IFPRI Discussion Paper 01477

October 2015

Adjusting to External Shocks in Small Open Economies The Case of

Samuel Morley

Valeria Piñeiro

Markets, Trade and Institutions Division INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE The International Food Policy Research Institute (IFPRI), established in 1975, provides evidence-based policy solutions to sustainably end hunger and malnutrition and reduce poverty. The Institute conducts research, communicates results, optimizes partnerships, and builds capacity to ensure sustainable food production, promote healthy food systems, improve markets and trade, transform , build resilience, and strengthen institutions and governance. Gender is considered in all of the Institute’s work. IFPRI collaborates with partners around the world, including development implementers, public institutions, the private sector, and farmers’ organizations, to ensure that local, national, regional, and global food policies are based on evidence. IFPRI is a member of the CGIAR Consortium.

AUTHORS Samuel Morley (s. [email protected]) is a visiting senior research fellow in the Markets, Trade and Institutions Division of the International Food Policy Research Institute (IFPRI), Washington, DC.

Valeria Piñeiro ([email protected]) is a research coordinator in the Markets, Trade and Institutions Division of IFPRI, Washington, DC.

Notices 1. IFPRI Discussion Papers contain preliminary material and research results and are circulated in order to stimulate discussion and critical comment. They have not been subject to a formal external review via IFPRI’s Publications Review Committee. Any opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by the International Food Policy Research Institute. 2. The boundaries and names shown and the designations used on the map(s) herein do not imply official endorsement or acceptance by the International Food Policy Research Institute (IFPRI) or its partners and contributors.

Copyright 2015 International Food Policy Research Institute. All rights reserved. Sections of this material may be reproduced for personal and not-for-profit use without the express written permission of but with acknowledgment to IFPRI. To reproduce the material contained herein for profit or commercial use requires express written permission. To obtain permission, contact [email protected]. Contents

Abstract v Acknowledgments vi 1. Introduction 1 2. Honduras’s Macroeconomic Environment 3 3. The Model 11 4. The Macro Simulations 15 5. Conclusions 23 Appendix A: Econometric Analysis of the Determinants of Output Growth 25 Appendix B: Supplementary Tables 32 References 35

iii Tables

2.1 Components of 4 2.2 Sector shares in gross domestic product 5 2.3 Growth rates of productivity since 1960 (percent) 6 2.4 Average shares in the capacity to import 8 3.1 2008 macro-SAM for Honduras, in billions of Honduran lempiras 12 4.1 Percentage change in macro variables in response to reduction in remittances and foreign savings 16 4.2 Percentage change in macro variables in response to a 10 percent oil price shock 17 4.3 Percentage change in sectoral output, real wages, and unemployment in response to a 10 percent oil price shock 18 4.4 Percentage change in macro variables in response to and maquila productivity shocks 19 4.5 Percentage change in real wages and unemployment in response to reductions in prices and productivity 20 4.6 Percentage change in macro variables in response to a 10 percent increase in investment financed by foreign savings 21 4.7 Percentage change in sectoral output, real wages, and unemployment in response to 10 percent investment 22 A.1 Regression 1 27 A.2 Johansen cointegration test 28 A.3 Normalized cointegrating coefficients, one cointegrating equation 28 A.4 Engle-Granger first step 29 A.5 Engle-Granger second step 29 A.6 Third step Engle Yoo 30 A.7 Granger causality test 31 B.1 Data for regression 1 32 B.2 Sectors of the disaggregated micro-SAM 33

Figures

2.1 GDP per capita in constant local currency units, 1960–2013 3 2.2 Investment rate and growth in gross domestic product 5 2.3 Gross domestic product and capacity to import, 1974–2011 7 2.4 Remittances 9 2.5 Exports of goods and maquila in real terms 10 3.1 Flow of goods from producers to the national composite commodity market 13

iv ABSTRACT

Like the other small economies of , the economy of Honduras is a challenge for all those searching for an adequate and sustainable growth strategy. After growing strongly for the two decades prior to 1980, the economy suffered through more than 20 years of growth so slow that only in 1998 did per capita income surpass the level it had reached in 1980. Then things seemed to change. For five years (2004–2008) the economy grew by an average of almost 6 percent per year, a performance unmatched since the late 1970s. But then the world financial crisis of 2009 put an end to the boom and pushed Honduras into . While the economy subsequently recovered, it has never come close to the growth rates of the early 1990s. Indeed, the growth rate appears to have settled back to around 3.3 percent, enough to produce only a slight increase in per capita income. In this paper we address several growth-related questions. First, what are the drivers of growth, how have they changed, and are the changes related to the cyclic nature of growth in Honduras? Second, how important are negative exogenous balance-of-payments shocks in explaining the many periods of lackluster growth? Third, what can policymakers do to offset the exogenous external shocks and increase the growth rate? We rely on several fundamental inputs in addressing these questions: first, observed patterns of sectoral growth and their changes over time; second, changes in two key determinants of growth, capital and the capacity to import; and third, changes in important exogenous variables such as the real exchange rate and the terms of trade. Finally we develop and present a model of the economy with which we will simulate the impact of various exogenous shocks as well as the policy responses to ameliorate or offset the effect of negative shocks.

Keywords: general equilibrium models, Honduras, economic development, macro shocks, foreign exchange constraints

v ACKNOWLEDGMENTS

This project was financed by the Inter-American Development Bank. We wish to thank Alejandro Quijada Briceño and Jose David Sierra Castillo for their comments and support.

vi 1. INTRODUCTION

Small open economies like that of Honduras present difficult macro policy challenges to policymakers as they search for the least harmful way to adjust to negative macro shocks imported from abroad. These economies are likely to be quite sensitive to such shocks because of their openness and their limited ability to shift production toward the traded-goods sector with the speed and volume necessary to offset the shock. This makes it all that much more important that policymakers be aware of the challenge they face as they attempt to choose an appropriate policy response to a negative external shock. In this paper we develop a computable general equilibrium (CGE) model based on data for Honduras to explore policy options available to the government. We are particularly interested in how the outcomes of various policies are affected by both the structure of the economy and the degree of real wage and exchange rate rigidity. The first eight years after the turn of the millennium were good for Honduras, with gross domestic product (GDP) growth averaging more than 6 percent for the five years 2002–2007. Then in 2008, the first external shock hit in the form of a rapid rise in the prices of petroleum and other imports for which there were few domestic substitutes. To complicate matters further, two of the major sources of foreign exchange, maquila exports and remittance inflows, have both declined significantly in real terms since 2007. These are major shocks for an economy as open as that of Honduras. The question we wish to address here is the effect of these shocks on the Honduran economy under a number of different policy scenarios. If we use a CGE model in which we assume or posit full employment of both capital and labor, we know what the effect of a negative-balance shock will be. There will have to be a real devaluation sufficient to offset the shock at full equilibrium. That means that either the nominal exchange rate itself must be devalued or, if it is fixed, domestic prices and wages have to fall by an amount sufficient to produce the same real devaluation that would have been produced by permitting the nominal exchange rate to rise. In either case the relative price of tradables has to rise, but in one case that rise is produced by a rise in the domestic price of tradables because of the devaluation, and in the other it is produced through a decline in the price of nontradables. The CGE model will not show much difference between these two alternatives, assuming that wages and prices are flexible. But in actual practice there is a world of difference between them. With flexible exchange rates there will be a general rise in the price level. Bondholders will lose, and so will those unwise enough to have borrowed in US dollars. If the government tries to defend the nominal exchange rate, prices and wages have to go down. The only way that can happen is through a recession whose economic function is to force that downward adjustment. In this case bondholders gain while debtors and workers in the nontraded goods sector lose. The problem is far more complicated if we introduce several real-world factors relevant to Honduras into the adjustment process. The first is the minimum wage. Honduras has a fixed nominal minimum wage for unskilled labor in the formal sector. It also has a crawling peg exchange rate policy. In such a world, most of the adjustment necessary to offset the original balance-of-payments shock will have to be to the prices of nontraded goods. Those prices have to fall in relative terms, and in absolute terms if there is a fixed exchange rate, in order to produce the real devaluation required to reach a new equilibrium in the balance of payment. But with a fixed minimum wage and some short-run price stickiness in the nontraded goods sector, the economy is going to have a quantity adjustment instead of a price adjustment. During that adjustment it is going to have a recession deep enough to force wages and prices down as well as to choke off enough import demand to offset the initial balance-of-payments shock. Here the level of economic activity during the adjustment will be highly sensitive to the availability of foreign exchange.

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A recession is an unpleasant yet probably unavoidable part of adjusting to a negative balance-of- payments shock, particularly in an economy with a fixed exchange rate. How long and how big the recession has to be depends on the structure of the economy as well as the stickiness of prices and wages. We are not going to model that dynamic adjustment process. Rather our purpose in this paper is to combine the recently updated social accounting matrix (SAM) for Honduras with a simple CGE model to examine the changes in domestic production, wages, and prices that are to be expected after the economy completes its adjustment in response to balance-of-payments shocks given its particular production structure and its minimum wage and exchange rate policies.

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2. HONDURAS’S MACROECONOMIC ENVIRONMENT

Some Characteristics of Growth in Honduras To set the stage for a deeper consideration of the current growth problem, it is useful to look back at the performance of the Honduran economy in the past. We take as our starting point 1960. Figure 2.1 tells the story visually. It shows the time path of GDP per capita from 1960 to 2013. The main point to be drawn from the figure is the good performance of the Honduran economy from 1960 to 1980 and the dramatic change in performance after that date. As shown, the economy grew quite rapidly before 1980, but then it lost so much ground in the 1980s and 1990s that it took 19 years for the country to reach the per capita income it had in the previous peak year, 1979. But then in 1999 set the economy back again, and it was not until 2002 that the economy began a new period of rapid growth, this one brought to an end by the financial crisis in 2009. What is relevant to us here is that with the economy apparently stuck at a growth rate expected to be no more than 3.3 percent in 2013, it has yet to reach the per capita income level it had in 2008.

Figure 2.1 GDP per capita in constant local currency units, 1960–2013

Source: (2013).

To help us understand the changing characteristics of growth in Honduras, consider first the striking changes in the composition of GDP over time displayed in Table 2.1. The most obvious and important is the role of exports and investment. In the 1960–1980 boom period, exports and investment drove the economy. Government spending was relatively small though it grew faster than income over that period. While imports grew slightly faster than exports, the trade account was roughly in balance. All this changed dramatically, particularly after 1990. In the 1990s Honduras embraced the Washington consensus. Tariffs were dramatically reduced, and there were also reforms in tax policy and liberalization of the capital account. The result was a very sharp increase in capital formation in the economy, which unfortunately was not matched by an increase in the overall growth rate of the economy. While both household and government consumption fell a bit during the 1990s, the big increase in capital formation was to an increasing extent financed by current account deficits offset by capital inflows and remittances.

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In the new millennium, capital formation continued at a very high level (until the last several years), but now was accompanied by a big increase in both components of consumption. While exports and imports both rose thanks to the increasingly important maquila sector, there was also a big increase in the current account deficit. It appears a permissive fiscal policy as well as a very large increase in remittance and foreign direct investment inflows permitted a rise in both public and private consumption, not at the expense of capital formation but rather by an increasingly serious current account imbalance. While this imbalance could be sustainable if external trends are favorable, there are many reasons to fear that they are not, among them stagnation in maquila exports and remittances, and unfavorable movements in the terms of trade.

Table 2.1 Components of gross domestic product

Component 1960–1980 1980–1990 1990–2000 2000–2011 Household consumption (% of GDP) 72.84 71.92 66.14 76.08 General government final consumption expenditure (% of GDP) 11.25 13.43 10.73 15.87

Gross capital formation (% of GDP) 18.96 18.45 30.32 27.42 Exports of goods and services (% of GDP) 29.29 28.78 41.51 51.89 Imports of goods and services (% of GDP) 32.35 32.57 48.70 71.25 External balance on goods and services (% of GDP) -3.05 -3.79 -7.19 -19.36 Source: World Bank (2013). Note: GDP = gross domestic product.

Next, consider the changes in the share of GDP disaggregated by value-added per sector (Table 2.2). For the long-run comparison we will use World Bank data, which go back to 1960. Far more disaggregated data are available from the Central Bank starting in year 2000. The data do not match exactly, but do allow one to make some important conclusions about sectoral shifts over time. There are several important patterns to note here. First is the big shift in production out of agriculture into industry and services, all typical of developing countries as they grow. Note that all the gains in industry after 1990 came from manufacturing. Construction and electricity value-added actually shrank as a fraction of GDP after 1990. Most of that growth in manufacturing must have reflected the explosive growth in maquila after 1990. The other important pattern here is the change in the nature of growth after the financial crisis in 2009. The share of value-added coming from the production of goods shrank. GDP growth after 2008 was driven by services, particularly financial intermediation and communications.1 The reason this is particularly significant for Honduras is that it is a very open economy, whose growth depends on its ability to generate a sufficient amount of foreign exchange. Prior to 1980 that foreign exchange to a large extent came from exports. But that has not been the case since 2008, as we shall see in a moment. Maquila has been unable to maintain the growth it had between 1990 and 2008, and while there has been a very recent boom in agricultural exports (especially coffee and in 2011–2012), that has been insufficient to offset what has happened to maquila. That must be one of the reasons why growth in the last three years, slow as it has been, has caused such a large increase in the current account deficit, as shown in Table 2.1.

1 The publishes a more disaggregated table of value-added shares, which confirms the patterns shown in the World Bank data. Agriculture and manufacturing accounted for 15.1 percent and 23.4 percent of GDP, respectively, between 2000 and 2008. Both of those shares fell in the period 2009–2011, to 14.0 percent for agriculture and 21.8 percent for manufacturing. Meanwhile, the share of financial intermediation and communications grew from 15.1 percent to 23.3 percent of GDP over the same period (Central Bank of Honduras 2013).

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Table 2.2 Sector shares in gross domestic product

Sector 1960–1980 1980–1990 1990–2000 2000–2008 2008–2011 Agriculture, value-added (% of GDP) 33.35 21.61 20.75 13.64 13.01 Industry, value-added (% of GDP) 21.86 24.41 29.91 29.75 27.17 Services and so on, value-added (% of GDP) 44.79 53.98 49.34 56.61 59.83 Total 100 100 100 100 100 Manufacturing 13.95 14.94 18.26 21.14 18.76

Source: World Bank (2013).

Investment, Capacity Creation, and the Growth of Productivity Development economists and supply-side theorists emphasize the importance of fixed capital formation as a driver of economic growth. It contributes in two ways, first by increasing the capital available per worker thus increasing worker productivity. Since new machines are more productive than old ones, investment should raise total productivity as well. That being the case, one would expect that increasing the investment share should lead to faster growth rates. Indeed economies that do not save and invest generally do not grow rapidly. But the relationship between investment and growth in Honduras is ambiguous. As Table 2.1 shows, the positive relationship between investment and growth is confirmed during the pre-1980 period, but not afterward. Investment fell in the 1980s, along with GDP, but then it recovered quite strongly after 1990. In fact the investment share in Honduras reached an average of more than 30 percent of GDP in the 1990s, and 27 percent in the new millennium, a rate which should have dramatically increased the growth rate of the economy. But it didn’t except for the five year boom after 2002. Generally speaking it does not look like investment leads to growth in Honduras. To see that we have plotted the investment share against the growth rate of per capita income in Figure 2.2. As the reader can see, there appears to be no relationship at all.

Figure 2.2 Investment rate and growth in gross domestic product

Source: World Bank (2013).

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Relatively low investment shares in the pre-1980 period are associated with relatively high growth rates, whereas much higher rates of investment in later years are coupled with very low or even negative growth rates in the economy. That is undoubtedly because the debt crisis of those years forced a retraction in demand so that the increases in potential output or capacity made possible by higher levels of investment were not matched by higher levels of demand. If that was the case, it is surprising to find so much investment in the 1990s at a time when, according to the capacity argument, there must have been a good deal of idle capacity. What also requires explanation is that the high level of investment during the 1990s did not lead to much growth in that decade, possibly because of Hurricane Mitch in 1998.2 It is also possible that high investment in the 1990s may have built the capacity that permitted faster growth after 2000. As we have seen, Honduras has had very slow growth in per capita income since 1980. One possible, but not valid, explanation for this performance is a low rate of capital formation. On the contrary, since the mid-1980s, Honduras has had one of the higher investment rates in the region, greater than 25 percent of GDP on average ever since 1990. (Some part of the high investment rate after 1998 was undoubtedly directed at repairing the destruction caused by Hurricane Mitch.) Whatever it was that slowed down the growth rate after 1980, it was not a lack of capital. That implies that either the growth process is now far more capital intensive than it used to be, or Honduras is investing unwisely, or there is a demand-side problem that has caused the economy to produce below its potential capacity level. To look more closely at this phenomenon, we have used the reported investment series to build up an estimate of the capital stock. We then calculate a series of potential and actual GDP from 1960 on using the estimated growth rates of capital, the observed population growth rates as a proxy of the growth rate of employable labor, and the shares of capital and labor from the 2008 SAM. Total factor productivity (TFP) growth can then be calculated as the difference between the observed rate of growth of the economy and the weighted average growth rates of capital and labor (Table 2.3). When we do that, we find that between 1960 and 1980, when the economy grew at more than 5 percent, the implied growth rate of productivity was 1.6 percent per year. If we apply the same methodology and assume that the economy was at capacity in 1998 and again 2008, we find that productivity growth falls to less than zero between 1980 and 1998, recovers a bit in the following 10 years, but falls again after 2008.3 Over the entire 31-year span after 1980, productivity grew by less than 0.1 percent per year. That means that the weighted average growth rate of the factors of production is just about equal to the growth rate of the economy, and there is no increase in average productivity at all after 1980, in spite of the large investment effort.

Table 2.3 Growth rates of productivity since 1960 (percent)

Category 1960–1980 1980–1998 1998–2008 2008–2011 Productivity growth 1.56 -3.27 1.34 -1.88 Growth rate of gross domestic product 5.08 2.97 4.39 1.38 Growth rate of capital 4.32 4.13 4.76 3.19 Growth rate of labor force 3.22 2.93 2.39 3.28 Source: Authors’ calculations using data from World Bank (2013).

2 Note that when the revision of the national accounts is extended backward to the 1980s and 1990s, this statement may need to be adjusted slightly. That is, the growth rate in the 1990s may have been higher than the one shown in Table 2.1, which is based on the unrevised national accounts. 3 Loayza, Fajnzilber, and Calderon (2002) made a comparable estimate of TFP growth in Honduras. They estimate TFP growth of 0.97 percent in the 1960s, 1.04 percent in the 1970s, -0.98 percent in the 1980s, and -0.89 percent in the 1990s.

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The Capacity to Import and the Determinants of Growth It seems clear that more than a simple model based on capital accumulation will be required to find out what determines growth in Honduras. As shown earlier, there is little or no relationship between investment and growth, at least in the short run. Following the old two-gap model (see Appendix A for econometric analysis), our hypothesis is that in a small open economy like the one in Honduras, output in the short run is likely to be constrained as much by the available supply of foreign exchange as it is by the supply of fixed capital. This is a very open economy in which any increase in aggregate demand requires a significant increase in imports, both of consumption goods (including intermediate inputs, especially fuel) and of capital goods. All of these require foreign exchange. One could therefore think of foreign exchange and fixed capital as necessary complementary inputs in the production of GDP. Along a long-run equilibrium growth path, the economy produces the capital as well as the foreign exchange it needs to fully utilize that capital and produce an equilibrium rate of growth. But that may not be true in the short run, where we observe periods like the 1980s and 1990s, when the productive capacity of the economy appears to have been underutilized, and other periods like 2002–2008, when the big rise in foreign exchange permitted a very rapid growth rate with scarcely any increase in the rate of investment. By the available supply of foreign exchange we mean the net yearly inflow of foreign exchange from exports, remittances, development assistance, foreign investment, and increases in foreign debt minus interest payments on foreign debt. We will call this measure the capacity to import, and we expect it to be quite closely correlated to the observed output of the economy, not simply because of high import coefficients but also because key intermediate inputs such as fuel cannot be produced locally. To visualize the relationship between GDP and capacity to import, consider Figure 2.3, a scatter diagram of GDP in constant local currency and the capacity to import in current dollars deflated by the import price index, for the period 1974–2011.

Figure 2.3 Gross domestic product and capacity to import, 1974–2011

Source: Authors’ calculations using data from World Bank (2013). Notes: GDP = gross domestic product; LCU = local currency unit.

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As shown, there is a fairly tight relationship between the level of GDP and the capacity to import, particularly before 2008. There are outliers, of course, such as 1999 and 2004. In 1999 Honduras was recovering from Hurricane Mitch, which hit the country late in 1998. The country received a great deal of official development assistance in 1999 but output was virtually constant. In 2004 there was another big increase in the capacity to import, this time because of an increase in foreign borrowing and official development assistance coupled with a fall in debt service. Some of that capital inflow undoubtedly financed the expansion in output in 2005 rather than 2004. Finally, in 2009 the worldwide financial crisis as well as the removal of the president caused a sharp reduction in the capacity to import and a 2 percent reduction in real GDP. In the following two years both the capacity to import and GDP began to grow again, although at lower rates than they had previously. What is more worrisome is that due to stagnation in both exports and remittances, the inflow of foreign exchange appears to have become permanently smaller. We now examine changes in the contributions of the various components of the capacity to import to determine what the main producers of foreign exchange are and how they have changed over time (Table 2.4). Before 1980, exports and official development assistance composed more than 90 percent of the supply of foreign exchange to the Honduran economy. New borrowing was significant, but it was just about offset by interest payments on the outstanding debt. The picture is dramatically different after the turn of the millennium, and particularly in the recent recovery after 2008. To an increasing extent Honduras has come to depend on transfers rather than exports as a source of foreign exchange. After 2008, exports and official development assistance made up only 67 percent of the capacity to import, compared with 92 percent prior to 1980. Meanwhile, the share of remittances grew from less than 5 percent of the capacity to import to more than 27 percent. Official development assistance and new debt were both important sources of foreign exchange throughout the 1980s and 1990s, but both contracted sharply in the new millennium. Indeed, debt service is now a bigger drain on foreign exchange than increases in debt. The reason all these trends are significant is that maquila, one of the biggest and most dynamic of the export sectors, is no longer growing as it did between 2000 and 2008. Remittances also appear to have topped out or are no longer growing fast enough to offset the loss of dynamism in the export sector. If our hypothesis about the relationship between output and the capacity to import is correct, this shift poses major challenges for the Honduran economy.

Table 2.4 Average shares in the capacity to import Component 1974–1980 1980–1990 1990–2000 2000–2008 2008–2011 Exports/capacity to import 0.8 0.735 0.79 0.706 0.62 Transfers/capacity to import 0.04 0.101 0.14 0.219 0.26 FDI inflows/capacity to import 0.01 0.019 0.03 0.074 0.07 Debt service/capacity import 0.17 0.210 0.23 0.053 0.05 Official development assistance/capacity to 0.09 0.179 0.18 0.077 0.05 import Change in debt/capacity to import 0.18 0.176 0.06 -0.024 0.03 Source: Authors’ calculations using data from World Bank (2013). . Note: FDI = foreign direct investment.

Remittances Remittances are a major feature of the macro environment in Honduras. Figure 2.4 displays the historical record of remittances as reported by the Central Bank of Honduras, deflated by the same import price index used in our capacity-to-import calculations. Several important patterns are apparent in the figure: first the explosive growth from 2000 to 2007, and second the significant decline starting in 2008 and

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continuing through 2012. As late as 1997, remittances amounted to only US$150 million, representing less than 10 percent of the value of total exports and 3 percent of GDP. Ten years later, remittances had gone up by 16 times, to more than US$2.8 billion, to the point where in 2007 they were almost equal to the total value of goods and services exports, including maquila, and amounted to 30 percent of the total value of GDP. But what is even more significant is what has happened since 2007. In an earlier paper we reported that growth in the inflow of remittances was expected to slow down after 2008, but to remain positive (Morley and Piñeiro 2008). The reality has turned out to be far worse than that. In current dollars, personal remittances peaked at US$2.8 billion in 2008, then fell through 2011. When corrected for the rise in import prices, remittances by 2012 were 16 percent lower than at their peak in 2007.

Figure 2.4 Remittances

Source: Central Bank of Honduras (2013).

Remittances represent a supply of foreign exchange for which the economy does not have to produce exports or use productive resources. They increase aggregate demand and, according to most studies, are devoted to private consumption since they go to families. They must be a major reason why there was a big increase in the share of private consumption in GDP between 2000 and 2011, shown in Table 2.1. Remittances have been good for Honduras, for not only do they support aggregate demand, but they also provide crucial foreign exchange needed to satisfy the imported component of that demand. That is why the stagnation in the growth of remittances is such a serious problem for future growth in Honduras.

Maquila and the Growth of Exports Finding a successful export strategy is likely to be a central element of any sustainable growth plan for Honduras. Yet as shown in Table 2.4, the share of the capacity to import contributed by exports has declined by almost 20 percentage points since 2000. One of the main reasons for this is that production and exports from the maquila sector have virtually stagnated since 2008. The growth of maquila is one of the great success stories of Honduras. In 2000, maquila (mainly the production of underwear, socks, and battery harnesses) made up about 38 percent of total value-added in manufacturing and almost 20 percent of GDP. By 2008 the sector had more than doubled in size and accounted for more than 40 percent of manufacturing and 54 percent of the total value of goods exports

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from Honduras. But then growth stopped. In real terms, the value-added of this key sector fell by 15 percent between 2008 and 20124 (Figure 2.5). Equally serious, the dollar value of exports from the sector also stagnated, registering only 1 percent growth over the period.

Figure 2.5 Exports of goods and maquila in real terms

3,000

2,500 Maquila 2,000

1,500 Goods Millions of dollars 1,000

500

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 . Source: Central Bank of Honduras (2013).

Luckily for Honduras some of the dollars no longer provided by maquila exports were provided by other exports, three fourths of which come from Agriculture half of which come from three products, coffee, palm oil and . Coffee in particular has enjoyed a boom, with both prices and export volumes doubling between 2008 and 2012. Coffee alone was responsible for just under 70 percent of the total increase in agricultural exports and 53 percent of the increase in all goods exports over the period. Whether or not these important gains can be maintained in the face of declining coffee prices (which began in 2012) and the expected decrease in production due to coffee rust fungus, or roya, remains to be seen.5 But it is worrisome when so much of the total export capacity of the country is concentrated is so few sectors, particularly when one of those sectors, maquila, is no longer growing as it once did.

4 Note that the nominal value of maquila exports peaked in 2008, but because of rising import prices, real value peaked in 2006. 5 Roya has been devastating coffee production in Central America in the last few years; this fungus is disseminated by air and can be controlled with fumigation. The age of plants and the ambient temperature affect the spread of the fungus. The Asociación Nacional del Café (Anacafé) has estimated that the region will need to spend US$300 million to fight the disease, and the Organización Centroamericana de Exportadores de Café (ORCECA) has estimated that renewing the plantations will cost US$1 billion. The disease has already claimed one-fifth of the total Central American coffee production for the 2012– 2013 harvest.

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3. THE MODEL

We built a SAM for Honduras for 2008. This SAM is disaggregated into the 49 sectors shown in Table A.2 in the appendix. We report data separately for labor (disaggregated into skilled and unskilled, formal and informal), for land, and for capital. Table 3.1 displays the macro-SAM that results from aggregating all the columns and rows of the full SAM.6

Components of the Model The static CGE model used in this part of the research was built based on the standard model used by IFPRI (Lofgren, Harris, and Robinson 2001), which follows the neoclassical-structuralist tradition originally presented by Dervis, de Melo, and Robinson (1982). Basic data for CGE models are obtained from a social accounting matrix (SAM). A SAM is a comprehensive, economywide data framework, typically representing the economy of a country. The CGE model has three components. The first shows the payments that are registered in the SAM, following the same disaggregation of factors, activities, commodities, and institutions shown in the matrix. The second contains the equations that represent the behavior of the different institutions present. The third contains the system of constraints that have to be satisfied by the whole system covering the factor and goods markets, the balances for savings/investments, the government, and the current account of the rest of the world. Each producer maximizes profits under constant returns to scale and perfect competition. There are two factors of production, labor (differentiated by skill) and capital. Production is related to factor inputs in a constant elasticity of substitution (CES) production function, which allows the producers to substitute these two inputs until they reach the point where the marginal revenue of each factor equals the factor price (wage or rent). The second choice the producers make is the amount of intermediate inputs they will use. This specification is made assuming fixed shares that specify the appropriate amount of intermediate inputs per unit of output and labor/capital (value-added). Finally, output prices depend on the value-added (cost of labor and capital), intermediate inputs, and any relevant taxes and subsidies.

6 The full SAM can be requested by email from [email protected].

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Table 3.1 2008 macro-SAM for Honduras, in billions of Honduran lempiras Activities Commodities Factors Households Government ROW Savings/ Direct Import Sales TOTAL investment tax tax tax

Activities 540.42 540.42 Commodities 290.64 60.05 215.57 38.09 154.93 95.45 854.73 Factors 249.78 249.78 Households 249.78 53.92 5.8 36.13 345.62 Government -1.82 15.95 3.02 19.35 36.5 ROW 231.9 231.9 Savings/ investment 60.18 -7.39 42.66 95.45 Direct tax 15.95 15.95 Import tax 3.02 3.02 Sales tax 19.35 19.35 TOTAL 540.42 854.73 249.78 345.62 36.5 231.9 95.45 15.95 3.02 19.35

Source: Authors’ worksheets. Note: ROW = rest of the world.

12

Figure 3.1 shows the flow of a single commodity from producers to final demand. First, there is the combination of goods from all producers into an aggregate commodity output. This is achieved using a CES product demand system with the intention of leaving the option to buyers as to how much to buy of each individual product (maximizing their consumption). The aggregate output is sold domestically or internationally. The producers’ allocation between domestic sales and exports is specified via a constant elasticity of transformation (CET) function, assuming imperfect transformability between exports and domestic sales. The producers will sell their products to the market with the highest profitability. The domestic price is the international price times the exchange rate plus any possible export taxes or export subsidies. The domestic good is combined with imports to produce the composite commodity. For this the Armington7 specification is used, which means that the domestically produced and imported goods are imperfect substitutes.

Figure 3.1 Flow of goods from producers to the national composite commodity market

Imports Consumption

Commodity CES Composite Government output good consumption activity 1 Domestic Investment Aggregate CET sales CES output Intermediate Commodity use output activity n Exports

Source: Authors. Notes: CES = constant elasticity of substitution; CET = constant elasticity of transformation.

In this model there are four institutions—households, enterprises, government, and the rest of the world—that do three things: produce, consume, and accumulate capital. Households save a constant fraction of their disposable income and buy consumption goods with the remainder. Enterprises hire labor supplied by households and distribute all their profits to households. Household income is the sum of salaries, profits, and transfers from government and the rest of the world. Household consumption of goods and services is determined by a linear expenditure system. Firms buy intermediate goods, hire factors of production, produce commodities and services, and sell them in the market. Government receives taxes, consumes goods and services, and makes transfers to households. The capital account collects the savings from the households, firms, government, and rest of the world, and buys capital goods (investment). As was mentioned previously, our CGE model contains detailed information on the demand and supply of 49 economic sectors and commodities. Labor is disaggregated by qualification (skilled or unskilled), sector (formal or informal), and region (rural or urban). Workers can migrate between sectors according to labor demand, but total labor supply does not change. Our treatment of labor is the supply curve for unskilled labor in the formal sector. For this factor we assume a fixed minimum wage and assume that there is an excess supply of unskilled labor, at least over the range of solutions that we analyze. Effectively that means that the supply curve of labor is flat, or in other words that the wage is fixed and employment is endogenous. But since the entire

7 Achebe, N., and P. Armington. 1969.

13 model is a real model, or is expressed in terms of the consumer price index numeraire, that the wage of unskilled labor is fixed in real terms.

Closures and Assumptions on Factor Supplies The closures are the mechanisms that determine how various macro constraints are satisfied. (1) For rest- of-the-world closure, the exchange rate is assumed to be flexible or endogenous to the model, and foreign savings are fixed. (2) For the government, a balanced growth closure is imposed across simulations. (3) In equilibrium, total savings must equal total investment. There are various ways to guarantee this. In all the simulations, we fixed the savings rates of households and government, which makes total savings and investment positively related to the level of income. (4) In the labor markets, we have assumed that there is an excess of unskilled labor and a fixed real wage. For skilled labor, the supply is assumed fixed and the wages are endogenous to the model. (5) Capital is fully employed and sector specific, which means that profit rates are free to vary across sectors.

14 4. THE MACRO SIMULATIONS

Honduras faces an external environment that has deteriorated quite dramatically in the last year. The inflow of remittances has leveled off and, in real terms, is now 20 percent below what it was in 2007. Maquila has suffered similar declines, and coffee, one of the recent success stories, is afflicted by a disease that is cutting output even as world prices decline from their peak in 2011. Meanwhile oil prices continue to be very high. Thus it is virtually certain that the balance of payments is going to be a serious problem in the years ahead. Given the economy’s openness and its structural characteristics, it is useful to have an instrument with which to examine the implications of balance-of-payments shocks and to simulate alternative policy responses. That is what our CGE model is designed to do. The comparative statics simulations show the changes in relative prices and sectoral output levels that are required to keep the economy on a long-run growth path when there are changes in exogenous external conditions such as capital inflows, remittances or import prices. In terms of the discussion in Section 2 of this paper, the simulations show the effect on the economy of changes in the capacity to import due to exogenous external shocks. Our choice of shocks to simulate reflects the importance of the capacity to import to a small open economy such as Honduras’s. The advantage of the CGE for this sort of analysis is that it endogenizes the relative price, sectoral output, and employment responses to the shock. What the comparative statics exercises do not show is the manner in which the economy produces the necessary changes in relative prices and sectoral output levels. In this section we report the results of simulations of several balance-of-payments shocks of the sort recently observed in the country. We will start by examining the effect of reductions in remittances.

Remittance Shocks We now put our CGE model to work examining how the Honduran economy would react to a reduction in remittances. We start with a sensitivity analysis in which we vary the shock from 5 percent to 20 percent of base-level remittances. This is equivalent to between 1.1 percent and 4.6 percent of base-level exports, or between 0.7 percent and 2.8 percent of household income. Table 4.1 shows the impact of this change when the minimum wage is fixed in real terms. The first thing to note is that this shock has very little effect on total output, even with a fixed minimum wage. Mainly this change affects absorption and foreign trade. In all cases, economies adjust to shocks by some combination of changes in relative prices, output, and absorption. In this case, since the reduction in remittances directly reduces household income, most of the adjustment comes from reduced consumption and investment, not a change in relative prices and output. Reducing remittance inflows is contractionary. Prices fall across the board and there is a reduction in the standard of living of Hondurans, but there is not much of an effect on the level of GDP. The shock forces the economy to reduce absorption by replacing foreign savings with domestic savings and reduced investment. Either way, there has to be a shift in production from nontraded to traded goods. That means both an increase in exports and a reduction in imports. Since the minimum wage for unskilled labor is fixed in real terms, the real exchange rate has to fall far enough to make exports or domestic import substitutes more competitive. The export row in Table 4.1 shows that with a 5 percent shock, there is a real devaluation of 0.27 percent and exports rise by 0.61 percent. With a 20 percent shock, the real devaluation is 1.10 percent and exports rise by 2.46 percent. Export elasticity with respect to the real exchange rate is around 2.5. Import elasticity appears to be far lower, even though a substantial part of the change in imports must result from reduced consumption rather than changes in the exchange rate. One reason for this relative insensitivity of imports to the real exchange rate must be the high fraction of imports that are used in the production of maquila exports. This is one reason why external adjustment is difficult in Honduras. The reduction in absorption is spread over the three domestic sources of demand according to the adjustment scheme imposed by the closures in the model. We are using what we call a balanced closure here, in which adjustments in absorption are spread proportionately across the three domestic components of demand: household consumption, investment, and government spending (using the shares from the

15 base). At the same time, since by construction foreign savings is fixed (that is, the capital account is exogenous and fixed), any remittance shock has to be matched by an improvement in the trade balance through an increase in the production of traded goods. But since total production falls a bit, there must be a switch out of the nontraded goods sector to traded goods. If we classify agriculture and manufacturing as traded goods, and construction and services as nontraded goods, this is exactly what happens. With a 20 percent contraction in remittances, manufacturing, led by textiles, rises by 1.36 percent and agriculture rises slightly, while construction falls by 1.82 percent and the service sector (excluding government) falls by 0.34 percent.

Table 4.1 Percentage change in macro variables in response to reduction in remittances and foreign savings

Variable Base Remit. -5% Remit. -15% Remit. -20% Foreign savings Absorption 349.11 -0.53 -1.60 -2.13 -2.82 Private consumption 215.57 -0.53 -1.59 -2.12 -2.81 Fixed investment 89.28 -0.61 -1.85 -2.46 -3.27 Government consumption 38.09 -0.43 -1.31 -1.75 -2.30 Exports 154.93 0.61 1.85 2.46 3.27 Imports -231.90 -0.37 -1.10 -1.47 -1.94 Gross domestic production 272.14 -0.02 -0.06 -0.08 -0.11 Real exchange rate 1.00 0.27 0.82 1.10 1.46 Source: Authors’ worksheets.

It makes a difference what causes the contraction in the supply of foreign exchange. In the right- hand column of Table 4.1 we show the effect of a reduction in the supply of foreign exchange equal to that in the -20 percent remittance case. Thus the two right-hand columns are directly comparable. It turns out that reducing foreign savings (that is, capital inflows) does more damage than reducing remittances by the same dollar amount. That is because the entire adjustment to the foreign exchange shock has to come from the trade balance. There is no first-round contraction in consumption and imports due to the loss in household income from reduced remittances. That means that exports have to rise by more and imports have to fall by more to offset the shock, which is why there is a bigger depreciation of the real exchange rate under this scenario. As a result, total welfare as measured by absorption falls quite a bit further in this case than it did in the -20 percent remittance case, despite the fact that the fall in total production is only marginally smaller in this instance.

The Effect of an Oil Price Shock In this set of simulations we examine the effect of an increase of 10 percent in the price of oil, when the minimum wage for the unskilled is fixed and when it is endogenous, which we are calling the neoclassical case. What the simulation shows, first of all, is the sensitivity of the Honduran economy to oil price fluctuations. With a fixed minimum wage, a rise in the price of oil of just 10 percent reduces GDP by 1.32 percent. All of the components of GDP decline, including exports, despite the fact that the real exchange rate depreciates by almost 1.5 percent. Oil is a key intermediate input to all sectors, including especially agricultural exports and maquila. With a fixed real wage for unskilled labor and rising input costs for fuel and fertilizer, agricultural exporters are hard hit. Overall, agricultural production falls by 1.85 percent. But the effect on maquila is even greater. Value-added by maquila, which makes up 47 percent of base-period

16 manufacturing, falls by more than 2.7 percent. That has a big effect on employment and unemployment, as we will see further on. In this simulation, since we have fixed a key relative price (of unskilled labor) the economy is forced to make what we would call a quantity adjustment, in which output falls by enough to drive down imports by 4.58 percent, even with higher-priced oil. That is enough to offset the 2 percent fall in exports.

Table 4.2 Percentage change in macro variables in response to a 10 percent oil price shock

Variable Base Fixed wage Neoclassical Absorption 349.11 -3.19 -2.35 Private consumption 215.57 -3.38 -2.67 Fixed investment 89.28 -3.61 -2.55 Government consumption 38.09 -1.67 -0.45 Exports 154.93 -1.99 0.81 Imports -231.90 -4.58 -2.77 Gross domestic production 272.14 -1.32 -0.19 Real exchange rate 1.00 1.45 1.19 Source: Authors’ worksheets.

In the right-hand column of Table 4.2 (the neoclassical case) we show the results of the oil price simulation, except that now we release the minimum wage assumption and posit neoclassical factor market clearing in all labor markets. The purpose is to see how much difference a fixed minimum wage makes when the economy is forced to adjust to an exogenous shock. This means that all factors will remain employed at their base-period levels and any changes in sectoral output that occur must be due to changes in relative prices triggered by the exogenous rise in the price of oil. Full price and wage flexibility reduces but does not completely eliminate the fall in GDP we saw in the fixed wage case. If one measures welfare by total absorption, the oil price shock still reduces total welfare, but the reduction is about 1 percentage point lower than it was in the fixed wage case. That is mainly because total production stays nearly constant rather than contracting by 1.3 percent as it did in the fixed wage case. The big change is in imports and exports. Now instead of declining by 2 percent, exports rise by 0.81 percent. That means that a much greater fraction of the rising import bill can be offset by additional exports rather than forcing down domestic demand for imports through a quantity adjustment alone. Table 4.3 displays the impact of the oil shock on value-added by sector, and on real wages and unemployment in each of our factor markets. When the minimum wage is fixed, total output falls, with most of the decline coming from the traded goods sector, particularly textiles, despite the real devaluation we noted above. Services-sector output falls far less thanks to the tourism sector of hotels and restaurants, which must mean that the decline of 6 percent in the real wage of the skilled more than offsets the negative impact of rising fuel costs. Looking more closely at sectoral exports in the neoclassical case, in which total exports increase, we find that freeing the minimum wage has a big effect across the board, but particularly in maquila, manufacturing, and agriculture. Clearly, rising fuel (and fertilizer) costs have a big negative impact on agriculture. Rural skilled wages fall by almost 7 percent and the return on land falls even further.8 When the minimum wage is fixed, rural unskilled workers suffer a rise in unemployment from 3.3 percent to 4.27 percent. In the market clearing case, the unemployment rate for these workers is constant but their wages fall by more than 6 percent.

8 Our model has regional detail for agriculture, so we calculated the simple average return over the six regions in the model.

17 Table 4.3 Percentage change in sectoral output, real wages, and unemployment in response to a 10 percent oil price shock

Variable Base Fixed wage Neoclassical

Agriculture 31.12 -1.85 -0.46 Coffee 5.26 -1.66 -0.64 1.49 -2.26 -1.17 Manufacturing 51.78 -2.75 -0.03 Textiles 26.13 -4.51 -0.36 Electricity + water 3.43 -3.08 -1.73 Construction 17.64 -2.89 -1.86 Commerce, hotel, restaurant 51.09 1.17 1.40 Transportation + communication 16.45 -0.55 -0.74 Finance 26.37 -0.08 0.20 Government, personal services 47.16 -1.14 -0.19 Real wage Base Fixed wage Neoclassical Urban skilled 0.437 -6.22 -4.94 Urban unskilled, formal 0.437 0.00 -6.06 Urban unskilled, informal 0.437 -6.09 -4.38 Rural skilled 0.437 -7.08 -5.22 Rural unskilled 0.437 0.00 -6.19 Capital 0.087 -6.02 -4.65 Land 0.437 -8.83 -7.19 Unemployment Base Fixed wage Neoclassical Urban skilled 13.33 13.33 13.33 Urban unskilled, formal 6.27 8.00 6.27 Urban unskilled, informal 4.93 4.93 4.93 Rural skilled 2.44 2.44 2.44 Rural unskilled 3.33 4.27 3.33 Source: Authors’ worksheets.

The basic point of Tables 4.1 and 4.2 is that when there is a significant negative balance-of- payments shock, there has to be a real devaluation. If real wages are flexible, then most of the adjustment is seen in the real wage of the unskilled, which reduces the size of the required real devaluation. In effect, with flexible wages for the unskilled, Honduras becomes more competitive by driving down these workers’ real wage. Our comparative static model does not show the adjustment process that makes that happen. But it is likely to require a recession whose length and severity will be determined by how sticky or rigid nominal wages and prices are.

Maquila and Coffee Honduras has a quite narrow export base. Two of its main exports are maquila and coffee, the former comprising fully half of total export revenue and the latter an additional 8 percent (in 2011). The economy is therefore quite sensitive to anything that affects these two key activities. Unfortunately, both are experiencing significant difficulties. For coffee, the problem is a parasitic fungus called roya or coffee rust, which is expected to cut yields by 25 percent in the next couple of years. Prices, which peaked in

18 2011, have also started to decline. The situation in maquila is less clear, but the sector, which suffered a sharp decline in the crisis of 2009, has only managed to level out at a slightly higher point than it reached in 2008. It does not appear to be able to provide dynamic leadership in the creation of additional import capacity in the years ahead. To show how serious this situation could be for Honduras, we ran simulations in which we reduced both the price and the productivity of each of the sectors separately. For coffee, we hypothesized a 2 percent reduction in exogenous technical change coefficient coupled with a 30 percent reduction in prices, quite close to the declines being observed in 2013.9 For maquila, we supposed a 10 percent reduction in export prices and a 10 percent decline in productivity. These numbers are entirely arbitrary, but they do serve to emphasize the extreme sensitivity of the Honduran economy to what happens in these two sectors. Table 4.4 displays the macro effects of these negative shocks.

Table 4.4 Percentage change in macro variables in response to coffee and maquila productivity shocks

Variable Base Coffee Maquila Absorption 349.11 -0.28 -3.46 Private consumption 215.57 -0.25 -3.37 Fixed investment 89.28 -0.43 -5.29 Government consumption 38.09 -0.19 -0.24 Exports 154.93 -0.59 -15.68 Imports -231.90 -0.64 -11.41 Gross domestic production 272.14 -0.15 -3.63 Real exchange rate 1.00 0.67 7.17 Source: Authors’ worksheets.

As one would expect, negative productivity and price shocks are more serious for maquila than for coffee. Cutting both by 10 percent for maquila causes GDP and total absorption to fall by 3.63 percent and 3.46 percent, respectively. There is a very large real devaluation that actually helps the agricultural sector, causing output to increase by more than 4 percent. Exports fall more sharply than anything else in the economy, and that forces a substantial reduction in imports and in the import share. The coffee shock has a much smaller effect, partly because it is a smaller part of the economy and partly because we assume that the coffee fungus cuts productivity by only 2 percent. Table 4.5 shows the effect of these two shocks on sectoral output, wages, and unemployment rates. There is an inverse relationship between output effects in these two traded-goods sectors. If coffee output declines, as it does in the coffee simulation, the depreciation of the exchange rate stimulates an increase in production and exports in maquila. Even within agriculture there is a shift to other commodities so that total agricultural output decreases by far less than the decline in coffee. These patterns are more dramatic in the maquila case. Maquila exports are cut almost in half, and the associated 7 percent real depreciation of the exchange rate makes all agricultural production and exports more profitable. There is also a shift in production toward services, the principal nontraded goods sector.

9 Since all coffee is processed before exporting, coffee is combined with other processed food, and the weighted average export price of this sector (total) is assumed to fall by 5 percent. Note also that for this simulation we assumed that the allocation of land by is fixed at base-year levels.

19 Table 4.5 Percentage change in real wages and unemployment in response to reductions in prices and productivity

Variable Base Coffee Maquila Agriculture 31.12 -2.04 4.34 Coffee 5.26 -14.22 14.55 Mining 1.49 0.30 3.77 Manufacturing 51.78 1.31 -20.26 Textiles 26.13 2.10 -46.33 Electricity + water 3.43 0.08 -7.21 Construction 17.64 -0.37 -4.41 Commerce, hotel, restaurant 51.09 0.26 2.06 Transportation + communication 16.45 0.32 5.04 Finance 26.37 0.17 0.19 Government, personal services 47.16 -0.13 -0.08 Real wages Base Coffee Maquila Urban skilled 0.437 -0.281 -5.706 Urban unskilled, formal 0.437 0.000 0.000 Urban unskilled, informal 0.437 -1.837 -3.666 Rural skilled 0.437 -1.807 -6.727 Rural unskilled 0.437 0.000 0.000 Capital 0.087 -0.573 -4.641 Land 0.437 -4.270 16.750 Unemployment Base Coffee Maquila Urban skilled 13.33 13.330 13.330 Urban unskilled, formal 6.27 6.327 10.045 Urban unskilled, informal 4.93 4.929 4.929 Rural skilled 2.44 2.443 2.443 Rural unskilled 3.33 3.447 4.240 Source: Authors’ worksheets.

Increasing Investment and Foreign Savings In our last set of simulations, we explore the impact of an increase in investment financed by foreign borrowing, or in terms of our model, an increase in foreign savings. In an economy where the capacity to import is a significant determinant of output, any increase in the supply of foreign exchange will have a short-run positive impact on output and welfare irrespective of its long-run impact on productivity and growth. Here we increase fixed investment by 10 percent, which, if financed by foreign savings, necessitates an increase of 22 percent in foreign savings. Note that one could think of this as speeding up the completion of infrastructure projects that are already in the pipeline, but for which disbursement is held up by delays or lack of domestic counterpart. It may seem counterintuitive that workers building roads on a project financed by the Inter-American Development Bank earn foreign exchange, but in effect, they do. In fact, in the short run, this may be a faster or surer way to earn additional foreign exchange than increasing maquila or agricultural exports.

20 Table 4.6 gives the macro results of this investment experiment, first where there is no link between increased investment and TFP, and then, in the right-hand column, where the investment raises TFP by 1 percent in all sectors. Looking first at the 10 percent investment column (without TFP), we find that the increase in foreign exchange that finances the investment allows absorption and consumption to increase because it permits much of the increase in investment to be financed by an increase in the trade deficit. Exports decline and imports increase as the real exchange rate appreciates by 1.74 percent. If the additional investment raises TFP by 1 percent, then all the positive growth rates also increase by about 1 percent, and the decline in exports is reduced from 3.5 percent to 1.9 percent.

Table 4.6 Percentage change in macro variables in response to a 10 percent increase in investment financed by foreign savings

Variable Base 10% investment 10% investment + TFP Absorption 349.11 3.60 4.49 Private consumption 215.57 1.15 2.42 Fixed investment 89.28 10.00 10.00 Government consumption 38.09 3.06 4.03 Exports 154.93 -3.50 -1.93 Imports -231.90 2.81 3.86 Gross domestic production 272.14 0.23 1.37 Real exchange rate 1.00 -1.74 -1.63 Source: Authors’ worksheets. Note: TFP = total factor productivity.

While the increase in foreign exchange significantly raises absorption and welfare, it does not seem to have much effect on the level of production. The reason for that is that the real appreciation of the exchange rate causes a shift out of the traded-goods sectors in favor of nontraded goods (Table 4.7). Manufacturing and agriculture decline, while construction and personal services grow with an increase in investment and foreign savings, thanks to the large real appreciation of the exchange rate. In the factor markets, we see that land rents go down, while unemployment of the unskilled goes up. In the urban sector the patterns are just the opposite, with skilled wages going up while unemployment goes down for the unskilled. It appears that because capital inflows favor nontraded goods, they also widen wage inequality, particularly in the urban sector. Rural-sector skilled wages go up if productivity increases but fall if it doesn’t.

21 Table 4.7 Percentage change in sectoral output, real wages, and unemployment in response to 10 percent investment Investment. + Variable Base 10% investment TFP Agriculture 31.12 -0.74 0.45 Coffee 5.26 -1.84 -1.43 Mining 1.49 0.16 1.27 Manufacturing 51.78 -2.10 -0.49 Textiles 26.13 -3.97 -2.01 Electricity + water 3.43 -0.30 1.00 Construction 17.64 7.29 7.59 Commerce, hotel, restaurant 51.09 -0.48 0.61 Transportation + communication 16.45 -0.89 0.21 Finance 26.37 -0.22 0.88 Government, personal services 47.16 1.76 2.85 Real wage Base 10% investment Invest. + TFP Urban skilled 0.437 2.077 3.174 Urban unskilled, formal 0.437 0.000 0.000 Urban unskilled, informal 0.437 1.246 2.258 Rural skilled 0.437 -0.142 1.118 Rural unskilled 0.437 0.000 0.000 Capital 0.087 1.497 2.587 Land 0.437 -1.789 -0.600 Unemployment Base 10% investment Invest. + TFP Urban skilled 13.33 13.330 13.330 Urban unskilled, formal 6.27 5.737 5.522 Urban unskilled, informal 4.93 4.929 4.929 Rural skilled 2.44 2.443 2.443 Rural unskilled 3.33 3.520 3.356 Source: Authors’ worksheets. Note: TFP = total factor productivity.

22 5. CONCLUSIONS

For a small open economy like the one in Honduras, finding a way to produce an adequate supply of foreign exchange to support the full use of domestic capacity—or even better, a growing supply of foreign exchange to support rapid economic growth—is a critical policy issue. In Section 1 we documented how exports, foreign investment, and remittances have all slowed down since 2008, with the result that the official forecasts of growth for the next several years are less than 3.5 percent. And even that is if all goes well. The historical growth evidence that we have presented suggests that both capital and the supply of foreign exchange are key complementary inputs to achieving a higher and more sustainable growth rate. Increasing the rate of capital formation has not been sufficient in the past nor is it likely to be in the future. Small open economies like that of Honduras need to have enough foreign exchange or import capacity to support the higher output level made possible by high rates of capital formation. That has important policy implications. Import capacity does two things. It allows higher rates of investment and it supports the import of final consumer goods as well as petroleum and the other intermediates needed to support production for export as well as for the internal market. All of this underlines the central importance of the export sector to an economy like that of Honduras. Unless one is willing to continue to depend on rising remittances or foreign direct investment to reach a satisfactory growth rate, it will be necessary to invest in and to promote the export sector in order to guarantee the future health of the economy. The purpose of this paper is to show the impact of various balance-of-payments shocks for an economy in which the supply of foreign exchange is likely to have a significant impact on the level of realizable output. We built a CGE model that shows the changes in production structure and relative prices that are necessary in an open economy like the one in Honduras when it confronts sharp negative balance-of-payments shocks. The model also demonstrates the difficulties that follow from a fixed real minimum wage policy for unskilled labor. It shows that negative balance-of-payments shocks drive the economy into a recession whose severity depends on whether the minimum wage adjusts or not. What sort of policy implications follow from all of this? 1. In our opinion, export expansion is going to have to be a key objective if Honduras is going to reach a satisfactory and sustainable growth rate. That is the message of both the historical evidence and the CGE model. Therefore the development strategy should be built around sectors with actual or potential comparative advantage. These are the sectors on which growth, employment, and ultimately, prosperity depends. Identifying those sectors and what they need in order to become or stay competitive should be a central objective of the government. The expansion of export capacity in nontraditional activities should be a priority of the government. 2. If one accepts that the export sectors are critical, then policies on the exchange rate and minimum wage are also critical. Honduras now has a quasi-flexible exchange rate or a crawling peg. That is a big improvement over a fixed nominal rate. But the authorities and the public have to accept that negative balance-of-payments shocks are going to require a real depreciation. That can come about through small nominal devaluations with constant prices and wages, or it can come about through a fixed nominal exchange rate and falling domestic prices and wages. Our CGE model reminds us that when a country suffers a significant negative balance-of-payments shock, there has to be either a real devaluation or a permanent reduction in output. The basic point of the model is that the real devaluation is the same in either case. But there is one exceedingly important difference, and that is in how the economy gets to a new comparative statics equilibrium. If the exchange rate is flexible, there will be a nominal devaluation and a slight rise in other prices. It is conceivable that this could be accomplished without much real disturbance to employment and output. But if the exchange rate is sticky, or slow to adjust, then the real devaluation that has

23 to occur is going to require that all wages and domestic prices go down. And that is unlikely to happen without a recession whose length and severity will be determined by how sticky or rigid downward wages and prices are. We think that permitting a nominal devaluation in response to a balance-of-payments shock is the easiest route to the necessary real devaluation because we are convinced that the adjustment to rising prices and the exchange rate is likely to be easier and less costly in terms of lost output than the alternative way of getting the real devaluation, through recession. 3. The export development plan should be designed to include projects that could be financed by international donors or would attract foreign investment. In an economy where the capacity to import is a significant determinant of output, our simulations show that any increase in the supply of foreign exchange will have a short-run positive impact on output irrespective of its long-run impact on productivity and growth. That effect is enhanced if the increased investment is channeled into productivity-increasing projects. 4. Our CGE model shows that the danger to the economy of external shocks is magnified by the concentration of exports into a very few sectors and by the minimum wage policy. If the minimum wage is fixed in real terms, and if it applies across all the formal-sector unskilled labor markets, then our model demonstrates that external shocks will cause a permanent decline in output and a rise in unemployment. The point here is that the combination of the openness of the economy and the particular factor requirements of the big export sectors makes the determination of the appropriate level for the minimum wage a very import policy decision. If it is fixed in real terms and there is a shock, exports cannot respond adequately to the need for more foreign exchange, and the economy will be pushed to a lower long-run equilibrium level of output and employment even if the nominal exchange rate is flexible.

24 APPENDIX A: ECONOMETRIC ANALYSIS OF THE DETERMINANTS OF OUTPUT GROWTH

In the long run, theory leads us to expect that the rate of growth of output in a country should be a function of the growth rate of the capital stock, the labor force, and changes in productivity. But Honduras is a very open economy. That means that the supply of foreign exchange to finance the purchase of imports that make up such a large share of the total demand for goods is likely to impose a short-run ceiling on the feasible level of output. Given enough time, relative prices and production can adjust to shortages of foreign exchange, but that is likely to be harder the smaller the economy or the larger the share of imports in total demand. In other words, in the short run, the supply of capital or productive capacity may be a necessary but not a sufficient condition to determine the level of output in an economy like that of Honduras. As seen in Section 2 of this paper, the data support the idea that the capacity to import, or the supply of foreign exchange, is an important determinant of the level of observed output. In this appendix we will more thoroughly analyze this relationship by estimating a model for GDP in Honduras in which we include a number of variables that we might expect to have a causal relationship to GDP. Based on the empirical evidence discussed above, one such variable is the capacity to import. Another should surely be the productive capacity of the economy, proxied by the capital stock. Formally, we set the level of observed income as the lesser of the capacity of the economy, determined by the supply of factors of production, capital, and labor, and a production function and mcapconadj, the economy’s capacity to import divided by the import ratio M/Y = m. That is,

Y = min(g(K,L), mcapconadj/m). (1)

As noted earlier, mcapconadj is defined as the sum of exports, capital inflows, development assistance, foreign investment, and changes in foreign debt and remittances, less interest payments on foreign debt. The import ratio itself is a negative function of the real exchange rate and the terms of trade, and a positive function of the share of investment in GDP, because for a country like Honduras most capital goods are imported. Therefore whenever there is an investment boom the import ratio goes up. That may be partially offset by an increase in the inflow of foreign capital to finance that investment. Since the capacity to import depends on exports, and since exports themselves are thought to be a positive function of the real exchange rate (REXR) and the terms of trade (TTRADE), we can rewrite equation (1) in a more general form as

Y = y(K,L, REXR, TTRADE, MCAP). (2)

Equation (2) is the theoretical equation that we will look into in analyzing the main factors that affect the growth of GDP and the possibility of the economy’s being constrained by the balance of payments. In trying to answer these questions, we use a methodology that is reported in three stages:10 1. Ordinary least squares (OLS) regression with the variables that affect GDP as independent variables 2. Analysis of the existence of cointegration between GDP, capital stock, and the capacity- to-import variable 3. Analysis of the coefficients of the regression, paying special attention to the relationship between GDP, capital stock, and the capacity-to-import variable.

10 This section is based on Piñeiro (2006).

25 First Stage We begin with a time-series regression intended to identify the significant variables that affect GDP. Following our hypothesis, we include variables that have an impact on the level of GDP. The independent variables used are (1) capital stock (kstock)11; (2) capacity to import (mcapconadj)12; (3) terms of trade (ttrade)13; (4) real exchange rate (rexr)14; (5) dummy for Hurricane Mitch in 1999 (dum99); and (6) dummy for the financial crisis of 2009 (dum09) (see Table A.1 for the actual data). The two dummy variables are included to capture the effects of Hurricane Mitch and the financial crisis of 2009. We believe these two variables affect the level of production directly, the first by destroying infrastructure and the second by reducing remittances and exports.

Second Stage In time series analysis there is always the potential problem of nonstationarity15 of the variables. If both dependent and independent variables are trending upward over time, we can easily make misleading inferences from OLS results because of spurious regression. We tested for the existence of unit roots in order to determine if the variables in the regression were nonstationary, using the adjusted Dickey-Fuller (ADF) test. Running the ADF test on all the variables used in the regression, we could not reject the null hypothesis of nonstationarity of the variables, being integrated of order 1 (except that terms of trade is stationary). The variables gdpctelcu, kstock, mcapconadj, and rexr are nonstationary. They are trending upward, and they can be characterized as difference stationary, which means that it requires differencing to obtain a stationary series. A way of dealing with nonstationary series (in which a significant relationship is found between time series when none exists) is by differencing them. When we do so, in the case of gdpctelcu, kstock, mcapconadj, and rexr, we reject the null hypothesis16 using the Dickey- Fuller GLS test.17 We notice that the GDP and import capacity variables are somehow related, with one lag period. The previous year’s import capacity will affect GDP in the following year. With this thought, we run the same equation (2) but include the lagged import capacity variable.

11 We constructed a series of capital based on the observed levels of investment after 1960. We assumed an initial capital output ratio of 2.5 and a depreciation rate of 5 percent per year. We then built up an estimate of the capital stock based on those starting values and the observed values of gross investment. 12 Defined by the available supply of foreign exchange, meaning the net yearly inflow of foreign exchange from exports, remittances, development assistance, foreign investment, and increases in foreign debt, minus interest payments on foreign debt. 13 From World Bank (2013). 14 Calculated using the nominal exchange rate, the GDP deflator index, and the US wholesale commodity price index from

International Financial Statistics. 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐺𝐺𝐺𝐺𝐺𝐺 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 15 The variable will be stationary (or weak stationary) if its mean and variance are constant and independent of time, and if 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝐸𝐸𝐸𝐸𝐸𝐸 ∗ 𝑈𝑈𝑈𝑈 𝑝𝑝𝑝𝑝𝑖𝑖𝑐𝑐𝑐𝑐 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 the covariances given by two points in time depend only upon the distance between the two time periods, but not the time periods per se. 16 The capital stock and import capacity variables are stationary with an intercept, having a t-statistic of -3.19 and -10.13. The dependent variable is stationary with an intercept, showing a t-statistic of -4.08. 17 If one includes a constant, or a constant and a linear time trend, in an ADF test regression, following ERS (1996), which proposes a simple modification of the ADF tests in which the data are detrended so that explanatory variables are “taken out” of the data prior to running the test regression.

26 Table A.1 Regression 1 Dependent variable: D(GDPCTELCU) Method: Least S]squares Sample (adjusted): 1976–2011 Included observations: 36 after adjustments

Variable Coefficient St d. error t -statistic Prob.

C 2.24E+09 1.34E+09 1.672292 0.1056 D(KSTOCK) 0.224621 0.071705 3.132590 0.0040 D(MCAPCONADJ) 1.074293 0.553039 1.942525 0.0622 D(MCAPCONADJ(-1)) 0.969357 0.517134 1.874479 0.0713 D(REXR) -5.06E+08 5.83E+08 -0.868109 0.3927 TTRADE -19131834 28594472 -0.669075 0.5089 DUM99 -6.89E+09 2.30E+09 -3.000305 0.0056 DUM09 -9.06E+09 2.81E+09 -3.219270 0.0032

R -squared 0.542030 Mean dependent var. 3.38E+09 Adjusted R-squared 0.427537 S.D. dependent var. 2.90E+09 Std. err. of regression 2.20E+09 Akaike info. criterion 46.05216 Sum squared resid. 1.35E+20 Schwarz criterion 46.40405 Log likelihood -820.9389 Hannan-Quinn criter. 46.17498 F-statistic 4.734191 Durbin-Watson stat. 1.543902 Prob(F-statistic) 0.001315

Source: Authors’ calculations.

Third Stage As mentioned in the previous section, a way of dealing with nonstationary series is to difference them, but Granger (1969) proved that differencing these nonstationary time series could destroy potential valuable information about the long-run relationship between the variables. In order to make sure we are not missing any piece of information that will help us understand the determinants of GDP growth, we follow Granger’s approach by looking into cointegration where Y and X are cointegrated if there exists a linear combination of Y and X that yields a stationary series. Such a cointegrating relationship can be interpreted as a stable long-run relationship between the components of this time-series vector. The long- run economic relationship between the variables that are cointegrated prevents the residuals (see equation [3]) from becoming larger and larger in the long run. A number of different methods for estimating the long-run equation and the short-run error-correction model (ECM) are studied in the literature. We will concentrate on the Engle-Granger (EG) (Engle and Granger 1987) and Engle-Yoo (EY) (Engle and Yoo 1991) approaches. The first thing to be done is to test for cointegration between the variables, as mentioned previously. Using the Johansen cointegration test for identifying the number of cointegrating relationships between the variables, and for estimating the parameters of the long-run relationships, we have

GDPCTELCU = β1+ β2*KSTOCK + β3 * MCAPCONADJ + β4 * MCAPCONADJ(-1). (3)

27 Table A.2 Johansen cointegration test Unrestricted cointegration rank test (trace)

Hypothesized Trace 0.05 no. of CE(s) Eigenvalue statistic critical value Prob.**

None * 0.403360 33.00060 29.79707 0.0207 At most 1 0.340244 15.44157 15.49471 0.0509 At most 2 0.037556 1.301491 3.841466 0.2539

Source: Authors’ calculations. Note: CE = Cointegrating equations. Trace test indicates 1 cointegrating eqn(s) at the 0.05 level. * denotes rejection of the hypothesis at the 0.05 level. ** MacKinnon-Haug-Michelis (1999) p-values.

The likelihood ratio test indicates one cointegrating equation at the 5 percent significance level.18

Table A.3 Normalized cointegrating coefficients, one cointegrating equation Normalized cointegrating coefficients GDPCTELCU KSTOCK MCAPCONADJ(-1) 1.000000 -2.549710 137.2849 (0.61839) (36.5051) Source: Authors’ calculations. Note: Sstandard error in parentheses.

Normalizing the equation by the GDP variable gives the long-run relationship of

GDPCTELCU = β1+ β2*KSTOCK +β3 * MCAPCONADJ (-1). (4)

It is necessary to check the residuals from the cointegration regression, testing the null hypothesis that the residual has a unit root against the alternative, that the series is stationary. If we accept the alternative hypothesis, there is cointegration. All the residuals for cointegration equation (4) pass the test for stationarity, with the null hypothesis being rejected at the 1 percent confidence level. The tricky part of using cointegration is to find a way to use the coefficients given by the regression. Engle and Yoo (1991) proposed a three-step estimator that gives t-ratios with normal distributions. The solution (in the simplest case) is to regress the residuals from the ECM19 on the I(1) variables. As explained in the following theorem (Engle and Granger 1987, 262), the two-step estimator of a single equation of an error-correction term with one cointegrating vector, obtained by taking the estimate α^ of α from the static regression in place of the true value for estimation of the error-correction form at a second stage, will have the same limiting distribution as the maximum likelihood estimator using the true value of α. Least squares standard errors in the second stage will provide consistent estimates of the true standard errors. The EY three-step estimation technique overcomes two of the limitations of the two-step EG model: (1) it deals with the problem that even though the long-run static regression gives consistent estimates, they may not be efficient, and (2) it fixes the restriction of the interpretation of the significance of the parameters. In addition, the EY third step corrects the parameter estimates of the EG first step so that the standard tests, such as t-test, can be applied.

18 There is only one cointegrating equation that will allow us to use the EG and EY single equation–based approaches. These methods assume the uniqueness of the cointegrating vector. 19 The ECM uses first differences and levels for the cointegrating relationship, leaving us with the possibility of looking at the long-term relationship and testing for spurious regression problems at the same time.

28 In the EG first step, we estimate a standard cointegrating regression (see equation [4]) in order to get the residuals and the first-step estimates. One of the benefits of this technique is that the long-run equilibrium relationship (equation [4]) can be modeled by a straightforward regression involving the levels of the variables.

Table A.4 Engle-Granger first step Dependent variable: GDPCTELCU Method: Least squares Sample (adjusted): 1975–2011 Included observations: 37 after adjustments

Variable Coefficient Std. error t-statistic Prob.

C 1.53E+10 2.29E+09 6.682894 0.0000 KSTOCK 0.232309 0.016004 14.51609 0.0000 MCAPCONADJ 2.445513 0.966933 2.529145 0.0164 MCAPCONADJ(-1) 2.533373 0.989541 2.560150 0.0152

R-squared 0.988309 Mean dependent var. 9.39E+10 Adjusted R-squared 0.987246 Std. dev. dependent var. 3.51E+10 Std. err. of regression 3.96E+09 Akaike info. criterion 47.14091 Sum squared resid. 5.19E+20 Schwarz criterion 47.31506 Log likelihood -868.1068 Hannan-Quinn criter. 47.20231 F-statistic 929.9134 Durbin-Watson stat. 0.646009 Prob(F-statistic) 0.000000

Source: Authors’ calculations.

The EG second step involves using the ECM equation to estimate a short-run model with an error-correction mechanism, using the lagged residuals from the cointegrating regression as an error- correction term.

Table A.5 Engle-Granger second step Dependent variable: D(GDPCTELCU) Method: Least squares Sample (adjusted): 1976–2011 Included observations: 36 after adjustments

Variable Coefficient Std. error t-statistic Prob.

C 1.72E+09 8.48E+08 2.025825 0.0515 D(KSTOCK) 0.122638 0.067456 1.818034 0.0787 D(MCAPCONADJ) 1.558464 0.588243 2.649353 0.0126 D(MCAPCONADJ(-1)) 0.990142 0.584202 1.694862 0.1001 RESEG1(-1) -0.305538 0.118567 -2.576925 0.0150

R-squared 0.316840 Mean dependent var. 3.38E+09 Adjusted R-squared 0.228690 Std. dev. dependent var. 2.90E+09 Std. err. of regression 2.55E+09 Akaike info. criterion 46.28542 Sum squared resid. 2.02E+20 Schwarz criterion 46.50535 Log likelihood -828.1375 Hannan-Quinn criter. 46.36218 F-statistic 3.594334 Durbin-Watson stat. 1.615247 Prob(F-statistic) 0.016098

Source: Authors’ calculations.

29 Note that the estimated coefficient of the ECM (reseg1(-1) in Table A.5) should have a negative sign and be statistically significant; to avoid an explosive process, the coefficient should also have a value between -1 and 0. This is a necessary condition for the variables to be cointegrated. It is interesting to notice that in the second step of the EG method there is no possibility of having a spurious regression because the stationarity of the variables is ensured. The combination of the two steps provides a model that incorporates a long-run component as well as the dynamic short-run components. However, it is necessary to do one more step (the EY third step) in order to have some judgment about the significance of the parameters. Now we use the EY third step to correct the estimates of the EG second step. The residuals from the EG second step are regressed on the intercept, and the coefficients of the residuals from the EG first- step variables are lagged.

Table A.6 Third step Engle Yoo

Dependent variable: RESEG2 Method: Least squares Sample (adjusted): 1976–2011 Included observations: 36 after adjustments RESEG2= C(1)+ C(2)*-0.305538*KSTOCK(-1)+C(3)*-0.305538 *MCAPCONADJ(-1)+C(4)*-0.305538*MCAPCONADJ(-2)

Coefficient Std. error t-statistic Prob.

C(1) -2.14E+09 1.40E+09 -1.526504 0.1367 C(2) 0.027619 0.032968 0.837762 0.4084 C(3) -1.437797 1.931529 -0.744383 0.4621 C(4) -1.564233 1.986191 -0.787554 0.4368

R-squared 0.073820 Mean dependent var. -6.49E-07 Adjusted R-squared -0.013009 Std. dev. dependent var. 2.40E+09 Std. err. of regression 2.42E+09 Akaike info. criterion 46.15318 Sum squared resid. 1.87E+20 Schwarz criterion 46.32912 Log likelihood -826.7572 Hannan-Quinn criter. 46.21459 F-statistic 0.850177 Durbin-Watson stat. 1.813156 Prob(F-statistic) 0.476813

Source: Authors’ calculations.

The final estimates are the sums of the ones obtained in the EG first step plus the corrections obtained from the EY third step. The standard errors are the ones from EY third step: • KSTOCK= 0.232309 + 0.027619168 = 0.259928 • MCAPCONADJ = 2.445513 -1.437797 = 1.007716 These results corroborate the results we obtained in the previous regression, in which capital stock and capacity to import were found significant and had a positive effect on GDP. One last exercise is to include the Granger causality test. The Granger (1969) approach to the question of whether x causes y is to see how much of the current y can be explained by past values of y and then to see whether adding lagged values of x can improve the explanation. Thus y is said to be Granger-caused by x if x helps in the prediction of y, or equivalently if the coefficients on the lagged values of x are statistically significant.

30 Table A.7 Granger causality test

Null hypothesis: F-statistic Prob. DGDPCTELCU does not Granger-cause MCAPCONADJ 1.49 0.24 MCAPCONADJ does not Granger-cause DGDPCTELCU 3.01 0.04

Source: Authors’ worksheets.

For this example, we cannot reject the hypothesis that GDP does not Granger-cause mcapconadj, but we do reject the hypothesis that mcapconadj does not Granger-cause GDP. Therefore it appears that Granger causality runs one way, from mcapconadj to GDP, and not the other way.

31 APPENDIX B: SUPPLEMENTARY TABLES

Table B.1 Data for regression 1

Gross domestic Capital Capacity to Real Terms of Year production stock import exchange rate trade 1974 4.2076E+10 91107727140 2256948907 0.2262 44.09 1975 4.2972E+10 97388952034 2364176741 0.2224 45.79 1976 4.7485E+10 1.01002E+11 1768888326 0.2270 46.41 1977 5.2416E+10 1.05042E+11 3578376914 0.2381 43.03 1978 5.766E+10 1.12033E+11 3582360820 0.2331 42.49 1979 6.035E+10 1.21605E+11 3482410384 0.2262 35.96 1980 6.0753E+10 1.31217E+11 3467647733 0.2242 29.53 1981 6.2292E+10 1.38839E+11 2940123207 0.2171 28.07 1982 6.1426E+10 1.43844E+11 2548024000 0.2207 28.66 1983 6.0858E+10 1.44483E+11 2928062619 0.2335 26.84 1984 6.3502E+10 1.45505E+11 3004430106 0.2363 26.12 1985 6.6162E+10 1.48548E+11 3730705652 0.2477 24.36 1986 6.664E+10 1.52076E+11 3778088652 0.2670 28.04 1987 7.066E+10 1.53295E+11 3887324720 0.2675 32.72 1988 7.3917E+10 1.57133E+11 3665170241 0.2750 41.22 1989 7.7114E+10 1.63992E+11 4034631041 0.2804 39.11 1990 7.7189E+10 1.6996E+11 5270963921 0.6737 49.77 1991 7.9699E+10 1.76088E+11 2805463800 1.0908 71.49 1992 8.4182E+10 1.84531E+11 5129681797 1.2206 69.57 1993 8.9426E+10 1.94697E+11 4437102062 1.6104 64.77 1994 8.8261E+10 2.10026E+11 3782395092 2.6599 69.26 1995 9.1847E+10 2.26424E+11 4053532362 3.5966 59.50 1996 9.5149E+10 2.42786E+11 3713563379 5.3888 52.86 1997 9.9901E+10 2.56761E+11 4018326229 7.2964 46.66 1998 1.028E+11 2.72096E+11 4372765779 8.5808 45.85 1999 1.0086E+11 2.87211E+11 5794467052 10.0278 44.53 2000 1.0665E+11 3.03894E+11 5113375274 12.8543 32.07 2001 1.0956E+11 3.18872E+11 5005986050 14.3879 29.51 2002 1.1367E+11 3.31922E+11 5645298607 16.4617 33.23 2003 1.1884E+11 3.42997E+11 6202694603 17.4900 32.65 2004 1.2625E+11 3.54864E+11 7636031206 18.4257 31.82 2005 1.3389E+11 3.72503E+11 7039672905 18.8323 28.53 2006 1.4268E+11 3.88852E+11 7253168462 18.8946 25.63 2007 1.5151E+11 4.06907E+11 7305190197 19.4168 26.33 2008 1.5792E+11 4.33307E+11 7974126173 19.0714 29.20 2009 1.5455E+11 4.62548E+11 6369314667 21.8633 32.20 2010 1.5884E+11 4.67043E+11 6984836623 21.6066 30.87 2011 1.646E+11 4.76514E+11 7307728660 21.8663 31.66 Source: Authors’ calculations.

32 Table B.2 Sectors of the disaggregated micro-SAM

Category Account Description Activities/commodities acere ccere Cereals averd cverd Vegetables afrut cfrut aolea colea Seeds and oleaginous fruits aflor cflor Live plants, flowers, and flower seeds acafe ccafe Coffee ataba ctaba acazu ccazu aotcu cotcu Bananas and other aanim canim Livestock aotan cotan Other livestock asilv csilv Products of forestry and logging apesc cpesc Fish and other fishing products ammet cmmet Metal ores amnom cmnom Nonmetallic minerals acarn ccarn Processed meat appes cppes Processed fish apvef cpvef Processed vegetables and fruits aacei cacei Vegetable and animal oils alact clact Dairy products amoli cmoli Milling apana cpana Bakery products aazuz cazuz acaca ccaca Cocoa, chocolate, and sugar confectionary abebi cbebi Beverages aptab cptab Tobacco products atext ctext Textiles, , and leather amade cmade products, cork, and others apape cpape Pulp, paper, paper products, and printing aquim cquim Chemicals and chemical and petroleum products acauc ccauc Rubber and plastic products avidr cvidr Glass and other nonmetallic products apmet cpmet Metal products, machinery, and equipment amueb cmueb Furniture aotma cotma Other manufactures aelea celea Electricity and water aconst cconst Construction

33 Table B.2 Continued

Category Account Description acome ccome Commercial services ahore chore Hotels and restaurants atran ctran Transportation asfin csfin Financial services asinm csinm Real estate services asalq csalq Rental services asgov csgov Public administration asens csens Education services assal cssal Health services ascult cscult Recreational and cultural services asdiv csdiv Other services asdom csdom Domestic services Transaction costs trc Transaction costs Transportation costs trt Transportation costs Factors flabf-ursk Labor, urban, skilled flabf-urns Labor, urban, unskilled flabc-ur Labor, informal flabf-rusk Labor, rural, skilled flabf-runs Labor, rural,unskilled fcap Capital FlndR1 Land, region 1 FlndR2 Land, region 2 FlndR3 Land, region 3 FlndR4 Land, region 4 FlndR5 Land, region 5 FlndR6 Land, region 6 FlndR7 Land, region 7 Enterprises ent Enterprises Households htegu hsped hourb Other urban hrura Rural Government gov Government Taxes dtax Direct taxes mtax Import tariffs itax Sales tax sub Subsidies Savings and investment s-i Savings and investment Source: Authors’ worksheets. Note: The agricultural activities are also disaggregated by region. For instance, there is one activity cereal region 1, one activity cereal region 2, one activity cereal region 3, and so forth (for all seven regions); they are all combined into one commodity cereal.

34 REFERENCES

Achebe, N., and P. Armington. 1969. “A Theory of Demand for Products Distinguished by Place of Production.” IMF Staff Papers 16: 159–176. Central Bank of Honduras. 2013. Producto Interno Bruto por Ramas de Actividad Económica database. Accessed November 2013. http://www.bch.hn/cuentas_nacionales_anuales.php. ———. 2013. Balanza de Pagos database. Accessed November 2013. http://www.bch.hn/balanza_pagoshon.php. Dervis, K., J. de Melo, and S. Robinson. 1982. General Equilibrium Models for Development Policy. Cambridge, UK: Cambridge University Press. Engle, R. F., and C. W. J. Granger. 1987. “Co-integration and Error Correction: Representation, Estimation and Testing.” Econometrica 55: 251–276. Engle, R. F., and B. S. Yoo. 1991. “Cointegrated Economic Time Series: An Overview with New Results.” In Long- Run Economic Relationships, edited by R. F. Engle and C. W. J. Granger, 237–266. Oxford, UK: Oxford University Press. Elliott, G., T. J. Rothenberg, and J. H. Stock. 1996. “Efficient Tests for an Autoregressive Unit Root.” Econometrica 64: 813–836. Granger, C. W. J. 1969. “Investigating Causal Relations by Econometric Models and Cross-spectral Methods”. Econometrica 37 (3) 424-438. Loayza, N., P. Fajnzilber, and C. Calderon. 2005. Economic Growth in and the Caribbean: Stylized Facts, Explanations, and Forecasts. Working Paper 265. Santiago: Central Bank of Chile. Lofgren, H., R. L. Harris, and S. Robinson. 2001. A Standard Computable General Equilibrium (CGE) Model in GAMS. Trade and Division Discussion Paper 75. Washington, DC: International Food Policy Research Institute. MacKinnon, J., A. Haug and L. Michelis. 1999. ‘Numerical distribution Functions of Likelihood Ratio Tests for Cointegration.” Journal of Applied Econometrics 14 (5): 563–577. Morley, S., and V. Piñeiro. 2008. The Impact of CAFTA on Employment, Production, and Poverty in Honduras. Development Strategy and Governance Division Discussion Paper 748. Washington, DC: International Food Policy Research Institute. ———. 2011. A Dynamic Computable General Equilibrium Model with Working Capital for Honduras. Markets, Trade and Institutions Division Discussion Paper 1130. Washington, DC: International Food Policy Research Institute. ———. 2013. A Regional Computable General Equilibrium Model for Honduras. Markets, Trade and Institutions Division Discussion Paper 1252. Washington, DC: International Food Policy Research Institute. ———. 2009. A 2004 National Social Accounting Matrix (SAM) for Honduras. Washington, DC: International Food Policy Research Institute. Piñeiro, V. 2006. “The Impact of Trade and Policy Liberalization on ’s Agricultural Sector: Technology Adoption in a Dynamic Model.” Ph.D. thesis, University of Maryland, College Park, US. Thurlow, J. 2004. A Dynamic Computable General Equilibrium Model for . Discussion Paper 100. Washington, DC: International Food Policy Research Institute. World Bank. 2013. World Development Indicators database. Accessed on December 2013. http://data.worldbank.org/data-catalog/world-development-indicators.

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