Coping with Recession in Rural China

April 21, 2018

Coping with Recession in Rural China: Strategic Labor Supply Decisions in Periods of Boom and Bust

Linxiu Zhang, Center for Chinese Agricultural Policy, CAAS, Beijing Amelia Hughart, Research Associate Department of Economics, Stanford University Scott Rozelle, Dept. of Agriculture and Resource Economics, University of California, Davis Jikun Huang, Center for Chinese Agricultural Policy, CAAS, Beijing

We would like to thank the staff members of the Center for Chinese Agricultural Policy, students of the LEADS21 Winrock Ph.D. Program and members of China Rice Research Institute (CAAS) for time and effort spent in collecting the 1996 data. We would also like to thank the officials, village leaders, and farmers in our sample area for their assistance with our survey.

Thanks to the Ford Foundation, Beijing, and the Ginanni Foundation, the University of California, for their generous financial support of this project. Coping with Recession in Rural China: Strategic Labor Supply Decisions in Periods of Boom and Bust

Abstract:

One of agricultural sector’s least explored roles in a rapidly industrializing economy is as a safety net for families of urban and rural non-farm workers who lose their jobs. This is especially pertinent to present day China, a nation with a large, landed rural population, a rapidly expanding mobile labor force, and a stop and go economy. This paper describes labor shifts in response to China’s cycles of boom and bust, explores the farmer’s and family’s decision to enter or exit the off-farm labor force at different points in the business cycle, and assesses the welfare implications of these shifts. The paper has presented evidence that China’s agricultural sector, at least that in the sample areas in Northern Jiangsu Province, has played an important stability- increasing role in the nation’s development in the reform era. It is also observed that with the emerging of labor market, human capital becomes important factor in determining off-farm employment. However, there observed some biases against female in off-farm employment although not all of them gained or lost in the business cycle. Coping with Recession in Rural China: Strategic Labor Supply Decisions in Periods of Boom and Bust

One of agricultural sector’s least explored roles in a rapidly industrializing economy is as a safety net for families of urban and rural non-farm workers who lose their jobs. Although researchers have explored intersectoral labor linkages between agriculture and the rest of the economy (Hirschman, 1970; Johnston and Mellor, 1961), such studies tend to focus on the movement of labor out of rural areas into urban areas and mostly ignore the reverse flow of labor during recession times. In many nations, however, agriculture acts as a buffer against the impact of economic contraction: not only producing labor during economic expansions, but also absorbing labor when people lose their off-farm jobs. Despite the lack of formal study, agriculture’s role as safety net is a common phenomenon. Agriculture’s capacity to produce and absorb labor, has been documented in many cases in history – the 19th century United States, post

World War II Japan, and Taiwan and Korea during their eras of miracle growth in the 1960s and

1970s (Todaro, 1976; Nafziger, 1997; Jia, 1991; Jayasuriya and Shand, 1986; Misawa, 1969;

Takeya, 1991; Christiansen, 1990; and Abey, Booth, and Sundrum, 1981). More recently, flexible labor markets have facilitated labor movement back into rural areas during recessions in many countries, such as, Peru, Korea, and Thailand (Lee, 1980; Laite, 1985; Stretton, 1985;

Vanderkamp, 1972). In the current economic crisis in Asia, large numbers of unemployed urban workers are returning to rural areas, where individuals subsist by rejoining the agricultural labor force (Richburg, 1998).

Agriculture’s ability to assimilate labor also is an implicit assumption behind many labor movement models. Conventional economic models, including Todaro’s and the Fei-Ranis revision of the Lewis model, typically assume that laborers remaining in rural areas can find sustenance and employment while waiting for opportunities in the off-farm or urban sector (Gillis

1 et. al., 1996). Traditional analytical frameworks also assume that workers will lose their industrial jobs if their marginal productivity falls below the wage level and agricultural workers on family farms will continue to work as long as the marginal product of their labor is higher than the family’s value of leisure. More specifically, although often unspoken, small family farms are assumed to support family members, employing them on the farm when they are laid off or when an individual has not yet found employment in the off-farm economy (Rosenzweig and

Binswanger, 1993).

Understanding agriculture’s role as both a source of labor and as a safety net is especially pertinent to present day China, a nation with a large, landed rural population, a rapidly expanding mobile labor force, and a stop and go economy. Faltering growth rates of rural enterprises and recent government announcements of proposed layoffs in state-owned enterprises and government bureaucracy raise the prospect of rising levels of urban unemployment (Kwang,

1998). Worries that the Asian crisis will spread to China, valid or not, also raise interest in agriculture’s capacity to absorb unemployed off-farm labor and unsuccessful job seekers. If unemployed rural workers can support themselves by moving back to the countryside (or postponing migration), there may be less risk of political and social unrest as the result of the current or future economic fluctuations.

The strength of agriculture’s capacity to absorb and release labor also has important implications for household incomes and for the development of a strong and healthy rural economy, whether economic times are good or bad. If good rural labor markets help absorb laid- off workers during economic dips, the start and stop record of China’s economy in the recent past and prospects of continuing uneven employment growth in the future make it even more imperative for China to foster labor market development.1 If families can depend on agriculture to support off-farm workers who are laid off or under employed during recessions, family members will be more willing to accept the risks of joining the off-farm sector as wage earners or entrepreneurs (Giles, 1997).

2 Despite the potential importance of the linkage between the agricultural and nonagricultural labor markets in China, there still is relatively little research on the dynamics of the household’s and individual’s labor supply decision and the movement of labor between the agricultural and off-farm sectors during different phases of the business cycle. Many questions exist concerning the decisions of rural family members to take jobs in these sectors during periods of recession or expansion, and the welfare implications of such off-farm labor shifts.

Where do workers seek employment? How big are labor shifts between sectors? Does the agricultural sector in China absorb labor during recessions? Does the family labor allocation change should a member working off-farm lose his job? Who loses off-farm jobs when the economy contracts and who enters the off-farm labor force during times of economic expansion?

The overall goal of our paper is to answer some of these questions and help increase our understanding of labor supply in China during times of recession. To meet this goal, our paper describes labor shifts in response to China’s cycles of boom and bust, explores the individual’s and family’s decision to enter or exit the off-farm labor force at different points in the business cycle, and assesses the welfare implications of these shifts. The following sections present our data, outline our analytical approach, present our results, and offer conclusions and draws policy implications.

Space constraints and data limitations have forced us to narrow the focus of our inquiry.

While an important part of the ability of agriculture to absorb and release labor also involves other questions of labor demand, we focus on labor supply behavior. Moreover, because meeting our objectives requires examining data over time on labor supply and entry and exit decisions at different points of recent business cycles, we need household time allocation data which covers both bust and boom periods. One of the only such data sets we know of was collected by the authors in northern Jiangsu in 1988, 1992, and 1996. While the local nature of the sample limits casual attribution of the findings to all of China, the data’s high quality and unique feature of following the same households and individuals in the households over a 9-year period allow us to

3 examine many interesting questions of labor supply in times of economic stress. We also exploit the panel nature of the data in our choice of econometric techniques.

Boom and Bust in Reform China

China’s reform period is characterized by remarkable economic growth in both agriculture and industry. National GDP rose from 896 billion yuan (1997 value) in 1986 to 6.9 trillion yuan in 1996 (State Statistical Bureau, 1997). Much of the credit for this growth goes to a series of agricultural and rural industrial reforms implemented beginning in 1978 (Naughton,

1995). The reforms provided new opportunities for farmers, allowing them to respond to market signals instead of central planning commands. Agricultural production shifted to the household responsibility system, and as farm families also took greater control of labor allocation decisions, production increased as farmers responded to the new incentives (Lin, 1992). Led by the rapid growth of township and village enterprises (TVEs) and expanding urban labor markets, many farmers and their family members began to supply their labor to off-farm activities, resulting in the rise of non-farm employment from 67 million to 130 million between 1985 and 1996 (State

Statistical Bureau, 1997).

China-wide Business Cycles

China’s economy, however, has not grown at a uniform even pace, a fact that scholars agree on, but the causes of which remain controversial (Figure 1, Panel A). Naughton (1995) describes a complicated cycle of reform and retrenchment. Yusuf (1994) details a policy and inflation cycle. Zhu and Brandt (1995) blame financial and fiscal policy. Whatever the cause, the economy surged ahead in the mid- and late-1980s, slowed following the retrenchment of 1989, and after recovering slowly in the early 1990s, boomed again in the mid-1990s.

Employment typically has risen and fallen with economic growth. After increasing rapidly in the 1980s, both off-farm and urban employment growth rates fell during the 1989-91 recession and then climbed again in the mid-1990s (State Statistical Bureau, 1997). Construction

4 employment, one of the largest and most cyclical employers of rural labor, rose in the 1980s, fell as GDP growth declined in 1989 and 1990, and grew once GDP growth rates recovered in the mid-1990s (Figure 1, Panel B). During recessions, some cities experienced reversals of rural to urban migration as rural-urban migrants lost jobs in the city and local government officials encouraged workers to return to rural areas (Cheung, 1990).

Economic Fluctuations in Jiangsu Province

The economic growth of Jiangsu Province has followed the same general trends found in

China during the reform era. Growth averaged more than 20 percent in the mid-1980s, before dropping sharply in the early 1990s (Figure 2, Panel A). The province experienced its highest growth in 1993 and 1994. As shown in Rozelle and Jiang (1995), the cyclical trends are even more pronounced in the northern part of the province, the location of our study’s sample villages.

Northern Jiangsu growth rates in boom times exceed those of the south, in part because the north’s economy started at a lower level. Growth rates, however, fell further and recovered later, making an even more exaggerated boom and bust cycle.

Just as in the case of China, Jiangsu’s off-farm employment trends mirrored those of

GDP. Construction employment rose rapidly in the 1980s, fell in the early 1990s, and recovered in the mid-1990s (Figure 2, Panel B). If construction employment trends are indicative of general off-farm employment trends, as in national data, we may conclude that the recession hit off-farm employment in Jiangsu with substantial force, especially in the northern part of the province

(Rozelle and Jiang, 1995).

Differential Labor Flows

Not all sectors employing rural labor, however, have grown or contracted the same

(Figure 3). Rural employment growth rates in some major industrial and transportation sectors experienced the same cyclical pattern as construction. The rise and fall of employment in the retail and service sectors were less sharp. Employment in some types of firms had strong growth throughout the late 1980s and 1990s despite the recession (State Statistical Bureau, 1997). For

5 example, employment in firms funded by foreign direct investment grew 25 percent per year before 1988, 19 percent between 1988-92, and 18 percent in the mid-1990s. These patterns suggest that employment opportunities for those workers most likely to find jobs in various sectors may differ overtime. Respondents in our sample villages confirmed that by the early

1990s, jobs in export-oriented foreign and domestic firms could be found more easily, despite difficulties faced in other sectors.

The sponge-like effect of labor leaving and returning to the farm is illustrated by counter- cyclical rural employment growth rates of industry and agriculture in China between 1986 and

1996 (Figure 4). During the recession of the early 1990s, labor forced out of slow growing off- farm sectors may have been able to shift back into agriculture. In contrast, during times of rapid growth in the late 1980s and mid-1990s, labor shifted out of agriculture into the off-farm sector.

Supply Decisions in the Survey Households

Households in our sample villages experienced the same recession-expansion cycle that swept China and Jiangsu Province from the late 1980s to the mid-1990s. Mean per capita family income fell from 5 to 58 percent in each of the villages between 1988 and 1992 and grew from 16 to 167 percent between 1992 and 1996. On average, deflated per capita family income fell by nearly 30 percent between 1988 and 1992, and had recovered and exhibited healthy new growth by 1996 (Table 1, row 1). Off-farm income fell between 1988 and 1992 and rose between 1992 and 1996, accounting for almost all of the change to total income. Agricultural earnings trends rose between 1988 and 1992, partially offsetting the shock in total income caused by family members losing their off-farm jobs.

Labor allocation of households followed similar patterns, displaying evidence that it was responding to macro-economic pressures. As the economy sagged in the early 1990s, total off- farm labor allocation of our respondents fell by about 20 percent, from 104 to 84 days per year

(Table 1, row 2). In contrast, average agricultural labor allocation of the total sample jumped 63 percent during the recession, increasing from 51 to 83 days per person (row 3). The opposite

6 labor allocation pattern occurred when the growth of the economy picked up again in the mid-

1990s; off-farm labor rose (although less than it had originally fallen) and household labor input into agriculture fell by about 30 percent. Entering and exiting trends mirror participation rates

(rows 10 and 15).2 Appendix A contains a description of other traits of rural laborers.

Hypotheses and Econometric Analysis

The above descriptive analysis suggests that the economic recession in the early 1990s had a significant impact on households in rural China. Such an impact, however, differs among different population groups. Most importantly, there is evidence showing that apart from the traditional roles that the agricultural sector has been playing (i.e., providing food and industrial labor for the economy), it also acts as a safety net for rural households during economic downturns, helping buffer family incomes by absorbing the laid-off laborers. Likewise, agriculture then re-releases labor during the next economic expansion.

Our strategy for more rigorously testing the validity of these observations is to undertake a series of empirical activities examining how individuals and households respond with their labor decisions to buffer the negative shocks of recessions. First, we examine if individuals in our sample are being “hurt” in terms of access to off-farm jobs and wages by the recessionary forces that swept China, in general, and northern Jiangsu Province, in particular, in the early

1990s. Next, we estimate the agricultural labor response of households, investigating how they used their labor in 1992. Finally, we explore what happens when an individual loses an off farm job and how other members of the household respond when one loses a job.

Model Specifications

Off-farm work participation

In this study, a probit model is used to estimate off-farm work status determinants. The basic form of the model is:

7 Y = aX1 + bX2 + cX3 + dX4 + eX5 +   where Y is a dummy variable equal to 0 if the individual did not work off-farm, and 1 otherwise.

The sets of explanatory variables include human capital characteristics (X1--age and age-squared, education and education squared), family characteristics (X2--both on the consumption side, such as the number of children under age 6 at home, number of elderly at home, number of working age family members, and on the production side, such as land size), a gender variable (X3- equaling 1 if the individual was female), village effects (X4--to hold constant the differential impact village characteristics might have on employment participation), and two year dummies

(X5- one for 1992, the recession year, and one for 1996, the second boom year). A test for the impact of the recession, will be that, everything else constant, we will find a negative coefficient for the year dummy of 1992. In an alternative specification, we also take advantage of the panel nature of our data, replacing non-time varying household characteristics and village dummies with a set of dummy variables for each individual. The fixed effects specification eliminates all unobserved latent heterogeneity from the estimation.

Exit and entry models

The second set of equations is used to identify the determinants of exit / entry of off-farm sector of the sample individuals:

Ei = aX1'+ bX2 + cX3'+ dX4 + eX5 + , for i = 1 and 2, (2)

where E1 is a dummy variable equal to 1 if the individual exited the off-farm sector from one period through the next, and where E2 is a dummy variable equal to 1 if the individual entered.

The explanatory variables are similar to those included in participation model, except that in order to examine the propensity of different groups of individuals in the sample to have identifiable and different exit and entry behavior, age variable are interacted with the gender

dummy variable. The human capital matrix, X1', only includes education and education squared,

and the gender and age matrix, X3', includes five terms that interact gender with three age

8 categories (the excluded base category being young males). The age categories are defined in

Appendix Table A1. The sample for the entry analysis is limited to those who in the labor force who do not work off-farm at the beginning of either of our two periods of study;3 the sample for the exit analysis is limited to all of those in the work force who had off-farm jobs in the beginning of either of our two study periods. The definition of the dependent variables makes the estimator of equation (2) almost like that of a first differences model, a function that makes the analysis nearly equivalent to using a fixed effects framework.

Wage equation

In order to analyze the determinants of off-farm wages, a Heckman two-stage least squares model is used. The basic logic is that if we directly estimate the wage equation in a single equation model, we would potentially have biased results because the sample did not include those individuals which chose not to work since we have no wage observations on those who did not work off the farm for a wage. But the behavior of non-working individuals includes information that can help identify the determinants of wages. At the wage that they face in the labor market, such individuals do not choose to supply labor to the market. Our estimation allows us to include all individuals in the analysis.

The solution to this problem is to estimate the wage equation in two stages. The first stage is to estimate a probit equation of the choice whether or not the individual chooses to work

(similar to equation 1). From the first stage of the analysis, one can recover the Inverse Mills

Ratio (IMR), which measures the propensity for a person to participate in the labor market. Its inclusion in the second stage, the determinants of wage equation, corrects for the bias that would otherwise affect estimates of the wage equation with the censored sample.

To get better identification on the coefficients of the wage equation (better that is than just relying on the inclusion of the IMR), one also wants to include variables in the estimation of the participation (probit) that are significant determinants of whether or not to work, but have no effect on the independent variable in the second stage of the model, the wage. In our case, we

9 assume that land size, family size, the number of children, the numbers of elders at home, and the characteristics of the households, affect labor participation, but do not affect the wage rate which is determined by labor market traits and the individuals human capital. The first stage of the analysis is similar to equation (1). The second stage wage model is:

Ln(Wage) = aX1 + cX3 + eX5 + fX6 +, (3) where the dependent variable is a measure of the daily wage net of mandatory, work-related expenses; X1 and X5 are matrices of human capital variables and year effects as in equation 1, X3 is a gender variable, and X6 is a matrix of sector-specific dummy variables, since the wage of an individual will be affected if the job that they are in is in a factory, construction firm, or trading enterprises (with the excluded base category representing all others, a category dominated by the service sector). Because men’s and women’s wage determinants are likely to differ, we also include separate regressions by gender.

Agricultural labor allocation equations

For a direct test of whether or not agriculture helps buffer the effect of a recession, an ordinary least square equation, with and without fixed effects, is used to estimate the determinants of individual and family agricultural labor allocation, measured in standard labor days (8 hours) per year. In the household labor response equation, a 1992 dummy variable and a measure of the rest of the household’s off farm work status are included to estimate the propensity of the family to increase their on-farm use of labor when layoffs hit its members. We also run a similar equation for individual labor response. While holding the individual’s own off farm work status constant, we include a measure of the percentage of family members (not including the individual) that is working off the farm as a direct test of the “buffer effect.”

Allocation to the off farm sector should rise as the proportion of family members working off farm falls.

Because of the possibility that these measures of the rest of the families off farm labor allocations also depend on the individual’s (and household’s) agricultural labor decisions, we

10 need to worry about endogeneity. Generally we try four approaches: Include both the 1992 dummy (recession year effect) and uninstrumented family off farm work status; include the year dummy variable by itself; include the instrumented off farm work status variable by itself; and include both the year effect and the instrumented variable. We identify off farm work status with demand side variables that are unrelated to family or individual labor decisions, such as the year effect, the growth of county employment and output, and the total employment in the village-run factories. Unfortunately, although these variables pass the Hausman-Wu exclusion restriction tests, and are the best instruments we could find, they only explain a small fraction of the variability of off farm labor supply by other household members, and as such their predictions may not be a very good proxy for demand-driven labor shocks to the family. In addition, the same household traits (X2), gender (X3), village effects (X4), and year effects (X5) variables, the predicted wage from equation (3) (without sector-specific dummies) is used in individual labor response equation. In our family agricultural labor allocation model, since the entry and exit happens before the current year’s labor allocation decision, simultaneity bias is not a problem.

General Performance of the Econometric Models

To implement our testing procedure, we estimate sets of equations for determinants of off-farm work status, entry and exit, wages, and household and individual labor supply. Most of all the models perform well in terms of the goodness of fit. For example, the adjusted R-square statistics of the agricultural labor supply equations and the wage equations that are estimated by

OLS are all above 0.30. The goodness-of-fit measures of the probit equations for off-farm labor participation, entry and exit of off-farm labor force show similar precision.

The signs of the coefficients of many of the explanatory variables also are as expected and significant. For example, in the initial set of equations explaining the determinants of off- farm employment status (Table 2), the human capital variables (in the equation pooling all years, columns 1 and 2) show that increases in experience (age) and education has a positive, but

11 diminishing, effect on the decision by individuals to move off the farm. The signs and significance of the human capital coefficients in the year-specific regressions (1988- columns 3 and 4, 1992- columns 5 and 6, and 1996--columns 7 and 8) show a progression of signs that demonstrate the emergence of China’s labor markets. Moving from 1988 to 1996, the signs and t-ratios on the coefficients are consistent with a labor market that is providing greater rewards for higher human capital in the mid-1990s. In contrast, the household traits, such as size of the land holdings and village characteristics, which play an important role in determining off-farm participation in the 1980s, become less important in the 1990s, an indication of a developing, more impersonal labor market. The signs of most coefficients in the other equations have intuitive explanations.

Results

Impact of Recession

Our off-farm participation model shows that the recession in the early 1990s did adversely affect employment in the off-farm sector (Table 2). The negative and significant sign on the 1992 Year dummy variable means that, all other factors held constant, individuals in the sample had a 13 percent less chance of being employed in the off farm sector (column 1 and 2).

In the equation using only observations from 1992, (column 5 and 6), we see that farmers in villages 3, 4, and 5 were laid-off proportionately more than those in the most industrialized village (the base village), a result that we would expect if local governments continue to operate their firms to keep employment up even when business is poor. When fixed effects are added to the model, the results are robust (Appendix Table B1). Unless people were systematically returning to agriculture on their own volition (a scenario that is quite unlikely given the low farm

12 prices in 1992), the results show the effect of the recession on labor allocated to the off farm sector.

The results of the exit and entry models (Table 3) suggest a similar pattern to that found in the off-farm participation equation. Individuals exit (or are laid off) more frequently (21.1 percent more often) in the 1988-92 period than in the later period (1992-96--the negative sign meaning they exit less often). Additional evidence of employers indiscriminately cutting workers in the recession can be found in columns 3 and 4. Education variables play little role in discriminating who got laid off in the 1988-92 period. But, in the period when they were hiring again during the boom period of the mid-1990s, employers rewarded education in workers by giving them jobs, allowing them to enter more frequently than those without education (Table 7, columns 7 and 8).

The two-stage Heckman estimates show that recession also affected wages (Table 4, column 1). The negative coefficient on the 1992 dummy variable on the first stage probit regression is the same size and magnitude as that in the participation equation (Table 2). The negative sign on the gender variable in the wage equation (-0.13) implies that wages of women during the period, other things accounted for, are 13 percent lower than men’s. The employment of men, in particular, was adversely affected in 1992 (Table 4, column 3). Women’s employment participation, in contrast, did not change significantly in 1992 (column 5); their wage, however, did fall significantly. Although the wage for men in 1992 was not lower than that in 1988, it was

65 percent lower than the wage in 1996. In summary, our results are unambiguous in their portrayal of 1992 as a year when off-farm labor employment was down, workers generally were both laid-off in increasing numbers and had difficulty finding new jobs, and wages for most were lower than those in times of greater economic prosperity.

Coping with Recession

Household labor response in times of boom and bust demonstrate the role that agriculture plays in buffering layoffs in the off farm sector (Table 5). At the household level, the findings

13 are strong and robust to different estimators and specifications (column 1 to 4). In the equations in which only the instrumented number of family members are included (i.e., there is no 1992 year dummy in both the models with and without fixed effects), the results show that each time a family member gets laid off, ceteris paribus, the number of days worked on the farm by the family goes up by over 200 days (equations 3 and 5). Agriculture in this part of China has shown an amazing ability to absorb labor laid off. The rapid growth of yields and gross value of output during this time period certainly helped maintain incomes even during the times when off-farm jobs were hard to find.

The results of the individual agricultural labor response equations also mostly support the findings of the household-level analysis (Table 6). In all of the equations (both those with and without fixed effects) that include a time dummy for 1992, it can be seen that individuals have raised their labor allocation effort to agriculture during northern Jiangsu’s recession (columns 1,

2, 4, and 5). When the individual’s own work status and a variable representing the number of family members working off the farm are included in their uninstrumented form, we can also see the expected correlation between agricultural labor response and the off-farm labor status of everyone (the individual- strongly significant--and other members- the right sign but insignificant) in the family (column 1).

The equations with instrumental variables (for off-farm work status of the individual, himself or herself, and for that of the other family members), however, are sensitive to the specification of the equation and sometimes do not clearly show the relationship between individual labor response and work status of the family. The t-ratios on the coefficients of the instrumented variables are small and the signs vary (column 3). While the inability to find significant signs on the variables in these equations slightly weakens the definitiveness of the results, it may very well be that the result of interest disappears because of the relatively weak set of instruments. In fact, despite the potential weakness of our instruments, when we include a set

14 of village and instrument interaction terms, we find that in several of the villages the sponge effect appears (Appendix Table B2).

Welfare Effects

Women are more vulnerable to recession. Within our sample, while everyone works, participation in the off-farm labor force is significantly lower for women (Table 2). Even accounting for human capital levels and the structure of the family, women have a 28.2 percent less chance of working off the farm. They are 22.4 percent more likely to be laid off during the recession (Table 7, columns 1) and 10.3 percent less likely to enter the off-farm sector in all periods and even less likely (21.5 percent) during the boom period (Table 7 – columns 5 and 7).

For some reason, women are being systematically excluded from the labor market. Our analysis suggests that there is some cultural, gender discrimination, or other unmeasured factor that is keeping down women’s employment off the farm.

The recession impact, however, affects different age groups differently. At the time in the early 1990s when most were being laid off, young women were not exiting nearly as fast as men (Table 3, column 3). Young married women were more likely to be employed (Appendix

Table B4) and were actually entering at a faster rate than any other group during this time

(column 7). Although in 1988 and 1996, the boom years, young married women worked significantly less than other groups, in 1992 they worked significantly more. These contours of employment show that women, who are disproportionately employed in the more-recession proof service and export-oriented sectors, apparently are taking advantage of the fact the males in the family have returned to the farm. Although the family may not find it profitable to send young single and married women to these jobs when men have opportunities to work off-farm, when men do not have jobs, the employment opportunities become attractive as the opportunity cost of having the women leave the farm are lower. Job-switching is another way that households are attempting to buffer the effect of recession.

15 Conclusion

Our paper has presented evidence that China’s agricultural sector, at least that in the sample areas in Northern Jiangsu Province, has played an important stability-increasing role in the nation’s development in the reform era. Families and individuals in these areas are making strategic labor allocations, taking into account the decisions of other members. When layoffs increased and hiring slowed in the late 1980s and early 1990s, those who lost their jobs returned back to the agricultural sector. Increased labor use in agriculture certainly has reduced the fluctuation of incomes that would have been suffered if there was no work on the farm. A new item should be added to Johnston and Mellor’s list of five contributions of agriculture to development.

If employment in the off-farm sector increases status, women have paid the price of the household’s strategy to send some members off farm and keep others at home doing agricultural work. Women work absolutely more on the farm in all periods and allocate a far greater proportion of their labor to farm work. When women do get jobs off the farm, their wages are lower and they are more likely to be laid off.

Not all women, however, are hurt by all recessions. An argument can be made that some women gained from the recession of the early 1990s. When men in their households returned home and began to increase the amount of farm labor, some young married and single women found that they were freed to move into jobs in those industries less hurt by recession. While it is too early to tell, the experience gained by these women, even if they are denied opportunity to work off the farm in the next boom (because men find it more profitable to re-enter the job market), it may help their status and position in the village. Work experience off the farm often teaches women skills and provides an experience in which they have directly earned income and contributed to the household in ways that could not have been as easily seen if they had stayed on the farm.

16 Not all recessions, however, may be so gender-friendly. In fact, China’s current economic difficulties are in part due to the Asian crisis which has disproportionately slowed down the export sector. These labor-intensive sectors have traditionally hired large numbers of rural women. With its slowdown, women of all ages have suffered layoffs and wage cuts. Hard evidence is not available to prove these assertions, but many accounts of the current recession in

China suggest that women have born an even greater proportion of the downfall in terms of reduced off-farm employment.

In more general terms, the stability-increasing role of agriculture may disappear as the sector begins its transformation to a modern farming system. This may help free women and others to take better advantage of off-farm opportunities and give them more leeway in planning and pursuing a career. But, there is a price of moving to larger scale, more heavily capitalized farms. Healthy agricultural sectors using appropriate technology in developing countries can add much needed stability to the disequilibria that always accompany development.

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21 Endnotes

22 11 A decline in the number of rural-to-urban migrants is one result of the current economic slow down as central and local governments put pressure on SOEs to reduce employment. One estimate places the number of recent migrants moving back to the countryside at thirty million (Becker). The proportion of the rural labor force working in urban areas fell from 6 to 7 percent in 1995 to 3 to 4 percent in 1997 (O’Neill). Future industrial and governmental reforms will certainly lead to even greater urban layoffs and slow down in hiring in the short run. 2 2 An enterer is an individual who is in the sample in both periods, does not have a job in the first period, but has a job in the second. In contrast, an exiter has a job in the first, but not the second. Exiting rates between 1988 and 1992 exceeded those between 1992 and 1996; the propensity of our sample individuals to enter is just the opposite (Table 1, rows 12 and 15). Families in our sample appear to have attempted to cushion the effect of losing off-farm jobs in the early 1990s by increasing on farm labor.

38 The sample includes all of those in the workforce without off-farm jobs in 1988 who were still present in the workforce in 1992 (period 1) and all of those without off-farm jobs in 1992 who were still present in the workforce in 1996 (period 2).