Barriers to Health Care: the Relief Effect of Remittances in Tajikistan

Sandra Pellet and Florence Jusot

Abstract

While the impact of remittances on consumption and investment is well studied, there are fewer studies exploring their influence on healthcare utilization. In a context of cost barriers to healthcare and heavy reliance on migration, it is legitimate to ask whether remittances alleviate the budget constraint and allow healthcare consumption in migrant- sending households. Here we investigate the effect of remittances on foregone care, access to care and expenditure using a two-part modeling, using data from the 2007 Tajikistan Living Standards Survey. Indeed, in Tajikistan, out-of-pocket expenditure increased substantially during the last two decades. In the meantime, labor migrations and the remittances increased, reaching 50% of GDP. At the same time, health needs may have been a factor in sending someone abroad to face tremendous out-of-pocket expenses, inducing endogeneity. Using the 2SLS procedure, we address this hypothesis. We find an income effect of currently abroad migrants' remittances on healthcare access.

Sandra Pellet PSL Research University Université Paris-Dauphine, LEDa-LEGOS Place Maréchal de Lattre de Tassigny 75016 Paris (France) Contact: [email protected]

Florence Jusot PSL Research University Université Paris-Dauphine, LEDa-LEGOS Place Maréchal de Lattre de Tassigny 75016 Paris (France) Contact: [email protected]

Keywords: Migration, Remittances, Health care use, Unmet needs, Postsocialist Transition.

JEL codes: D6; F2; I1; I3

1. Introduction

Migrants’ transfers play a critical role in the economy of the country of origin. An indicator of their role is the significant contribution to GDP that remittances make in many developing countries. Small developing economies tend to show much higher remittance dependency than giant emergent economies, such as India and China, which are the top recipient countries of remittances in total amount. For example, 25% of households in Perù receive migrants’ transfers, which accounted for more than one fifth of the recipients’ income (Cox et al., 1998) and remittances in 2014 accounted for 42% of the GDP in Tajikistan, 30% in Kyrgyz Republic, and 29% in Nepal (Ratha, 2016). These examples show the dependency many developing economies have on private transfers sent from abroad by households’ members or relatives. Remittances may also have a long-term effect on economic growth through their impact on capital and human capital accumulation (Rapoport and Docquier, 2006; Taylor and Mora 2006; Clément 2011). Since the 2000s, migration studies have increasingly investigated the use of remittances for non-durable and durable consumption, as well as for productive investment in local enterprises. These studies provide mixed results on the impact of remittances. Nevertheless, they all confirm the key role that migrants’ transfer play in emerging economies. The effect of remittances on human capital investment and human capital accumulation has also been explored in response to the potential longer- term impact these investments may have on endogenous growth (Gatskova et al. 2017). For example, some studies have focused on the effect of remittances on children's educational outcomes (Hanson and Woodruff 2003; McKenzie and Rapoport 2006 ; Bennett et al. 2013). There is also a growing body of literature that is focused on the potential impact of remittances on healthcare expenditure and health outcomes (Hildebrandt and McKenzie, 2005; Amuedo-Dorantes and Pozo, 2011). The demand for health care is in some aspects comparable to the demand for food, which is necessary to satisfy short-term needs. This demand also corresponds, as the demand for education does, to an investment in human capital and more specifically, to its health component, which contributes to health capital accumulation. According to the health capital model of Grossman (1972), individuals have incentive to invest in their health capital in order to compensate the natural depreciation of health with age. On one hand, it is a consumption motive because the stock of health capital is one of the arguments for utility function, although on the other hand, it is a productive motive since the stock of health capital determines the individuals’ ability to work and thus produce a disposable income. Within this framework, remittances may release the intertemporal budget constraint and give individuals additional possibilities of investment in health capital via either additional health expenditures or additional time dedicated to health improvement. In recent studies focused on health issues, most investigate the effect of migrants’ transfers on health outcomes (Kanaiaupuni and Donato, 1999; Franck and Hummer, 2002), whereas less attention has been paid to their impact on access to care (Valero-Gil, 2009). However, health expenditures are of particular interest since they constitutes a particular type of expenditures. First, even if they occur more or less often depending on initial health status, socioeconomic characteristics or local opportunities (healthcare supply, transport, accessibility etc.), they may be exceptions and not necessarily predictable. Second, when health cares needs occur, consecutive health expenditures may be catastrophic, if compared to other consumption. Thus, these costs may act as a barrier to the consumption of care and this restricts care access and cause care renunciation. As a consequence, remittances are expected to improve access to expensive health care, or to health care with large out-of-pockets payments such as in liberal healthcare systems or corrupted systems. For instance, in former Soviet areas, where the healthcare system was inherited from the universal socialist system, the share of out-of-pocket expenses is very high due to the existence of informal payments. It is the case in Tajikistan where out-of-pocket expenditures increased substantially through formal and informal channels in the past two decades (Khodjamurodov and Rechel, 2010). In this study, we investigate the effect(s) of remittances on care access in Tajikistan. Few studies have analyzed the impact of remittances on healthcare expenditures, and most of them don’t account for health needs such as in Kan (2016). Therefore, these investigations fail to provide evidence on the impact of remittances on the appropriateness of healthcare use according to the needs of the population. To fill this gap, we analyzed the impact of remittances on healthcare use after controlling for health needs that were encoded by three indicators: the existence of any health expenditure (in order to measure the impact of remittances on access to care), the conditional amount of health expenditure (in order to measure their impact on the intensity of the health care received) and the existence of forgone care during the previous year (in order to assess their impact on unmet needs). We also attempt to distinguish between health expenditure components. Remittances are not likely to affect unpredictable and predictable health expenditures in the same way, and this difference likely depends on the type of health problem. Either households call on the migrant regularly for paying predictable care, for example, because of the presence of a chronically ill or pregnant person in the household; or remittances are mobilized in cases of an unexpected emergency, for instance, an unexpected and severe surgery. To this end, not only do we control for health status, but we have also dichotomized health expenditure into two components: outpatient health expenditure and hospital health expenditure. This likely did not entirely reflect the difference in predictable and unpredictable spending, however, it does have the merit of looking more closely at whether remittances affect all of the expenditure components in the same way. Households may ask migrants for regular help in dealing with a chronic illness. In addition, health care needs within the household in the country of origin may be the purpose for the original migration and for remitting. As a consequence, a naïve estimate of the impact of remittances on access to care may suffer from endogeneity bias, as is discussed by Amuedo-Dorantes and Pozo, (2011) and Kan, (2016). The motives of remitting are well documented since the 1980s and summarized by Rapoport and Docquier (2006). The motives are various across migrants and often numerous in an individual migrant. Motives can be both individualistic (pure altruism, exchange of service, strategic) and resulting from a familial arrangement, as is suggested in the rich literature that has focused on informal mutual insurance and familial investment (Katz and Stark, 1986; Stark and Lucas, 1988; Stark, 1995; Shaw, 1988; Lambert, 1994; Schrieder and Knerr, 2000). We might predict that health shocks would affect the receipt of remittances in cases where the "altruistic", “exchange of service” or "insurance" models apply or dominate others. Rapoport and Docquier (2006) and Cox et al. (1998) note that transfers are often targeted to the unemployed and sick people and this is in line with the altruistic, exchange of services and insurance models. Moreover, catastrophic healthcare expenditures can be considered as one of the probable, adverse short-run shocks in recipients’ income that affect the receipt of remittances (Ambrosius and Cuecuecha 2013). In the case of altruism, the migrant helps the household to cope with (potentially catastrophic) health expenditure following a shock, but this is not necessarily the reason why he/she migrates. In the case of the exchange of service model, the remaining members, such as the wife or elderly member(s) of the household nurse the children in exchange for an income surplus and then the health expenditure may be endogenous. In the instance of the insurance model, the migrant and household, as agent and principal, have a contractual agreement: the household helps the migrant to migrate (investment), provided that he pays for health care (dividend). Under this model the health expenditure is endogenously related to remittances. We contribute to the discussion regarding the endogeneity of health expenditure by investigating the effect of health variables on intention to migrate and by using a 2sls estimator (sections 4). According to what has been said above, predictable and regular expenditures are also more likely to be endogenous to migration.

2. Background: barriers to healthcare and reliance on remittances in Tajikistan

This study investigates the role of remittances in facilitating the access to healthcare in Tajikistan. Tajikistan represents an interesting combination of a high reliance on labor migration, mostly in Russia, and an increasing rate of ambulatory and hospital fees inducing catastrophic expenditures. After the collapse of Soviet Union, Tajikistan overcame five years of civil war followed by an economic crisis. Unemployment jumped from 0.6% in 1993 (officially) to 46% in 2005 (Hohmann, 2010, quoting IMF figures). During the war, a first wave of emigration occurred via fleeing “ethnic Russian” and war refugees. In the early 2000s, there was a second wave that consisted of labor migration attracted by the economic recovery in Russia. These migrations were 90% male and mostly temporary. Labor migrants either work a few years and plan to return after, or they work seasonally and return in the off season. There were between 600,000 and 1.2 million Tajiks in Russia in the year 2000 and some estimates were as high as 2 million (Laruelle, 2006). Remittances became the main source of income for many households. At the macro level, migrants’ transfers contributed massively to the Tajik GDP. In 2007, when the Tajikistan Living Standards Survey was conducted, the estimates of their contribution ranged between 37% (Reeves, 2012) and 45% of the Tajik GDP (Danzer et Ivaschenko, 2010). Remittances made up 49.6% of the Tajiks GDP in 2008 (Clément, 2011; World Bank, 2010). More recently, although GDP and transfers remain volatile, their share of the GDP is still one of the highest in the world at 49.6% in 2013, 41.7% in 2014 (World Bank, 2016) and 28.8% in 2015.1 The

1 This figure is computed, based on the Migration and remittances database (World Bank website, November 2017). use of remittances in Tajikistan has been well documented. For instance, their use in wedding expenditure (Cleuziou 2013, Reeves 2012) and in durable goods, dwelling, education and health have all been reported in detail (Clément 2011, Kan 2016). However, during these years private health expenditure increased dramatically in the reaching 70% of total health expenditure in 2007. The Tajikistan healthcare system is characterized by a high level of out-of-pocket expenditure, while the system is still supposed to be universal and free according to the Constitution. Indeed, informal payments to doctors, which already existed in USSR, have become the rule and increased sharply due to state crises and arrears in staff salaries. Some medical procedures, tests and entrance fees have now become officially chargeable. In addition, treatments, hospital materials, and food are now entirely charged to patients. In turn, despite the new National Strategy for heath and the great efforts by the state, which are supported by international donors, public health expenditure are still the lowest in Central Asia and represent 5.7% of public spending and 1.7% of GDP in 2007 (Khodjamurodov and Rechel, 2010). Health remains a major concern for people and often induces either catastrophic expenditure due to a lack of insurance or delayed and forgone care. In the context of high out-of-pocket expenses and dependence on migration, families can urgently call upon the migrant to pay for medical care or rely on his regular support for other, more predictable expenses. To explore this hypothesis, a qualitative field survey was conducted during the spring of 2014 and summer of 2015 (Pellet, 2018). The results of 40 interviews with patients, doctors and health-related NGO members indicate that most households receiving remittances claim they were able to afford more and better care, which is attributable to support from their relative abroad. Moreover, some of these individuals insisted that they sent one member abroad in order to cope with predictable, catastrophic care expenditure. For instance, Malika, a 28 years old mother of three whose eldest son underwent severe eye surgery, said “My son has a problem with his eyes. He needs care and several operations. He already had one in September, last year. That's why my husband is in Russia. All this is very expensive! Where can I find the money for the operation? There's very little money here. [...] That's why he's in Russia; it takes a lot of money to pay for that here [...]. He's a good person, he will come back with the money in September because my son needs a second eye surgery” (Dushanbe, August 2015). Malika is from a poor background with few opportunities. It is possible her husband migrated anyway for other reasons. Nevertheless, what is interesting in her interview is how she links together her son’s chronic disease and the departure of her husband to Russia.

3. Materials and methods

3.1. Database

This study uses the data obtained from the 2007 wave of the Tajikistan Living Standards Measurement Survey led by the Government Statistics Department and supervised by the World Bank. This is a nationally representative survey with a two-stage stratified sampling. The stratification is based on five regions (three oblasts,2 Dushanbe, Districts of Republican Subordination or DRS) and two types of area (urban or rural), which yields nine strata as the capital Dushanbe is only urban. In the first stage, a random selection of 270 primary sampling units, the clusters, was run at the community level. In each cluster, 18 households were randomly selected. The total sample included 4,860 households or 29,798 individuals. These data consisted of 15 thematic modules that included demographic, socioeconomic, individual and household characteristics (education, labor market participation, migration, etc.) as well as health questions (health status, access to ambulatory and hospital care, individual health expenditure, HIV awareness). A partial second round was organized two months later. The aim was to interview the migrants that returned from abroad (mainly Russia, at 96%). We used the second round to gather health information because two questions regarding medicine expenditure were included, which made the computation of total households health expenditure more relevant. We argue that the main socioeconomic characteristics were not changed during the interim period. Because we prioritize the second round, to compute health expenditure, we loose around a hundred households who were not re-surveyed. In previous literature on the determinants and use of remittances, we find migration and remittance reception as a mix of individual choice and familial arrangements (Chort and Senne 2015) (Rapoport and Docquier, 2006). Even if individual health expenditure and migrant characteristics were provided in the database, we conducted the analysis at the household level. More than 26% of households surveyed had at least one migrant (current or recent), i.e. a member who is currently abroad or who has already been in migration recently (1,231/4705). However, only 15.6% of households had a migrant currently abroad. Most of the current migrants were found to remit to their household (87%).3

3.2. Variables of interest:

We investigated the impact of remittances on three types of health care variables: the probability of having positive expenditure (ambulatory and hospital separately), the amount of expenditure conditionally on using care services (ambulatory and hospital) and the renunciation to needed care (partially or completely). The variable of ambulatory expenditure in the last four weeks was calculated as the sum of medicine cost, formal and informal payments and average transportation cost. Because healthcare supply is quite heterogeneous in the territory, we add transport

2 “Oblast” is the Russian word for the regional level of administration. 3 The information about remittance reception is missing for the migrants back to their household. This could be problematic because, although those households no longer receive remittances, some of them used to get remittances in the past 12 months. Yet, hospital expenditures are asked for the last stay in hospital, in the last 12 months. So, the information about their remittances is censored and we cannot say that they are “non- recipient household” in the estimation of hospital expenditure. We did the estimation with and without them; the results do not change significantly. Here, we present only the results taking them into account. expenditure to capture the entire cost of health care consumption. Hospital expenditure for the last twelve months was calculated as treatment cost, formal and informal payments, food and other costs implied by hospitalization. The transportation cost to hospital, however, was not reported, which may result in an underestimation of hospital expenditure compared to ambulatory ones. Two dummy variables that indicate the existence of any ambulatory and hospital expenditure were used to measure the impact of remittances on basic access to out-patient and in-patient services, i.e. health care consumption at the extensive margin. Two continuous variables measuring the conditional amount of ambulatory and hospital expenditure among users were used to assess the impact of remittances on health care consumption at the intensive margin, i.e. on the intensity of care received. We argue that ambulatory services are more accessible and generally less expensive in terms of formal cost (laboratory tests and medicine, including automedication) and informal costs such as payments to the staff are smaller in ambulatory. The sickness or injuries treated are less serious4 than in hospital services and in consequence, the impact of remittances can be very different. For this reason we split the sample. Hospital expenditure is more exceptional. In terms of predictability, ambulatory and hospital expenditure can be predictable, thus we imperfectly capture the difference. Nevertheless, in the case of chronic diseases for example, consultations that are mainly in ambulatory services will be predictable, whereas an emergency surgery is much less predictable. As remittances are conditional on migration, itself contingent on a number of factors, the more predictable the expenditure, the more it is affected by remittances. We predict that hospital expenditure is the least elastic, whether this is because they are more unpredictable or because they are more serious and unavoidable. Because of this, we expect ambulatory expenditure to be significantly more affected than the hospital expenditure in those receiving remittances. Finally, we investigated their effect on partial and total renunciation to care, indicators that refer to the feeling of needing medical help. For this purpose, we use the question about having delayed or forgone care among the households declaring health needs for at least one of their members. Despite the subjectivity of those indicators and their sensitivity to the report of health needs (which may be related to previous health care use), they are very relevant as they directly measure their unmet needs and thus reflect their dissatisfaction with the health care system. In effect, it is very difficult to identify the inadequacy of health care consumption, based on health care consumption, due to the difficulty to properly assess health needs using a health interview survey. Nevertheless, the report of renunciation to care is difficult to interpret because the more a patient has important health care needs, the more likely he is to report that he has already waived or delayed medical help. Therefore, individuals may report unmet needs even if they have access to the healthcare system and if they have used health care during the last year. That is the reason we propose to distinguish total renunciation to health care, declaring to have

4 This is also due to general preferences and the habit to delay care in Tajikistan. Indeed, the soviet mentalities are still affecting people decision nowadays, « secondary » care is still perceived as better than primary care and specialists as better doctors, that is why many people report to wait and delay seeking care until it becomes unbearable and then go to hospital directly instead. However, the ongoing reform is targeting this change in mentalities by enhancing primary care. delayed or foregone health care, and reporting null health expenditure from partial renunciation, declaring to have delayed or foregone health and reporting positive health expenditure.

Table 1: The different types of forgone care

Having positive No health health Delay/renounce expenditure expenditure Total Out of scope (no health care needs) 1234 802 2036 Within the scope (declare needs) 812 1857 2669 No, never 516 1182 1698 Yes, once or more 296 675 971 Total renunciation 296 0 296 Partial renunciation 0 675 675 Total 2046 2659 4705

To assess the impact of remittances on health care utilization, we consider a dummy variable Receiving remittances (RR) corresponding to the declaration of household members remaining in Tajikistan to having received remittances in cash during the last 12 months from a member abroad. “Did [member abroad] remit to this household, in cash, at any point during the last 12 months?”. Even if the exact value of remittances sent during the last 12 months was collected in the survey, we decided not to consider this second variable in our analysis due to potential measurement errors and to the difficulties to instrument truncated variables. Indeed, while our instrument predicts the probability of remitting (extensive margin) well, it does not predict the amount remitted (intensive margin) well. Finally, in order to control for other resources that may allow access to health care, we built an asset-based wealth index using principal component analysis. The PCA took into account the potential presence of main assets (dwelling, land) and all assets that were determined in the questionnaire (radio, TV, computer, cattle, car, etc.). This measure of wealth does have limitations and depends on the subjective representations of the survey designers. However, it presents less risk of measurement error than reported income and less risk of collinearity with healthcare use than the aggregated value of consumption expenditure and autoconsumption which is usually used when direct income measures are unreliable (see Pellet 2016, for a discussion on this point).

3.3. Two-part model

To analyze the determinants of healthcare consumption, we used a two-part model, introduced by Cragg (1971) and widely used in health economics (Maddala 1985, Mullahy 1998, Jones 2000). This model separately estimates the determinants of the probability of having positive health expenditure, whatever the intensity of care utilization, and the determinants of health expenditure among health care users. This choice was initially motivated by the fact that health expenditure has a zero- inflated and rightly skewed long-tailed distribution rightly bounded by zero, which prevents us to estimate the health expenditure using a linear regression, considering it as a continuous variable. Indeed, in our sample 43% of the households do not have any health expenditure, neither ambulatory nor hospital. This methodology is also justified by the fact that seeking medical help, or not, and the amount of healthcare consumed (i.e. the amount of money spent for treatment and future consultations) are two decisions that are not identically motivated. On the one hand, households may have no expenditure either because they have no needs (possibly because they do not perceive their needs) or because they are not seeking medical help despite recognized health needs. This could occur when health care is not affordable or when the cost of care and consecutive deprivation of other consumptions are too high compared to the utility provided by healthcare. On the other hand, the level of health expenditure is instead determined by the supply side, through physician prescription or informal payments (see Pellet, 2016 on the equity effect of informal payments). That is the reason we chose a two-part model, consisting of two separate equations of access and expenditure, instead of a Tobit model, which is more restrictive since the effects at the extensive and intensive margins are supposed to be generated by the same process. This two-part model is estimated by taking into account the risk of heteroscedasticity, but without taking into account the potential selection bias in the second part, as it is usually done for the estimation of health expenditure. Indeed, we are interested in the impact of receiving remittances in the current level of health expenditure among households that currently use healthcare and not in the hypothetical outcomes that would be realized without the obstacles of select users. Angrist (2001) reminds us that the TPM is more appropriate than sample selection models when there is a choice (to work or to consume healthcare) that is to say when the censored latent variable is the difference between the utility and the marginal rate of substitution with other consumptions. In the first part of TPM, we estimated the probability of having positive expenditure, using the Probit model and LPM. Because the results were very similar and for simplification reasons, we report in tables 3a and 3b the marginal effects estimated by LPM. In the second part of TPM, we estimated the conditional amount of health expenditure through a log-linear model as recommended by Angrist (2001). Among healthcare consumers, the variance is extremely high with some outliers declaring thousands somoni5 for several operations in the year. We chose to keep the outliers because in this system, healthcare expenditure can be easily catastrophic due to informal payments and formal contribution to materials and medication.6 People who can afford for those treatments are significantly richer than average households. The logarithm of health expenditure allows us to adjust for the huge variability.

3.4. Instrumentation

5 Somoni is the Tajik currency (TJS). In 2007, 1USD was equivalent to 3.44 TJS. 6 We also tested the results without the outliers and they are convergent. Some articles (Kan, 2016, Ambrosius and Cuecuecha, 2013) and our observations on the field suggest that there may be a link between health status and migration, which compelled us to account for the potential endogeneity of remittances. There are two potential sources of endogeneity. Endogeneity of remittances could first be explained by a reverse causality. Facing health troubles may encourage households to send a member abroad or to ask a migrant already abroad to start sending remittances (or to increase the amount of his transfers) in order to pay for health care. In that case, the existence and/or the amount of remittances are determined by the existence and/or the amount of the health expenditure.

Second, as we cannot properly control for unobservable characteristics, and because information about households and community infrastructures are limited, there might be some omitted variables affecting both health expenditure and remittances. The bias induced could be positive or negative, local or individual. For example, at the local level, the decommissioning (the level of abandon) of medical supply due to the withdrawal of a local authority, which is not directly observable, would induce a negative bias. If the infrastructures in the worst state of neglect are precisely where people do not have other opportunities, other than sending migrants to pay for the materials and medication and to incite doctors and maintain them in the area through informal compensations, then the level of state withdrawal will be negatively correlated with both health expenditure and received remittances. Moreover, according to the “healthy migrant hypothesis” (McDonald and Kennedy, 2004), immigrants have on average a better health status than non-migrants, since healthy people have better opportunities during migration. However, at the household level, migrants and non-migrants can share some unobservable health characteristics, such as a good genetic inheritance, which results in a negative correlation between the reception of remittances and health expenditure of household members remaining in Tajikistan. Finally, the bias could be positive if the migration of some household members (most often husbands or sons) is detrimental to the health status of those who stay in Tajikistan, most often wives and mothers, due to isolation or harder working conditions. Therefore, sending remittances and using healthcare services are both caused by the same factors.

To test the results found in previous literature that has investigated these risks, we proceed to a two-stage estimate thanks to an instrumental variable, which is likely to strongly influence current migrations, and then the reception of remittances, and not health expenditure. We chose to use the potential network of migrants locally available. Instrumenting remittances by the density of migrants at community level is very common in previous work (Lokshin and Glinskaya 2009; Kan 2016), because it approximates the network levels the migrant can find abroad. This network maximizes his chances to migrate with less transactional costs and his chances to find a job. It also increases the net benefit of migrating. This network effect is particularly important when the country of destination is the same. In our sample, 96% of participants are going to Russia, which makes this type of instrument stronger. Different ways of capturing the migrant's network have been implemented. Bohme et al. (2015) used the variation in destination of Moldave migrants and difference in growth rate in the country of destination to build a proxy of “network-growth-interaction”. Lokshin and Glinskaya (2009) used the lag-density7; Amuedo-Dorantes and Pozo (2011) used the temporal and spatial variation in existing historical data of Mexican migrations in the different localities. In the case of Tajikistan, Kan (2016) used the density of migrants' households at the community level for the 270 primary sample units of the Tajikistan Living Standards Measurement Survey. Using such a local instrument raises the issue of its variability and the need to control for other local characteristics, which may be correlated with health expenditure of those who do not receive remittances and those who received remittances. In order to reintroduce some individual variability, we built a quasi-random instrumental variable based on the probability of each household having a household with a migrant currently abroad among his neighbors as a proxy of the potential network abroad. In each cluster, there were 18 households, except in a few, where one or two households were missing. For each household, we randomly selected 8 households among 17 and reported the proportion of migrant's household among his neighbors. As each household of the cluster had around a 50% chance to be selected for each of them, the instrumental variable varied at the household level in addition to the cluster level.8 Our instrumental variable, the proportion of migrants in the neighborhood, predicted the aggregated local density of migrants' households well but had the advantage to vary at the household level. It also predicted the chances to receive remittances well. Seemingly, we built a good enough proxy of the potential network of migrants disposable for each household when a member plans to migrate.9

4. Results

4.1 Exploratory analysis

Receiving remittances

In table 2, we compare the main characteristics of each category of households: households who do not received any remittances (NRH), those who receive remittances (RRH) and households having “recent migrant”, i.e. with at least one migrant who came back in Tajikistan in the last 12 months. The former category corresponds to households who were not asked to answer questions about remittances since they no longer receive

7 We also tried this method but had to give it up because the instrument was too weak and the variability too small (past density was calculated based on the 2003 wave, in which the clusters were not able to merge with 2007 clusters). 8 Results were recomputed by selecting either 5 or 11 households per cluster instead of 8. The choice of the number of households selected slightly affected the weakness of the instrument: the IV based on a probability of 1/3 of being selected is often a weak instrument, while the two others were strong enough instruments. 9 We are very grateful to François-Charles Wolff for his help in the development of this instrumentation strategy. transfers, but who might have received remittances in the past 12 months that may have influenced their health care consumption in the last 12 months. As is reported in table 2, RRH are on average slightly poorer, less educated and more rural than other types of households. They also have higher ambulatory expenditure but lower hospital expenditure. This is why we preferred to distinguish the analysis of the two types of expenses. RRH have a lower share of members that were active on the labor market. They were more often living in mountainous regions in that they live at an average altitude of 949m. Also, 7% of RRH were from Badakhshan (vs 3% of NRH). They had lower rates of university degrees. Finally, the medical supply was less important in their locality than in the localities where other categories of households lived. However, the proportion of RRH using medical services was slightly higher than the proportion among NRH (3 percentage points) both ambulatory services and hospitals. Indeed, in accordance to the hypothesis of a reverse causality from health needs to remittances, RRH having at least one member suffering from a chronic disease, are in higher proportion in the sample (38% versus 35%). If their ambulatory expenditures are higher on average, RRH have lower hospital expenditure. Finally, RRH are less often declaring complete renunciation to care than others (6% versus 12%), either because migrant’s support help them to seek medical help more often when it is needed or because health needs explain both remittance receipt and renunciation. In order to explore further, the hypothesis of endogenous relation between health needs and remittances, we regressed (logistic regression) the intention to migrate on different health variables and controlled for other observable characteristics.

Table 2: Mean of main covariates

Receiving (Mean) All No remittance Remittances Recent migrant Proportion receiving remittances 0,12 0 1 ? Amount of remittances (TJS10) . . 4075,6 ? Users of ambulatory care 0,46 0,45 0,48 0,46 Users of hospital services 0,26 0,25 0,28 0,31 Ambulatory exp. (among users) 101,8 91,25 142,6 125,3 Hospital exp. (among users) 529 544,9 498,4 471,1 Renunciation (complete) 0,11 0,12 0,06 0,09 Renunciation (partial) 0,3 0,31 0,31 0,24 Wealth Q1 0,20 0,21 0,21 0,14 Wealth Q2 0,20 0,20 0,24 0,16 Wealth Q3 0,20 0,19 0,22 0,24 Wealth Q4 0,20 0,19 0,19 0,25 Wealth Q5 0,20 0,21 0,14 0,21 At least 1 illness 0,25 0,24 0,23 0,28 At least 1 Chronic disease 0,36 0,35 0,38 0,39 Family size 6,43 6,33 6,36 7,14

10 In 2007, 1USD was equivalent to 3,44 somoni, 4075,6 somoni was equivalent to 1184,8 USD. Sex of HHH = male 0,81 0,82 0,65 0,87 Share of active 0,29 0,30 0,25 0,27 Share of women 0,52 0,51 0,56 0,50 Share of child. < 6 y.o. 0,12 0,12 0,12 0,12 Share of +65 y.o. 0,05 0,06 0,04 0,03 Primary/basic educ. 0,18 0,18 0,25 0,13 Secondary educ. 0,60 0,59 0,60 0,69 Higher educ. 0,18 0,19 0,12 0,15 Tajik 0,77 0,76 0,78 0,80 Altitude (100m) 8,13 7,94 9,49 7,96 Gorno-Badakhshan Autonomous Region 0,04 0,03 0,07 0,02 GBAO*Urban 0,01 0,01 0,01 0,00 Districts of Republican Subordination 0,20 0,19 0,25 0,20 DRS*Urban 0,03 0,03 0,03 0,02 Khatlon region 0,32 0,34 0,26 0,29 Khatlon*Urban 0,06 0,07 0,02 0,05 Sogd region 0,33 0,31 0,35 0,40 Sogd*Urban 0,10 0,11 0,06 0,12 Water supply 0,51 0,54 0,37 0,51 Local sewage system 0,23 0,25 0,16 0,21 Medicine availability 0,65 0,66 0,60 0,66 Presence of hospital 0,36 0,36 0,33 0,35 Presence of polyclinic 0,36 0,37 0,31 0,38 Presence of feldsher 0,52 0,50 0,58 0,59 Presence of drugstore 0,51 0,51 0,47 0,54 Headcount 4705 3573 637 495

Sources: Authors’ computations, based on the data from the Tajikistan Living Standards Survey (2007).

Health needs and intention to migrate

We wondered whether households were more likely to send a migrant abroad in the near future, when someone in the household had a health shock or a chronic disease, had to face catastrophic expenditure or postpone or waive care. According to tables 3a and 3b, experiencing renunciation to care, whatever the type, did not significantly contribute to the planning to migrate or not. On the contrary, having one member suffering from poor health; facing catastrophic expenditure, relative to financial capacity; and, to a lesser extent, facing positive ambulatory expenditure (significant at 13%) increased the chances of having at least one member in the household intending to migrate. The mean probability of planning to migrate was 0.066, 0.073 for those households with a member in poor health (59%) and 0.054 for those without (41%), all other things being equal. The computed marginal effect was 0.017 percentage points, which represents more than one quarter of the total probability. Facing catastrophic expenditure (6.8% of the sample) increased the chance of planning a migration by 0.021, which was the strongest effect. Facing positive ambulatory expenditure (43%) was also increased by 0.01 percentage points the chance to intend to migrate. This finding reflects the above-mentioned case of Malika (section 2). We also decomposed the variable “poor health” to see more precisely what drives the effect. The presence of chronic disease was not the stronger factor if compared to recent illness or injury. Health shocks were more likely to suggest intent to migrate than a long lasting chronic disease. After differentiating among members, it appears that poor the health of the mother or the spouse is more likely to influence migration of the son or husband (complementary results available on demand) than the poor health of other members. These results are robust, if one adds district dummies: poor health has exactly the same coefficient, positive ambulatory expenditure is significantly more influential on positively migration (coefficient=0.30, dydx=0.016, significant at 5%) and catastrophic expenditure also has a stronger estimated impact (coefficient=0.51, dydx=0.026, significant at 5%). Total and partial renunciations were still not significantly influential on the intention to migrate. This first exploratory result encourages us to treat endogeneity in the following regressions. Because health needs and some variables of healthcare consumption are positively affect plans to migrate, we expect a positive bias; i.e., the more households that use medical care, the more likely one of them will migrate and there will be a higher probability that the households are receiving remittances.

Table 3a: Is health influencing the intention to migrate?

Dep. Var (1) (2) (3) (4) (5) (6) Planning to migrate Logit Logit Logit Logit Logit Logit Poor health 0.329** (0.133) Complete renunciation -0.403 (0.312) Partial renunciation -0.00936 (0.201) Positive ambul. exp. 0.202 (0.131) Positive hosp. exp. -0.0581 (0.159) Catastrophic health exp. 0.416* (0.237) A migrant currently abroad 0.569*** 0.638*** 0.618*** 0.593*** 0.588*** 0.583*** (0.164) (0.208) (0.219) (0.163) (0.163) (0.163) Having a migrant recently back 2.596*** 2.636*** 2.537*** 2.601*** 2.601*** 2.606*** (0.155) (0.187) (0.193) (0.155) (0.155) (0.155) Mig. neighbor pct (1/2) 1.372*** 1.722*** 1.661*** 1.345*** 1.360*** 1.349*** (0.392) (0.507) (0.535) (0.398) (0.394) (0.396) Wealth Q2 -0.0309 0.00614 0.0819 -0.0262 -0.0265 -0.0201 (0.231) (0.302) (0.308) (0.229) (0.228) (0.230)

Wealth Q3 0.174 0.119 0.184 0.168 0.171 0.172 (0.221) (0.295) (0.300) (0.221) (0.221) (0.222) Wealth Q4 0.260 0.257 0.305 0.254 0.239 0.242 (0.235) (0.299) (0.311) (0.235) (0.236) (0.237) Wealth Q5 0.758*** 0.477 0.408 0.735*** 0.743*** 0.757*** (0.255) (0.347) (0.362) (0.255) (0.254) (0.259) Family size 0.0228 0.0264 0.0249 0.0283 0.0335 0.0297 (0.0234) (0.0295) (0.0308) (0.0235) (0.0232) (0.0238) Sex of HHH -0.0983 0.147 0.126 -0.124 -0.116 -0.122 (0.186) (0.259) (0.272) (0.183) (0.185) (0.184) Share of active on LM -0.168 -0.556 -0.741 -0.163 -0.201 -0.181 (0.366) (0.504) (0.520) (0.370) (0.369) (0.371) Share of women -1.534*** -1.975*** -2.021*** -1.520*** -1.484*** -1.527*** (0.363) (0.502) (0.523) (0.362) (0.359) (0.358) Other HH characteristics YES YES YES YES YES YES Community characteristics YES YES YES YES YES YES Constant -3.955*** -3.062*** -2.859*** -3.833*** -3.806*** -3.807*** (0.700) (0.859) (0.887) (0.707) (0.707) (0.717)

Observations 4,705 2,599 2,304 4,705 4,705 4,705 Pseudo R2 0.23 0.23 0.22 0.23 0.23 0.23 Notes: Other controls were included. See annex C, for an exhaustive version of the table 3a. Sources: Authors’ computations, based on the data from the Tajikistan Living Standards Survey (2007).

Table 3b: Average marginal effects of health explanatory variables

dy/dx Std. Err. z P>|z| Poor health 0,017** 0,007 2,48 0,013 Complete renunciation -0,021 0,016 -1,29 0,198 Partial renunciation -0,001 0,011 -0,05 0,963 Positive ambul. exp. 0,010 0,007 1,53 0,125 Positive hosp. exp. -0,003 0,008 -0,37 0,715 Catastrophic health exp. 0,021* 0,012 1,76 0,078

Sources: Authors’ computations, based on the data from the Tajikistan Living Standards Survey (2007).

4.2 The impact of remittances on health expenditure

Tables 4a and 4b report the results on the effect of remittances on health expenditure at the extensive margin (the probability of having ambulatory or hospital expenditure) and at the intensive margin (the amount of ambulatory or hospital expenditure). According to the specifications, without district fixed effects (annex A), remittances do not increase the probability of access to ambulatory care (extensive margin), but do increase the amount of expenditure among those who already consume (intensive margin), whether by increasing the number of consultations when needed or by accessing better quality care11. After instrumentation, the coefficient and the significance were increased greatly at the extensive margin. At the intensive margin, the value of the coefficient increased as well, but became insignificant due to the increase in standard error. Contrarily to what was expected due to the positive effect of health needs on the intention to migrate, this result suggests that there is a negative bias of endogeneity: some omitted variables are positively correlated to health expenditure and negatively correlated to migration and remittance reception. We can think about the level of development or the remoteness of a place, since the inclusion of transport costs into ambulatory expenditure automatically induces higher health expenditure for people living in remote areas. For example, local labor market (mining, agriculture) will decrease migration but increase health needs and healthcare use. Remoteness will increase health expenditure among consumers but decreases the probability of migrating due to transaction costs. Another explanation is the common poor genetic inheritance that may explain high health care needs within the household and a low ability to send a member abroad in accordance with the “healthy migrant hypothesis”. This local variability could be approximated using district dummies, which captures the level of development and the specificities of healthcare supply. The district level is relevant because healthcare systems have district levels (district polyclinic, district hospital and administration). The smaller level (community level) in rural areas was not relevant; it covered only “medpunkt” (a nurse) or rural health house providing primary care (one or two general practitioners and nurses). According to these specifications, including district dummies (table 4a), receiving remittances did not significantly impact the access: the coefficient was high (0.5), but not significant. Yet, the instrument was strong enough (F-statistic reaches 15.2) and endogeneity was less obvious (Hausman test p-value increases from 0.01 to 0.08). There is a 92% chance of being right to reject exogeneity (instead of 99% without district dummies inclusion, see annex A), according to the Hausman tests. 12 This suggests that after controlling for the huge geographical variability in terms of access to care and supply-side characteristics (spatial inequality, lack of transportation, difference in informal payments asked by the staff etc..), that there is less or no negative bias. The endogenous relation of remittances to ambulatory expenditure is due to local disparities rather than household characteristics or choices. At the intensive margin, after instrumenting and controlling for district fixed effects we found no effect of remittances on ambulatory expenditure among users. However, the exogeneity hypothesis of remittances among healthcare users was not rejected (p-value 0.35), which suggests that the OLS estimates are privileged. Remittances are not enough to counteract all of the barriers to healthcare, but once a household has overcome these barriers, remittances had a positive effect on their consumption. Regarding other covariates, the only significant factors of expenditure were health needs, the number of household members, and education. Higher education levels are correlated with a higher probability of accessing ambulatory care and higher ambulatory

11 Because the results estimated by the Probit model and computed marginal effects are sensitively equivalent, we have reported here the LPM to ease reading. 12 Under the null hypothesis that the specified endogenous regressor can actually be treated as exogenous, the test statistic is distributed as chi-squared. expenditure among users. This could be a problem of access due to the cost barriers. However, because wealth index is not significant and education is not playing on hospital access (table 4b), we think this is instead a sign that good preventive habits, prophylactic and regular consultations are more common among educated people. Table 4b shows the results for hospital expenditure. Whatever the specification, remittances did not affect hospital expenditure and their endogenous relation to hospital expenditure is not proven (p-values are 0.14 and 0.86).13 The fact is hospital expenditure is less flexible and less predictable (emergency), fees are more non-negotiable (like treatment), and hospitals are less accessible. Barriers are of a different nature. For instance, remittances are not sufficient if there is no transport to go to hospital. Also, people often delay seeking medical help and, when they get to the hospital, they may already be in serious condition. Hospital care then becomes more serious and unavoidable. More generally, in the case of hospitalization, the determining factors were health needs and wealth index quintiles. This is understandable when one compares the cost of the last hospital stay and the last months expenditure in ambulatory services (table2). Household composition played a role. The number of young children increased the use of hospital care but it decreased the amount of expenditure, probably because on average infantile diseases (mostly infectious, emergency) are less durable than chronic and related- to-age diseases. Hence, the number of elderly had the opposite effect. As a whole, remittances had only an impact on the more predictable expenditure and only among people who already used ambulatory care, but not on the probability of access. Secondly, remittance regressor did not seem to be endogenous, which suggests that remittances are not sent with the sole aim of providing funds for accessing healthcare while needed. However, they are still needed help to increase and/or improve consumption. Nevertheless, the two-part model has some limits due to the inherent characteristics of censored variables (health expenditure). As Angrist (2001) reports, “instrumental variables that are valid for estimating the effect of Di on Yi are not valid for estimating the effect of Di on Yi conditional on Yi>0”. As reported here in tables 4a and 4b, the instrument, which is a good instrument at the extensive margin of health expenditure, is quite weak on the sub-sample of users. It happens that the instrument is not valid on the conditional estimation. This may explain the failure to identify endogeneity. IV procedures involving endogenous binary regressors induce an “expansion bias” (Rigobon and Stoker, 2009).14 This is another reason for testing other indicators of access to care, such as renunciation.

Table 4a: The effect of remittances on ambulatory expenditure

First Part (extensive margin) Second Part (Intensive margin) Dependent Var. Positive ambulatory exp. Amount ambulatory exp. (1) (2) (3) (4)

13 There is a little doubt on (4): the instrument is weak on this subgroup of users of hospital care, so the estimated coefficient of RR can be underestimated. 14 Even if the instrument is valid in the “true data” (uncensored plus censored data), correlated with the regressor and not with the disturbance, but correlated with the censored part of the regressor, then the instrument might not be valid for the equation with the censored variable. LPM IV OLS IV Receiving remittances 0/1 -0.0222 0.514 0.258** 1.533 (0.0207) (0.348) (0.102) (1.560) Wealth Q2 -0.00809 -0.0259 -0.0966 -0.147 (0.0221) (0.0258) (0.108) (0.131) Wealth Q3 0.00959 -0.0159 0.0420 0.0208 (0.0226) (0.0293) (0.104) (0.111) Wealth Q4 -0.0137 -0.0362 0.0521 0.0463 (0.0236) (0.0291) (0.121) (0.126) Wealth Q5 0.0157 -0.0100 0.115 0.0981 (0.0291) (0.0350) (0.152) (0.157) At least 1 illness 0.0870*** 0.0889*** 0.121 0.103 (0.0164) (0.0173) (0.0743) (0.0797) At least 1 Chronic disease 0.111*** 0.106*** 0.340*** 0.312*** (0.0149) (0.0161) (0.0692) (0.0789) Family size 0.0168*** 0.0167*** 0.0556*** 0.0593*** (0.00287) (0.00305) (0.0134) (0.0146) Sex of HHH -0.00965 0.0463 0.0521 0.146 (0.0199) (0.0425) (0.0928) (0.146) Share of active on LM -0.0828** -0.0304 0.0850 0.257 (0.0361) (0.0511) (0.185) (0.288) Share of women 0.0293 -0.000535 -0.187 -0.264 (0.0395) (0.0461) (0.195) (0.220) Share of child. < 6 y.o. -0.0270 -0.0292 0.0164 -0.134 (0.0525) (0.0547) (0.242) (0.309) Share of +65 y.o. 0.0165 0.0877 0.385 0.535* (0.0495) (0.0693) (0.247) (0.307) Primary/basic educ. 0.0740* 0.0305 0.370** 0.256 (0.0379) (0.0494) (0.179) (0.226) Secondary educ. 0.0637* 0.0315 0.291* 0.214 (0.0372) (0.0447) (0.175) (0.198) Higher educ. 0.0882** 0.0632 0.375* 0.305 (0.0401) (0.0455) (0.194) (0.212) Tajik 0.0222 0.0166 0.0380 0.0117 (0.0212) (0.0228) (0.102) (0.109) Constant 0.210*** 1.882*** (0.0742) (0.339)

District dummies YES YES YES YES Observations 4,705 4,705 2,026 2,026 R-squared 0.165 0.116 F-stat 15.19 4.35

Endog.test P-val 0.08 0.35 *** p<0.01, ** p<0.05, * p<0.1

Note: Robust standard errors in parentheses, Kleibergen-Paap Wald rk F statistic used for testing the validity of instrument; Hausman test for endogeneity (H0 = exogeneity); district dummies added (local fixed effects have been partialled out in the 2SLS regression). Sources: Authors’ computations, based on the data from the Tajikistan Living Standards Survey (2007).

Table 4b: The effect of remittances on hospital expenditure

First Part (extensive margin) Second Part (Intensive margin) Dependent Var. Positive hospital exp. Amount hospital exp. (1) (2) (3) (4) LPM IV OLS IV Receiving remittances 0/1 -0.0133 -0.373 0.118 0.319 (0.0194) (0.264) (0.0799) (1.128) Wealth Q2 0.0268 0.0397* 0.129 0.114 (0.0208) (0.0237) (0.100) (0.129) Wealth Q3 0.0412* 0.0624** 0.0642 0.0430 (0.0216) (0.0275) (0.103) (0.157) Wealth Q4 0.0349 0.0561* 0.0364 0.0220 (0.0230) (0.0287) (0.105) (0.125) Wealth Q5 0.0630** 0.0854*** 0.156 0.138 (0.0272) (0.0322) (0.129) (0.161) At least 1 illness 0.0833*** 0.0821*** 0.0595 0.0619 (0.0169) (0.0175) (0.0651) (0.0645) At least 1 Chronic disease 0.206*** 0.211*** 0.205*** 0.207*** (0.0151) (0.0160) (0.0632) (0.0618) Family size 0.0231*** 0.0239*** -0.115*** -0.116*** (0.00290) (0.00309) (0.0125) (0.0127) Sex of HHH 0.0453** 0.00635 0.129 0.137 (0.0181) (0.0342) (0.0909) (0.100) Share of active on LM -0.00372 -0.0433 0.150 0.188 (0.0318) (0.0441) (0.184) (0.276) Share of women 0.0410 0.0593 -0.124 -0.165 (0.0351) (0.0386) (0.186) (0.289) Share of child. < 6 y.o. 0.106** 0.103** -0.452* -0.468* (0.0491) (0.0508) (0.240) (0.244) Share of +65 y.o. -0.0650* -0.112** 0.771*** 0.809** (0.0394) (0.0537) (0.272) (0.340) Primary/basic educ. 0.0524 0.0847* 0.00406 -0.0179 (0.0353) (0.0434) (0.162) (0.198) Secondary educ. 0.0265 0.0527 0.0198 0.00792 (0.0346) (0.0403) (0.162) (0.167) Higher educ. 0.00559 0.0232 0.0986 0.0853 (0.0372) (0.0404) (0.173) (0.179) Tajik 0.0473** 0.0539*** 0.0927 0.0992 (0.0187) (0.0204) (0.0972) (0.100) Constant -0.0610 3.616*** (0.0686) (0.326)

District dummies YES YES YES YES Observations 4,210 4,210 1,082 1,082 R-squared 0.155 0.251 F-stat 16.82 3.69

Endog.test P-val 0.14 0.86 *** p<0.01, ** p<0.05, * p<0.1

4.3 The effect of remittances on forgone care

The existence and amount of health expenditure are indicators of consumption and non- consumption, but not direct indicators of access to appropriate care with regard to health needs. We turn now to partial renunciation and total renunciation, i.e. those who completely renounce to seek medical help, mostly for economic or geographic reasons. In table 5, the population is limited to those who declare to have at least one member that needs health care in the last 12 months. One may suspect that there is an endogenous link between migration and difficult access to care through the same mechanism as before. We noticed that, without taking into account district dummies, receiving remittances decreased the probability that care was renounced and this effect was stronger after controlling for endogeneity. This is corroborating the result of the two-part model at the extensive margin (proxy of access); that there seems to be a negative bias. Those who are the most excluded from healthcare were also less likely to send a migrant and receive remittances. After dealing with this, RR became the biggest factor of renunciation reduction, and probably the biggest factor, which contributed to care access. However, adding district dummies canceled this result. In table 5, unlike table 4a and 4b, all instruments passed the “rule of thumb” validity test in the sub-population of those who declared having needed care (table 5), meaning the instruments are strong enough15. The exogeneity null hypothesis cannot be rejected according to the Hausman tests (p-values between 0.35 and 0.60). So, under these specifications, there was no suspicion of weak instrument inducing the failure of endogeneity identification. Suspected endogeneity is then owed to local characteristics. Remittance reception was not endogenously related to healthcare access after controlling for local dummies. For these reasons, we have focused on Probit results (1), (3) and (5). Receiving remittances affects all types of renunciation. RR is reduced by 0.06 percentage points partial renunciation, that is to say it is diminished from 28% (sample mean and predicted value are 29.7%) to 22%. RR had a smaller effect on complete renunciation as it reduced it from 11% to 8.4%. Regarding other negative factors of complete renunciation, wealth quintiles and education level were also found to be important. One can notice the differentiated impact of education on complete renunciation (reduces it) and on partial renunciation (increases it). This can be explained using the same reasoning we made in section 3 with regards to the construction of the variable itself. One is more likely to declare having a delayed / renounced care if she already had access and knows she needs it.

15 In specification (6) in table 4, the instrument is weaker (F stat = 8.5). However, according to Stock and Yogo (2002) critical values, its maximum size bias is 15%, which corresponds more or less to the Staiger-Stock rule-of-thumb, and makes this instrument acceptable. An unexpected result was the estimated effect of the share of women in the household, which was positively correlated to complete renunciation to care (controlling for the size of household, health status and so on). Either women are not treated as a priority or they are more prone to report health care needs (Caroli and Weber, 2016), and this in turn raises greater awareness of having to give up for financial reasons. For example, there are many home deliveries due to the inability to pay the doctor in the maternity ward. We find our instrumentation procedure to be robust because it takes into account the local specificities (district fixed effects). This makes an important contribution, because local disparities explain a large part of the inequity to care access (Pellet 2016) and the endogenous relation between migration and health expenditure. It respects the exclusion- restriction hypothesis (contrarily to migrant density) and it varies at a household level. Previous papers (Kan, 2016) found endogeneity probably due to the estimation of health outcomes (including health expenditure), without controlling for health status, the utilization of an invalid instrument, the lack of local characteristics controlled for, and because she does not account for the censored type of health expenditure distribution and remittances.

Table 5: The effect of remittances on forgone care16

(1) (2) (3) (4) (5) (6) Dependent Var. Delay or forgone care Complete renunciation Partial renunciation VARIABLES Probit dydx IV Probit dydx IV Probit dydx IV Receiving remittances 0/1 -0.0766*** 0.101 -0.0261* -0.175 -0.0576** 0.280 (0.0290) (0.329) (0.0139) (0.226) (0.0269) (0.372) Wealth Q2 -0.105*** -0.112*** -0.0426*** -0.0546** -0.0680** -0.0867** (0.0305) (0.0354) (0.0126) (0.0242) (0.0295) (0.0385) Wealth Q3 -0.142*** -0.155*** -0.0384*** -0.0448 -0.107*** -0.140*** (0.0304) (0.0403) (0.0134) (0.0281) (0.0290) (0.0434) Wealth Q4 -0.128*** -0.137*** -0.0250 -0.0258 -0.107*** -0.133*** (0.0331) (0.0399) (0.0160) (0.0278) (0.0310) (0.0427) Wealth Q5 -0.265*** -0.289*** -0.0669*** -0.0847** -0.215*** -0.256*** (0.0335) (0.0486) (0.0155) (0.0342) (0.0315) (0.0497) At least 1 illness 0.140*** 0.127*** 0.0237* 0.0261* 0.133*** 0.129*** (0.0228) (0.0209) (0.0121) (0.0145) (0.0228) (0.0224) At least 1 Chronic disease 0.151*** 0.135*** 0.00957 0.00836 0.157*** 0.143*** (0.0213) (0.0207) (0.0114) (0.0146) (0.0208) (0.0214) Family size -0.00507 -0.00521 -0.00909*** -0.00949*** 0.00177 0.000796 (0.00424) (0.00397) (0.00235) (0.00266) (0.00404) (0.00400) Sex of HHH 0.0163 0.0283 0.00403 -0.0110 0.0178 0.0439 (0.0302) (0.0377) (0.0149) (0.0260) (0.0295) (0.0401)

16 As robustness tests, we run also biprobit procedures for all types of renunciation with all types of instruments (and record only the results with the strongest instrument in annex B). The results are convergent. The Wald test indicates that rho = 0, then the log likelihood for the bivariate probit models is equal to the sum of the log likelihoods of the two univariate probit models. A joint estimation is thus not necessary. Share of active on LM -0.0379 -0.00754 -0.0105 -0.0295 -0.0230 0.0277 (0.0587) (0.0704) (0.0302) (0.0497) (0.0571) (0.0762) Share of women 0.0927 0.0744 0.0637** 0.0903** 0.0264 0.000148 (0.0635) (0.0599) (0.0320) (0.0392) (0.0613) (0.0646) Share of child. < 6 y.o. -0.0805 -0.0864 -0.0111 -0.00974 -0.0562 -0.0816 (0.0821) (0.0765) (0.0427) (0.0516) (0.0794) (0.0779) Share of +65 y.o. -0.0463 -0.0161 -0.0263 -0.0491 -0.0176 0.0381 (0.0729) (0.0825) (0.0382) (0.0606) (0.0703) (0.0925) Primary/basic educ. 0.0669 0.0462 -0.0555*** -0.0677 0.145** 0.0966 (0.0567) (0.0639) (0.0177) (0.0491) (0.0655) (0.0653) Secondary educ. 0.0600 0.0432 -0.0448* -0.0479 0.111* 0.0819 (0.0536) (0.0572) (0.0265) (0.0453) (0.0566) (0.0570) Higher educ. -0.0508 -0.0492 -0.0684*** -0.0929** 0.0338 0.0169 (0.0571) (0.0587) (0.0172) (0.0455) (0.0653) (0.0591) Tajik 0.0409 0.0431 0.0174 0.0194 0.0263 0.0321 (0.0318) (0.0305) (0.0153) (0.0203) (0.0313) (0.0322)

District dummies YES YES YES YES YES YES Observations 2,349 2,350 2,188 2,350 2,077 2,086 Pseudo R2 0.12 0.12 0.12 F-stat 11.25 11.25 8.50 Endog.test P-val 0.60 0.50 0.35

Note: Robust standard errors in parentheses, and Kleibergen-Paap Wald rk F statistic used for testing the validity of instrument; Hausman test for endogeneity (H0 = exogeneity); district dummies added (local fixed effects have been partialled out in the 2SLS regression). Sources: Authors’ computations, based on the data from the Tajikistan Living Standards Survey (2007).

5. Discussion and conclusions

This paper investigates the effects of remittances on foregone care, access to care and healthcare expenditure using data obtained from the 2007 wave of the Tajikistan Living Standards Survey. This study also addresses the potential endogeneity of remittances with regards to health care needs. The results here confirm that remittances help to increase health expenditure at the intensive margin and also to reduce the likelihood of abandoning or postponing care when needed. Outpatient ambulatory expenditure was found to be more likely affected by remittances. Those findings have been obtained using a dichotomous indicator of receiving remittances since the variable used for instrumenting remittances failed to explain the amount of remittances received. This positive impact of remittances on health care utilization can first be interpreted as an income effect, with remittances lowering financial barriers to care. However receiving remittances may also address other symbolic barriers to healthcare system, such as "wait and see" habits (rather than consulting immediately). Indeed, a small part of the literature is concerned with non-monetary or "social" remittances, i. e. the transmissions of habits, good or bad practices, new experiences, and changes in preferences that stem from migration and passed on to the remaining household members (Amuedo-Dorantes and Pozo, 2009). For example, a migrating parent may be inspired to encourage the custodial parent to take the child to the doctor or dentist more often, through remittances, and adopt less common prophylactic methods. Contrary to the existing literature and exploratory results, this paper doesn’t support the hypothesis of the endogeneity of remittances, but highlights the importance of considering the effects of local unobserved heterogeneity. Previous results suggest that if we control for the local specificity (unequal accessibility to an unequal supply, unequal development etc..), then remittances are not endogenously related to health expenditure and to renunciation of care. This means that because there are so many reasons to migrate that health needs are probably not dominant. Those findings could be seen as weakly consistent with those of our exploratory results regarding the effects on intention to migrate. We found that health problems contribute to households’ expectations of migration. However, expectations of migration are not effective migration. Plans to migrate do not mean they will be able to effectively migrate due to the cumulative local obstacles to migration, which is shown by the negative bias without local dummies. To conclude, those findings seem to be consistent with the model of altruism: even if migrants do not send remittances due to health expenditure or lack of access, remittances help to improve care consumption levels or quality and reduce slightly the delayed and forgone care.

Acknowledgements:

We are very grateful to François-Charles Wolff for his valuable advice in the development of this instrumentation strategy. We are also grateful to all the discussants who provided us with substantial feedback in the conferences and to the organizers (DIAL conference and JMA 2017, EuHEA conference and workshop 2016, 38th JESF). We thank the Laboratory of Economics in Paris-Dauphine and more particularly the LEGOS team for their material and scientific support to our research. We also thank the French Institute for Central Asian Studies for its material assistance in the field.

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Annex

Annex A:

Positive amb. exp. Conditional ln(expenditure) (1) (2) (3) (4) VARIABLES LPM 2SLS OLS 2SLS

Receiving remittances 0/1 -0.00545 0.521** 0.257** 1.578 (0.0238) (0.238) (0.108) (0.970) Wealth Q2 -0.00235 -0.0220 -0.0418 -0.0877 (0.0247) (0.0265) (0.106) (0.116) Wealth Q3 0.0120 -0.0175 0.119 0.0996 (0.0261) (0.0313) (0.105) (0.108) Wealth Q4 -0.00809 -0.0438 0.220* 0.196 (0.0295) (0.0354) (0.122) (0.126) Wealth Q5 0.0398 -0.00173 0.315** 0.275* (0.0379) (0.0450) (0.151) (0.156) At least 1 illness 0.0635*** 0.0663*** 0.119* 0.101 (0.0184) (0.0189) (0.0691) (0.0738) At least 1 Chronic disease 0.100*** 0.0943*** 0.355*** 0.323*** (0.0164) (0.0170) (0.0700) (0.0775) Family size 0.0172*** 0.0174*** 0.0485*** 0.0520*** (0.00332) (0.00346) (0.0134) (0.0138) Sex of HHH -0.0129 0.0454 0.00819 0.115 (0.0208) (0.0350) (0.0918) (0.124) Share of active on LM -0.0728* -0.0274 0.0778 0.252 (0.0378) (0.0462) (0.212) (0.249) Share of women 0.0157 -0.0194 -0.181 -0.271 (0.0414) (0.0450) (0.207) (0.228) Share of child. < 6 y.o. -0.0140 -0.0209 0.0925 -0.0228 (0.0554) (0.0577) (0.230) (0.247) Share of +65 y.o. 0.00281 0.0748 0.280 0.450* (0.0496) (0.0626) (0.238) (0.255) Primary/basic educ. 0.0592 0.0200 0.380** 0.265 (0.0406) (0.0466) (0.172) (0.195) Secondary educ. 0.0681* 0.0387 0.282* 0.206 (0.0379) (0.0428) (0.160) (0.169) Higher educ. 0.0790* 0.0601 0.365** 0.304 (0.0409) (0.0449) (0.181) (0.187) Tajik 0.000416 -0.00227 0.124 0.0849 (0.0286) (0.0304) (0.0971) (0.103) Altitude (100m) -0.00385 -0.00692* 0.0166 0.00269 (0.00361) (0.00358) (0.0134) (0.0185) Gorno-Badakhshan Autonomous Region -0.0175 -0.0719 -0.714** -0.709** (0.0878) (0.0869) (0.301) (0.310) GBAO*Urban 0.244*** 0.276*** -0.115 -0.107 (0.0714) (0.0673) (0.352) (0.360) Districts of Republican Subordination 0.115** 0.0635 0.143 0.0104 (0.0573) (0.0643) (0.203) (0.221) DRS*Urban -0.0556 -0.0429 -0.248 -0.200 (0.0554) (0.0598) (0.273) (0.266) Khatlon region 0.0125 -0.0324 0.144 0.00144 (0.0665) (0.0702) (0.213) (0.244) Khatlon*Urban -0.0115 0.0486 -0.134 0.0683 (0.0684) (0.0744) (0.205) (0.257) Sogd region 0.133** 0.0708 0.365* 0.188 (0.0599) (0.0694) (0.209) (0.260) Sogd*Urban -0.0838 -0.0373 -0.475*** -0.352* (0.0633) (0.0689) (0.173) (0.210) Water supply 0.0535* 0.0736** 0.0116 0.0483 (0.0302) (0.0326) (0.106) (0.110) Local sewage system 0.0581 0.0567 0.0997 0.0646 (0.0417) (0.0430) (0.133) (0.131) Medicine availability -0.0433 -0.0468 0.0980 0.0491 (0.0297) (0.0300) (0.101) (0.110) Presence of hospital 0.0177 0.0143 -0.135 -0.138 (0.0299) (0.0311) (0.104) (0.108) Presence of polyclinic -0.0655** -0.0450 0.0404 0.0932 (0.0290) (0.0317) (0.103) (0.115) Presence of feldsher 0.0528 0.0472 0.167* 0.152 (0.0322) (0.0335) (0.0997) (0.104) Presence of drugstore 0.0220 0.0216 0.0110 0.0140 (0.0325) (0.0319) (0.111) (0.115) Constant 0.176** 0.172* 2.148*** 2.248*** (0.0869) (0.0896) (0.328) (0.346)

Observations 4,705 4,705 2,026 2,026 R-squared 0.059 0.070 F-stat 48.33 15.85 Endog.test P-val 0.01 0.13