rafmacalaba/datause-encoder-data huggingface dataset:
Hammer, Jeffrey S., 1997. "Economic Analysis for Health Projects,' World Bank Research Observer, 12(1):4771.

Heller, Patrick, 1996. "Social Capital as a Product of Class Mobilization and State Intervention: Industrial Workers in Kerala, India," Wld Develogmg, 24(6):1055-1071.

Hossain, S. 1989. "Effect of Public Programs on Family Size and Child Education and Health." Journal of Development Economics. 30(1): 144-158.

Ii, Masako, 1996. "The Demand for Medical Care: Evidence from Urban Areas in Bolivia," LSMS Working Paper No. 123. The World Bank, Washington, DC.

Jamison, Dean T., W. Henry Mosley, Anthony R. Measham, Jose-Louis Bobadilla eds., 1993. Disase Control Priorities in Develoging Countries, Oxford Medical Publication.

Kim, Kwangkee, and Philip M. Moody, 1997. "More Resources Better Health?: a Cross -National Perspective," Social Science and Medicine, 34(8):837-842.

Koenig, Michael A., Vincent Fauveau, and Bogodan Wojtyniak, 1991. "Mortality reductions from health intenrentions: the case of immunization in Bangladesh," Population and Development Review 17:87-104 Kunstadter, Peter, 1985. "Remarks (on Good Health at Low Cost]," in Halstead et al eds.(l985) Lavy, Victor, and Jean-Marc Germain, 1994. "Quality and Cost in Health Care Choice in Developing Countries," Living Standards Measurement Study working paper 105. The World Bank.

Lavy, Victor, John Strauss, Duncan Thomas, and Philippe de Vreyer. 1996. "Quality of Health care, Survival and Health Outcomes in Ghana." Journal of Health Economics. 15:333-357.

Lewis, Maureen a., Gerald M. La Forgia, and Margaret B. Sulvetta, 1996. 'Measuring Public Hospital Costs: Empirical Evidence from the Dominican Republic," Social Science zmd Medicine, 43(2):221-234.

Lopez, Alan D., 1993. 'Causes of Death in Industrial and Developing Counties: Estimates for 1985-1990," in Disease Control Priorities in Dleveloing Countries, Jamison et al eds.

Makinen, Marty and Laura Raney, 1994. "Role and Desirability of User Charges for Health Services,' Draft Working Paper. Bethesda, MD.: Abt Associates.

Manniing, Willard G., Joseph P. Newhouse, Naihua Duan, Emmett B3. Keeler, Arleen Leibowitz, and M. Susan Marquis, 1987. "Health Insurance and tie Demand for Medical Care: Evidence from a Randomized Experiment," _Arian_ EcnomicRvie, 77(3):251-77.

Martines, Jose, Margaret Phillips, and Richard g. Feqchem, 1993. "Diarrheal Diseases," in Disease Contro Prioris in Developing Countries, Jamison et al eds.

Menken, Jane, and James Phillips, 1990. "Population Change in a Rural Area of Bangladesh, 1967-87," 1a Anrals of the American Academy, 510:87-101.

53
Policy Research Working Paper 9740

# **Abstract**

In the presence of credit constraints, temporary migration abroad provides an effective strategy for workers to accumulate savings to finance self-employment when they return home. This paper provides direct evidence of this link and its effects on workers’ employment trajectories by using a new, large-scale survey of temporary migrants from Bangladesh. It constructs and estimates a dynamic model that establishes connections between asset accumulation and credit constraints, and, thus, between workers’ migration and self-employment decisions. Interlinked impacts also emerge from simulations of three key policy interventions that target migration costs or domestic credit constraints for entrepreneurship. Lowering migration costs increases emigration, reduces the age at which workers depart, and reduces the duration of their time abroad, which together lead to higher savings and domestic self-employment. Reducing the interest rate for entrepreneurial loans reduces migration and savings levels, undercutting the positive effects on business creation at home. Correcting workers’ inflated perceptions about overseas earnings potential reduces emigration rates and durations, triggering a decrease of both repatriated savings and self-employment in Bangladesh. The findings, which have implications for migrant-sending countries, highlight the need for policies to take into account the linkages between migration and self-employment decisions.

This paper is a product of the Social Protection and Jobs Global Practice and the Development Research Group, Development Economics.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www. worldbank.org/prwp. The authors may be contacted at lbossavie@worldbank.org, josephsimon.goerlach@unibocconi.it, cozden@worldbank.org, and hwang21@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
establishment of a playgroup for four to six-year-olds, typically offered two hours a day, three times a week. This package (community facilitation, block grants, teacher training and playgroups) is effectively the intervention evaluated in this paper.

# **_The Evaluation Design_**

Given the scope of the project, the government originally decided to roll out implementation in three batches roughly nine months apart. Thus during the design of the evaluation the decision was made to randomly assign villages to planned phases of the project. Analysts worked with the government to hold a public lottery and assign some villages randomly to the first batch - batch 1 (100 villages) and some villages to the last batch - batch 3 (120 villages). Villages in the second batch (batch 2) were not included in the evaluation since it was felt that nine months was too little time to gauge the impact of such an intervention. Thus according to this design some villages were randomly assigned to control group status but only for 18 months. At the end of this period, batch 3 villages also received the intervention. Given that after this period, there would be no villages without the intervention, analysts also collected information on a group of villages that would never receive the project (90 villages). These villages were not randomly selected. Instead they were identified by government officials as being similar to the villages that had been randomly assigned to batches 1 and 3. Together these three groups of villages (batch 1, batch 3 and villages that never received the project) constitute the source of the data used in the impact evaluation.

# **_The Evaluation in Practice_**

Due to issues with timing of budget disbursements, in reality the design above was not adhered to. In reality the government rolled out the program in two phases. The first roll-out comprised what was originally referred to as batch 1 and roughly half of batch 2. The second roll-out comprised the remaining half of batch 2 and batch 3. Given that project implementation deviated from its original design, for the purposes of this evaluation, we use actual date of funds disbursal to confirm when villages actually received the project. As a result, we end up with 105 villages in batch 1 and 112 villages in batch 3. The remaining 93 villages never received the project. Thus the evaluation in practice only has small deviations from the evaluation in design.<sup>5</sup>

> 5 See tests of balance between original and actual batches as reported in Hasan, Hyson and Chang (eds.), 2013.

8
# **3.2. Comparison of Ground‐ and Satellite‐Based Yield Measures on Pure Stand Plots**

We begin the evaluation of satellite VIs by presenting simple bivariate relationships between VIs on single dates and the objective ground‐based yield measures (Fig. 3). For brevity, correlations with the subjective SR yields are not presented in this section, but they are generally lower than those for the objective yield measures.

Four important features are evident in Figure 3: (1) Correlations were generally higher between VIs and FP yields than between VIs and CC yields, which is consistent with the notion that full‐ plot crop cutting provides a better measure of plot‐level productivity. (2) Correlations tended to improve when excluding the smallest plot sizes, consistent with the results in BL17. A likely explanation for this is the increased importance of georeferencing errors and mixed pixels on the smallest of plots. For example, a 0.05 ha plot covers an area of just five 10x10m Sentinel‐2 pixels, and most of these pixels are likely to span the edge of the plot and contain some contribution from neighboring plots. (3) The MTCI consistently outperformed the other VIs on both image dates. The MTCI was designed to be sensitive to canopy chlorophyll concentration (Dash and Curran, 2004), which is likely a good proxy for yield in the low nutrient setting of Uganda. Perhaps more importantly, MTCI is much less sensitive to atmospheric conditions than other VIs such as NDVI or GCVI (Curran and Dash, 2005), because it uses the difference in reflectance between two nearby bands that will be similarly affected by atmospheric scattering. In both images, significant amounts of haze are evident above many of the plot sites in both the raw reflectance and NDVI or GCVI images. However, the MTCI images exhibit much lower sensitivity to haze (Fig. A1). (4) Finally, Figure 3 indicates that a substantial fraction of FP yield variability is captured by VIs on both dates, with MTCI capturing 37 percent of yield variability on plots at least 0.10 ha on May 30, and 49 percent on June 19. These values are similar to the amount of FP yield variability captured by CC yields on these plots (R<sup>2</sup> = 47 percent).

Satellite‐based yields were estimated for all plots which did not contain clouds on either May 30 or June 19 (397 out of 463 total plots). The “calibrated” satellite yield estimates, obtained from a regression of FP yields vs. MTCI on May 30 and June 19, captured slightly more than half of yield variability for the pure stand plots above 0.10 ha (R<sup>2</sup> = 0.55, Fig. 4a). For comparison, calibration using CC rather than FP yields resulted in roughly half the amount of variability captured by satellite (R<sup>2</sup> = 0.26, Fig. 4b). Interestingly, though, the coefficients of the two regressions were very similar, with the model calibrated to CC yields having a slightly lower range of predicted yields. As a result, this model did nearly as well predicting FP yields (R<sup>2</sup> = 0.54) as the model calibrated to FP yields.

15
‘conflict’ (30 homicides per 100,000) and is well above the rate of any country in Africa, some of which were engaged in civil wars.<sup>3</sup>

**Figure 1: El Salvador, Intentional homicides (per Figure 2: Intentional homicides (per 100,000 people)**

<!-- Start of picture text --> 100,000 people 1995–2014) by countries, 2014<br>150 80 75<br>130 70 64<br>110 60<br>50<br>90<br>40<br>70<br>30<br>50 23<br>20<br>30<br>10 5<br>10<br>0<br>‐10<br>(per 100,000 people)<br>(per 100,000 people)<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 HNDSLVVENJAMBLZZAFGTMCOLTTOBRALACPRIDOMMEXSSTUGACRIPHLRUSBRBPRYECUURYARGGRDMNGPERKENLTUWLD<br><!-- End of picture text --> _Source:_ World Bank, World Development Indicators _Source:_ World Bank, World Development Indicators In this context, PATI was introduced in 2009 by combining income support with training opportunities, as a response to the international global financial and fuel crisis, which resulted in a significant contraction of the economy (gross domestic product [GDP] declined 3.1 percent in 2009) and seriously affected employment (between 2008 and 2009, more than 100,000 Salvadorans, or 3 percent of the labor force, became unemployed or under-employed).

The objective of the program was dual: (a) to provide short-term income support to poor and vulnerable individuals in urban areas, and (b) to increase beneficiaries’ medium-term employability by offering training opportunities in addition to them gaining experience working in productive social activities. PATI provided each participant a cash benefit of US$100 per month for a maximum of six months, conditional on participation in community projects, occupational training, and labor market orientation courses. Eligible beneficiaries had to be at least 16 years old, with neither formal work (self-employed, under-employed, or unemployed) nor studying. In each eligible settlement, there was a preregistration process, and eligible individuals were prioritized

> 3 Chioda (2017).

6
looked for people who were strong, hardworking, obedient, healthy, spoke English and were not alcoholics. Perhaps due to the newness of international migration in Vanuatu, it was not the poorest households who applied and had workers selected for the program, with communities more concerned with sending workers who would represent the village well, and the poorest households not necessarily having information about the program in the first year, or having the resources to finance the costs of the travel process.<sup>5</sup> Typical work under the RSE includes working on vineyards to prune vines and pick grapes, harvesting apples and kiwifruit and other fruitpicking, and working in the packhouse to sort, grade, and pack the fruit. The work was typically physically demanding, and included work in both cold and hot conditions. In part due to the nature of the work, the majority of RSE workers recruited were male: in the first year in our sample, 82 percent of the ni-Vanuatu RSE workers and 87 percent of the Tongan RSE workers were male. This corresponds closely to the gendermix in the population of RSE workers from these countries in the first year – official data on all workers from Tonga and Vanuatu recruited by 22 May 2008 show that males comprised 78 percent of the ni-Vanuatu and 91 percent of the Tongans recruited by that date (effectively the first season).<sup>6</sup> The RSE has been viewed as a success from the New Zealand point of view. An evaluation of the first two years conducted by the New Zealand Department of Labour (2010, p. xvii) concluded that “Overall, the RSE Policy has achieved what it set out to do” The policy is found to have provided employers in the horticulture and viticulture industries with access to a reliable and stable workforce, with productivity gains starting to emerge as workers return for another season. The main concerns raised about temporary labor programs have been mitigated: the evaluation finds little displacement of New Zealand workers; almost all workers have returned, with overstay rates of about 1 percent in the first season and less than 1 percent in the second; and concerns about worker exploitation have at most arisen in a couple of isolated cases. The question this paper addresses is then whether the RSE has also lived up to the policy goal of improving development in the Pacific.

# **3. Our surveys and estimation methodology**

# **3.1 The surveys**

There was keen interest from national governments on both sides of the migration relationship and from the World Bank in learning whether the new RSE policy would have the development impacts envisioned as one of the core rationales for the program. It was therefore decided ex ante

> 5 Employers are required to cover half the cost of the return airfare, and often provide loans to workers for the worker share. But workers still had to meet the costs of a passport, visa, police clearance, medical check-up, and local transport to and from their home to the airport.

> 6 Official data provided as a custom table by the New Zealand Department of Labour.

6
# Table 6.5 (Cont.)

||Based<br>in D|on Value-Ad<br>omestic Price|ded<br>s|Based<br>in|on Value-A<br>World Prices|dded<br>|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|In (Middle Management)||-022E-3<br>(0.21E-3)|||0.30B-4<br>(026E-3)||
|In (Upper Management)<br>In (Wage BM of<br>Temporaiy Workers)<br>InK<br>_Tume_|0.14E-3<br>(0.15E-3)<br>0.349<br>(0.078)<br>4.076*<br>(0.029)|0.13E-3<br>(0.31E-3)<br>0.20B-3<br>(0.14E-3)<br>_OA29'_<br>(0.072)<br>.0081<br>(0.031)|024E-3*<br>(0.14E-3)<br>0.377*<br>(0.079)<br>4.090*<br>(0.029)|0.39E-3'<br>(0.19E-3)<br>0.194<br>(0.126)<br>4.070e<br>(0.036)|0.541-4<br>(0.39E-3)<br>0.46E3<br>(0.19E-3)<br>0.251<br>(0.135)<br>4.068<br>(0.041)|0.45AE4<br>(0.18B-3)<br>0.274<br>(0.125)<br>4.089W<br>(0.08)|
|R2|0.897|0888|0883|0.838|0808|0.801|
|N|115|115|115|109|109|109|

* - Significant at the _5_ % statistical test leveL

+ - Significant at the 10 % statistical test leveL The food industry includes 24 firms from the following sub-industies wsugar, o, fish, flour, and other food.
**Table 4: Studies with alternative estimated impact of transfer on alcohol or tobacco consumption**

|**Country**|**Program name**|**Temptation good**|**Impact**|**Detail on impact**|**Methodology**|**CCT/UCT**|**Reference**|
|---|---|---|---|---|---|---|---|
|**_Report pro_**|**_portion of expenditures_**|||||||
|Brazil|Bolsa Alimentaria & Bolsa<br>Escola|<br>Alcohol, tobacco, and<br>gambling|<br>−0.003 (0.002)||Difference in differences|CCT|Braido, Olinto, & Perrone 2012|
|Mexico|Oportunidades|Alcohol and tobacco|-0.0025 (0.0018)||RCT|CCT|Rubalcava, Teruel, & Thomas<br>2002|
|Mexico|PROGRESA|Tobacco|-0.02 (0.09)<br>-0.51 (0.398)|Benefit (dummy)<br>Benefit (level)|RCT|CCT|Schluter & Wahba 2004|
|Nicaragua|Red de Protección Social|Alcohol and tobacco|0.1||RCT|CCT|Maluccio & Flores 2005|
|**_Report pro_**|**_bability of alcohol abuse in_**|**_household_**||||||
|Mexico|Oportunidades|Alcohol abuse|-0.042***(0.016)||RCT|CCT|Angelucci 2008|
|**_Report pro_**|**_bability of consumption (for_**|**_adolescents only)_**||||||
|Mexico|Oportunidades|Alcohol<br>Tobacco|-11%*** (0.026)<br>-13%***(0.029)<br>-4%***(0.015)<br>-15%***(0.029)<br>-13%***(0.024)<br>-2%**(0.007)|Rural, 10 - 21 year olds<br>incorporated in 1998<br>Rural, 10 - 21 year olds<br>incorporated in 2000<br>Urban, 15 - 21 year olds<br>Rural, 10 - 21 year olds<br>incorporated in 1998<br>Rural, 10 - 21 year olds<br>incorporated in 2000<br>Urban, 15 - 21 year olds|Propensity score matching|CCT|Gutiérrez, Bautista, Gertler,<br>Hernández, & Bertozzi 2004|
|Mexico|Oportunidades|Alcohol|-40%~|Females|Instrumental variables|CCT|Galárraga & Gertler 2009|
|||Tobacco|-46%~|Males||||
|**_Report num_**|**_ber of days consumed in pa_**|**_st week (for children_**|**_1-7 only)_**|||||
|Uganda|WFP Cash Transfers to<br>UNICEF-supported ECD<br>centers|Beer|-0.198 (0.198)|ITT|RCT|CCT|Gilligan & Roy 2012|

Notes: * denotes statistical significance at the 10% level, ** denotes statistical significance at the 5% level, *** denotes statistical significance at the 1 % level, ǂ denotes that statistical significance is reported but not a standard error, and ~ denotes that statistical significance is not reported. CCT is Conditional cash transfer; UCT is unconditional cash transfer. ETT is estimate of treatment on the treated. ITT is the intent-to-treat estimator. RCT is randomized control trial. TASAF is the Tanzania Social Action Fund. The reported impacts on total expenditures (and corresponding standard errors) presented in 2012 PPP are calculated by inflating the impact in local currency in the various base years (the year the data were collected, or as close to that as could be inferred) to their 2012 values using the inflation GDP deflator (annual %), before dividing by the 2012 PPP conversion factors for private consumption (LCU per international $). Both indicators used in the PPP conversion come from the World Development Indicators database available at http://data.worldbank.org/data- <u>catalog/world-development-indicators</u> 24
In theory, strict employment protection legislation is expected to discourage hiring and rises the costs of employment adjustment. If employers find it difficult and costly to reduce employment during the downturn, they are reluctant to hire labor and increase employment during the upturn. This is a rational behavior aimed at minimizing expected labor costs.

It is tempting to attribute the low job creation rate in Moldova to strict employment protection. However, in order to establish such a link one first needs to determine if relevant regulations are observed and enforced. Available evidence suggests that this is not the case in Moldova. First, as shown earlier, the job destruction rate is in Moldova is relatively high, implying that costs of eliminating redundant jobs are not prohibitive.

Second, overstaffing is not a major problem in Moldova. Actually, it seems to be less of a problem than in some transition economies with apparently more flexible labor markets. For example, only 5 percent of Moldavian firms would reduce their employment 20 percent or more, were the regulations on dismissal absent, compared with 6 percent of firms in Hungary and 12 percent in the Czech Republic, i.e. countries where the employment protection legislation is relatively liberal and significantly less strict than in Moldova.<sup>27</sup> Finally, and perhaps most importantly, employers themselves do not regard labor regulations as an important problem negatively affecting their business. Or at least, problems associated with labor regulations are thwarted by others, such as those relating to custom procedures, permits, certificates, licenses and inspections. According to the results of a focus group (comprising managers from small and medium sized enterprises), labor regulations are perceived as the least important problem facing employers (score of 1 on a scale form 0 to 10).<sup>28</sup> All this means that, although stringent in theory, in practice labor regulations have little bite as they are mostly ignored and not enforced. In other words, the low job

> 27 These results  come from of the second round of the Business Environment and Enterprise Performance Survey (BEEPS II) conducted in 2002 by the World Bank and the EBRD.  Survey results are available at: http://info.worldbank.org/governance/beeps2002.

> 28 MAEWS Focus Group, Chisinau, Moldova, December, 2002.

37