rafmacalaba/datause-encoder-data huggingface dataset:
in household production. Table 3 shows statistically weak or insignificant relationships between remittances and unemployment. The combination of lower participation rates and no difference in unemployment among recipients suggests the latter motivation, a welfare increasing reallocation of household labor, may be playing a more important role in the Northern Triangle than would-be workers searching for higher wage jobs.

**Table 3.  Labor force participation and unemployment rates by gender, propensity score matching results**

|||<br>El Sal|<br>vador|<br>Guate|<br>mala|<br>Hon|<br>duras|
|---|---|---|---|---|---|---|---|
|||2006|2014|2006|2014|2006|2014|
|||Lab<br>|or force parti<br>|cipation<br>||||
||Treated|0.757|0.781|0.903|0.858|0.838|0.858|
||Control|0.819|0.831|0.936|0.894|0.880|0.872|
|Men|Diff (p.p.)|-0.062|-0.049|-0.033|-0.036|-0.042|-0.014|
||Diff (%)|-0.076|-0.059|-0.035|-0.041|-0.048|-0.017|
||t-stat|-7.48|-6.86|-4.44|-3.19|-6.23|-1.12|
||Treated|0.382|0.403|0.462|0.392|0.445|0.463|
||Control|0.558|0.537|0.523|0.468|0.506|0.555|
|Women|Diff(p.p.)|-0.175|-0.134|-0.061|-0.075|-0.062|-0.092|
||Diff(%)|-0.315|-0.250|-0.116|-0.161|-0.122|-0.166|
||t-stat|-21.010|-17.430|-5.390|-5.480|-7.780|-5.640|
||||Unemploym<br>|ent<br>||||
||Treated|0.046|0.058|0.025|0.032|0.053|0.079|
||Control|0.048|0.052|0.014|0.021|0.047|0.060|
|Men|Diff (p.p.)|-0.002|0.006|0.012|0.010|0.006|0.019|
||Diff (%)|-0.033|0.106|0.852|0.482|0.136|0.312|
||t-stat|-0.33|1.19|2.83|1.67|1.41|1.76|
||Treated|0.032|0.041|0.016|0.038|0.057|0.086|
||Control|0.026|0.037|0.015|0.021|0.051|0.083|
|Women|Diff(p.p.)|0.006|0.003|0.001|0.016|0.006|0.003|
||Diff(%)|0.218|0.088|0.085|0.766|0.116|0.035|
||t-stat|1.28|0.72|0.32|1.99|1.12|0.22|

_Source:_ Authors’ tabulations using SEDLAC (CEDLAS and the World Bank). _Note:_ This table reports the labor force participation rate and unemployment rate of the treated group (those who received remittances) and the control group (similar individuals who did not receive remittances).

Youth in Latin America are vulnerable to unemployment and economic inactivity; research on the “ _ninis_ , _”_ from the Spanish acronym for “not in school and not working”, finds high rates in the countries of the Northern Triangle (de Hoyos, Rogers, and Szekely 2016). Considering only those between the ages of 18 and 25, Table 4 reports the likelihood of being neither in school nor employed for youth who receive remittances compared to similar youth who did not, by gender. With the exception of Honduran women in 2006, youth who received remittances were statistically more likely to be _ninis_ than similar youth who did not receive remittances. For women, the effect of receiving remittances on being a _nini_ are in line with the results reported above for participation rates for all women. In 2014, young women in households that received remittances were 13 percent (Guatemala), 19 percent (Honduras) and 22 percent (El Salvador) more likely to be _ninis_ than similar young women without remittances. For comparison, overall, adult women that same year were about 16 percent (Guatemala and Honduras) and 25 percent (El Salvador) less 10
growth per worker accelerates between **1.1** and **2.4** percentage points per year.

|**Table 2. Trade, diversi**<br>_Dependent variable, Growth_<br>_Estimation Method: GMM-I_|**fication and gro**<br>_of real GDP per wo_<br>_V System Estimator (_<br>[1]<br>|**wth: Baseline speci**<br>_rker (5-year non-overlap_<br>_Arellano and Bover, 199_<br>[2]<br>|**fication.**<br>_ping averages)_<br>_5; Blundell and Bond,1998)_<br>[3]<br>[4]<br><br>|
|---|---|---|---|
|Initial GDP per worker<br>|-0.022<sup>***</sup><br>|-0.027<sup>***</sup><br>|-0.022<sup>***</sup><br>-0.020<sup>***</sup><br> <br>|
|_(in logs)_|(0.000)|(0.000)|(0.000)<br>(0.000)|
|Exports and Imports|0.034<sup>***</sup>|0.032<sup>***</sup>|0.016<sup>***</sup><br>0.017<sup>***</sup>|
|_(% of GDP, logs)_|(0.000)|(0.000)|(0.000)<br>(0.000)|
|Market Concentration|0.015<sup>***</sup>|0.020<sup>***</sup>|0.013<sup>***</sup><br>0.006<sup>***</sup>|
|_(Herfindahl Index)_|(0.000)|(0.000)|(0.000)<br>(0.000)|
|Product Concentration|-0.031<sup>***</sup>|-0.038<sup>***</sup>|-0.019<sup>***</sup><br>-0.020<sup>***</sup>|
|_(Herfindahl Index)_|(0.000)|(0.000)|(0.000)<br>(0.000)|
|Natural Resource exports||0.007<sup>**</sup>|-0.002<sup>**</sup><br>-0.005<sup>***</sup>|
|_(% of GDP)_||(0.002)|(0.002)<br>(0.000)|
|Secondary enrollment rate|||0.003<sup>***</sup><br>0.008<sup>***</sup>|
|_(initial level, logs)_|||(0.001)<br>(0.000)|
|ICRG Political Risk Index|||0.005<sup>***</sup><br>0.006<sup>***</sup>|
|_(principal components)_|||(0.000)<br>(0.000)|
|Credit to private sector|||-0.013<sup>***</sup>|
|_(% of GDP)_|||(0.000)|
|Government consumption|||-0.002|
|_(% of GDP)_|||(0.862)|
|Growth volatility|||0.222***|
|_(S.D. growth per worker)_|||(0.000)|
|Constant|0.044<sup>*</sup>|0.083<sup>***</sup>|0.142<sup>***</sup><br>0.130<sup>***</sup>|
||(0.024)|(0.000)|(0.000)<br>(0.000)|
|Observations|870|840|613<br>601|
|_AR(1)_|0.000|0.000|0.015<br>0.001|
|_AR(2)_|0.281|0.238|0.330<br>0.527|
|_Hansen_|0.072|0.198|0.477<br>0.386|
|_p_-values in parentheses<br>||||
|* _p_ <0.05,** _p_ <0.01,*** _p_ <|0.001|||

Export market concentration, measured by the Herfindahl-Hirschman index of exports from country _i_ to its trading partners ( _HHIM_ ), has a positive and significant coefficient in all specifications reported in Table 2. The coefficient estimate of _HHIM_ fluctuates between 0.006 to 0.020. A positive relationship between growth per worker and _HHIM_ can be rationalized as a country having fewer trading partners whose economic activities are highly synchronized (in the medium term). On the other hand, export product concentration, measured by the Herfindahl-Hirschman index of export products ( _HHIP_ ), has the expected negative sign and it has a robust relationship with growth per worker. The estimated coefficient of _HHIP_ ranges from -0.019 to -0.038, and this coefficient decreases in size as more control 12
Last, both knowledge and participation in pilot districts was higher than in non-pilot districts at baseline. The differences are all statistically significant, indicating that pilot districts are not necessarily a representative sample of districts in the country. However, the magnitude of the differences is not large enough to dismiss external validity of these results for the rest of the country. Moreover, while knowledge and participation were higher in pilot districts, they were still low in absolute terms.

# **3. Results**

# **_3.1 Balance at baseline_**

Before turning to the impact of the interventions, the first set of results we discuss are those that test the validity of the evaluation design. Because of the differences in the baseline and endline, described in the previous section, we perform two sets of tests. First, we compare summary statistics for available outcome variables at baseline for the treatment and control groups (Table 2). Second, we use the richer endline survey to compare summary statistics of fixed background variables of respondents in the treatment and control groups (Table 3).

The first set of balancing tests (Table 2) reveal no statistical differences between treatment and control groups in Tulungagung and Sumbawa in any of the baseline variables. However, in some cases, the magnitude of some differences is large with corresponding large standard errors. In part this is due to the relatively small number of observations in the baseline survey. A joint test for the joint significance of the variables (or indices) in predicting treatment fails to reject that they are jointly equal to zero, supporting the notion of baseline balance in these outcome variables.

# **Table 2: Summary statistics of available knowledge and participation variables at baseline**

14
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|TIMMS Math Score<br>(Standardized)|I|Core<br>Respondent|N/A|3|Total score from a professionally developed<br>test of mathematics based on the Malawian<br>school curricula for the grades the target<br>population was attending. Five questions (four<br>from the Fourth Grade test and one from the<br>eighth Grade test) from Trends in Mathematics<br>and Science Study (TIMMS) 2007, which is a|
|Non-TIMMS Math Score<br>(Standardized)|I|Core<br>Respondent|N/A|3|cycle of internationally comparative<br>assessments in mathematics and science<br>carried out at the fourth and eighth grades<br>every 4 years, were added to the Math test.|
|Cognitive Test Score<br>(Standardized)|I|Core<br>Respondent|N/A|3|The cognitive test score is based on Raven’s<br>Colored Progressive Matrices.<br>Competencies represent a set of skills that<br>were anticipated to be sensitive to education|
|Competencies Score<br>(Standardized)|I|Core<br>Respondent|Yes|4|and relevant for non-formal employment. The<br>overall competencies score is a standardized<br>index of the five competencies listed<br>immediately below.|
|Fertilizer Application<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of ability to follow<br>instructions for applying fertilizer to maize.<br>|
|Change Given<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of ability to make change<br>following hypothetical scenarios of market<br>transactions.|
|Sending a Text Message<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of ability to send a text<br>message saying "hello" to a specified phone<br>number.|
|Using a Calculator<br>(Standardized)|S3|Core<br>Respondent|No|4|Standardized index of ability to use a cell-<br>phone calculator to calculate 873*17.|

62
3 between. The RAND Health Insurance Experiment (HIE), conducted in California in the 1970s, is the most famous exception. It randomly assigned participants to different insurance plans, and found that cost-sharing had only limited impacts on health outcomes among the general population (Brook _et al._ 1983). However, as Chernew and Newhouse (2008) note, with the technological advances that have occurred in medicine since the study, the negative effects of cost-sharing on health outcomes are likely to be larger today than they were in the 1970s; furthermore, the HIE did find negative effects of cost-sharing among those on low incomes. Randomized encouragement is an alternative to randomized assignment. King et al. (2009) used this technique to estimate the impacts of Mexico’s _Seguro Popular_ health insurance program, distributing leaflets about the program to households in randomly chosen health facility catchment areas. They found no difference in health between the intervention and control group; they acknowledged, however, that the short follow-up period (just 10 months) may be the reason. Finkelstein et al. (2011) exploited Oregon’s decision to give a random selection of people the chance to apply for Medicaid. They found that 12 months after the lottery, among those given the chance to take out Medicaid around 25 percent did so. This “treatment” group reported greater use of preventive and curative medical care than the control group, and reported themselves to be in better physical and mental health than the control group.

Randomization is _sufficient_ to eliminate concerns over selection bias but not _necessary_ ; what matters is that a setting can be found where an individual’s insurance status is exogenous to his or her health. One such setting is where a program is rolled out in a staggered fashion so that over time the number of people entitled to or possessing health insurance increases, but the individuals affected do not decide their eligibility and may not
# **2 Contribution and Related Literature**

This paper is related to three strands of literature. First and foremost, we contribute to the literature looking at the economic impact of opening the domestic economy to foreign investors. The existing research finds positive effects of such financial liberalization on aggregate economic outcomes including domestic investment (Henry 2000a), economic growth (Bekaert, Harvey, and Lundblad 2005; Gupta and Yuan 2009; Levchenko, Ranci`ere, and Thoenig 2009), and international trade (Manova 2008; Defever and Suedekum 2014) while also raising concerns about potential adverse effects in terms of more volatile capital flows (Broner and Ventura 2016) and increased vulnerability to financial crises (Reinhart and Rogoff 2009).<sup>1</sup> More recently, the researchers started to look beyond the aggregate outcomes and explore the structural changes brought by foreign investors to recipient economies (e.g., Bekaert et al. 2007; Gopinath et al. 2017; Larrain and Stumpner 2017; Varela 2018).<sup>2</sup> One of the main challenges in this research is to capture the full structural impact of foreign investors relevant for a broad group of countries.

For one thing, the empirical work on allocative consequences of financial liberalization faces a trade-off between the cross-country coverage and comparability of the available data on the one side and their disaggregation level on the other side. Researchers thus often have to choose between utilizing disaggregated firm-level data and maximizing the number of countries across different stages of financial and economic development in their sample.<sup>3</sup> The framework of international trade allows us to use highly disaggregated

> 1 Papers examining the impact of financial liberalization on the outcomes in financial markets include, e.g., Bekaert and Harvey (2000), Henry (2000b), Kaminsky and Schmukler (2008).

> 2 Related research examining the impact of domestic financial development on allocation of physical capital includes, e.g., Wurgler (2000), Beck and Levine (2002), Love (2003), and Fisman and Love (2004, 2007).

> 3 Bekaert et al. (2007) use country-time panel data covering 50 countries between 1980 and 2002 and show that financial openness is an important factor in aligning growth opportunities with actual growth. Larrain and Stumpner (2017) rely on firm-level data from 10 post-communist countries in Central and Eastern Europe and provide evidence that capital account liberalization helps financially constrained firms to demand more capital and produce at more efficient levels. Gopinath et al. (2017) explore capital misallocation using firm-level data from Spain and extend part of their analysis to five other countries in Western Europe. Both papers rely on databases provided by ORBIS-AMADEUS that cover most of the manufacturing firms in the respective countries. Varela (2018) looks at impact of lifting the restrictions on international borrowing by using firm-level census data covering all manufacturing firms in Hungary.

3
been avoided, or would have been, had there been an all-weather road.

## **5.3 The focus group interviews**

The focus group interviews yielded, once more, both some striking uniformities and a few differences in the roads’ effects, as is clear from Table 14. In all villages, children and the chronically sick receive more regular care, and treatment has become more timely, thereby reducing long-term _sequelae_ . In five villages, doctors are also readier to make house-calls to treat those too sick to be transported to a hospital by an ordinary – vehicle though this raises questions about the quality of ambulance services, which exist precisely to deal with such cases. Despite these improvements in access, the respondents in three villages named the quality of care in the facilities themselves as the main problem, and those in two further villages were unclear on this point. Yet whatever their misgivings about the facilities on this score, the respondents in these five villages reported that people had largely ceased to rely on traditional healers. This revealed preference points to an improvement in welfare, but the respondents’ expressed opinions to unexploited complementarity in the provision of public services.

Whatever be the failings on this latter front, there was no doubt in the respondents’ minds that their new road had reduced mortality, which is surely a, if not the, leading outcome where evaluating its effects on health is concerned. Since this indicator is also measurable, the groups were asked how many of their fellow villagers had died due to untimely treatment in the year before their road became operational and how many in the crop year 2009-10. In communities wherein people know so much about their neighbors’ doings, such estimates should be fairly reliable. Averaging over the villages, the mean number fell from 3.25 to 0.75 annually. Taking the individual differences to rid the data of any village fixed effects, the corresponding value of _|t|_ is 5.00: with 7 d.f., this is an overwhelming rejection of the null. This quantitative finding stands in stark contrast to that yielded by the household data, and it lends support to the suspicion that the failure to find any effect in the latter can be put down to sampling – fluctuation with just 8 households per village.

# **6 Ranking the Benefits in the Three Domains**

Both the household survey and the focus group interviews provide strong support for the contention that the benefits in the domains of education and health are likely to 32
13 Economics‖ is yet another recent and reasonably comprehensive set of topics. The JHE uses yet another classification system, grouping articles into: production of health and health services; demand and utilization of health services; financing of health services; measurement of health; behavioral models of demanders, suppliers and other health care agencies; manpower planning and forecasting; the prevention of sickness; cost-benefit and cost-effectiveness analyses and issues of budgeting; and efficiency and distributional aspects of health policy.

In the event, we opted for the following 12 topic groups: (1) health and its value; (2) efficiency and equity; (3) determinants of health and ill-health; (4) public health; (5) health and the economy; (6) health statistics and econometrics; (7) demand for health and health care; (8) medical insurance; (9) supply of health services; (10) human resources; (11) markets in health care; and (12) economic evaluation. The classification scheme is explained more fully in the Appendix.

The assignment of topics to publications was done manually. To ensure consistency, process was completed by just one of us (Culyer). Many articles could be assigned readily on the basis of their titles but in more than half of cases the abstract was also used. In about one quarter of cases, the full pdf was downloaded in Google Scholar before the final assignment was made. Assigning a topic to all 32,000 publications was clearly infeasible. Instead, we assigned topics to all publications in each of the following groups: the most-cited 300 publications; the most-cited 50 publications in each of the four decades studied; the most-cited 50 publications by each of the following institutions: Harvard, Berkeley, MIT, Chicago, the World Bank, and the University of York; the most-cited 50 publications from each of the United States, the United Kingdom, Canada and the Netherlands. In total, a topic was assigned to 694 publications.

The allocation is a little arbitrary at times: some articles span multiple categories, as when, for example, they are methodologically innovative (which might get them into category (6) but also have a specific application, which will typically place them elsewhere. Three categories—(7), (10) and (11)—are somewhat prejudiced by being elements in a very large number of papers in all topic areas, whereas we have included in these three groups only papers whose particular focus appeared to be on one of these three.
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demand-side intervention, which was found to positively impact rates of timely antenatal and postnatal care, does not result in different outcomes when implemented in conjunction with the CHW incentives.

It could be that the financial rewards per service were not high enough. For the CHWs to increase effort towards a targeted service, the reward has to be perceived as at least equal to the cost incurred by exerting the additional effort. The cost, for example, can be in terms of the time the CHW will have to spend away from household tasks or income-generating activities. As shown Table 1, the per-service incentive amounts have been continuously reduced. In the follow-up survey, CHWs were asked to report the amount received in their last CPBF quarterly payment, excluding dividends from the cooperatives’ income-generating activities. The average reported amount was only about 7.3 USD. For reference, the GNI per capita was estimated to be 690 USD in 2014. This means that for an average Rwandan the CPBF payments would represent a very small change in income. The PBF scheme at health facilities, for comparison, increased staff salaries by 38 percent (Basinga, Gertler, et al. 2011).

Another factor that could have further diminished the expected return to effort exertion is the transmission of the financial incentives through the cooperative structure. When CPBF payments were transferred to the cooperative, at least 70% of the payments had to be invested in the cooperatives’ income-generating activities. It was up to the cooperatives to decide how to allocate the CPBF payments and dividends from the income-generating activities among an average of more than 100 members. Therefore, the reward for an individual CHW does not depend only on her effort but also on the efforts of the other cooperative members, the success of their income-generating activities and the cooperative’s revenue allocation decisions. Moreover, focus group discussions conducted early during the implementation of the program revealed that CHWs were confused about the payment mechanisms of the CPBF program (Condo et al. 2014).

The study was not set up to evaluate the organization of community health workers in cooperatives and cannot identify their impact as the same organization was used in all study arms. There are ways in which the organization of CHWs in cooperatives is theorized to positively impact the performance of the CHWs. For example, the income received from the cooperatives might permit the CHWs to spend more time on health activities rather than on individual income-generating activities. The cooperatives might also enable sharing of health knowledge and collaboration. However, transferring the financial incentives through the cooperatives might not serve as an optimal platform for inducing individual effort by the CHWs in their perspective villages as it can weaken the link between efforts exerted and perceived rewards.

17