rafmacalaba/datause-encoder-data huggingface dataset:
to identify the causal impact of PAF program on outcomes of interest. Using two rounds of survey data and difference-in-difference combined with instrumental variable estimation method, we find that there is a positive and significant “PAF effect” on key welfare indicators. The estimated _net program impact_ on real per capita consumption growth is 19 percent for PAF participants. Other impacts include a 19 percentage points decline on incidence of food insecurity (as defined as self-reported food sufficiency for six months or less) and 15 percentage points increase in school enrollment rate among 6-15 year olds. While improvements in nutrition and social empowerment indicators are seen for all groups (treated/untreated), the impact of the PAF program on indicators associated with child malnutrition and indicators of social capital (trust, respect, relationships between different ethnic groups within the community) is yet to be detected. The policy implications of these results should be of interest to the government and to development partners in determining what may be effective instruments to deliver services to marginalized communities in what remains a fragile and difficult political environment.

This study contributes to the existing literature on impact evaluation of social fund in a few ways. First, we present results of a large-scale intervention using an experimental impact evaluation design. Second, we estimate the causal impact even when there is no perfect compliance with randomized assignment. Imperfections in randomized evaluations are quite reasonable in real world settings of large-scale programs and allowing for such in fact can maintain external validity of the impact estimates. Third, in addition to using per capita household consumption, we also report impact on food security and access to education which are important indicators of vulnerability in conflict settings.

# **2. Evaluation Design**

<u>PAF Setting: As mentioned earlier, PAF interventions are predominantly income generating</u> activities (IGAs), particularly in the initial rounds, since the social cohesion required to motivate investments in public goods often takes longer to form, particularly in communities affected by conflict. The interventions typically have the following sequencing:

- PAF and its Partner Organization (PO) – local NGO- select the village in a targeted district (PO’s village selection depends on qualitative and quantitative assessment of need and feasibility)

- PO carries our community mobilization in the selected village on possible PAF interventions and household participation

- Community Organizations (CO) are formed with 25-30 households as CO members

3
Table 3: Probability of having a health-risk job based on marginal effects of the multinomial logit

||Model 1:<br>Raw<br>as|Model 2:<br>Raw gaps|Model 3:<br>Individual|Model 4:|Model 5:<br>Language|Model 6:<br>Barriers to|Model 7:<br>Networks|
|---|---|---|---|---|---|---|---|
|VARIABLES|gp|<br>by origin|character.|NUTS2 FE|<br>ability|certification||
|Foreign born|0.089***<br>(0.004)|||||||
|Origin EU15||0.015**|0.019**|0.007|-0.017**|-0.016**|-0.009|
|||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.011)|
|Origin NMS13||0.105***|0.114***|0.101***|0.065***|0.048***|0.040***|
|||(0.008)|(0.008)|(0.008)|(0.009)|(0.009)|(0.013)|
|Origin EFTA||0.020|0.014|-0.006|-0.014|-0.013|-0.029|
|||(0.023)|(0.023)|(0.021)|(0.020)|(0.020)|(0.037)|
|Origin other Europe||<br>0.104***|<br>0.114***|<br>0.109***|<br>0.065***|<br>0.061***|<br>0.076***|
|||(0.008)|(0.008)|(0.008)|(0.009)|(0.009)|(0.014)|
|Origin MENA||<br>0.066***|<br>0.095***|<br>0.065***|<br>0.025*|<br>0.020|<br>0.007|
|||(0.013)|(0.015)|(0.014)|(0.013)|(0.013)|(0.018)|
|Origin Sub-Saharan Africa||<br>0.164***|<br>0.171***|<br>0.157***|<br>0.118***|<br>0.117***|<br>0.143***|
|||(0.014)|(0.015)|(0.015)|(0.014)|(0.014)|(0.022)|
|Origin Asia||0.085***|0.106***|0.095***|0.044***|0.042***|0.037**|
|||(0.009)|(0.009)|(0.009)|(0.010)|(0.010)|(0.014)|
|Origin LAC<br>Origin non-Europe OECD<br>Age<br>Age^2||0.210***<br>(0.012)<br>-0.058***<br>(0.014)|0.213***<br>(0.012)<br>-0.054***<br>(0.015)<br>0.006***<br>(0.001)<br>0.000***|0.169***<br>(0.011)<br>-0.054***<br>(0.015)<br>0.006***<br>(0.001)<br>0.000***|0.146***<br>(0.011)<br>-0.062***<br>(0.014)<br>0.006***<br>(0.001)<br>0.000***|0.139***<br>(0.011)<br>-0.064***<br>(0.014)<br>0.006***<br>(0.001)<br>0.000***|0.159***<br>(0.018)<br>-0.071***<br>(0.019)<br>0.006***<br>(0.001)<br>0.000***|
||||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Male|||<br>-0.122***<br>(0.002)|<br>-0.119***<br>(0.002)|<br>-0.120***<br>(0.002)|<br>-0.120***<br>(0.002)|<br>-0.114***<br>(0.002)|
|Years of Education|||-0.004***|-0.004***|-0.004***|-0.004***|0.003***|
||||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Language – advance (reference||||||||
|<br>category is mother tongue)|||||0.039***<br>|0.037***<br>|0.033***<br>|
||||||(0.008)|(0.003)|(0.011)|
|Language – intermediate||||||||
|<br>(reference category is mother||||||||
|<br>tongue)|||||0.080***|0.072***|0.074***|
||||||(0.010)|(0.010)|(0.014)|
|Ln – binnr (rfrn||||||||
|aguage ege eeece<br>category is mother tongue)|||||0.055***|0.052***|0.060***|
||||||(0.013)|(0.013)|(0.016)|
|Barrier certification||||||0.069***<br>|0.056***<br>|
|||||||(0.014)|(0.016)|
|Job through networks*Migrants|||||||<br>0.037***<br>(0.009)|
|Job through networks*Natives|||||||<br>0.018***<br>(0.004)|
|Observations|395,284|395,284|394,291|394,291|393,300|393,300|<br>373,350|
|R-squared|0.003|0.006|0.078|0.088|0.089|0.089|0.088|
|NUTS2 division of residence FE|No|No|No|Yes|Yes|Yes|Yes|
|P(health-riskjobs)|0.180|0.180|0.182|0.179|0.179|0.179|0.175|

45
# **1. Introduction**

Individuals and households decide whether or not to migrate - and whether or not to send remittances if they do migrate - with the outcome of these choices depending upon their skills, wealth, risk preferences, ambition, drive, family ties, and a myriad of other observable and unobservable characteristics. This self-selection of migrants poses a severe challenge for researchers attempting to ascertain the impacts of migration or remittances on individuals, families, and communities. For example, suppose we observe that children are more likely to attend school in households with a migrant than in households without a migrant. This may reflect the income effect of remittances, but could just as easily reflect that children in households with migrants have higher quality parental education, or better language skills, or that it is parents who care most about the education of their children who migrate to earn the money needed to pay for schooling costs. As a result, even if we condition on a wide array of observable characteristics, comparisons of migrants and non-migrants are unlikely to give convincing estimates of the impacts of migration.

Experimental approaches to migration studies aim to overcome this difficulty by exploiting situations where the reason one household engages in migration or remits and another does not is truly the result of random chance. This may occur as a result of policy experiments, such as visa lotteries; through natural experiments whereby “nature” provides the source of exogenous variation; and through researcher-led field experiments which are explicitly designed to test specific theories of constraints to migration or remittance behavior. The purpose of this chapter is to introduce readers to the need for this approach, give examples of where it has been applied in practice, and draw out lessons for future work in this area. We begin with a short discussion to illustrate the perils and challenges of trying to estimate the causal impacts of migration or remittances using non-experimental approaches, and then discuss the different experimental approaches, before concluding with lessons for future work.
<!-- Start of picture text --> 50<br>29<br>wo= 40 26<br>=} 22<br>o<br>Ke)<br>g ME country<br>c 30 ME Sector<br>© EO year start<br>‘5 PY) Year End<br>® 20 WE size and Duration<br>o<br>Cc Ad<br>rd8a 175: "+17 AT<br>10 43<br>27<br>75<br>6.5 ' 61<br>0<br>Annual TVPI [N=2200] Home-Runs [N=2200] No-Tails [N=1982]<br><!-- End of picture text -->
# **The Appendix**

# **Table A.1.  Mean Characteristics for SADC, EAP and the similar-income sample**

||mean<br>SADC|mean<br>EAP|mean<br>Similar income as SADC|
|---|---|---|---|
|Foreign|0.175|0.135|0.108|
|Largest owner|0.813|0.678|0.753|
|ln(firm age)|2.058|2.354|2.324|
|Loss_crime|0.020|0.004|0.012|
|ln(GDPpc)|6.886|7.247|6.937|
|Fire_cost|55.152|88.344|64.715|
|interest_spread|11.822|3.542|9.335|
|govt_efficiency|-0.253|0.052|-0.275|
|ln(sales)|4.856|8.288|6.361|

Note. “Similar income as SADC” means income less than 4600 USD (in 2000 value) 32
# **Figure 7. GDP Per Capita and Real Exports for Mineral Exporters**

<!-- Start of picture text --> (a) GDP per Capita (1950=1) (b) Export Volume (1960=1)<br>1.20 1.6<br>1.10 1.4<br>1.00 1.2<br>0.90 1<br>0.80<br>0.8<br>0.70<br>0.6<br>0.60<br>0.4<br>0.50<br>0.2<br>0.40<br>0<br>Chile Peru South Africa Chile Peru South Africa<br>19601961196219631964196519661967196819691970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000<br>1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> Note: Countries’ GDP per capita and real exports are divided by the world’s. _Source: Maddison Project and WDI._
Although various theoretical models suggest quite opposing roles for agriculture in development, they do not necessarily contradict each other. The models are derived under different economic assumptions (e.g., openness to trade). Therefore, it is not surprising that they derive different policy implications. Because developing countries differ with respect to their economic environments, the role of agriculture for development might be re-evaluated in each specific case. This is in line with the 2008 World Development Report‘s message (World Bank 2007), which suggests that in agriculture-based economies, agriculture can be the main engine of growth, whereas in transforming countries, agriculture is already less important as an economic activity but is still a major instrument to reduce rural poverty. In urban countries, by contrast, agriculture plays the same role as other tradable sectors and subsectors with a comparative advantage can help to generate economic growth.

So far our discussion has mostly considered theoretical models. We now turn to the empirical investigation of the relation between the agricultural sector and economic growth, which has a long history. Early contributions by Kuznets, Chenery and others focused on sector changes accompanying economic development. In 1966, Kuznets observed that as economies develop, the share of agriculture in output and employment diminishes, which later empirical data have reconfirmed. Other important early contributions include Chenery and Syrquin (1975), who combined cross-section and time-series data over 1950 – 1970.

More recently, Timmer (2002) used a panel of 65 developing countries over 1960 – 1985 to show a positive correlation between growth in agricultural GDP and its lagged values and nonagricultural GDP growth. He suggests that this correlation can be explained by ―first-order‖ effects of agricultural growth on lower food prices, labor migration and capital flows from agriculture, as well as ―second-order‖ effects such as improved nutritional intake, which improves workers‘ productivity. Similarly, Self and Grabowksi (2007) established a positive relation between different measures of agricultural productivity and average growth of real GDP per capita over 1960 – 1995 for a cross-section of countries. However, on the basis of panel data from 52 developing countries during 1980-2001, Gardner (2005) concluded that agriculture does not seem to be a primary force behind growth in national GDP per capita.

The recent empirical literature considers that the effect of agricultural progress on poverty alleviation is highly positive. Mellor (2001) goes as far as arguing that it is not economic 9
is their comparability across countries and access to figures on sales and employment of US affiliates abroad. We use the latter figures in our robustness checks.

In the aggregate analysis, flow data from the IMF _International Financial Statistics_ are used. The IMF data capture inflows of FDI to each country from any country in the world. The aggregate analysis covers the period 1972-2005. Table 2 and its footnotes lists the 128 countries that are included in the aggregate analysis.<sup>14</sup> In both the analysis based on BEA sector data and in the analysis based on IFS aggregate data we use the log of FDI inflows as our dependent variable. To deal with zeros we add one US dollar to all observations before taking logs. To deal with negative values we follow Blonigen (2004) and Eichengreen and Tong (2005) and set all negative values to 0.1 US dollar before taking logs.<sup>15</sup> We follow the literature on FDI determinants (e.g. Wheeler and Mody, 1992; Javorcik, 2004b) and control for market size, labor costs, macroeconomic stability and business climate. Purchasing power of local consumers is measured as the log of GDP per capita. This variable could also be interpreted as a proxy for labor costs; thus the expected sign on the coefficient could be either positive or negative. Market-seeking FDI would be attracted to countries with high purchasing power, while FDI with the intention of cutting production costs is more likely to flow to countries with lower wage cost. GDP growth and the log of population size are proxies for the potential market size, and a positive coefficient is expected on these variables. The GDP and population variables are from the World Bank’s _World Development Indicators_ (WDI).<sup>16</sup> The inflation rate, provided by the IMF _International Financial Statistics_ , is a proxy for macroeconomic stability. High inflation indicates an unstable macroeconomic environment and

> 14 The number of years differs across countries, and the number of observations differs across econometric specifications due to the availability of control variables. The figure of 128 refers to relevant countries (countries from which we got a response, for which IPA existence could not be confirmed, or for which we know that they do not have an IPA) with FDI data in IFS.

> 15 As we were concerned about a significant number of cases with zero investment, we also used the Tobit specification including regional rather than country fixed effects. The Tobit results confirmed our findings. This was true both when the lower censoring limit for the dependent variable was set to log(1) and log(0.1), which corresponds to zero and negative FDI inflows, respectively. As a robustness check in the sector-level data, we also estimated the models from Table 9 without distinguishing between zeros and negative FDI flow values (i.e. we set both zero and negative values to 0.1 before taking logs). The effect of targeting was significant at the 1% level for all the specifications in the developing country sample (the coefficient varied between 1.018 and 1.454).

> 16 <u>http://publications.worldbank.org/WDI/</u>

17
Table 8: Short-term impacts of _Mais Educação_ on educational outcomes for schools that joined in 2010

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
||Panel A: Impacts on fifth-gr|ade variables||
|Dummy_Mais Educação_*|year 2011<br>0.064|–1.431**|–0.676|
||(0.164)|(0.710)|(0.653)|
|_R_ <sup>2</sup>|0.749|0.864|0.850|
|Observations|7,536|7,536|7,536|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
||Panel B: Impacts on ninth-gr|ade variables||
|Dummy_Mais Educação_*|year 2011<br>–0.238|–0.682|–0.775|
||(0.290)|(0.727)|(0.781)|
|_R_ <sup>2</sup>|0.796|0.847|0.804|
|Observations|6,886|6,886|6,886|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2010 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
(2019) who did find evidence of such an effect when studying the aggregate impact on a wider scale.<sup>16</sup> Considering the relationship between permission to work nights and female employment within a firm, column (2) of Table 5 shows that, on average across all firms, those that allow female workers to work nights shifts hire significantly more women. Once again, these results hold after controlling for a rich set of firm characteristics. The results presented in columns (3) and (4) of Table 5 show that, when the data are divided between manufacturing and construction versus services firms, the relationship between women’s freedom to work night shifts and the level of female employment is almost three times as strong in manufacturing and construction firms relative to firms in the services sector. This suggests that this freedom may have important implications for occupational segregation and the pay gap between men and women in the DRC.<sup>17</sup> The law in the DRC at this point in time did restrict women from night work for industrial undertakings.

The final relationship examined is that between women’s entitlement to maternity leave and the propensity of firms in the DRC to hire married women. The results, displayed in Table 6, show that, when women are entitled to maternity leave, it is significantly more likely that a firm will hire a married woman. This relationship holds after including a range a firm level controls, including a control for the total number of women currently employed by the firm (column (2)). From the perspective of a firm, this result makes economic sense – research has shown that women are more likely to return to work after having children if they can avail of maternity leave (Ruhm, 1998; Berger and Waldfogel, 2004; Baker and Milligan, 2008). Thus, it seems plausible that firms are more likely to invest in hiring and training an employee if she is more likely to return to work should she have children. The law does mandate maternity leave for women.

> 16 Islam et al. (2019) use a cross section of 94 economies but their data do not include micro firms, nor are they able to analyze restrictions at the level of the individual firm.

> 17 Differences by gender in terms of occupation and industry are, according to Blau and Kahn (2017), an important driver of the gender pay gap.

17
with itself are always 0, as ln Ω _m|b_ ( _x_ ) = ln(1) = 0.

In the above equation, _xβm|b_ is a vector containing our independent variables of interest.

The _J_ equations are solved as follows, to compute the predicted probabilities: The predicted probabilities will be the same regardless of the base outcome. While the estimated parameters are different, only the parameterizations are different (Long and Freese, 2006).

In the MNLM the dependent variable is a categorical variable, corresponding to the different types of business activities reported in two of the surveys (Ethiopia and Malawi). As the base category we take the households that are not operating a nonfarm enterprise. _xβm|b_ contains individual, household, and location characteristics. As individual characteristics we take the gender, age, and education of the household head. As household characteristics access to credit and shock variables, and as location characteristics the distance to the next population center.<sup>7</sup>

# **3.2.3 Heckman Selection Model**

To estimate the determinants of labor productivity in non-farm enterprises we account for possible sample selection effects, and use a two-step Heckmann selection model. As the determinants of labor productivity can be subject to selection effects, we might get biased estimates as a result if we do not control for this selection effect when estimating the determinants of labor productivity.

Formally we estimate, representing the selection stage of the model, where _zi_<sup>_∗_is a latent variable corresponding to</sup> the “outcome” productivity level, which will only be observed once a household operates a non-farm enterprise, thus _zi_ = 1 if _zi_<sup>_∗>_0and</sup><sup>_zi_= 0if</sup><sup>_z_</sup> _i_<sup>_∗≤_0.</sup><sup>_wi_isavectorcontaining</sup> the possible determinants of enterprise operation.

Once _zi_ is known, the outcome stage, the dependent variable is the log of labor

> 7 See Appendix B.3 for summary statistics and a detailed definition of the variables used in the MNLM model.

10
|China Health Reform Program|Dinesh M. Nair|
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|China Health Reform Program|Dinesh M. Nair|
|China Health Reform Program|Dinesh M. Nair|
|China Health Reform Program|Dinesh M. Nair|
|National Transmission Modernization I Project|AnthonyGranville|
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|Grid-Connected RooftopSolar Program|Amit Jain|
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|Indonesia: Geothermal EnergyUpstream Development Project|Peter Johansen|
|Indonesia: Geothermal EnergyUpstream Development Project|Peter Johansen|
|Indonesia: Geothermal EnergyUpstream Development Project|Peter Johansen|
|Indonesia: Geothermal EnergyUpstream Development Project|Peter Johansen|
|Indonesia: Geothermal EnergyUpstream Development Project|Peter Johansen|
|Indonesia: Geothermal EnergyUpstream Development Project|Peter Johansen|
|Indonesia: Geothermal EnergyUpstream Development Project|Peter Johansen|
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|Local Development for Jobs Project|Lorenzo Bertolini|
|Local Development for Jobs Project|Lorenzo Bertolini|