rafmacalaba/datause-encoder-data huggingface dataset:
28 supported by social marketing and free distribution through antenatal care.‖ _Health Policy and Planning_ 25 (September 14): 28-38.

Division of Malaria Control [Ministry of Public Health and Sanitation]. 2009. _Kenya Malaria_ _Programme Performance Review 2009_ . Nairobi, Kenya: DOMC, KNBS and ICF Macro. Division of Malaria Control [Ministry of Public Health and Sanitation], Kenya National Bureau of Statistics, and ICF Macro. 2011. _2010 Kenya Malaria Indicator Survey_ . Nairobi, Kenya: DOMC, KNBS and ICF Macro.

Fegan, G.F., A.M. Noor, W.S. Akhwale, S.C., and R.W. Snow. 2007. ―E ect of expanded insecticide-treated bednet coverage on child survival in rural Kenya: a longitudinal study.‖ _Lancet_ 370: 1035–39.

- Fernando, S. D, C. Rodrigo, and S. Rajapakse. 2010. ―The‘hidden‘ burden of malaria: cognitive impairment following infection.‖ _Malaria journal_ 9 (1): 366.

Grabowsky, M., T. Nobiya, M. Ahun, R. Donna, M. Lengor, D. Zimmerman, H. Ladd, E.

Hoekstra, A. Bello, A. Baffoe-Wilmot, and G. Amofah. 2005a. ―Distributing insecticidetreated bednets during measles vaccination: a low-cost means of achieving high and equitable coverage.‖ _Bulletin of the World Health Organization_ 83 (3): 195-201.

- Grabowsky, M., N. Farrell, W. Hawley, J. Chimumbwa, S. Hoyer, A. Wolkon, and J. Selanikio. 2005b. ―Integrating insecticide-treated bednets into a measles vaccination campaign achieves high, rapid and equitable coverage with direct and voucher-based methods.‖ _Tropical Medicine and International Health_ 10 (November): 1151-1160.

Guyatt, H. L, M. H Gotink, S. A Ochola, and R. W Snow. 2002a. ―Free bednets to pregnant

- women through antenatal clinics in Kenya: a cheap, simple and equitable approach to delivery.‖ _Tropical Medicine and International Health_ 7 (May): 409-420.

Guyatt, H. L, S. A Ochola, and R. W Snow. 2002b. ―Too poor to pay: charging for insecticidetreated bednets in highland Kenya.‖ _Tropical Medicine and International Health_ 7 (October): 846-850.

Guyatt, H. L, and R. W Snow. 2002. ―The cost of not treating bednets.‖ _Trends in Parasitology_ 18 (January): 12-16
# Has India’s Economic Growth Become More Pro-Poor in the Wake of Economic Reforms?

Gaurav Datt and Martin Ravallion<sup>1</sup> _World Bank_

> 1 The authors are grateful to Ann Harrison, Rinku Murgai and Abhijit Sen for helpful comments and to Dandan Zhang for excellent research assistance. These are the views of the authors, and should not be attributed to the World Bank.
**Table 4. Statistical mpact of SHG membership on capability indicators: complete sample**

|||Nearest-n<br>|eighbours<br>||Kernel<br>|-based mat<br>|ching<br>|
|---|---|---|---|---|---|---|---|
|Outcome|AT|E|A|TT|ATE|A|TT|
||Coef.|z-stat|Coef.|z-stat|Coef.|Coef.|t-stat|
|Health limits activities***|0.0789|3.38|0.0873|3.33|0.0719|0.0205|3.59|
|Able to meet socially***|0.0687|4.72|0.0774|4.56|0.0564|0.0571|4.15|
|Lost sleep from worry (mental health)|0.0138|0.72|0.0204|0.97|0.0092|0.0108|0.66|
|Able to enjoy recreation***|0.1017|6.60|0.1222|7.24|0.0975|0.1047|5.76|
|Own home***|0.0897|5.57|0.0918|4.85|0.0856|0.0836|5.09|
|Accommodation suitable***|0.0961|3.78|0.1086|3.55|0.0959|0.1017|4.92|
|Feel safe walking***|0.1059|4.10|0.1062|3.51|0.1095|0.1108|5.56|
|Risk of future assault|0.0085|0.46|0.0147|0.70|0.0195|0.0209|1.18|
|Risk of future discrimination|-0.0055|-0.28|0.0005|0.02|0.0111|0.0169|1.03|
|Influence local decisions***|0.1433|6.10|0.1403|5.33|0.1261|0.128|6.19|
|Freedom of political and religious<br>expression***|0.1380|6.17|0.1553|5.97|0.1204|0.1288|6.22|
|Support of family and friends**|0.0539|2.13|0.0686|2.24|0.0199|0.0244|1.20|
|Free to live life***|0.1099|5.08|0.1126|4.80|0.1105|0.1154|5.58|
|Freedom of creative expression***|0.1395|6.64|0.1368|6.00|0.1419|0.1365|6.66|
|Interesting work***|0.1794|8.45|0.1879|7.56|0.1547|0.1575|7.95|

Notes: Significance at less than 1% (***), less than 5% (**), less than 10% (*); Epanechnikov kernel estimates used <u>bandwidth 0.06, N=5433.</u> 42
Panel C of Table 1 presents information on educational attainment by generation III individuals in 1994. Three age groups, based on the likelihood that these individuals’ decisions on primary education may be affected by the passage of the HSAA are distinguished. First, primary education decisions for those between 0 and 5 years in 1994 would have been made after the amendment had come into force. By comparison, decisions for 13-18 years olds in 1994 would, under the assumption of elementary education being completed by age 13, all have been made before the HSAA had come into force. We thus define the ‘treated’ group as individuals who were less than 6 years old in 1994. These individuals satisfy two important conditions. First, they are young enough to respond to HSAA and begin their schooling investments after the reform, and second, they are old enough that we do observe their elementary school education in the 2006 REDS data.

The potential for spillovers raises the question of whether males are a valid control group. Our assumption is that HSAA has a direct effect on only female schooling. However, HSAA could have a spillover effect on males if an increase in girl’s education induced an increase in boy’s education (positive spillover) or family budget constraint causing an increase in girls education crowds out her brother’s education (negative spillover). While the (mean) differences in educational attainment of males in the two age cohorts is miniscule in Panel C, we do find that females in the younger cohort had slightly higher educational attainment than the older cohort. We also estimate the impact on educational attainment of younger and older cohorts separately for girls and boys to determine the extent to which our use of males as a control group drives the result. The coefficient on the young (0-5) cohort for the males-only regression is positive and insignificant, suggesting no dramatic effects of HSAA on educational attainment of boys. In contrast, the coefficient on the young cohort in the females-only regression is twice as high as the coefficient in the males only regression and is statistically significant (results available from the author) providing circumstantial evidence in support of using males as a valid control group. Furthermore, state level education policies are especially of interest because our outcome variable is schooling. If such policies had gender or cohort effects, these would not bias our estimates which use gender-cohort variation. The identification would only 20
# _4.4 Impacts on Extended Family_

Finally we consider the impacts of migration on one particular type of extended family, consisting of a household containing a parent or elder sibling of the PAC applicant. These households tend to be slightly smaller, containing an average of 3.7 individuals in the ballot loser households, and 3.5 individuals in ballot winner households. The partner relative is aged an average of 45 for elder siblings, and 54 for parents.

The impacts of having a child or younger sibling migrate through the PAC on these extended households are reported in Table 5. The only significant differences found are that families of PAC migrants receive more remittances and are slightly more likely to have bank accounts and an ATM card. However, the magnitude of additional remittances received is relatively low: NZ$215 annually in additional remittances, which is less than the weekly income gain to migrants and only 1.4 percent of annual total household income for ballot loser households. Given this small difference in remittances, it is unsurprising that we do not see impacts on other household outcomes for this extended family.

The limited impact is in a context where the households applicants lived in at the time of applying for the migration lottery are naturally dissolving as adults leave their parental homes and set up households of their own. Even had these individuals lived in Tonga, they would almost all be living in a separate dwelling from this extended family. As such, these extended households will have had to adjust to the absence of the applicant regardless of their migration outcome. One caveat that we should note is that it is possible that a key benefit of having a migrant abroad is as an insurance policy, who can provide help in the case of major shocks, something we are unable to measure with our data.

# **5. CONCLUSIONS AND DISCUSSION**

A long-term follow-up survey of Tongans who applied to migrate to New Zealand through a visa lottery enables us to measure the impact of international migration after a decade.

22
labor income, consumption, and health. Yet, the program prompted negligible effects on the labor market outcomes of Colombian workers (Bahar et al. 2021). It also reduced crime rates and the fiscal costs of hosting migrants in Colombia (Bahar et al. 2022; Ib ́a ̃nez et al. 2024).<sup>3</sup> Our analysis assesses the impacts of the PEP program on electoral turnout, support for left-wing, center, and right-wing political ideologies, and electoral competition in Colombia.<sup>4</sup> We examine the causal effects of the PEP program by comparing mayoral and firstround presidential election results in municipalities with higher and lower PEP take-up rates, before and after the program’s rollout in 2018.

We employ municipal data from six mayoral elections for 1,098 of the 1,122 municipalities in Colombia (recorded after the year 2000, when information about the electoral roll became available). Each municipal election is conducted independently and mayors are elected at the local level by plurality rule. As such, the analysis exploits data from more than six thousand individual election points. For robustness, we also examine the effects of the PEP program on first-round presidential elections. Presidents are elected nationally in the first round if they receive fifty percent of the votes, plus one. Hence, the presidential election analysis includes six individual election points.

Our analysis controls for municipal and election-year fixed effects, a rich set of baseline municipal characteristics, their interactions with election-year trends, and department election-year trends. The data supports the validity of the empirical strategy as we observe parallel trends for mayoral and presidential elections in municipalities with higher and lower PEP program take-up rates before the program’s onset in 2018. In line with

> 3Particularly, regularized migrants had higher incomes than those who remained undocumented. This prompted higher tax consumption payments. Moreover, regularized migrants also used the health system more efficiently, employing disproportionately preventive instead of emergency services. Finally, AmuedoDorantes et al. (2025) document that fertility rates for regularized households was reduced as they were granted access to better labor market opportunities and family planning services.

> 4We classified this following the methodology proposed by Fergusson et al. (2020) and employed in Rozo and Vargas (2021).

5
# **Are There More Female Managers in the Retail Sector? Evidence from Survey Data in Developing Countries**

Mohammad Amin* and Asif Islam** _Keywords_ : Gender, Labor market, Managers, Retail sector _JEL_ : J16, G30 _______________________________

*Enterprise Analysis Unit, Development Economics (DECEA), World Bank, Washington DC, 20433. Email: <u>mamin@worldbank.org.</u>

** Corresponding author, Enterprise Analysis Unit, Development Economics (DECEA), World Bank, Washington DC, 20433. Email: asif.m.islam@gmail.com
area-level model in Sri Lanka and slightly less accurate estimates in Tanzania. Generating additional empirical evidence on the relative performance of different types of statistical models is important because in practice their performance depends on the true data generating process, which in practical applications is unknown. Therefore, we compare in the Mexican context the household model estimates with two alternative small area estimation models: a sub-area model and an area-level model.

The sub-area model uses the same type of empirical best prediction model as the household model. However, the unit of analysis is the AGEB and the dependent variable is the estimated poverty rates for each AGEB from the 2014 MCS-ENIGH survey data. These poverty rates are then modeled as a linear function of covariates, with random effects specified at the area level. The inclusion of the area-level random effect allows the model to “borrow strength” from sample data in sampled municipalities, which contain relevant information for non-sampled AGEBs. Covariates are again selected using the plug-in (rigorous) LASSO procedure. The selected AGEBlevel covariates include the predicted shares of extreme and non-poverty from the CNN and the share of pixels classified as buildings at the AGEB level. At the municipal level, selected covariates include extreme poverty; share roads, forest, and grass; share rural; and ten state dummies. These variables explain 28 percent of the variation in AGEB-level poverty rates. The weights adjust both for sample weights and for heteroscedasticity in the dependent variable, due to the differing number of sample observations used to estimate AGEB-level poverty. As in the household model described above, the results are benchmarked to the state-level poverty estimates. The second column of Table A3 presents the coefficients from a post-LASSO OLS regression using the sample data, collapsed to the AGEB (sub-area) level.

In addition, for comparison, we also report results for a simple area-level model as initially described in Fay and Herriot (1979). Candidate predictor variables are aggregated to the municipality level, using 2010 population values from the census as weights. The prediction model is again selected using the “plug-in” rigorous LASSO, allowing for heteroscedasticity. The selected model contains seven predictors: The predicted municipal extreme and moderate poverty rates, the percentage of pixels classified as roads and background, and three state dummies. These variables explain 36 percent of the variation in estimated municipal poverty rates. The variance component of the model is estimated using the adjusted maximum likelihood approach for the linking model proposed by Li and Lahiri (2010), which guarantees positive variance estimates of 20
Figure 8: Absolute Score of Importance of Prenatal Care Services Notes: This graph reports the average of the absolute score that measures the importance given by clinics to seven different prenatal care procedures including initiating prenatal care prior to week 13 of pregnancy. The data were collected using a short online survey conducted in the clinics that participated in the experiment. (see Appendix D) The absolute scores range from 1 to 5, with 5 being the highest score in terms of importance. The respond was coded zero if the respondent reported that this procedure is inappropriate for a pregnant woman.

34
Table 1.

Use of investment vouchers in Lithuania as of September 1995

|Use of investme|nt vouchers||M|illion Litas|Perc|ent|
|---|---|---|---|---|---|---|
|Total||||9802||93|
|Public subscript|ion of shares|||5,833||55.3|
|Tenders||||415||4|
|Auctions||||165||1.6|
|Privatization of r|esidual state|ownership in J|SCs|392||3.7|
|Housing||||2,042||19.4|
|Agricultural ente|rprises|||410||4|
|Land<br>Source: Lithuania|n Departmen<br>|t of Statistics,<br>T<br>Basic Emplo|September 1995.<br>able 2.<br>yment Statistics<br>**Numb**|545<br>**er of Enterpri**|**ses with:**|5<br>|
|Number of|Number of|Employees per|_Less than 10 Betwee_|_n 10 and Betwe_|_en 50 and_|_Over 200_|
|Enterprises*|Employees|Enterprise**|_employees_<br>_50 em_|_ployees_<br>_200 e_|_mployees_|_employees_|
|**1995**<br>_5,807_|_583,753_|<br>_101_|<br>_686_|_3,091_|_1,476_|**_555_**|
||||_(11.8%)_|_(53.2%)_|_(25.4%)_|_(9.6%)_|
|**1996**<br>_5,347_|_543,691_|<br>_102_|<br>_305_|_3,101_|_1,435_|_506_|
||||_(5.7%)_|_(58.0%)_|_(26.8%)_|_(9.5%)_|
|**1997**<br>_7,185_|_560,394_|<br>_78_|<br>_449_|_4,645_|_1,626_|**_465_**|
||||_(6.2%)_|_(64.6%)_|_(22.6%)_|_(6.5%)_|

* - Enterprises with missing values for Total Assets, Revenues, or Employment were excluded from the sample. ** - Unweighted average.

33
# PRE Working Paper **Serieas**

||Iilli|~~~~~<br>~~Author|_12aQ_|Contact<br>for paaeer|
|---|---|---|---|---|
|WPS63|2 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Argentina|Carlos Alredo Rodrigtez|March 1991|R. Luz<br>34303|
|WPS63|3 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Thailand|Virabongse<br>Ramangkur<br>Bhanupongse<br>Nidhiprab|a March 1991<br>ha|R. Luz<br>34303|
|WPS63|4 Trends in Developing<br>Country<br>Expot's, 1963-88|Bela Balassa|March 1991|WDR Office<br>31393|
|WPS635|Exchange<br>Rates and Foreign Trade<br>in Korea|Bela Balassa|March 1991|C. Cuskelly-<br>Young<br>39413|
|WPS636|Economic Integration<br>in Eastern<br>Europe|Bela Balassa '|March 1991|C. Cuskelly-<br>Young<br>39413|
|WPS637|Poverty in Poland, 1978-88|Branko Milanovic|March 1991|WDR Office'<br>31393|
|WPS63|8 Researching<br>the Trade-Productivi<br>Link: New Directions|ty James Tybout|March 1991|D. Ballantyne<br>37947|
|WPS639|The'high Cost of Protecting<br>Uruguay's Autpmotive<br>Industry|Wendy E.Takacs<br>:|'9rch 1991|D.B3allantyne<br>37947|
|WPS64<br>C,,>|0 The Impact of Policy in African<br>Agriculture;,<br>An Empirical<br>Investigation|Wiliam Jaeger 'YMarch|<br>19|ideru<br>34663|

:V\ <!-- Start of picture text --> S~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ a<br>H~~~~~<br>.' ..-. ,<br>0 (~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~J<br>a~~~~~~~~~~~~~~~~~~~~~~~~<br>, -, .X0<br><!-- End of picture text -->
Table 6: ITT Effects on Household Outcomes

||Daily E|xpenditure|||Remittances||
|---|---|---|---|---|---|---|
||(1)<br>HH<br>Levels|(2)<br>Participant<br>Levels|(3)<br>MUE|(4)<br>Food<br>Insecurity|(5)<br>Levels|(6)<br>Index|
|||_Panel A: _|_Midline (6 _|_months)_|||
|ITT|-184.35|-644.36<sup>_∗∗_</sup>|0.075|0.111<sup>_∗∗∗_</sup>|-12313.70|0.07|
||(458.00)|(315.42)|(0.086)|(0.040)|(8308.04)|(0.07)|
||[0.644]|[0.168]|[0.604]|[0.040]|[0.465]|[0.604]|
|Observations|544|541|481|543|528|546|
|Control Mean<br>|7205.16|3565.36|-0.026|0.270|34309.80|-0.12|
|Adj. R<sup>2</sup>|0.198|0.041|0.074|0.124|0.169|0.041|
|||_Panel B: En_|_dline (18–_|_24 Months)_|||
|ITT|-123.61|-171.88|0.126|0.016|760.15|0.11|
||(447.25)|(297.17)|(0.096)|(0.040)|(6417.55)|(0.07)|
||[0.980]|[0.970]|[0.604]|[0.980]|[0.980]|[0.574]|
|Observations|544|540|476|543|526|535|
|Control Mean|6929.52|3443.70|-0.076|0.310|25547.62|-0.17|
|Adj. R<sup>2</sup>|0.183|0.013|0.047|0.091|0.072|0.094|

_Note:_ We winsorize daily expenditures, MUE, and remittances at the 99th percentile. Coefficients are ANCOVA estimates that control for the outcome at baseline, the respondent’s location, marital status, household size, number of children, age, network size and level of education. ITT shows traditional intent to treat effects. We calculate the MUE using consumption expenditures over the past week on seventeen food items, following the methods outlined in Ligon (2020). Higher values of the MUE indicate higher marginal utilities of expenditure, indicating that households are worse off. Food insecurity is a binary variable equal to one if the woman reports not having enough food more than once over the six months before the survey. Column (2) is included in column (1), however, we wanted to show the effect on the participating women expenditures as the information on the household corresponds to estimates the respondent thinks other members of the household have. Values in columns marked with heading _Levels_ are values in UGX. Index is an Anderson index of all outcomes using the swindex command in Stata. We report White robust standard errors in parentheses. Romano-Wolf multiple hypothesis test q-values are presented in brackets. Q-values include the same controls as above and are computed within pre-specified families of outcomes.

32
the dual practice sample was drawn from the universe of public MBBS doctors practicing in each district rather than the universe of providers practicing in sampled villages, as was the case for the representative sample); finally, we include controls for observable differences across the public and private practices of the doctors.

# **5.2 Completion of Essential Checklist of History Taking and Examinations**

Columns 1-3 in Table 3 present results from estimating equation 8 in the representative sample. Our outcome variable is ‘provider effort’, measured by consultation length and checklist completion. While the results are similar across the three specifications, we focus our discussion on the estimates in Panel B, because they compare relative performance within the same market (without controlling for provider characteristics), which is the relevant choice set for patients. Base levels of effort among representative public providers are low. The average public provider spent 2.4 minutes with the SP in a typical interaction and completed 16 percent of checklist items. Private providers spend 1.5 minutes more per patient and complete 7.4 percentage points more items on the checklist (62 percent and 47 percent more than the public providers respectively). When evaluated on the IRT scaled score, private providers score 0.61 standard deviations higher. Figure A.1 shows that time spent with the patient is strongly correlated with the number of checklist items completed, which points to the appropriateness of the checklist, as more time spent with the patient led to greater checklist completion.

Columns 4-6 repeat the analysis in the dual sample, with similar results. Public MBBS doctors appear to be more productive than the typical public provider in the representative sample (many of whom are unqualified) because they complete a slightly higher fraction of checklist items (18 percent) in 35 percent less time (0.9 minutes less). However, this additional productivity is not used to complete more checklist items in the public practice, but rather to reduce the time spent with patients (1.56 minutes versus 2.4 minutes in the representative sample). In their private practices, the same doctors doubled consultation length, completed 50 percent more checklist items, and scored 0.73 standard deviations higher on the IRT-scaled measure of quality. It is worth comparing these differences with those obtained in interventions that are regarded as highly successful. For instance, Gertler and Vermeersch (2013) look at checklist completion as a result of the introduction of performance pay in Rwanda. They find that performance pay increased checklist completion by 0.16 standard deviations; we find that the difference in checklist completion across public and private practices of the same doctor is five times larger.

provider fixed effects and the results are unchanged (but driven by variation in the asthma case).

19