rafmacalaba/datause-encoder-data huggingface dataset:
- Baird, S. J., C. Mcintosh, and B. Özler (2016, December). When the money runs out : do cash transfers have sustained effects on human capital accumulation ? Policy Research Working Paper Series 7901, The World Bank.

- Barham, T., K. Macours, and J. Maluccio (2019). Experimental Evidence from a Conditional Cash Transfer Program: Schooling, Learning, Fertility, and Labor Market Outcomes After 10 Years. Technical report, mimeo.

- Barrera-Osorio, F., M. Bertrand, L. L. Linden, and F. Perez-Calle (2011, April). Improving the Design of Conditional Transfer Programs: Evidence from a Randomized Education Experiment in Colombia. _American Economic Journal: Applied Economics 3_ (2), 167–195.

- Bastagli, F., J. Hagen-Zanker, L. Harman, V. Barca, G. Sturge, and T. Schmidt (2019). The impact of cash transfers: A review of the evidence from low- and middle-income countries. _Journal of Social Policy 48_ (3), 569–594.

- Basu, K., S. Das, and B. Dutta (2010). Child labor and household wealth: Theory and empirical evidence of an inverted-u. _Journal of Development Economics 91_ (1), 8–14.

- Becker, G. S. and N. Tomes (1976). Child endowments and the quantity and quality of children. _Journal of Political Economy 84_ (4, Part 2), S143–S162.

- Beegle, K., R. Dehejia, and R. Gatti (2009). Why Should We Care About Child Labor?: The Education, Labor Market, and Health Consequences of Child Labor. _Journal of Human Resources 44_ (4).

- Bharadwaj, P., K. V. Loken, and C. Neilson (2012). Early life health interventions and academic achievement. _IZA Discussion Paper_ .

- Black, M. M. (2003). Micronutrient deficiencies and cognitive functioning. _The Journal of nutrition 133_ (11), 3927S–3931S.

- Campillo Garcia, J. (1998). Official mexican standard (nom-169ssa1-1998) for social food assistance to at-risk groups. _Government of Mexico manuscript, Available at http://bibliotecas. salud. gob. mx/gsdl/collect/nomssa/index/assoc/HASH015c.dir/doc.pdf_ .

- Chetty, R., J. N. Friedman, and J. E. Rockoff (2014, September). Measuring the impacts of teachers ii: Teacher value-added and student outcomes in adulthood. _American Economic Review 104_ (9), 2633–79.

26
Eggers et al. 2006). Yet, to our knowledge, evidence is much scarcer in countries in lower income countries with less developed labor markets.

Based on our earlier discussion of linkages between national incomes and average wellbeing we should expect that job preferences, and the life satisfaction derived from work would vary considerably across countries with different income levels. This is also borne out by data from the World Value Surveys. When asked what they value most in a job, people in high income countries are much more likely to report that they value work that they perceive as “important” (Figure 1). This is in contrast to low income countries, where a much higher share of respondents reports a preference for jobs that generate higher earnings. There are also indications that job preferences interact with other outcomes in national labor markets. For example, people in countries with higher unemployment rates are more likely to report a preference for safe jobs and less likely to seek work they perceive as ‘important’. This suggests that, in addition to depressing overall wellbeing, higher unemployment rates also have a negative effect on people’s aspirations to carry out meaningful work. However, the correlations between national unemployment rates and job preferences in our sample are driven by a relatively small group of countries with very high unemployment rates.

**Figure 1: Preferences for jobs**

<!-- Start of picture text --> GNIpc and preference for 'high earnings' GNIpc and preference for 'important work'<br>0 10000 20000 30000 40000 0 10000 20000 30000 40000<br>GNI per capita (constant 2000 US$) GNI per capita (constant 2000 US$)<br>(mean) imp_income Fitted values (mean) imp_job Fitted values<br>Source: WVS 2005-7 and WDI 2005 data Source: WVS 2005-7 and WDI 2005 data<br>.8 .5<br>.6 .4<br>.4 .3<br>.2<br>.2<br>.1<br>0<br><!-- End of picture text --> 13
# **Tables and Figures**

Table 1: Randomization Verification: Clinic Census and Health Worker Pre-Training Survey

||(1)<br>Basic<br>Training<br>Mean|(2)<br>Extended<br>Provider<br>Training|(3)<br>N|
|---|---|---|---|
|_A. Provider-Reported Clinic Characteristics_||||
|Average Patient Load|30.371|-1.192|57|
||[22.172]|(1.207)||
|Clinic Has Microscopy|0.897|-0.111|57|
||[0.310]|(0.094)||
|Fraction Patients Tested with RDT|0.648|0.065|57|
||[0.221]|(0.057)||
|Fraction Patients Tested with Microscopy|0.473|-0.134|57|
||[0.306]|(0.083)||
|Fraction Tested Malaria Positive|0.543|0.062|57|
||[0.196]|(0.048)||
|_B. Provider Attendance_||||
|Number of Staf Attended per Clinic|3.862|0.000|58|
||[0.441]|(0.158)||
|Pre-Tests Submitted per Clinic|2.793|-0.061|58|
||[1.424]|(0.558)||
|Post tests submitted per clinic|3.897|-0.172|58|
||[0.900]|(0.293)||
|_C. Provider Pre-Test Knowledge_||||
|Knows Malian Malaria Policy|0.519|-0.045|160|
||[0.503]|(0.095)||
|Correct Meds for Simple Malaria Treatment|0.568|-0.038|160|
||[0.498]|(0.111)||
|RDT Detection Rate w. Low Parasite Load 90% or Higher|0.111<br>[0.316]|0.082<br>(0.052)|160|
|RDT Detection Rate w. High Parasite Load 90% or Higher|0.333<br>[0.474]|0.195<br>(0.128)|160|
|Correct Symptoms Severe Malaria|0.506|-0.024|160|
||[0.503]|(0.108)||
|Correct Time Interval to Read RDT|0.136|0.026|160|
||[0.345]|(0.092)||
|Number of Correct Answers|2.173|0.197|160|
||[1.473]|(0.384)||

_Notes_ : Panel A uses clinics census information (one observation missing). Panels B and C use the health worker pre-training survey. Robust standard errors clustered at the clinic-pair level in parentheses, standard deviation in brackets. All regressions include strata fixed effects. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

27
ation in Africa.

# **3 Data**

## **3.1 Individual and Household Data**

### **3.1.1 Afrobarometer Survey**

The Afrobarometer survey is a nationally representative survey of public attitudes on democracy, governance, economic conditions, and access to basic social amenities in over 35 African countries. The survey uses a two-stage stratified sampling strategy and focuses on individuals above the age of 18. Data from rounds 6 (2014-15) and 7 (2016-18) of the Afrobarometer are used for the analysis. The dataset is geo-referenced at the community level, making it possible to spatially match it with other datasets. Data from 25 . Sub-Saharan African (SSA) countries are used in this paper<sup>11</sup> I use data on employment status of individual(s), quality of electricity supply, socioeconomic attributes of the individual and their respective households, and commucharacteristics as well. status is measured based on to the nity Employment responses question: “Do you have a job that pays a cash income?”. Thus employment is defined as equal to 1 if a respondent reports having a cash-paying job, and 0 for a respondent without a cash-paying job but actively looking for a job.<sup>12</sup> In addition, using the information on their occupational history, respondents were classified into skilled vs unskilled workers, and agric vs non-agric sector employees. Quality of electricity supply is measured from the responses of households with electricity connection to the question “how often is the electricity actually available?”. Here the quality of electricity supply is classified

> 11Including Botswana, Burkina Faso, Cameroon, Cˆote d’Ivoire, Cabo Verde, Gabon, Gambia, Ghana, Guinea, Lesotho, Liberia, Madagascar, Malawi, Mauritius, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Swaziland, Zambia, and Zimbabwe

> 12In essence, the employment variable is returned missing for respondents that responded “No (not looking)” to the question.

10
**Figure 3** : Distribution of beds* that could potentially be repurposed by facility type (hospital vs. nonhospital) in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Kenya Sierra Leone<br>0 20 40 60 80 0 20 40 60 80<br>Repurposable beds Repurposable beds<br>Hospitals Non-hospitals Hospitals Non-hospitals<br>Tanzania<br>0 20 40 60 80<br>Repurposable beds<br>Hospitals Non-hospitals<br>.2<br>.08<br>.15<br>.06<br>.04 .1<br>Density Density<br>.02 .05<br>0 0<br>.08<br>.06<br>.04<br>Density<br>.02<br>0<br><!-- End of picture text -->

* For comparison with the above figure, health facilities with more than 100 inpatient beds are once again excluded

# Availability of medical staff

Adequate numbers of trained medical staff in facilities are paramount to ensure that patients receive necessary care during a pandemic, and to limit frontline health care provider burnout.<sup>12</sup> While the optimal mix of doctors, nurses, and other health care providers is unknown<sup>13</sup> (and will likely depend on facility characteristics, patient volume and characteristics), information on human resources to address a pandemic is crucial for planning and resource allocation. A preferred metric for assessing provider availability is the provider-to-patient ratio, however, the current data do not allow such estimation as inand out-patient volume during the pandemic period is likely to be different than that from the non-

> 12 Dewey, C. “Supporting Clinicians During the COVID-19 Pandemic”. _Ann Intern Med_ . 2020 Mar 20. <u>https://annals.org/aim/fullarticle/2763592/supporting-clinicians-during-covid-19-pandemic</u>

> 13 <mark>Forgacs, I. “The required number of physicians: is it an optimal figure?”</mark> _<mark>Cah Sociol Demogr Med</mark>_ <mark>. 2002 AprSep;42(2-3):269-82.</mark> <u><mark>https://www.ncbi.nlm.nih.gov/pubmed/12078328</mark></u>

9
middle-income countries in the last 10 years; however, few of them condition payments on health outcomes (Miller and Singer 2013).<sup>1</sup> In this paper we evaluate the impact of Plan Nacer on birth outcomes and neonatal mortality. Our identification strategy exploits the geographic phasing of the program across clinics over time. We use difference-in-difference models that allow us to control for time-invariant confounding factors that may exist between population groups receiving care from clinics incorporated into the program early compared to those included later, and time-varying factors within provinces that are common to both early and late adopters. Specification tests, used to evaluate preintervention trends, support a causal interpretation of our results from differencein-difference models.

Our analysis uses information obtained from birth certificate records in pubic maternity hospitals combined with administrative data sources for the seven provinces in northern Argentina for 2004 through 2008, covering the first phase of Plan Nacer scale-up. Birth certificates provide accurate data on prenatal care, birth outcomes, and in-hospital neonatal mortality. The size of the dataset allows us to investigate the impact of Plan Nacer on relatively rare events such as low birth weight and neonatal mortality, which is typically not possible in surveys with smaller samples.

Overall, these results show that Plan Nacer was effective in improving birth outcomes and reducing neonatal mortality, and that it achieved these gains through expanded access and improvements in the quality of health services in the public sector. Specifically, the program is associated with a significant increase in the number of prenatal care visits and the quality of prenatal care and delivery, measured by the probability of receiving a tetanus toxoid vaccine and a reduced probability of birth by cesarean section. We also find that beneficiary newborns had substantially better health outcomes in terms of less low birth weight and reduced neonatal mortality. Specifically, we find a 19 percent reduction in the probability of low birth weight, and a 74 percent reduction in neonatal mortality.

In addition to direct program impact, we also analyze program spillovers onto nonbeneficiary populations — that is, the impact of Plan Nacer on a nonbeneficiary who obtained care at a clinic incorporated into the program. Positive spillovers could be expected if program resources are invested in equipment, supplies, and system improvements in quality of care that affect both beneficiary and nonbeneficiary patients. On the other hand, we might expect negative spillovers

> 1. Such linking of finance to performance has also been used in state and local school reform in the United States (Race to the Top, No Child Left Behind) as well as incentive payments for teachers  (see, for example, Muralidharan and Sundararaman 2011).

3
with a lower environmental footprint. Alternatively, firms may become environmentally aware and therefore engage in green investments or green management practices.

The Enterprise Surveys allows for the possibility to test some of these channels. The main channels are largely infrastructure service disruptions such as water and power outages. There is some evidence of effects through labor productivity (sales per worker) and also limited access to finance – negative precipitation shocks decrease the likelihood than firms use banks to financing working capital and have access to overdraft facilities. A new channel uncovered in this study is governance. The intensity of negative precipitation shocks increases exposure to corruption. While there is no effect uncovered regarding weather shocks and investment in machinery and equipment, this could be due to the countervailing effects of limited access to finance and the need to replace damaged capital. However, extreme dry days do increase the probability of investing in land and buildings. Firms that are innovative in terms of process, have website ownership, and use technology licensed from foreign firms experience more muted effects of extreme dry days on firm performance. There is also some evidence that digital technologies and innovation can buffer against climate shocks (Zhao and Parhizgari, 2024; Liu et al., 2023). Finally, for a cross-section of 2019 surveys largely conducted in the Middle East and North Africa and Europe and Central Asia, a green module included in the survey instrument captures green investments and green management practices. This study leverages this new data for a subset of firms where it is available but finds no correlation between precipitation shocks and green management practices or green investments. One explanation could be that firms might adopt such practices only after repeated dry spells over time. However, these survey questions are limited. Adoption of green management practices or investments are captured through a binary variable – where a value of 1 means adoption and 0 implies no adoption - that pertains only to three years prior to the survey. Hence a firm that adopted any green management practices or made green investments 4 years prior may be coded as a zero. Therefore, these findings should be interpreted with care.

4
smaller in the formal than in the informal sector. For example, Hallward-Driemeier (2013) estimates that female-owned enterprises are 6 percent less productive than male-owned enterprises in the formal sector, compared with a gender gap of 50 percent in the informal sector. Similarly, Bruhn (2009), using data from Latin America and controlling for enterprise characteristics, finds gender differences in productivity for micro- and small enterprises, but not for medium-sized and large firms.

The present study builds on this literature to assess gender gaps in enterprise performance in the formal private sector. It improves on the existing literature in the following ways. First, to the best of our knowledge, this is the first global assessment of gender gaps in labor productivity in the formal private sector in developing and emerging economies. We use nationally representative data for 128 countries, which cover 46,000 firms in total. This not only increases the geographic scope of the analysis compared to earlier studies at the country or regional level, but also gives us more statistical power to detect gender differences in labor productivity. In addition, our data are more recent (2009-2016) than those used by previous studies, which often relied on Enterprise Survey data from the early 2000s (e.g. Bruhn 2009; Bardasi et al. 2011). Second, we use decomposition techniques to disentangle the various factors that explain gender differences in labor productivity and provide guidance to policy makers on how to reduce gender gaps in enterprise performance. And finally, in using comparable data and a consistent approach, the study allows for comparison across firm sizes, sectors and regions. The analysis in this paper complements other recent studies that have used global firm-level data to explore the factors and policies associated with women’s participation in firm management (Islam 2013; Islam and Amin 2016; Sekkat et al. 2015).

# **3. Data and Base Regressions**

# _Data source_

The data source for the analysis is based on the World Bank Enterprise Surveys (ES). The data set covers a cross-section of about 46,000 firms across 128 mostly developing countries, surveyed between 2009 and 2016 (list of economies presented in Table A1). The ES are nationally representative surveys of formal 6
Table 8: Structural Estimates - Industries

|**Parameter**||||**Esti**|**mate**||
|---|---|---|---|---|---|---|
|Parameter|Name|Whole|Agri|Manuf|Com|Serv|
|Average wage|_w_|104.2|103.64|104.63|104.7|104.6|
|||(15.9)|(16.1)|(15.4)|(15.8)|(15.7)|
|Ability - educ|_δ_1|0.51|0.62|0.35|0.52|0.45|
|||(0.079)|(0.118)|(0.042)|(0.076)|(0.17)|
|Capital share|_α_|0.230|0.321|0.186|0.292|0.213|
|||(0.057)|(0.023)|(0.073)|(0.019)|(0.021)|
|Labor share|_β_|0.499|0.519|0.617|0.696|0.561|
|||(0.059)|(0.023)|(0.074)|(0.019)|(0.024)|
|Borrow. constr.|_λ_|2.27|2.69|2.86|1.61|1.92|
|||(0.314)|(0.320)|(1.164)|(0.569)|(0.245)|
|Number of Obs.||10709|1150|2454|2268|5911|

Asymptotic standard errors in parenthesis Parameters _κ_ and _ρ_ are not included because geographical factors may not vary across industries.

for the whole sample, implying that a 10 percent increase in the population density increases firm productivity by 1.63%. For Douala and Yaoundé, these values are 0.065 and 0.91 which are lower than the national average for the obvious reason that the population density in these cities is already quite high. In other less populated cities such as Kribi and Bamenda, this elasticity is higher, estimated at 0.28 and 0.26 respectively. We estimated _ρ_ at 0.27 for the whole sample, implying that a 10 percent increase in paved road density increases firm productivity by 2.7%. This elasticity is higher in Garoua and Bamenda compared to the rest of the cities.

Before we perform counterfactual simulations, it is important to assess how well the model fits the data. We do this by comparing the model predicted moments and their sample analogs. Table 10 reports the model fit for the nine chosen moments, of which five were targeted in the gmm estimation as defined in Table 7 above, and four were not targeted in the gmm estimation. Apart from the proportion of formal entrepreneurs which is matched within a 20% discrepancy, all the remaining moments are matched within 10% or lower of their counterpart values in the data. A good fit in these moments can be seen as a validation of the model with data that have or have not been used directly in the estimation.

31
gradient may mask the urban-rural pattern of the disease, rather than an actual association between schooling and prevalence.

Second, gaps in knowledge exist and so there is a need to continue to improve the evidence base on the link between demographic and socioeconomic status and the prevalence and incidence of HIV/AIDS. The introduction and scaling up of antiretroviral therapy (ART) in most African countries profoundly affects the dynamics of the epidemic and has the potential to modify the links between demographic/socioeconomic variables and HIV. If ART is available for specific groups (such as wealthier or more urban populations), the prevalence of HIV will shift, controlling for changes in incidence. By reducing AIDS–related mortality, ART modifies the link between HIV prevalence and incidence, reinforcing the need for accurate measures of incidence (through, for example, panel or cohort studies including HIV tests), as a more appropriate indicator of the current state of the epidemic.

Finally, even with improved data sources, it will still to be difficult to generalize results across countries. Moreover, even within countries, patterns across regions (ethnic groups, urban/rural populations) can be starkly different. As shown in the studies cited and other results presented here, few consistent and significant patterns of prevalence by socioeconomic and demographic status are evident.

17
**Figure 6. Growth of Imports, Volumes and Prices by Category (index year 1993=100)**

<!-- Start of picture text --> 150<br>Total<br>130<br>110<br>90 Value<br>70<br>Price<br>50<br>30 Quantity<br>10<br>-10<br>-30<br>-50<br>Growth rate<br>1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> _Source_ : INDEC.

**A main feature of Argentina exports development in 2005-2008 was the dynamic performance of services exports.** The recovery in services following the 2001-02 crisis was initially weak, but since 2005 growth in the services sector picked up again. The poor performance in services after the crisis seems to be explained by a downward trend in the flows of insurance services in 2001-2007 (Figure 7). The recovery in services exports from 2005 is mainly attributable to recreational services (travel and tourism), combined with other business services, communications and IT. This points to scope for further growth of the services sector in the future.

**Figure 7. Argentina Net Flows of Services 1999-2007**

<!-- Start of picture text --> 2,000 Communication services<br>1,750 Construction services<br>1,500<br>1,250 Insurance services<br>1,000<br>750 Financial services<br>500<br>Computer & information<br>250 services<br>0 Other business services<br>-250<br>-500 Personal, cultural, &<br>recreational services<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 Govt services<br>Net Flow (million)<br><!-- End of picture text --> _Source_ : BOPS IMF 2008.

6
Table 2: The effect of orphanhood on probability of marriage by 2004 (Dependent variable: _ever married)_

||(1)<br>All|(2)<br>Women<br>(17-23 in 2004)|(3)<br>Men<br>(17-28 in 2004)|
|---|---|---|---|
|Male prime-age adult died|-0.042|-0.113|0.058|
||(0.088)|(0.128)|(0.121)|
|Female prime-age adult died|-0.139*|-0.064|-0.308***|
||(0.080)|(0.121)|(0.088)|
|Father died|0.071|0.126*|-0.005|
||(0.061)|(0.076)|(0.110)|
|Mother died|0.016|0.020|-0.021|
||(0.076)|(0.101)|(0.129)|
|Male|-0.501***<br>(0.049)|||
|_Baseline characteristics_||||
|Head has at least primary schooling|-0.029|-0.131|0.036|
||(0.054)|(0.081)|(0.075)|
|Head male|-0.093|0.002|-0.170|
||(0.074)|(0.114)|(0.105)|
|Age of household head|-0.003|-0.005*|-0.002|
||(0.002)|(0.003)|(0.002)|
|Log of expenditure per capita|0.014|0.005|0.002|
||(0.047)|(0.070)|(0.066)|
|Permanent flooring in dwelling|-0.160***|-0.196**|-0.106|
||(0.061)|(0.095)|(0.082)|
|Non-farm household|-0.031|-0.008|-0.037|
||(0.057)|(0.088)|(0.074)|
|Father lives in household|0.049|0.003|0.078|
||(0.066)|(0.102)|(0.087)|
|Mother lives in household|-0.003|-0.001|-0.032|
||(0.062)|(0.089)|(0.091)|
|Observations|683|320|363|
|Pseudo R-squared|0.198|0.233|0.209|

Notes: Results reported are marginal effects estimated using a Probit. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. Includes controls for age and age squared, district fixed effects and whether child was in school at baseline.

14