rafmacalaba/datause-encoder-data huggingface dataset:
in preschool availability during eligible ages—suppose that there few preschools when the child is aged 3 and the government builds more preschools when the child is aged 4.

Our annual panel affords us sufficiently frequent observations of mothers; however, our preschool data are only available roughly once in three years. Restricting our analysis to PODES years only could be problematic because we can only leverage variations as mothers transition in and out of eligibility with an older child growing out of, and a younger child reaching, preschool ages. Suppose that in year 2000, the mother only has one young child and in 2003, she has two young children. Her labor force participation calculus likely changes with the additional child. We must assume that individual preferences for work do not change over time and that variations in work participation are attributable only to variations in preschool availability. We must also assume that preschool availability does not affect individual preferences for children. Suppose that no preschools were available when a mother has a preschool-aged child and she decides not to have another child, so she can work. Since we only observe her three years later, we can only identify changes in preschool eligibility and not variations in preschool availability during eligible ages. We do not have to make these additional assumptions in DDD because we can compare across eligible mothers subjected to different preschool availabilities. We explore the validity and present the results of each strategy in the next section.

# **5. Results**

This section describes the effect of public and private preschool availability on female employment outcomes in Indonesia. We first begin by examining the extensive margin of employment: work participation. Do preschools serve as an adequate alternative to childcare, enabling women to partake in employment? We describe the results across different empirical strategies and different treatment of missing years, without preschool data in PODES. Subsequently, we explore the intensive margins, such as the types of occupations and sectors that women go into. Given that preschool only provides 3 hours of childcare per day on average, can it enable women to work in the formal sector with rigid hours?

# **_5.1 Work participation_**

Table 2 reports the regression results of our three empirical strategies with and without districtspecific trends in the most conservative PODES-restricted years. Columns 1-2 estimate the work participation of mothers with preschool-aged children on public (Panel A) and private (Panel B) 15
Poland, find a coefficient of 0.2 using a Fully Modified Ordinary Least Square (FMLOS) and 0.19 using a Dynamic Ordinary Least Square (DOLS).

The finance sector has a particular place in the debate about South Africa economic transformation (Rodrik (2008), Fedderke (2014)). Its size is disproportionate relative to the size of the economy and it has experienced a significant wave of innovation and integration in the international capital markets after 1994. It is therefore instructive to isolate the effect of increasing productivity of the financial sector on the relative prices. The results are displayed in Table (5).

||(1)|(2)|(3)|
|---|---|---|---|
||Agriculture|Manufacturing|Mining|
||_pt,N −pt,Agr_|_pt,N −pt,Man_|_pt,N −pt,Min_|
|∆_aAgr −aF in_|0.177***<br>(3.81)|||
|∆_aMan −aF in_||0.426***<br>(5.26)||
|∆_aMin −aF in_|||1.560***<br>(10.03)|
|Constant|0.0675*|-0.175***|-0.343***|
||(2.40)|(-6.40)|(-6.97)|
|_N_|25|25|25|

_t_ statistics in parentheses * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001 Table 5: Sectoral Effect of Finance Productivity The results are qualitatively in line with the general results, but the effects are quantitatively much larger for manufacturing and mining. For manufacturing, an increase in the difference in productivity of 1 % will increase the price differential between manufacturing and finance by almost half of a per cent. For mining, the change in relative prices is even stronger, with the change in productivity differential increasing the price differential by more than one to one.

19
determinants, is the inadequacy of the commonly used wealth index as a proxy for income/material well-being, in particular in the rural areas of Sub-Saharan Africa. We will discuss this issue at quite some length in our empirical analysis below. In our view, quite a few of the reviewed studies fail to discuss this possible source of error sufficiently.

A second shortcoming of the presented studies is their mere focus on reduced-form mortality equations that directly links socio-economic plus proximate determinants and child survival. Although the Mosley-Chen framework, as laid out above, also suggests examining the link between socio-economic and proximate determinants, we found very few examples for this type of exercise. This implies that the transmission channels (Are more educated mothers more likely to have their children vaccinated?) are often left in the dark.

Finally, the empirical literature is dominated by studies that examine a single country at a single point in time. Notable exceptions include the multi-country (not cross-country) studies by Lalou and LeGrand (1997), Brockerhoff and Hewett (2000), and Harttgen and Misselhorn (2006). Changes over time are only addressed in Hill et al. (2001) and Burgard and Treiman (2004). Although the use of multiple cross-sections (instead of panel data) does not allow for dynamic econometric analysis, these analyses do provide important insights into the factors that shape changes in child mortality. Therefore, the subsequent empirical analysis uses data from two years for each country in a multi-country comparison, attempts to address the difficulties with regard to the DHS wealth proxy, and stresses the transmission channels through which socio-economic factors eventually affect child mortality outcomes.

# New Evidence from DHS: Comparing Country Experiences in SSA

For our empirical analyses, we have selected four country pairs from different SSA regions: Guinea-Senegal and Mali-Niger in West Africa as well as Madagascar-Mozambique and Kenya-Uganda in East Africa. For all these countries, two relatively recent DHS from the late 1990s and early 2000s are available that we use for the statistical analyses. We opted for using two cross-sections for each country because this allows us to examine possible changes in child mortality determinants. Data availability has hence been one criterion for country selection. The selected countries with their respective child mortality levels are highlighted in Figure 2.

Before we present the empirical results, we shortly want to discuss the motivation, merits and shortcomings of our multi-country approach that will combine quantitative and qualitative elements. Although the quantitative analysis of child mortality determinants that broadly follow the studies presented above is at the core of the following empirical work, the 14
**Figure 2.6. Yemen: sectoral demand composition in 2020 (%)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>intermediate use* private cons government cons investment exports<br>percent<br><!-- End of picture text -->

*Includes trade and transport margins.

Source: Authors’ calculations based on the 2020 Yemen SAM.

# 2b. Literature overview

During the last decades, Yemen’s economy has been analyzed in a large number of applications of CGE models, an indication that it is relatively straightforward to apply such models even in a data-scarce environment like Yemen’s. This section provides a brief survey of the 15 Yemen studies that have been identified, with a focus on method and the issues addressed. The studies were published between 2007 and 2022 in academic journals, as book chapters, or as working papers. Most were undertaken by IFPRI as part of a research effort led by Clemens Breisinger; some work was also done at the World Bank. Readers interested in additional details are referred to the individual references for the studies.

The methodological framework of these models is rooted in the pioneering work at the World Bank in the late 1970s and early 1980s that is documented in Dervis, de Melo, and Robinson (1982). The approach that was developed may be described as neoclassical structuralist (Robinson 1989): its starting point is Walrasian general equilibrium theory, but it deviates from this theory whenever this is deemed necessary in order to incorporate essential features of the modeled economy.

In terms of the specifics of the Yemen applications, the IFPRI standard model (Lofgren et al. 2002), extended to recursive dynamics by Thurlow (2004), provides the mathematical structure and 10
# **1994-2004**

|**Type of safeguard**|**Number of i**|<br>**nvestigations**|**Percentag**|<br>**e breakdown**|**Wo. Measures/**<br>|
|---|---|---|---|---|---|
||**Conducted**|**Wo. Measures**|**Conducted**|**Wo. Measures**|**Conducted**|
|**WTO SAFEGUARDS**||||||
|**Rice**|**1**|**0**|**9,1**|**0,0**|**0,0**|
|**Shoes**|**1**|**0**|**9,1**|**0,0**|**0,0**|
|**Textiles and apparel**|**6**|**0**|**54,5**|**0,0**|**0,0**|
|**Home appliances**|**2**|**0**|**18,2**|**0,0**|**0,0**|
|**Taxis**|**1**|**1**|**9,1**|**100,0**|**100,0**|
|**TOTAL**|**11**|**1**|**100,0**|**100,0**|**9,1**|
|**SPECIAL SAFEGUARD**||||||
|**Chemicals and petrochemicals**|**3**|**1**|**30,0**|**20,0**|**33,3**|
|**Textiles and apparel**|**3**|**2**|**30,0**|**40,0**|**66,7**|
|**Chains**|**1**|**0**|**10,0**|**0,0**|**0,0**|
|**Home appliances**|**3**|**2**|**30,0**|**40,0**|**66,7**|
|**TOTAL**|**10**|**5**|**100,0**|**100,0**|**50,0**|
|**ANDEAN COMMUNITY**||||||
|**Agricultural products**|**7**|**0**|**58,3**|**0,0**|**0,0**|
|**Extra-neutral alcohol**|**1**|**1**|**8,3**|**33,3**|**100,0**|
|**Polypropylene bags**|**2**|**1**|**16,7**|**33,3**|**50,0**|
|**Triplex and particle boards**|**1**|**0**|**8,3**|**0,0**|**0,0**|
|**Iron & Steel Products**|**1**|**1**|**8,3**|**33,3**|**100,0**|
|**TOTAL**|**12**|**3**|**100,0**|**100,0**|**25,0**|
|**AEC 24 ALADI**||||||
|**Agricultural products**|**1**|**0**||||
|**TOTAL**|**34**|**9**|||**26,5**|

**Source: Calculations by the authors based on data from the Ministry of Trade. Note: agricultural products appearing in the Andean safeguard are rice and refined vegetable oils.**

# **_F. TWO CASE STUDIES_**

An analysis of the previous sections suggests that it is interesting to look into two sectors more deeply. The first one is the agricultural sector, from which rice was selected for this paper because of the persistence in time of the measures to protect it against imports. This case is illustrative of the difficulty to manage the liberalization process when political considerations seem to prevail over technical ones.

The second one is the textile-apparel sector, which reveals the problems that are created in a production chain when inputs become more expensive due to the application of duties or 26
29 To help answer these questions, this project conducted an RDM analysis of flood risk management in Ho Chi Minh City based on existing models and data. The RDM analysis found that Ho Chi Minh City’s soon-to-be completed infrastructure investments will reduce economic and population risk in the Nhieu Loc-Thi Nghe catchment compared to recent (pre-infrastructure) levels in current climate and socio-economic conditions. It will also keep risk below pre-infrastructure levels in the best-estimate future conditions. However, the analysis also identifies many plausible future conditions where this infrastructure may fail to reduce risk. This project thus demonstrates how RDM can help Ho Chi Minh City decision makers understand the range of future socioeconomic and climate conditions over which their infrastructure investments can successfully manage risk.

This analysis also identified adaptation and retreat measures that, in combination with the soon-to-be-completed infrastructure, could significantly expand the range of future socio-economic and climate conditions over which an integrated flood risk management strategy could reduce risk. Decision makers may find some types of adaptation and retreat measures technically, politically, and economically easier to implement than others. The RDM analysis provides tradeoff curves to help decision makers choose a strategy that provides an appropriate balance between risk reduction and implementation feasibility. In particular, the RDM analysis shows how adaptive strategies, in which some policy options are implemented immediately and others only implemented if needed in the future, can achieve almost the same level of risk reduction as a strategy that implements all such options immediately. In some cases, decision makers may find such adaptive strategies attractive because they can achieve high levels of risk reduction while deferring implementation of some, potentially more difficult or costly options, until it is clear they are needed.

# **RDM Can Improve Risk Management in Developing Countries**

These findings suggest that RDM can offer significant value-added to the challenge of developing effective risk management strategies in fast-changing and deeply uncertain developing country environments. In particular, this project helps demonstrate RDM’s capability to:

- Develop a detailed understanding of the combinations of future climate and socioeconomic conditions where a proposed flood risk management strategy will and will not meet its risk reduction goals;

- Use this information to compare the quantitative and qualitative tradeoffs among alternative strategies, including cost, feasibility, the impacts on different populations, and the impacts between economic and population risk;

- Consider adaptive strategies, which may more effectively reduce deeply uncertain risk by evolving over time in response to new information;

- Suggest signposts, which can help decision makers know when to trigger deferred investments and actions as part of an adaptive strategy; and
consumption of households in Ethiopia, with rainfall shortfalls and food price increases being the most important. A moderate drought reduces consumption by 9 percent for many rural households in drought prone areas and the rapid inflation witnessed in the first half of 2011 caused consumption losses up to 14 percent among many urban households. We find considerable heterogeneity of impacts, with uneducated households in urban areas suffering most from food price increases and those not covered by the safety net in rural areas suffering most from rainfall losses.<sup>4</sup> When covered by the safety net, beneficiaries are better able to withstand shocks than non-beneficiaries, although this cannot be fully attributed to the presence of the program.

Vulnerability to poverty using our approach is 42 percent in rural areas, much higher than poverty (headcount just under 30 percent); vulnerability is 5 percentage points less prevalent than poverty in urban areas. This is on account of poverty having been measured in a year of good conditions in rural areas (good weather) and bad conditions in urban areas (high food price inflation). It highlights how a one-year snapshot of poverty such as afforded by a national consumption survey is not necessarily reflective of the relative vulnerability of different groups in the population, and also, that drought and inflation do not always coincide. Analyzing our various vulnerability measures by gender of the household head yields surprising results: female-headed households are more vulnerable in urban areas, the opposite is true in rural areas. PSNP targeting in rural areas appears to be good, but there are many households in rural woredas that are not covered by the program, and many vulnerable households in urban areas too.

The paper is structured as follows. Section 2 outlines the empirical method and data used to estimate the impact of weather and price shocks on household consumption. Section 3 discusses the literature on vulnerability measurement and how the estimated impact of weather and prices on consumption can be used to measure vulnerability to poverty. Section 4 presents results on the impact of weather and prices on consumption. Section 5 presents vulnerability estimates for 2011, with a breakdown by location and gender. Section 6 concludes with some policy recommendations.

# **2 Quantifying the welfare impact of weather and price shocks**

## **2.1 Method**

A particular focus of the methodology employed in this paper is in identifying an internally consistent estimate of the welfare losses caused by shocks.

The welfare measure used is that used by the Government of Ethiopia in estimating poverty, namely total expenditure on food and non-food consumption per adult equivalent (Woldehanna and Porter, 2013). The setting in which the household operates (the economic, legal and political environment) and the household’s ability to cope with

> 4Households not covered by the safety net includes both beneficiary housheolds observed before the safety net was introduced and non-beneficiaries.

4
|Individual Age Squared|1,042|846.0|815.6|711.8|1,187|891.8|
|---|---|---|---|---|---|---|
|Number of Adults in HH|4.809|2.972|5.123|3.102|4.608|2.868|
|Number of Children Aged 0-5 in HH|1.206|1.393|0.873|1.187|1.418|1.472|
|Individual owns a Financial Account|0.220|0.415|0.392|0.488|0.111|0.314|
|Individual is Married|0.607|0.488|0.348|0.476|0.773|0.419|
|Individual is Divorced/Married/Separated|0.033|0.179|0.012|0.110|0.046|0.210|
|Individual Completed Secondary Schooling|0.082|0.275|0.147|0.354|0.041|0.199|
|Individual owns a Vehicle|0.434|0.496|0.496|0.500|0.394|0.489|
|Individual Works as Apprentice|0.030|0.171|0.049|0.216|0.0179|0.133|
|Individual Works as Manager|0.058|0.234|0.103|0.304|0.029|0.169|
|Individual Works as Skilled Worker|0.037|0.188|0.069|0.253|0.016|0.127|
|Individual Works as Family Worker|0.748|0.434|0.570|0.495|0.862|0.345|
|Individual Works in Agri. Sector|0.746|0.435|0.572|0.495|0.858|0.349|
|Individual Woks in Man. Sector|0.056|0.230|0.117|0.321|0.018|0.132|
|Individual Works in Service Sector|0.197|0.398|0.312|0.463|0.124|0.330|
|Is Female|0.611|0.488|||||
|**Number of Observations**|**10,591**||**4,295**||**6,296**||
|_Variables used in Monthly Business Revenues Regression_<br>_Decomposition_|||||||
|Asinh(Revenue)|11.34|2.710|11.85|3.003|10.91|2.353|
|Individual Lives in an Urban Area|0.398|0.489|0.400|0.490|0.396|0.489|
|Individual Age|38.62|10.84|39.41|10.50|37.95|11.09|
|Individual Age Squared|1,609|882.7|1,663|865.3|1,563|894.8|
|Number of Adults in HH|3.921|2.657|3.862|2.572|3.970|2.726|
|Number of Children Aged 0-5 in HH|1.163|1.257|1.279|1.270|1.065|1.237|
|Individual owns a Financial Account|0.435|0.496|0.633|0.482|0.268|0.443|
|Individual is Married|0.867|0.340|0.885|0.319|0.851|0.356|
|Individual is Divorced/Married/Separated|0.069|0.253|0.0267|0.161|0.104|0.305|
|Individual Completed Secondary Schooling|0.036|0.188|0.0567|0.231|0.019|0.139|
|Individual owns a Vehicle|0.682|0.466|0.879|0.326|0.516|0.500|
|Business has Electricity|0.079|0.271|0.140|0.347|0.029|0.168|
|Business has Running Water|0.014|0.119|0.025|0.156|0.006|0.075|
|Business has a Phone|0.888|0.316|0.945|0.228|0.839|0.367|
|Business is Registered|0.033|0.180|0.065|0.247|0.006|0.081|
|Business Maintains Accounting Records|0.054|0.226|0.082|0.274|0.030|0.172|
|Business has Machines|0.132|0.339|0.196|0.397|0.078|0.269|
|Number of Men Employees|0.372|3.272|0.784|4.793|0.025|0.326|
|Number of Women Employees|0.170|7.256|0.283|10.72|0.075|0.415|
|Number of Days HH Female Member Worked|134.0|168.1|23.34|95.62|227.3|159.2|
|Number of Days HH Male Member Worked|119.6|171.8|254.2|170.1|6.025|42.38|
|Business is in Food Sector|0.387|0.487|0.243|0.429|0.509|0.500|
|Business is in Clothing Sector|0.269|0.443|0.254|0.436|0.281|0.450|
|Business is in Construction Sector|0.126|0.332|0.182|0.386|0.080|0.271|
|Business is in Service Sector|0.516|0.500|0.615|0.487|0.431|0.495|

32
1

# Stunted Growth: Why Don’t African Firms Create More Jobs?<sup>1</sup>

_Leonardo Iacovone (World Bank) Vijaya Ramachandran (Center for Global Development) Martin Schmidt (University of Maryland)_ JEL: D22, D24,L2, L25, O12, O14, O55

> 1 The authors are grateful to Alan Gelb, Christian Meyer, Simon Quinn, Mans Soderbom, Gaiv Tata, Michael Wong, and seminar participants at the annual conference of the Center for the Study of African Economies at Oxford University and the Africa Finance and Private Sector Group at the World Bank, for comments and suggestions.
downward mobility. In particular, more than two-thirds of the extreme (food) poor (i.e., people living under the food poverty line) moved up one or two welfare categories. Factors such as education achievement, having a female household head, urban residence, and formal work contract are strongly correlated with movement out of poverty. These factors and others such as migration, belonging to other ethnic groups, working in the non-agriculture sector, having access to social capital,<sup>6</sup> having experienced no disastrous shock, and having no disability are correlated with households being protected from falling into poverty. Distinguishing between those who are chronically poor from those who have recently fallen into poverty yields interesting insights. Notably, factors such as weak social capital, exposure to natural disasters, and limited access to quality community infrastructure are more strongly correlated with downward mobility than with chronic poverty. Out of all the correlates, education appears to be most strongly linked to escape from poverty.

The paper comprises five sections. We provide a brief overview of the country background and data description in the next section, which is then followed by discussion of the synthetic panel method in Section III. Section IV provides estimation results and Section V concludes with policy recommendations.

# **II. Country Background and Data Description**

# **II.1. Data Description**

We analyze two cross sectional rounds of the household Senegal Poverty Monitoring Surveys (Enquete de Suivi de la Pauvreté au Sénégal _,_ or in short, ESPS) in 2005-2006 (ESPS1) and 2011 (ESPS2). Both surveys were implemented by the national statistical agency (ANSD) with

> 6 Access to social capital is defined as the household that receive financial help from family, neighbors or friend in case of need.

6