rafmacalaba/datause-encoder-data huggingface dataset:
TABLE A20: Impact of text messages on ECD outcomes for children of leaders versus nonleaders

|(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|
|ITT<br>-0.01|-0.02|-0.01|
|(0.03)|(0.07)|(0.04)|
|Child of Leader<br>0.10<br>(0.07)|0.24<sup>_∗_</sup><br>(0.12)|0.03<br>(0.08)|
|ITT X Child of Leader<br>-0.06|-0.02|-0.08|
|(0.08)|(0.13)|(0.10)|
|Observations<br>2393|747|1646|
|ITT + ITT X Child of Leader =0<br>0.39|0.73|0.32|

_Note:_ All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months old, and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, Memory and self-control) of children aged 36 to 83 months. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variable for a set of households surveyed in 2016, as well as village fixed effects. Five villages without leaders were excluded from the sample. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 68
# I. Introduction

Substantial work has been done on the take-up of social programs and on the role of stigma in reducing take-up (for early work in this area see Moffitt, 1981, 1983; for literature reviews, see Andrade, 2002, and Currie, 2004). The evidence from recent empirical studies is mixed as to whether stigma is pervasive and has an impact on take-up.

On one side of the debate, Hernandez et al. (2007) show that take-up of income support is lower than take-up of housing benefits and council tax benefits among pensioners in the United Kingdom in part due to greater stigma associated with income support. Levinson and Rahardja (2005) use data from the National Survey of America’s Families in the United States which includes questions on stigma to show that families that do not enroll in Medicaid tend to have more negative perceptions of welfare, suggesting that stigma reduces take-up. Riphahn (2001) shows that in Germany more than half of all eligible households do not claim social assistance transfers in part due to stigma. Henderson et al. (2006) show that stigma associated with substance abuse makes it difficult in the United States to identify individuals who could benefit from substance abuse services. Again in the United States, Stuber and Kronebush (2004) use data from community health center patients to show that stigma reduces take-up of welfare and Medicaid benefits.

On the other side of the debate, there are also several studies that suggest that stigma may not be as pervasive as commonly believed, or may not have a larger impact on takeup. For example, using data on the use of food stamps in San Diego County, Breunig and Dasgupta (2003) do not find evidence supporting the assumption of a welfare stigma associated with the use of food stamps. Similarly, Bingley and Walker (2001) suggest that in the United Kingdom, stigma associated with housing benefits is low.

The fact that empirical studies send mixed messages as to the magnitude of stigma and its impact on take-up may be due to differences in stigma between programs. Such differences may in turn be related to the fact that stigma and take-up are likely to be jointly determined. If there is a high (low) degree of participation in a program, stigma is likely to be lower (higher) since the more eligible persons participate, the less one can single out and 1
These higher rates of pass-through for food items give a first indication of the distributional consequences of the depreciation. Since poorer households devote a greater share of expenditure to food items, the price changes associated with the depreciation would have had a larger effect on them. In the next section of the paper I turn documenting in more detail the welfare effects of these price changes.

## **3.  Estimating the Welfare Impact of Exchange Rate Induced Price Changes**

The next step is to take the estimates of the changes in prices induced by movements in the exchange rate and calculate their welfare effects.

## **3.1 Empirical Framework**

I use the compensating variation as a standard measure of welfare effects of price changes. In particular, let e(u,p) denote the expenditure function, i.e.

# * (6) e(,pu*) ≡ min 'p x .t.s u(x) > u

where p is an nx1 vector of prices, x is an nx1 vector of quantities demanded, u(x) is a well-behaved utility function, and u* is a reference level of utility. Let p0 denote the reference prices prevailing in 2000, the time of the initial household survey, and let Δp ≡ p1 − p 0 denote the vector of price changes that were caused by the depreciation between 2000 and 2005, that I have isolated empirically in the previous section. The compensating variation measures the change in expenditure that would be required in order for households to achieve their pre-depreciation utility u* at the post-depreciation set of prices, p1: I will empirically approximate the compensating variation using a second-order Taylor expansion of the expenditure function around the initial period prices:<sup>9</sup>

> 9 This approach is also taken by Levinsohn and Friedman (2002).   An alternative is Vartia (1983), who shows how to get the exact comparison of utility in time 0 and time 1, by numerically

10
- Buvinic, M., & van de Walle, D. (2019). The Debate about Headship in Poverty and Gender Studies. https://www.cgdev.org/blog/debate-about-headship-poverty-and-gender-studies

- <mark>Caria, S., Crepon, B., ElBehairy, H., Fadlalmawla, N., Krafft, C., Nagy, A., ... & El Assiouty, S. (2022).</mark> _<mark>Child Care Subsidies, Employment Services and Women's Labor Market Outcomes in Egypt: First Midline Results</mark>_ <mark>. World Bank.</mark>

- Chant, Sylvia. (2003). “ _Female Household Headship and the Feminization of Poverty: Facts, Fictions and Forward Strategies”,_ LSE Gender Institute, New Working Paper Series, Issue 9 (London: London School of Economics).

- CAPMAS. (2014). “Poverty Indicators according to the Household Income, Expenditure and Consumption Survey 2012/2013”. <u>http://capmas.gov.eg/pepo/a.pdf.</u>

- <mark>Clark, S., Kabiru, C. W., Laszlo, S., & Muthuri, S. (2019). The impact of childcare on poor urban women’s economic empowerment in Africa.</mark> _<mark>Demography</mark>_ <mark>,</mark> _<mark>56</mark>_ <mark>(4), 1247-1272.</mark>

-

- Dang, Hai Anh H., and Elena Ianchovichina. (2018). “Welfare dynamics with synthetic panels: the case of the Arab world in transition.” _Review of Income and Wealth_ , Vol. 64, pp. S114S144.

- Dang, Hai-Anh and Peter Lanjouw. (2023).  “Measuring poverty dynamics with synthetic panels based on cross-sections.” _Oxford Bulletin of Economics and Statistics_ , 85(3): 599-622.

- Dang, Hai-Anh and <mark>Cuong Viet N</mark> guyen. (2021). “Gender Inequality during the COVID-19 Pandemic: Income, Expenditure, Savings, and Job Loss.” _World Development_ , Vol. 140, 105296.

- Datt, G., D. Jolliffe, and M. Sharma. (2001). “A profile of poverty in Egypt.” _African Development Review_ , Vol. 13(2), pp. 202-237.

- Deaton, A., & Paxson, C. (1998). Economies of scale, household size, and the demand for food. _Journal of Political Economy_ , 106(5), 897-930.

- DeGraff, Deborah S., and Richard E. Bilsborrow. (1993). “Female-headed households and family welfare in rural Ecuador.” _Journal of Population Economics_ , Vol. 6, pp. 317-336.

- <mark>De Vreyer, P., & Lambert, S. (2021). Inequality, poverty, and the intra-household allocation of consumption in Senegal.</mark> _<mark>World Bank Economic Review</mark>_ <mark>,</mark> _<mark>35</mark>_ <mark>(2), 414-435.</mark>

- <mark>Deere, C. D., & León, M. (2003). The gender asset gap: Land in Latin America.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>31</mark>_ <mark>(6), 925-947.</mark>

- <mark>Deere, C. D., Alvarado, G. E., & Twyman, J. (2012). Gender inequality in asset ownership in Latin America: Female owners vs household heads.</mark> _<mark>Development and Change</mark>_ <mark>,</mark> _<mark>43</mark>_ <mark>(2), 505-530.</mark>

29
Small and Medium Enterprises (MSME) Survey for the year 2006–2007 suggests that 13% of firms are owned by a woman and 11% are female-managed (Chaudhuri et al., 2020).

**Stylized Fact #2** : Female-managed formal firms do not seem to differ widely in their demand for finance relative to male-led firms, but the former have limited access to the amount of credit, especially in traditional countries. Unconditional averages suggest that firms managed by a woman are more likely to apply for credit, their credit applications are less likely to be rejected and, consequently, female-managed firms are more likely to have an open line of credit. Nevertheless, on average, the credit received by female-managed firms is lower than that in male-managed firms (Table 1). Three points are worth noting here: _First_ , among the firms that did not apply for credit, 67% of female-managed firms and 62% of male-managed firms did not apply for credit because they believed their firms did not need loans, implying that women’s perception of the need for formal finance may be different. _Second_ , although both female and male-managed firms in our sample primarily use own funds to finance their fixed assets and working capital, there are modest gender differences in traditional countries (Appendix Table A.3). _Lastly_ , when separating the sample by social perceptions about women, differences in the unconditional average value of debt are higher in traditional countries (Table 1). These points suggest that while women-led firms are equally likely to apply for credit, they remain credit-starved. The reasons for not applying for formal finance may be embedded in their perception of the financial needs of the firm, or traditional social settings where finding own sources of finance may be more acceptable.

**Stylized Fact #3** : Female and male-managed firms differ in the underlying attributes that may matter for their demand and access to credit. On average, female-led firms are younger, smaller, have lower sales per worker and are more likely to operate in low-tech manufacturing. However, they are also more profitable and more likely to export relative to male-led firms (Table 1). Finally, female-led firms are not significantly different from their male counterparts on their risk profile and total factor productivity (TFP). The higher profitability and comparable TFP of female-led firms, although unexpected, aligns with the findings of Morazzoni and Sy (2022) and can be rationalized by the more rigorous selection of women managers in formal firms with over five employees.

(50-249 employees) and large-size (over 250 employees) firms, respectively (Basiglio et al., 2023).

6
# **5  Empirical Strategy**

Based on the propositions put forward by our theoretical model and the specification applied by Kahn (2005), we derive the following reduced-form specification for our baseline estimates: Our dependent variable is the number of deaths, _DEATH ijt_ , at event _i_ in country _j_ at year _t_ . Our main variable of interest is foreign aid in period _t_ − 1 per capita, _Ln_ ( _AID j_ , _t_ − 1) . This variable a) accounts for the fungibility effect of past foreign aid and b) serves as our empirical proxy for the crowding-out effect i.e. the amount of anticipated ex-post aid that might crowd out national collective hazard management activity. To account for a country's level of development and its general ability to install protective measures we include _GDP_ per capita. In order to reduce potential reversed causality between disaster fatalities and income we use the first lag of _GDP_ p.c., _Ln_ ( _GDPj_ , _t_ − 1 ) . We expect that richer countries suffer c. p. less deaths from natural disasters.

In contrast to general belief, the correlation between _GDP_ p. c. and _AID_ p. c. in each of our three sub-samples is moderate (between -0.104 and -0.152). This is in line with the results of empirical studies (e.g. Frey & Schneider, 1986, Alesina & Dollar 2000) that identify political and strategic considerations as the driving forces behind foreign aid.

To account for a nation's size and density of population we introduce the natural log of population, _Ln_ ( _POPjt_ ) , and the natural log of population density, _Ln_ ( _POPDENS jt_ ) , respectively. Gassebner, Keck & Teh (2009) show a negative relationship between the occurrence of disasters and a nation's trade volume. Alesina & Dollar (2000) find that bilateral aid flows are positively related to a nation's openness for trade. To control for potential spurious correlation that might stem from this interrelationship, we include a nation's openness for trade, _OPEN jt_ , in our specification.

Apart from these socio-economic variables, the main explanatory factors for disaster fatalities are a disaster's magnitude, and additional climatic, geographical and topographical factors. All else equal, more powerful disasters should kill more people. We include wind speed measured in kilometers per hour as a magnitude variable for our storm sub-sample, the number of months per year _t_ in the country _j_ , with a monthly precipitation that is one standard deviation above the long time precipitation mean in country _j_ for our flood sub-sample and the Richter scale for the earthquake sub-sample.

Nations that are more exposed to natural disasters could also be the subject of more international aid contributions. Thus, we control for a nation's exposure to each disaster type via a proxy variable. Using GIS-data on the mortality risk and a shape file of a country's national boundaries we are able to construct the mean mortality risk from each disaster type for each nation, 11
The student preferential fare for Metro is regressive both on the benefit and the funding side, implying almost a neutral impact although the net effect is somewhat progressive. However, judging by the error of exclusion of this subsidy it can be seen that very few poor households gain from it.

The only policy with a positive distributive impact is the direct monetary transfers implemented in 2004, 2005 and 2006 described in Box 1. This policy was much more effective in reaching the poor. The error of inclusion is quite low, as far as subsidy policies go.

# _Mumbai, India_

A case study of Mumbai, India, shows similar results for supply side subsidies (Cropper, 2007). Two supply side subsidies are analyzed in that study, one to cover operating losses for bus services (representing around 30% of fares) and another for rail services (a small shortfall of 2% of costs). The incidence of these subsidies is presented in Table 7. This table presents the cumulative percentage of the population by income groups and the cumulative percentage of total subsidies accruing to each group. It is simply a tabulated representation of the relative beneficiary curve. The final column presents the Ω coefficient for two different definitions of the poverty line; one assumes poverty is defined to include households earning less than 5K (27% of households) and the other assumes it includes households earning less than 7.5K per month (54.4% of households).

**<u>Table 7: Subsidy incidence in Mumbai, India</u>**

|**Income group**|<br>**Cumulative**<br>**percentage of**<br>**households**|<br>**Cumulative**<br>**percentage of**<br>**bus subsidy**|<br>**Cumulative**<br>**percentage of**<br>**rail subsidy**|**Ω**<br>**Bus/rail**|
|---|---|---|---|---|
|<5K|26.6|19.1|15.5|0.72 / 0.81|
|5001-7500|54.4|44.9|38.0|0.83 / 0.85|
|7501-10000|76.3|68.0|61.8||
|10001-20000|94.1|92.2|88.3||
|>20K|100.0|100.0|100.0||

_Source_ : own calculations based on Cropper (2007) The results show that these subsidies are not pro-poor. More than a proportional amount of the subsidy accrues to middle and higher income households. The errors of inclusion, shown in Table 8 are quite high, while the errors of exclusion are quite low. This is typical of universal subsidies that benefit rich and poor alike.

**<u>Table 8: Errors of inclusion and exclusion, Mumbai, India</u>**

|**Subsidy**|**Errors of exclusion**|**Errors of inclusion**|
|---|---|---|
|Bus|10.0|92.6|
|Rail|26.1|86.2|

_Source_ : Cropper (2007) 38
# Deals versus Rules: Policy Implementation Uncertainty and Why Firms Hate it

_For my friends, anything; for my enemies, the law._ Oscar R. Benavides, President of Peru, 1933-1940

## **_Introduction_**

The Investment Climate Enterprise Surveys have conducted detailed face-to-face interviews of over 80,000 entrepreneurs in over 100 countries, including 11,150 in 34 SubSaharan countries. The issue firms most frequently identify as an obstacle to their growth is “regulatory and economic policy uncertainty” (Smith and Hallward-Driemeier 2005). In Africa, 60 percent of surveyed firms regard “economic and regulatory policy uncertainty” as an obstacle to their firms’ growth and almost 40 percent regard it as a “severe” or “major” obstacle (figure 1). This finding creates a puzzle. The other obstacles in the top 5 of Figure 1 are easy to understand: lack of electricity creates obvious production problems, macro-economic instability has its obvious consequences, firms everywhere and always complain about taxes, and “access to finance” as been widely investigated as a limit to firm expansion. Moreover, these other obstacles--infrastructure, macroeconomic instability, access to finance--are relatively easily linked to related literatures on country aggregate growth and to programmatic activities. But until we understand what “regulatory and policy _uncertainty,_ ” is, such that firms hate it so, we cannot investigate its impacts at the aggregate level nor what could be done about it.

2
# **Table A7. Liquidity and Diversification Requirements: Selected Responses**

||**ALL**|**ADV**|**EMDEs**|
|---|---|---|---|
|**A. Overview**|**% Yes**|**% Yes**|**% Yes**|
|Are there any regulatory rules or supervisory guidelines regarding asset<br>diversification?|56<br>(46)|59<br>(49)|55<br>(45)|
|Are there regulatory rules or supervisory guidelines regarding diversification of<br>fundingsources of banks' liquiditymanagement?|75|86|72|
|Are there regulatory rules or supervisory guidelines regarding contingency funding<br>plans,includingstress testingof banks' liquiditymanagement?|86|94|83|
|Is banks' liquidity management of foreign currencies requirements in place in your<br>jurisdiction?|73|71|74|
|Is regulatory minimum ratio on liquid assets (e.g. as a percentage of total balance<br>sheet or deposit base)requirements inplace inyourjurisdiction?|74|67|77|
|Is maturity mismatch/'gap' limit requirements in place in your jurisdiction?|55|63|53|
|**B.**<br>**Risk Concentration**|**% Yes**|**% Yes**|**% Yes**|
|Are banks prohibited from making loans abroad?|20<br>(9)|0<br>(3)|28<br>(11)|
|Are banks limited in their lending to a single borrower or a group of inter-related<br>borrowers?|99<br>(97)|97<br>(92)|100<br>(99)|
|**C. Reserve Requirements**|**% Yes**|**% Yes**|**% Yes**|
|Is Central Bank reserve and/or deposit requirements in place in your jurisdiction?|86<br>(90)|69<br>(76)|93<br>(95)|
|Are banks required to hold reserves in foreign currencies or other foreign-<br>denominated instruments?|35<br>(25)|26<br>(8)|39<br>(31)|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

# **Section 8: Deposit (Saving) Protection Schemes**

Section 8, including 29 questions, covers a wide variety of issues related to depositor protection schemes, such as the authority of the deposit insurance agency, the membership and coverage requirements for the deposit insurance scheme, and the depositor insurance reimbursement practices among other topics. More than three quarters of respondents reported having an explicit deposit insurance protection system for commercial banks in their jurisdictions (Table A8). Of the countries with such an explicit system, almost 80 percent have a legally separate deposit insurance agency (Figure A5). Furthermore, in 95 percent of jurisdictions, participation of domestic banks in the deposit insurance system is compulsory. However, in 51 and 71 percent of the countries the deposits at foreign branches and subsidiaries of domestic banks, respectively, are excluded from the deposit insurance system.

36
7 a version of the cooperative model developed in Browning and Lechene (2001) to analyze the impact of adult inequality on child welfare<sup>7</sup> .

I deal with the above two issues by a) confining the samples employed to strictly nuclear type of households, b) examine the effect of child gender by an index based on the number of children in the household. However, age of children may exert some influence on adult goods consumption quite apart from those due to child gender; hence I control separately for age of children. To do so, I employ the following version of the Working-Leser functional form on which almost all previous studies are based (except for a separate gender index here).

where _wj_ denote budget share of adult goods _j_ , _xh_ is household total expenditure , _nh_ is household size, _na / nc_ indicate proportion of kids in different age groups _a_ over total number of children _nc_ , _ng / nc_ is the proportion of girls in the household and constitutes our child gender index, _z_ is the vector of additional variables, regional dummies; variables specific to the analysis of child gender bias, such as dummies for the mother’s age, and her educational level; their interactive terms, etc.; ε a normal error term. This is the standard semi-logarithmic Working-Leser functional form, which could be augmented if there is a curvature effect for the logarithm of per capita total expenditurein which case a quadratic term for total expenditure can be added to the LHS of (1) giving I provide least square estimates by (1) or (2) for each _j_ =4 adult goods in this study, depending on whether there is a significant total expenditure curvature effect on _j_ . All expenditures are on annual basis, and the definition of total expenditure employed

> <sup>7</sup> This study demonstrates that such a task requires the abandonment of the assumption of efficient outcomes on which the core cooperative model, see Browning et al. (1994), rests; the introduction of non-co-operative structure ‘considerably complicates the theoretical analysis and also makes greater data demands’, (p. 5). Indeed, many years of budget surveys are required to distinguish distributional regions, and large number of parameters involved results, for some of the regions, in ‘very imprecise estimates’ (p.15).