rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Gender norms are often emphasized as key to help explain remaining gender gaps in the labor market (Bertrand, 2020; Goldin, 2021).<sup>1</sup> For example, the stereotypical gender roles that men should be the main income earners or “breadwinners” and women should primarily be responsible for the care of children and household chores or “caregivers” in the household are widely acknowledged normative constraints to women’s economic empowerment (Jayachandran, 2021). Despite the recognition of the importance of social norms for gender equality among economists and policy makers, very little development-funding is currently devoted to programs that directly influence norms-related change.<sup>2</sup> In this paper we collect and analyze data from 111 countries to characterize how gender beliefs and norms relate to economic behavior across the world. We use the findings to help highlight some potential entry points for policy to address gender norms.

Women’s economic decisions may be constrained by a range of social norms that influence the types of roles and responsibilities that are acceptable for men and women and uphold widely shared conceptions of masculinity and femininity (Bicchieri, 2016; Marcus and Harper, 2014). Akerlof and Kranton (2000, 2002, 2010) translated theories of social identity into an economics framework and propose that social identity influences economic outcomes because deviating from the prescribed behavior is inherently costly. Norms (often subconsciously) encourage behaviors that are socially valued and discourage behaviors that elicit social sanctions and stigma (Bernhardt et al., 2018). Gender norms are likely to significantly constrain women’s choices about whether and which types of work to pursue, and equally may prevent men from engaging in care and domestic chore activities.

Our interest in this paper is to contribute to a growing literature that examines societal norms as a barrier to female labor market outcomes (e.g. Bernhardt et al. (2018); Bursztyn and Yang (2022); Giuliano (2021); Jayachandran (2021)). While there is general acceptance that gender norms play a role in explaining gender differences in labor market outcomes, there is currently limited empirical evidence on the relationship between norms and economic outcomes (Field et al., 2021). Existing research has been concentrated in specific regions and countries, and persistent gaps in obtaining gender-disaggregated data have prevented research at scale. Studies also have relied on relatively small sample sizes and the majority of research has focused on how aggregate, country-level attitudes relate to rates of female labor supply. More recently studies have emphasized the importance of complement-

> 1 Gender norms, a subset of social norms, are defined as shared expectations about how women and men should behave in a particular social group or culture.

> 2For example, at the World Bank social norms are not an operational focus and are often simply considered the “enabling environment” under which capital- or skills-related programs operate.

2
||**_Table10: A_**|**_ccesstoimprovedsani_**|**_tationservices_**||
|---|---|---|---|---|
||**(1)**<br>**Withoutaccessto**<br>**sanitation1990**|**(2)**<br>**Withoutaccessto**<br>**sanitation2000**|**(3)**<br>**Averageannual rateof**<br>**change1990-2000(%)**|**(4)**<br>**Averageannualrateof**<br>**changenecessaryto**<br>**achievegoal(%)**<br>**(2000-2015)**|
|All**developing**countries|56|48|-1.5|-3.6|
|EastAsiaandPacific|62|53|-1.6|-3.6|
|**Europe**andCentralAsia|**..**|._|_..|_..||
|LatinAmericanand<br>Caribbean|28|22|-2.4|-3.0|
|MiddleEastandNorth<br>Africa|22|17|-2.6|-2.9|
|South Asia|69|64|-0.8|**4.1**|
|Sub-SaharanAfnica|45|45|0.0|-4.6|

Source: WHO

# IV. **Development progress and the policy environment**

Why have some countries done better than others in working toward the development goals and what implications does this have for how best to use development assistance to reach the goals? Recent empirical studies have shown that the _policies_ countries follow are important determinants both of economic growth<sup>5</sup> and of whether the poor share in the benefits of this growth6. Graph 1 below, from Collier and Dollar (2001), shows that countries with better economic policy saw faster increases in the growth of income of the poor in the 1990s. These countries also saw greater progress in the human development goals, such as primary education and reductions in infant mortality. Uganda is an example of a poor country that has made significant improvements in the policy framework, including macroeconomic and structural policies, public sector management and social inclusion, and which has registered substantial progress in most of the international goals (see Box 1).

> 5 Studies linking good policies with growth include Burnside and Dollar (2000), Hansen and Tarp (2000) and Collier and Dollar (2000).

> **6** Collier and Dollar (2001) link the quality of economic policy with the growth of incomes of the poorest quintile in 80 countries and find a positive correlation.

13
employment and growth, and capital formation has not accelerated. Trade surpluses have not been typical for most SSA countries, except in the resource-rich oil economies such as Angola and Equatorial Guinea. While there have been differences across countries, in Africa, the low savings rates have led to a greater dependence on overseas capital and aid, and investment rates have averaged below 20 percent of GDP. By many measures, this has resulted in an increasing debt overhang in the African context and the rise of debt service payments (partially corrected with HIPC). This is in marked contrast to China, which began its reforms with low levels of international debt. Finally, conflict and spillover from neighboring conflict have also played a role in diminish SSA aggregate growth rates and leading to greater volatility.

One interesting phenomenon when comparing the two is recognizing that both China and SSA have very high levels of inequality. If Sub-Saharan Africa were a single country, large national inequalities would just become large provincial inequalities. The gap in per capita income between the richest country in Africa – South Africa – and the poorest – Somalia – is about 20 to 1 in 2007. Generally, richer countries like Ghana and Nigeria are coastal, while poorer countries such as Niger and Central African Republic are landlocked, similar to provinces in China. From an analytical perspective, the standard deviation of a country’s GDP per capita is much lower in Africa than in Asia or Latin America, but the standard deviation of country’s Gini’s is much more than in the other two continents (Milanovic, 2003). In other words, compared to China and other Asia countries, there is less variation in Africa in GDP per capita but more variation in inequality between and within countries. Also, resource endowments in Africa have strong influence on economic destiny as arid and difficult climates hinder economic growth in many landlocked countries, particularly in the Sahara and the Sahel.

Similarly, interprovincial disparities and interregional differences in China are significant, especially after the onset of reforms. In this regard, China provides a laboratory for examining the relationships between growth and inequality as well as the dynamics between the center and the provinces. Chinese statistics show that the differential in per capita GDP between 8
# **Recurrent Climatic Shocks and Humanitarian Aid: Impacts on Livelihood Outcomes in Malawi**

Nancy McCarthy<sup>†</sup> , Talip Kilic<sup>‡</sup> , Joshua Brubaker<sup>#</sup> , Alejandro de la Fuente<sup>*</sup> , and Siobhan Murray<sup>^ 1</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _May 2021._ This version is updated on _June 2021._ To obtain the originally published version, please email prwp@worldbank.org.

**JEL Codes:** D60, I38, Q12, Q54.

**Keywords:** Droughts, Floods, Humanitarian Aid, Smallholder Agriculture, Farm Production, Household Consumption, Malawi, Sub-Saharan Africa.

1† ‡ Principal Researcher, LEAD Analytics, Washington, DC, <u>nmccarthy@leadanalyticsinc.com.</u> Senior Economist, Development Data Group, World Bank, Washington, DC, tkilic@worldbank.org.<sup>#</sup> Research Analyst, LEAD Analytics, Washington, DC, <u>brubaker.josh@gmail.com.</u><sup>*</sup> Senior Economist, Poverty and Equity Global Practice, World Bank, Washington, DC, <u>adelafuente@worldbank.org.</u><sup>^</sup> Technical Specialist, Development Data Group, World Bank, Washington, DC, <u>smurray@worldbank.org. The authors would like to thank (a) Carlo Azzarri, Maribeth Black, Nicole</u> Carn, Kathy Derore, Lazarus Gonani, Leslie Lipper, and Osborne Sibande for their comments on the earlier drafts of this paper, and (b) the Malawi National Statistical Office (NSO) management and field staff that worked on the 2013 and 2016 rounds of the Integrated Household Panel Survey (IHPS) and the 2015 Flood Impact Assessment Survey. The funding for the research was provided by the World Bank Research Support Budget-funded project “Evaluating the Negative Impacts of Extreme Weather on Household Agricultural Production and Consumption in Malawi.”
Policy Research Working Paper 10141

# **Abstract**

Measurement of food access typically relies on a consensus of different indicators. However, there is a growing list of surveys in which the Food Insecurity Experience Scale is one of the few food access indicators captured, likely because it is an official measure for tracking progress toward the Sustainable Development Goal of zero hunger. This paper uses a nationally representative, multipurpose household survey conducted in Nigeria to investigate the validity of the Food Insecurity Experience Scale. It compares the Food Insecurity Experience Scale to monetary poverty and a widely used food access metric that has been more extensively validated, the Food Consumption Score. Although it is possible for food access metrics to be poorly aligned and capture different dimensions of poor food access, empirically supported assumptions in standard consumption models result in many dimensions of poor food access being concentrated among the poorest segments of the population. However, the paper demonstrates that the Food Insecurity Experience Scale does not appear to correctly identify the population with poor food access—it finds little difference in the share with poor food access among poor and nonpoor Nigerians. Moreover, even the very richest and very poorest households have a similar prevalence of poor food access, according to the Food Insecurity Experience Scale. These patterns are in stark contrast to the Food Consumption Score, which suggests that food access is significantly lower for poorer Nigerians. Combined, the results demonstrate the importance of measuring food access with more than one indicator, and they call into question the notion of using the Food Insecurity Experience Scale alone, despite the measure being a key Sustainable Development Goal food security indicator.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jlain@ worldbank.org, standon3@worldbank.org, and tvishwanath@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
relatively little difference across turnover bands.<sup>6</sup> It also confirmed that tax compliance costs in South Africa, as in most other countries, are regressive and often burdensome for the smallest firms. The second report focused on differences in tax compliance costs between the nine provinces of South Africa.

The other two surveys listed above were both co-financed by USAID and carried out by Citizen Surveys of South Africa.<sup>7</sup> The direct survey of SMMEs, based on a representative sample of almost 1000 SMMEs registered with SARS (and stratified based on turnover quintiles between R70,000 and R14 million in the SARS database for the year 2005), also confirmed the regressive pattern of tax compliance costs and broadly confirmed the costs of outsourcing. It showed that SMMEs devote in the range of 150 - 260 hours per year on general accounting and tax compliance.<sup>8</sup> This paper utilizes the survey of formal SMMEs, and analyses their reported practices of outsourcing tax preparation to professional tax practitioners.

# **Brief overview of the survey**

The fieldwork for the Tax Compliance Cost Survey of Formal SMMEs in South Africa was completed during the second half of 2007. Interviews were conducted over the phone. This effort has resulted in a nationally representative sample of 998 SMMEs registered with the South African Revenue Service (SARS) from all provinces of the country, all sectors of the economy and turnover groups from just over 70,000 rand to 14 million rand of reported turnover in 2005. The latter group proved to be rather upward moving, that is why the final sample has about 10% of respondents with turnover over 14 million rand. To ensure the maximum accuracy of data analysis a weighting factor was also computed ensuring fine tuning of sample characteristics.

Questionnaires were developed through a joint effort of the National Treasury, SARS, FIAS, the Public Sector Governance Group of the World Bank, and the contractors. Questionnaires were tested and adapted before the main fieldwork.

# **Research goal**

This paper is aimed at providing a more detailed analysis of patterns and practices of tax compliance outsourcing followed by South African SMMEs. Specifically, we attempt to provide a more detailed analysis about which SMMEs do or do not outsource, what tasks are outsourced, why they are outsourced, the extent to which the use of IT may substitute

> 6 These tax compliance costs were clearly separated by the tax practitioners from the cost for general accounting services, which averaged about R12,000 per year for SMMEs who outsourced such work. 7 USAID (2008), “Formal SMME Tax compliance Survey Report: Prepared for National Treasury Republic of South Africa”, available at http://www.fias.net

> 8 SMME respondents found it difficult to separate tax compliance from general accounting; those who provided an answer believed it was about half and half.

3
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# **Table 1: Intergenerational Persistence and Coresident Sample Bias:  Bangladesh**

# **(All Children)**

||**Intergene**|**rational Regress**<br>**(IGRC)**|**ion Coefficients**<br>Test of|**Intergene**|**rational Correl**|**ations(IGC)**<br>Test of|
|---|---|---|---|---|---|---|
||**Full**|**Co-resident**|Equality<sup>2</sup>)|**Full**|**Co-resident**|Equality<sup>2</sup>)|
|**Father's Education**|**0.55*****|**0.42*****||**0.51*****|**0.46*****||
|(Standard Error)|(0.0149)|(0.0134)|91.30|(0.0138)|(0.0146)|9.16|
|Observations|14,017|5,599||14,017|5,599||
|**Mother's Education**|**0.86*****|**0.64*****||**0.47*****|**0.44*****||
|(Standard Error)|(0.0245)|(0.0200)|140.23|(0.0134)|(0.0138)|11.01|
|Observations|14,527|5,523||14,527|5,523||
|**Parent's Education**|||||||
|**(average)**|**0.73*****|**0.59*****||**0.52*****|**0.48*****||
|(Standard Error)|(0.0172)|(0.0167)|132.83|(0.0123)|(0.0136)|22.55|
|Observations|18,505|5,806||18,505|5,806||
|**Biases**||**Bias (IGRC)**|Co-residency<br>Rate||**Bias (IGC)**||
|Father's Education||**31%**|40%||**11%**||
|Mother's Education||**34%**|38%||**7%**||
|Parent's Education(average)||**24%**|31%||**8%**||

NOTES: (1) * significant at 10%; ** significant at 5%; *** significant at 1% (2) The Bias is defined as [(Estimate from Full Sample – Estimate from Coresident Sample) *100] / Estimate from Coresident Sample
do not see any evidence of a positive impact when we evaluate the last community group. Partial exposure, net of time trends, remains negative and insignificant.

# **7. Discussion**

In order to put our results in context, we compare the estimates to those found in previous studies. (See Appendix 1 for a more detailed explanation of the previous literature.) As mentioned before, the Oportunidades rural evaluation sample covers localities incorporated in 1998. Therefore, we can compare our results for the first group of rural communities to the existing evidence in the rural areas. Experimental evidence suggests that children 12 to 36 months at baseline were about 1 cm taller after one year in the program. Analyses that evaluate one additional year of exposure after both treatment and control groups were in the program, only find positive effects on children living in more disadvantaged households (1 to 1.5 cm). We choose to convert our results for a 2 year-old boy, as it lies in between the 12 to 36 month range of previous estimates. Our baseline estimated effect of 0.88 standard deviations translates into 2.68 cm. Our second specification shows estimates in the range of 0.18 to 0.28 standard deviations. A middle point, 0.23 sd., translates into 0.7 cm. Given that these estimates represent an average additional exposure of two years, under the assumption of linear effects we get an estimated impact between 0.35 cm and 1.34 cm for an additional year. As we see, this range is consistent with previous evidence.

Even though our results cover a wide range of values, our preferred specification for this locality group is the one that uses all children and all locality groups in the estimation. By using all locality groups we can control for nation-wide time trends, and by including all children we are maximizing the sample of children in control communities without going too far away from the target population in community-group 1, as participation rates are very high and variation in socio30
for electricity generation. Eskom’s power stations were designed to burn low-grade coal which in 2004 was priced at R55 per ton as compared to the higher grade exported coal whose price varied between R200 and R400 per ton during the same year.<sup>14</sup> While this reliance on low-quality coal enhanced South Africa’s export potential--in 2002, South Africa was the world’s third largest net coal exporter (73.7 mmst)--it also created serious environmental challenges. As a result, in recent years Eskom has sought to diversify into other forms of energy and renewable energy resources. Still, the biggest portion of its plant mix consists of coal fired base load power stations)—11 operational stations with a total capacity of 32,066 MW. Two other coal fired stations—Grootvlei and Komti—with a total installed capacity of 2,200 MW have been mothballed, but are now being returned to service. Moreover, the Camden Power Station is in the process of re-commissioning two of its production units which would add 1,600 MW of capacity to the system.

**<u>Table 1  Eskom’s plant mix</u>**

|**Operational coal fired**<br>**stations**<br>**Mothballed coal fired**<br>**stations**|Arnot: 2 100MW<br>Duvha: 3 600MW<br>Hendrina: 2 000MW<br>Kendal: 4 116MW<br>Kriel: 3 000MW<br>Lethabo: 3 708MW<br>Majuba: 4 110MW<br>Matimba: 3 990MW<br>Matla: 3 600MW<br>Tutuka: 3 654MW<br>Camden: 1 600MW<br>Grootvlei: 1 200MW<br>Komati: 1 000MW|Middelburg, Mpumalanga<br>Witbank, Mpumalanga<br>Hendrina, Mpumalanga<br>Witbank, Mpumalanga<br>Kriel, Mpumalanga<br>Sasolburg, Free State<br>Volksrust, Mpumalanga<br>Ellisras, Northern Province<br>Kriel, Mpumalanga<br>Standerton, Mpumalanga<br>Ermelo, Mpumalanga<br>Balfour, Mpumalanga<br>Middelburg, Mpumalanga|
|---|---|---|
|**Nuclear station**|Koeberg: 1 840MW|Melkbosstrand, Western Cape|
|**Conventional hydro**<br>**stations – Orange River**|Gariep: 360 MW<br>Vanderkloof: 240 MW|Norvalspont, Free State<br>Petrusville, Northern Cape|
|**Pumped storage**<br>**schemes**|Drakensberg: 1000 MW<br>Palmiet: 400 MW|Bergville, KawZulu Natal<br>Grabouw, Western Cape|
|**Gas fired stations**|Acacia: 171MW<br>Port Rex: 171MW|Cape Town, Western Cape<br>East London, Eastern Cape|
|**Total Installed Capacity:**|**41,860 MW (38,060 MW net)**||

_Source_ : Eskom Generon Communication GFS 0004 (2005).

Eskom operates Africa’s only nuclear power station, Koeberg, with installed capacity of 1,840 MW and located near the major load center of Cape Town, at the opposite end

> 14 Eskom (2005).  Engineering News: 14 June 2005.

12
Financial Regulation and Development billion birr, and the seventh bank, Construction and Business Bank of Ethiopia (CBB), with assets below four billion birr.

Therefore, we set the hypothesis that large banks are also endowed with lower unit cost (a better technology) than smaller banks: thus, larger banks match the _ηL_ case and smaller banks match the _ηH_ case. For this reason, given that the largest six banks are more than twice as large as the remaining eight, we classify these banks as “more efficient” (hence presenting a lower cost of branch opening, _ηL_ ) and we define a dummy variable “Big Bank” taking unit value for all of these. The remaining are categorized as “less efficient” (embedding the parameter _ηH_ ).<sup>10</sup> Once both the time-series and cross-sectional variation is clear, we present the following tests of our proposition.

A. Main Results. In this section we verify the predictions of Proposition 1 and 2 on the following databases.

1. Balance sheet data. Using monthly data, we verify that safe asset purchases increase after the policy, that new deposits are collected in old branches, and loan volume also increases (Section 2.2.1).

2. Branch map. Using monthly data, we give evidence that branch installation increases more markedly after the policy and more cities see their first branch installed after the policy (Section 2.2.2).

3. Annual reports. Using yearly data, we show that bank profitability does not decline in absolute value and actually increases for larger banks. We also show that the number of employees grows much more after the policy, as banks start to expand further (Section 2.2.3).

B. Robustness Checks. In Section 2.3 we explore a variety of factors which might confound our estimates and verify the soundness of our results.

# **2.2 Main Results**

## **2.2.1 Balance Sheet Evidence**

The policy change creates a large exogenous variation in the aggregate _s_<sup>_R_</sup> , and from the point of view of the theoretical model, this leads to more deposits, lending, and safe assets. Because private banks are equally affected by the policy, but respond differentially based on their parameter _η_ , we can produce a variety of tests to study Propositions 1 and 2 empirically. The following two are performed and presented.

> 10In Appendix D, we provide a direct test of our hypothesis and show that “big banks” are not just larger, but also present 40% lower administrative costs over assets and 45% lower administrative costs over personnel. This result, though not a comprehensive test of a variation due to _η_ , provides some evidence to support our identification.

18