rafmacalaba/datause-encoder-data huggingface dataset:
Married women need permission to initiate legal proceedings First available year of data: 1960-1965 <!-- Start of picture text --> IBRD 40366=5 UNCODIFIED - CUSTOMARY LAW a : a<br>endorsementpeeor acceptance of math bemdories. [__] noon<br><!-- End of picture text --> Married women need permission to initiate legal proceedings Latest available year of data: 2010 <!-- Start of picture text --> IBRD 40367=5 UNCODIFIED - CUSTOMARY LAW a ‘ a<br>endorsementpeeor acceptance of math bemdories. [__] noon<br><!-- End of picture text -->
In most countries a gradient can similarly be observed for expenditures, although it is less pronounced than for the asset index. The mean of the household per capita expenditures for each of the steps of the welfare ladder is presented in Figure A3 (Appendix 3).

Regardless of the correspondence between subjective relative income and rankings based on objective welfare, a uniform distribution of answers to the relative income distribution would be expected in theory. If there was no systematic error in the way households placed themselves on the ten-step scale of relative income, the same proportion in every country (10 percent) should be found on each decile. Figure 2 shows the raw distribution of the responses to the economic ladder question by country. It can be seen that the distribution of subjective welfare positions is concentrated in the middle, with 60 percent of households placing themselves in the fourth, fifth or sixth deciles, and in many countries skewed to the left.

**Figure 2:** Distribution of economic ladder question responses, by country

<!-- Start of picture text --> Albania Armenia Azerbaijan Belarus BiH Bulgaria<br>Croatia Czech Estonia FYROM France Georgia<br>Germany Hungary Italy Kazakhstan Kosovo Kyrgyzstan<br>Latvia Lithuania Moldova Mongolia Montenegro Poland<br>Romania Russia Serbia Slovakia Slovenia Sweden<br>0 5 10<br>Tajikistan Turkey UK Ukraine Uzbekistan<br>0 5 10 0 5 10 0 5 10 0 5 10 0 5 10<br>Subjective household relative income position<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>Density 1<br>.5<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> _Source: LiTS II, 2010._ There could be many factors explaining differences between households’ subjective views on their relative position in the economic ladder and their objective position as measured by their actual relative income or consumption. Aside from measurement error in our household consumption aggregate, we may have miscalculated “actual” relative income for example because we do not account for cost-of-living differences or because of errors in constructing 7
younger and female workers are more likely to lose their job. However, exposure to unemployment is not concentrated in particular income groups, as these individuals are found across the whole welfare distribution (see Figure 5).

_Figure 5: Unemployed workers by decile of the welfare distribution_ <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Household income decile<br>population<br>Newly unemployed as a share of total<br><!-- End of picture text --> This result suggests that the ultimate distributive impact of the unemployment increase would depend heavily on the social protection programs available to those who lost their job and, of course, to the whole population. However, it is unlikely that these programs and, more generally, public transfers and expenditure will not suffer from the shrinking oil royalties following the drop in oil price. The oil price shock, indeed, puts government expenditure under pressure and its reduction is likely to have affected differently individuals located in different parts of the welfare distribution.

Taking advantage of the availability of data on public transfers in a different survey, the RLMS (Russian Longitudinal Monitoring Survey), our second simulation estimates the effect of a 10 percent cut in public transfers. The impact on poverty and shared prosperity depends on the incidence of the transfers along the welfare distribution: if poor households depend relatively more than the richer ones on public transfers, the final effect of the oil shock could be reversed once the cut of transfers is taken into account.

Most of the beneficiaries of public transfers<sup>21</sup> and pensions are indeed located in the first decile of the consumption distribution (see Figure 6) while the share is much smaller for households located in the top 30 percent of the distribution.

> 21 Transfers are defined as the sum of children, rent, utilities, fuel and unemployment benefits.

20
**Figure 10: Due to the fast growth of cheap capital, skilled worker supply cannot keep up in many cases and their wages tends to rise.**

<!-- Start of picture text --> Cumulative change in skilled wages 2015-50 (percent)<br>Baseline Faster productivity growth<br>Education convergence Mobilized savings<br>ER convergence ER convergence + gender parity<br>All interventions<br>200<br>150<br>100<br>50<br>0<br>Botswana Namibia South Africa Lesotho and<br>Swaziland<br><!-- End of picture text --> Source: Authors’ simulation results 21
original baseline household to form their own household. In the second wave of data collection, the number of panel households increases to 3,924; and in the third wave, to 5,010 households. The number of observations for individuals went up from 16,709 in the baseline to 20,599 in the second wave and 25,412 in the third. The attrition rate for individuals was 7.5 percent.

The NPS follows the same households and all household members age 15 or older (excluding live-in servants). We use a balanced sample from the three survey rounds. The full panel consists of 3,082 households and 14,552 individuals, but the sample size for this analysis varies with the outcome variable. For the highest grade completed, the panel is 4,112 children (from 2,241 households) who have once attended school and were aged 6-18 in the first wave. Similarly, for the PSLE variable, we used a panel of 3,101 households having at least one PSLE-eligible child aged 6-18, and for the FIVE variable, a panel of 2,696 households with at least one FIVE-eligible member aged 16-24.

# **4. Results**

# _4.1. Summary statistics_

Tables 2–4 present summary statistics. All point estimates are weighted to allow inferences to the population of either individuals or households, depending on the variable. Table 2 presents demographic characteristics of the sample in all three waves, disaggregated by individual characteristics, household characteristics, and characteristics of the household head. Tanzania has a very young population: in the NPS the average age is 21 years in the baseline, 23 in the second wave, and 24 in the third. For those who have attended school, the average age at start of school is 8, which is higher than the Sub-Saharan Africa average of 7 years. In all three waves, average household size is about 5, and about half of the individuals are children aged 6-18 years.

Parental and household head characteristics are important for the analysis because the effect of assets on child education mostly operates through parental decisions about child labor, schooling, and intra-household resource allocation. Parental education is measured by ‘maximum parent’s 16
# **Implementing Energy Subsidy Reforms An overview of the key issues***

## Maria Vagliasindi**

### **JEL: H2, H30, H31, Q4**

### **Energy and Mining (EM) Sector Board**

* This paper is based on a broader study (Vagliasindi, 2012) aimed at providing the emerging lessons from a representative sample of case studies that could help policy makers to address implementation challenges, including overcoming political economy and affordability constraints, by looking at complementary instruments to compensate vulnerable groups for energy-price increases. My special thanks go to Vivien Foster for her advice and comments. I am most grateful to Nicolas Jost, Evgenia Shumilkina for their research assistance and Ani Balabanyan, Ulrich Bartsch, Husam Beides, Benu Bidani, Kwawu Gaba, Franz Gerner, Mudassar Imran, Ashish Khanna, Migara Jayawardena, Todd Johnson, Eduardo Ley, Rinku Murgai, Kari Nyman, Dejan Ostojic, Silvia Pariente-David, Giovanna Prennushi, Caterina Ruggeri, Jon Strand, Gary Stuggins, Mike Toman, Vlado Vucetic, Ruslan Yemtsov and Ariel Yepes, most helpful comments and suggestions.

**Maria Vagliasindi, Lead Economist, Energy Unit, Sustainable Energy Department, World Bank
almost half (46%) of the world’s urban poor by the $1 a day line are found in South Asia, and another third (34%) are found in SSA; these proportions fall appreciably when one focuses on the $2 a day line, for which 39% and 22% of the urban poor are found in South Asia and SSA respectively.

In the aggregate and in most regions, we find that poverty incidence fell in both urban and rural sectors over the period as a whole (though with greater progress against rural poverty in the aggregate). LAC and SSA are exceptions. There rising urban poverty came with falling rural poverty. The (poverty-reducing) population shift and rural components for LAC and SSA were offset by the (poverty-increasing) urban component.

While the urban poverty rate for the developing world as a whole was relatively stagnant over time for $1 a day, this is not what we find in all regions. Indeed, the urban poverty rate is falling relative to the national rate in both East Asia and ECA, attenuating the urbanization of poverty; indeed, in ECA the urban share of the poor is actually falling over time — a “ruralization” of poverty — even while the urban share of the total population has risen, though only slightly. (There is the hint of a ruralization of $2 a day poverty in East Asia from the late 1990s, again due to China.) The ruralization of poverty in ECA is not surprising, as it is consistent with other evidence suggesting that the economic transition process in this region has favored urban areas over rural areas (World Bank, 2005). This has also been the case in China since the mid-1990s (Ravallion and Chen, 2007).

South Asia shows no trend in either direction in the urban poverty rate relative to the national rate, and the region has also had a relatively low overall urbanization rate, with little sign of a trend increase in the urban share of the poor. The population shift component of poverty reduction is also relatively less important in South Asia.

The urban poverty rate relative to the national rate has shown no clear trend in SSA, although rapid urbanization of the population as a whole has meant that a rising share of the poor are living in urban areas.

15
<!-- Start of picture text --> Figure 5a: Components of Adequate Food Security<br>Components of Adequate Food<br>86 86<br>80 81<br>78<br>72<br>69<br>67 66 67 66<br>55 54<br>50 51 49<br>45<br>40<br>37<br>33<br>31 30 30<br>26<br>22<br>13<br>BGD (HK, '11) BOL ('08) IDN (RKD, '10) NPL ('11) ZWE ('10)<br>BGD (IFPRI, '11) ETH ('11) KHM ('10) PER ('12)<br>Dietary diversity > 5 months Exclusively breastfed < 6 months<br>Meal frequency Food security<br>Source: Author estimates.<br>Note: (1) Dietary diversity: 4 out of 7 food groups. In Indonesia based on household (not child specific) dietary diversity;<br>(2) Meal frequency depends on age<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text --> Applying stricter standards by requiring simultaneous access to adequate levels of each component of food security, less than one‐half of the young children in the study countries appear to have access to adequate food security. In fact in six out of the 9 surveys less than one‐third of the children had access to adequate food (Figure 5b). In Ethiopia, with the lowest access to adequate food security, only 12 percent of the children met the criteria for adequate food security. As figure 5a above clearly indicates, this is primarily due to the very low prevalence of adequate dietary diversity (13 percent). Bolivia and Indonesia were the only countries where more than half of the children had access to adequate food security, at 51 and 68 percent, respectively. However, in Indonesia the dietary diversity was not measured for each child, and the measure is based exclusively on the dietary diversity at the household level so it is not comparable with the other studies. Depending on how different the child’s diet is from the general diet of the family, the true prevalence of adequate food security may be much smaller.

In most countries, the improvements in access to adequate food security (as defined above) have been modest (Cambodia, Peru and Zimbabwe) to none (Ethiopia, Nepal) in the 5 to 10 years between surveys. The decrease in Bolivia in access to adequate food security is due to the change in the definition. In 2003 no information was collected on meal frequencies and thus the minimum acceptable diet variable could not be constructed. In 2008 the information was available and it was included as part of the food adequacy measure, thus making the 2008 definition a more comprehensive one than the 2003 measure. Furthermore, based on the Helen Keller data, in Bangladesh the access to adequate food halved between 2010 and 2011. Comparing the second 23
- Owoo, N. S., Oduro, A., and Ackah, C. (2019). Exploring Firm Performance and Growth among Own-Account and Micro Enterprises in Ghana. _International Journal of Entrepreneurship and Small Business_ , 38 (3), 294- 311

- Ruderman, M. N., Ohlott, P. J., Panzer, K., and King, S. N. (2002). Benefits for multiple roles for managerial women. _Academy of Management Journal_ , 45(2), 369–386.

- Sikweyiya, Y., Addo-Lartey, A.A., Alangea, D.O. et al. (2020). Patriarchy and genderinequitable attitudes as drivers of intimate partner violence against women in the central -

- region of Ghana. _BMC Public Health,_ 20, 682 (2020). <u>https://doi.org/10.1186/s12889 020-08825-z</u>

- Unger, J. M., Rauch, A., Frese, M., and Rosenbusch, N. (2009). Human capital and entrepreneurial success: A meta-analytical review. _Journal of Business Venturing,_ 26, (3) 341–358. https://doi.org/10.1016/j.jbusvent.2009.09.004.

- UNIDO (2001). Women entrepreneurship development in selected African countries. Working Paper No. 7, United Nations Industrial Development Organization, Vienna.

- Wang, X., Zhu, X., Deng, S. (2020). Female entrepreneurs’ gender roles, social capital, and willingness to choose external financing. Asian Business and Management, 21, 432–457 <u>https://doi.org/10.1057/s41291-020-00131-1</u>

- Welter, F., and Smallbone, D. (2011). The embeddedness of women’s entrepreneurship in a transition context. In E. G. Candida Brush, Colette Henry, Anne de Bruin (Eds.), Women entrepreneurs and the global environment for growth: A research perspective (pp. 96– 117). Cheltenham: Edward Elgar.

- Yadav, V. and Unni, J. (2016). Women entrepreneurship: research review and future directions”, _Journal of Global Entrepreneurship Research_ , 6(12).

24
The results described above highlight the importance of risk-weights and quality of capital and the effect they may have on bank risk. As capital essentially preserves solvency by absorbing liquidity, information and economic shocks, the quality of capital determines how effective it performs as a buffer. Because lower quality capital is made up of components that are more opaque and subject to information asymmetries, lower quality capital can be severely undervalued during times of distress and may not be able to absorb shocks. Risk weights on banks’ assets are also an important element of risk-based capital ratios. Banks can meet capital requirements by either increasing capital or decreasing risk-weighted assets. Since the current regulations provide substantial discretion to banks in determining risk-weights, a key concern has been that individual banks may try to minimize capital requirements by underestimating the risks of assets on their balance sheets.<sup>23</sup> Consistent with the notion that there is significant discretion in the computation of regulatory capital ratios, Demirguc-Kunt et al. (2013) find that stock returns of banks during the financial crisis were more sensitive to simple leverage ratios using common equity than to regulatory risk-based ratios.

To test these tenets more formally, we examine how bank risk is related to the quality of capital and risk-weights using bank level data. We obtain bank level financial information by merging the now discontinued Bankscope database (1999-2014) with the Bank Focus (2015-2017) database. We limit our sample to commercial banks with at least $10 million USD in assets. Our sample includes more than 20,000 banks in 158 countries. We use the following regression models:

> 23 Jones (2000) discusses techniques that banks can use for regulatory capital arbitrage and provides evidence on the magnitude of these activities in the Unites States.  Le Leslé and Avramova (2012) provide evidence of heterogeneity with respect to the calculation of risk-weights.  Recent growth in financial innovation and financial engineering may have also made it easier for financial institutions to manipulate regulatory risk measures. Acharya, Schnabl, and Suarez (2013) show that banks have used various forms of securitization to reduce bank capital requirements and that these actions have led to greater concentration of risk.

29
Pathfinder survey<sup>13</sup> , the final sample consists of 109 communities (91 rural and 18 urban) and 2,700 women, of which just over 2,000 remain after excluding never married and never partnered women.

# **3.1 Estimation Strategy**

Our approach is to first estimate the determinants of the decision on whether to place a program _P_ in area _k_ and then to estimate the effect of the program on the individual outcome _yi_ (fertility). The equations are: where _X k_ is a vector of exogenous variables that are area specific, and _Zk_ is a vector of area specific exogenous variables that affect program placement but do not affect the individual fertility decision. Individual characteristics are captured by _X i_ . Whether a program is available in the area, _Pk_ , is the main variable of interest and _β_ 3 measures the program’s impact on the outcome of interest. The main outcome of interest is the number of children ever born. In addition, to probe into the channels through which family planning affects fertility and to distinguish its effect from the presence of the health facilities through which the family planning services are provided, we estimate the effects of family planning on various measures of child mortality, recent birth or pregnancy, and whether last birth or pregnancy was wanted. Unfortunately, lack of birth histories in the data means that we cannot examine how the timing of births responds to family planning.

Using a modified two-stage method, _β_ 3 can be estimated under relatively relaxed conditions (Wooldridge 2002, Chapter 18). The first stage estimates the determinants of the placement decision using a Probit model and the fitted probabilities of having a program are calculated. In the second stage, the individual decision equation is estimated by IV, using the fitted probabilities from the first stage for _Pk_ , **Xk** and **Xi** as instruments. Identification comes from _Zk_ in the first stage. An attractive feature of this approach is that the results are robust even if the placement equation is not correctly specified (Wooldridge 2002, p. 623).

In addition to the instrumental variable results, for comparison we also present OLS results, where

> 13Because of incompleteness in the WHFC survey and the census, another 25 communities could not be merged to

6
<!-- Start of picture text --> California schools<br>$4000 fe Zo SS Os<br>= beet or ee ore<br>pe es 5<br>Step 0 Oo Caliper.<br><!-- End of picture text --> <!-- Start of picture text --> OECD health systerns<br>qooo -|..- : eee re PT,<br>B8 gon | GANSSThe SSSSO<br>é AOD pe SoG a :<br>20 ne ae ge PE<br>5 : 10<br>5<br>Step og Caliper<br><!-- End of picture text -->

<!-- Start of picture text --> Education - PISA<br>g 2800 SM SCORE CCMA |<br>E00 ee i re<br>10 an = i” 15<br>Step o 0 Caliper;<br><!-- End of picture text -->

<!-- Start of picture text --> Vietnam hospitals<br>ea CSS a -.<br>@g 180 )---"" GOLaOSS SS |ae<br>2 bet . veh i Prost ' 4<br>10 wee ane eee Meee vey<br>4 a0 ms 4<br>2 2<br>Step o 0 Caliper<br><!-- End of picture text -->
different strata may be matched. To avoid this, rankings need to be rescaled within strata. If possible, the sensitivity of the method to the rescaling should be assessed, for example, by recalculating the decomposition with subsamples of the original data.

This section has outlined the practical issues of applying the ASN methodology to the multidimensional headcount H. We now turn to two different empirical applications to illustrate the performance of the methodology, which highlight the importance of the issues described.

# **4 to cross section data: The case Application repeated of Colombia**

We apply the proposed methodology in the context of the decline in multidimensional poverty observed in Colombia between 2008 and 2012. The Colombian case is interesting for a couple of reasons. Being a middle-income developing country, a large share of Colombians is still deprived in the dimensions considered for cross country calculations of MPI measures. On the other hand, the pace of decline has varied over the and multidimensional declined years. Monetary poverty sharply from 2003 to 2008; while over the last five years, the decline has been less sizable though steady.

## **4.1 Description of the data, the MPI and trends**

We use data from the Colombian Quality of Life Survey for the years 2008, 2010 and 2013. This is the survey that the National Administrative Department of Statistics ( _Departamento Administrativo Nacional de Estad ́ıstica_ , DANE) uses for multidimensional poverty calculations. The survey included around 50,000 households in 2008; while the sample size increased by about 5 percent and 38 percent in the next two rounds, respectively. We were able to replicate the official poverty measures published by DANE closely. For a detailed summary, see Angulo (2010).

The multidimensional poverty index for Colombia includes 15 dimensions grouped into five broad categories: education, childhood and youth, labor, health and standard of living<sup>7</sup> . Each of the categories has a weight of 0.2, which is distributed evenly

> 7This index is broader that the calculations of Alkire and Santos (2010) for Colombia. Their selec-

18