rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4889

# **Abstract**

Increasing adult mortality due to HIV/AIDS in SubSaharan Africa raises considerable concerns about the welfare of surviving children. Studies have found substantial variability across countries in the negative impacts of orphanhood on child health and education. One hypothesis for this variability is the resilience of the extended family network in some countries to care for orphans—networks under increasing pressure by the sheer number of orphans in many settings. Using household survey data from 21 countries in Africa, this study examines trends in orphanhood and living arrangements, and the links between the two. The findings confirm that orphanhood is increasing, although not all countries are experiencing rapid rises. In many countries, there has been a shift toward grandparents taking on increased childcare responsibility—especially where orphan rates are growing rapidly. This suggests some merit to the claim that the extended network is narrowing, focusing on grandparents who are older and may be less able to financially support orphans than working-age adults. However there are also changes in childcare patterns in countries with stable orphan rates or low HIV prevalence. This suggests future work on living arrangements should not exclude low HIV/AIDS prevalence countries, and explanations for changes should include a broader set of factors.

This paper—a product of the Poverty Team and Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to study the well-being of children. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at beegle@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
15 There can also be significant variation in public services within the urban and rural sectors, underscoring the point made above. For example, Selowsky (1979) finds significant variation in educational, health and public utility services among urban areas of different sizes. Meerman ('979) fmds significant variation in many public services among different rural provinces of Malaysia.

# In-Kind Transfers and Informal Markets

The potential for resale of in-kind goods provided by the government exists in both developing and developed countries. However, this potential may be greater in most developing countries due to their larger informnal sectors. To the extent that informal sector resale occurs, the in-kind good provided to one person may confer benefits on several more individuals. For example, sellers of in-kind goods receive the benefit income from the sales net of queuing costs but buyers receive the consumer surplus from the good.1<sup>6</sup> Such transactions will make tracing the benefits of public services particularly difficult, as individuals are unlikely to reveal in household surveys the resale of in-kind goods.

# Government Benefits and the Intrafamily Distribution of Resources

While many studies in developing countries use the household as the unit of analysis, this ignores important effects of government programs on both intrafamily transfers among related households and the intrahousehold distribution of resources. A first set of issues concerns the difficulty of defining what constitutes a family in developing countries. Often the most appropriate resolution of this issue would depend on the culture of the country, but available survey data may not support culturally-relevant family definitions. For example, in Jamnaica, fathers may have children by several women while being married to none of them. Yet the father may provide partial support to the children and the mothers. Identification of such families may not be possible with existing data.

Another set of issues concerns the potential of government benefits to displace the existing pattem of intrafamily transfers.1<sup>7</sup> For exnample, Cox and Jlimenez (1992) estimate that the institution of social security payments in Peru reduced private transfers from young to old by 20 percent.
reserves, which had dwindled to $5 billion prior to the 1991 crisis, was enhanced to about $75 billion by the early 2000s. For most of this period monetary policy was broadly set in sync with domestic considerations.

A second phase can be said to last from 2003-04 to 2007-08, when India experienced a surge in capital flows. The surge reflected prominently in all components of capital inflows—portfolio flows, FDI flows and other flows, Figure 5. Capital inflows accelerated to an average $ 44 billion a year in the five years between 2003 and 2007, compared to an average of 10 billion in three prior years, and at its peak in 2007-08 exceeded $ 100 billion in one year, Figure 6. A sudden stop in 2008-2009, when capital flows declined precipitously to $ 7 billion, then started the third phase, the genesis of which can be traced to the collapse of Lehman Brothers. Capital flows, particularly portfolio and other flows, fluctuated sharply in subsequent years.<sup>12</sup> Another bout of capital outflows and volatility was evident during the “tapering talk” episode in 2013q3.<sup>13</sup> The discussion below is organized around these three phases of the capital flow cycle.

**Figure 6: A Surge and Stop Capital flow Cycle was evident in India starting in the early 2000s**

<!-- Start of picture text --> 120,000<br>Phase 3: Global<br>Financial Crisis<br>100,000 Phase 2: 2003-04-2007-08: and Sudden Stops,<br>Surge in Capital inflows. 2008-09, and 2013-14<br>Inflows peaked at<br>80,000<br>$ 107 bn in 2007<br>60,000<br>Phase I: early 1990s-early 2000s:<br>Steady liberalization<br>40,000 of capital flows, modest<br>pick up in inflows<br>20,000<br>0<br>Mln US $<br>1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14<br><!-- End of picture text --> Source: RBI Handbook of Statistics on the Indian Economy. Capital flows refer to net capital flows.

> 12 Using methodology standard in the literature, using quarterly data we identify 2008q3-2009q2 as the period of a sudden stop. The criteria used is similar to that in Forbes and Warnock (2012b) wherein a sudden stop is identified if inflows fall one standard deviation below the average of previous 20 quarters for at least consecutive two quarters; and if in at least one quarter, flows fall two standard deviations below the prior years’ average.

> 13 However, since this event lasted only for a quarter, it does not qualify as a sudden stop as per the standard criteria used in the literature, which require the reversal of capital flows to last more than a quarter.

11
likely be the first one in a developed country asking questions about sexual orientation and gender identity. In Uruguay, an effort is underway to conduct a census among transgender people.

# **4. Methodological overview: How developmental outcomes for LGBTI people have been measured through surveys**

In the following section, we will discuss four surveys of LGBTI people that have attempted to assess development outcomes: the previously mentioned survey in Nepal, a survey in India; the FRA survey from Europe; and the largest survey of intersex people from Australia. For each survey, we will focus on the methodologies, constraints, and lessons.

## **Nepal**

The Williams Institute and UNDP conducted one of the <u>most recent surveys on sexual orientation, gender identity and/or expression</u> in cooperation with a local Nepalese NGO, Blue Diamond Society.<sup>36</sup> Respondents were asked a set of questions, including open-ended questions, on their sexual orientation, gender identity and/or expression covering the selfidentification, behavioral, and attraction dimensions of all three. A number of survey enumerators from the Blue Diamond Society were recruited, trained and ultimately able to reach 1,178 respondents.<sup>37</sup> Most of the respondents were recruited through local LGBTI networks based on drop-in centers the Blue Diamond Society manages throughout the country. This venue-based snowball convenience sample<sup>38</sup> survey was developed to fit local circumstances. The sampling strategy is a combination of three distinct recruiting methods: Venue-based, snowball, and convenience. Venue-based refers to the drop-in centers that were managed by Blue Diamond Society that served as a primary venue to recruit respondents. The survey furthermore employed a snowballing approach where an initial number of subgroup members serve as ‘seeds’ and recruit further respondents of the subgroup until the required sample size is reached. These ‘seeds’ were selected on a non-probability basis simply because they were conveniently accessible. All three methods are commonly used to survey hard to reach populations but they have important limitations.

> 36 This included a feedback on the methodology, survey tool and data analysis.

> 37 UNDP; Williams Institute. 2014. Surveying Nepal’s Sexual and Gender Minorities: An Inclusive Approach. 38 This sampling strategy is a combination of three distinct recruiting methods: Venue-based refers to the drop-in centers that were managed by Blue Diamond Society; the snowballing is <mark>a nonprobability approach to sample design where respondents help recruite new respondents; and lastly a convenient sample which in this case refers to the intial group of respondents that were interviewed because they could be easily accessed through</mark> Blue Diamond Societies existing networks.

12
diversi...cation for a given ...rm, incorporating the number of products exported by ...rm i to the world market in the initial year of the trade relationship, NProductsci. Transport costs are proxied with bilateral distance between origin country c and destination country j in logs, Distancecj<sup>12</sup> . We also include a dummy variable that equals one if importing and exporting countries share a border, Contiguitycj. Bilateral trade can be fostered by countries’cultural proximity. Similarity in culture can indeed increase the quality of the match between varieties produced in exporting country and tastes of consumers in the destination country. We control for this proximity by introducing two dummies, respectively equal to one if a language is spoken by at least 9% of the population in both countries, Com_languagecj, or if both partners have had a colonial relationship, Colonycj. Appendix 8:E provides summary statistics of the main variables used in our analysis.

Equation (2) is estimated under partial likelihood (Cox, 1972). Since there may be unobserved variation across exporter-sector pairs–to avoid biasing the standard errors downwards–in all tables we report robust standard errors clustered at the exportersub-sector (HS4) level. The coe¢ cients can be interpreted as semi-elasticities, as they measure the percentage point change in the hazard rate as a result of a unit change in the right-hand side variables.

A common feature of survival data is censoring. First, we observe ‡ows in the ...rst year of our sample but do not know how long they have been in existence. Second, we observe ‡ows in the ...nal year of our sample but do no know how long they will continue to exist. The problem of right-censoring is accounted for in the Cox estimation procedures<sup>13</sup> . Left-censoring presents a more serious problem. Given the short time span, our approach is simply to ignore left censoring in our main estimations. As a robustness test, we drop all observations which are left-censored and determine the sensitivity of our results to left-censoring<sup>14</sup> .

# 4 Data

## 4.1 African Firm Data

Our analysis relies on a novel dataset collected within the frame of the Export Survival Project, implemented by the International Trade Department of the World Bank<sup>15</sup> . The dataset combines ...rm level export data collected by customs authorities in ...ve African reporting countries–Ghana, Mali, Malawi, Senegal and Tanzania. The dataset

> 12Distances are calculated as the sum of the distances between the biggest cities of both countries, weighted by the share of the population living in each city.

> 13Stata incudes a dummy variable taking vaue one if the spell is still existing the last year of the sample.

> 14Results not reported, available upon request.

> 15We thank Denisse Pierola and Paul Brenton.

12
In Table 7, we present robustness checks in which we vary the Raven’s score cut-offs used to categorize children as low or high ability.<sup>27</sup> Raven’s scores ranges from 0 to 18. In Table 5, we use the sample mean of 6.05 as the threshold, with lower ability children defined as those with scores between 0 to 6 and higher ability children those with scores between 7 to 18. We reproduce those results in column 3. In the other Table 7 columns, we decrease and increase the ability cut-off to verify that our results do not depend on the chosen cut-off. We find that for low ability children, CCTs consistently outperform UCTs irrespective of the ability threshold used. _5.3 Impacts on Attendance_ In Table 8, we analyze the impact of cash transfers on school attendance rates of all children ages 7-15, unconditional on their enrollment. This is a broad measure of school participation with direct policy relevance that accounts for enrollment and attendance effects and is not confounded by changes in the share of the sample enrolled. We rely on attendance taken from school ledgers collected at each school.<sup>28</sup> For each child, we compute the percentage of school days attended for the entire academic year. Children who are not enrolled receive an attendance rate of zero. The attendance results are consistent with the marginal child hypothesis described for enrollment.<sup>29</sup> CCTs increase school attendance at round 3 for all children and in all subgroups. UCTs increase attendance for non-marginal children (those enrolled at baseline, boys, older children, and higher ability children). For marginal children (those not enrolled at baseline, girls, young children, and low ability children), CCTs significantly outperform UCTs as we can reject the equality of the coefficients, while we cannot reject that equality non-marginal children.

> 27 Results (not shown) using the two alternative empirical specifications lead to similar conclusions. 28 We also collected parental self-reports on attendance for the two weeks prior to the survey and so the results are not directly comparable with the school-based data. Moreover, since some villages were surveyed after the end of the school year, this self-reported measure is potentially less-reliable. Nevertheless, the analysis using parental selfreports of attendance yield results similar and consistent with the Table 8 results.  .

> 29 Similar results using the other empirical specifications are presented in Appendix Table 4.

22
# **4.2 Distributional and Poverty Results for the BaU**

A moderate decrease in poverty between 2001 and 2015 results from micro-simulating the identified key structural trends on the Brazilian household data. Considering the full sample of all households, the headcount poverty ratio (P0) declines by about 6 percentage points (see Table 5). The reduction of the average normalized poverty gap (P1) and the poverty severity index (P2) indicates that those who remain poor move closer to the poverty line.<sup>30</sup> Inequality changes very little, as indicated by the 0.1 decrease in Gini coefficient (or as in the Theil or other inequality indices, not reported).

These average indices indicate that some progress in reducing aggregate poverty and inequality is achieved in a Business as Usual scenario, but these aggregate measures may conceal relevant distributional changes at a more disaggregated level. In fact, reaching stronger poverty reduction may require specific pro-poor policies which often rely, for their successful implementation, on more detailed information about disaggregated distributional effects.

A first obvious way to gather more detailed information is to analyze the poverty and inequality impacts separately for the agricultural and non-agricultural households.

_<u>Table 5: Poverty and inequality in the BaU scenario, by sectors</u>_

||2001<br>level<br>All hous|2001-15<br>change<br>eholds|2001<br>level<br>Non-agric<br>househ|2001-15<br>change<br>ultural<br>olds|2001<br>level<br>Agricul<br>househ|2001-15<br>change<br>tural<br>olds|
|---|---|---|---|---|---|---|
|PC income|314.9|1.5|351.9|1.2|148.3|2.3|
|Gini|58.6|-0.1|57.1|0.6|56.6|-0.7|
|P0|23.6|-5.6|18.6|-3.1|46.2|-13.8|
|P1|9.6|-3.0|7.1|-1.6|21.0|-8.0|
|P2|5.3|-1.8|3.7|-0.9|12.3|-5.2|
|Population %|100||81.8|3.3|18.2|-3.3|
|Contr. to P0|||64.4|8.8|35.6|-8.8|

Source: Authors’ calculations. Note: PC income is per capita income in 2001 R$ and the change is given as annual growth rate. All levels are in percent and changes in percentage points.

A household is classified as “agricultural” when its head or at least two of its members are employed in agriculture. In 2001, according to this classification, agricultural

> 30 A short note on the interpretation of the reported poverty measures: The income-gap ratio, i.e. average income shortfall (of the poor) divided by the poverty line, can be calculated as P1/P0. This ratio is 0.4 for all households in our case, i.e. the perfectly targeted cash transfer needed to lift every poor person out of poverty is 40 percent of the poverty line times the number of the poor. Thus, 0.4 times the percentage point change in P0 (here 2.4) provides a percentage point change benchmark for evaluating the change in P1, as this would be the change in P1 that we would observe had the average income of the poor stayed constant while the headcount declined.

30
benefit rural communities.<sup>11</sup> Prevailing market structures, including market power in downstream segments of the production/value chain may also weaken the link between world and local farm gate prices for farmers, especially in developing countries.<sup>12</sup>

**Figure 2. The Relative Rate of Assistance to Developing-Country Agriculture, %.**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04<br>-10<br>-20<br>DCs incl. K/T HICs incl. ECA<br><!-- End of picture text --> _Source_ : Anderson (2009) and www.worldbank.org/agdistortions Within developing country agriculture, there is a sharp difference in the rates of protection provided to import-competing agriculture and to export-oriented agriculture, as shown in Figure 3. The almost complete elimination of taxation of agriculture has sharply reduced the cost of distortions on export-oriented agriculture in developing-countries. The rise in protection to import-competing agriculture has substantially raised the costs associated with this form of

> 11 See, e.g., Aksoy and Hoekman (2010).

> 12 See for example McCorriston, Sheldon and Sexton (2004), Sheldon (2006), Sexton,. Sheldon, McCorriston and Wang (2007) and Porto, Chauvin and Olarreaga (2011).

11
# 1. **Introduction**

In recent years there has been a surge of interest in the consequences of governance and misgovernance for development. This has been accompanied by a proliferation of data measuring subjective perceptions of various aspects of governance. In this paper, we describe a new governance database containing over 300 such governance measures compiled from a variety of sources.<sup>1</sup> We provide a detailed description of each of these indicators and sources, and construct six aggregate indicators corresponding to six fundamental governance concepts. We conclude with new empirical evidence that governance matters, in the sense that there is a strong causal relationship from good governance to better development outcomes such as higher per capita incomes, lower infant mortality, and higher literacy.

In order to organize our data collection and dissemination efforts, we require a working definition of governance itself. 2 We define governance broadly as the traditions and institutions by which authority in a country is exercised. This includes (1) the process by which governments, are selected, monitored and replaced, (2) the capacity of the govemment to effectively formulate and implement sound policies, and (3) the respect of citizens and the state for the institutions that govern economic and social interactions among them.

The focus of our data collection effort is a set of indicators which measure subjective perceptions regarding the quality of governance in different countries. Our data are drawn from two types of sources: polls of experts, which reflect country ratings produced by commercial risk rating agencies and other organizations, and cross-country surveys of residents carried out by international organizations and other nongovernmental organizations. We do not attempt to compile or present the wide array of available quantitative and descriptive data on cross-country differences in political and social institutions.<sup>3</sup> While these are certainly important determinants of the crossIPending appropriate authorization from original sources, we plan to have the database itself ready for dissemination on the Web late in 1999. 2There does not appear to be a single accepte **d** efinition of govemance. Our-definition of govemance isin part motivated those suggested by the Institute for Governance, IDEA, and the IMF.

> 3 A database of such objective institutional indicatori **s** currently being compiled as a joint effort by Phil Keefer (World Bank), Robert Bates (Harvard University), and David Epstein and Sharyn O'Halloran

1
West Bengal Figure 11a: Annual Percentage Change In Stamp Duty Revenues and Rates Figure 11b: Stamp Duty Revenues and Rates <!-- Start of picture text --> West Bengal<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>-200<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br><!-- End of picture text --> <!-- Start of picture text --> West Bengal<br>40000 25.0%<br>35000<br>30000 20.0%<br>25000 15.0%<br>20000<br>15000 10.0%<br>10000 5.0%<br>5000<br>0 0.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rate (%)<br>Rs. Lakhs<br><!-- End of picture text --> 77
<!-- Start of picture text --> 3000-<br>c<br>= . %E 750- |<br>=<br>_<br>Qo, a ir<br>. 2000 =<br>g<br>g - 7 .<br>o wo<br>2o%4500- **<br>o<br>.<br>= * ao °<br>Ss a Py<br>© 1000- = :<br>3 * ra<br>= ~ 250-<br>3gj<br>2<br>(e) ai . *<br>. Oo2 e.<br>*<br>O- O-<br>fh) 1000 2000 3000 fn) 250 500 750<br>Predicted number of cases per week Predicted number of cases per hexgrid cell<br><!-- End of picture text -->