rafmacalaba/data-use-mentions-extended huggingface dataset:
complete the missing country-years of our main data set, after verifying that the newly added estimates are consistent with the estimates that we already had for other years.<sup>54</sup> **GDP Share of FIRE.** The _National Accounts Official Country Data database_ of United Nations (2020a) reports when available the GDP share of various sectors – using both the ISIC Revision 3 and Revision 4 – from the 1960s to date. The data are patchy, however, and we employ them only to obtain the GDP share of FIRE c. 2020 (observations from 2015-2020).

> 53For manufacturing, we use the series “Manufacturing, value added (% of GDP)”. For services, we use as our baseline the series “Services, etc., value added (% of GDP)”. When estimates of the service share are not available, we rely on another WDI series: “Services, value added (% of GDP)”.

> 54 We do not use SNA-AMA as our baseline database. Indeed, when comparing WDI + the yearbooks and SNA-AMA, it appears that many SNA-AMA estimates were extrapolated.
Policy Research Working Paper 10074

# **Abstract**

To understand the role of family background in intergenerational mobility, a large literature has focused on the conditional mean of children’s economic outcomes given parent’s economic status, while ignoring the information contained in conditional variance. This paper explores the effects of family background on the conditional variance of children’s outcomes in the context of intergenerational educational mobility using data from three large developing countries (China, India, and Indonesia). The empirical analysis uses exceptionally rich data free of sample truncation because of the nonresident children at the time of the survey. Evidence from all three countries suggests a strong influence of father’s education on the conditional variance of children’s schooling. The analysis finds substantial heterogeneity across countries, gender, and geography (rural/ urban). Cohort-based estimates suggest that the effects of father’s education on the conditional variance have changed qualitatively; in some cases, a positive effect in the 1950s cohort turns into a substantial negative effect in the 1980s cohort. A methodology is developed to incorporate the effects of family background on the conditional variance along with the standard conditional mean effects. This paper derives risk-adjusted measures of relative and absolute mobility by accounting for an estimate of the risk premium for the conditional variance faced by a child. The estimates of risk-adjusted relative and absolute mobility for China, India, and Indonesia suggest that the existing evidence using the standard measures of mobility substantially underestimates the effects of family background on children’s educational opportunities, and thus gives a false impression of high educational mobility. The magnitude of underestimation is especially large for the children born into the most disadvantaged households where fathers have no schooling, while it is negligible for the children of college educated fathers. The standard (but partial) measures may lead to an incorrect ranking of regions and groups in terms of relative mobility. Compared to the risk-adjusted measures, the standard measures are likely to underestimate the gender gap and rural-urban gap in educational opportunities.

This paper is
regions could be due to a welfare criterion in which poorer regions or poorer people receive more transfers. However, Columns 3 and 4 show that overrepresented areas are neither poorer nor more unequal than underrepresented areas. We thus interpret the results in Table 8 as providing evidence that stronger legislative representation translates into more political influence for overrepresented regions. Moreover, since overrepresented regions are more likely to vote for parties associated with pre-democratic regimes (as shown in Section 4.1), voters living in these regions may associate the higher transfers with these parties and may continue voting for them, thus re-enforcing and extending the political influence of pre-democratic elites.

# **5. Concluding Remarks**

In this paper, we argue that pre-democratic elites can strategically create malapportionment in the electoral system during the transition to democracy in order to safeguard their economic interests in a newly established democracy. Our results show that higher levels of malapportionment foster transition to democracy, presumably because it makes pre-democratic elites feel less threatened by the policies that might be implemented during the new democratic regimes.

Using within-country data, we find – in line with the argument we propose – that overrepresented electoral districts are more likely to vote for parties that are close to former nondemocratic regimes. Moreover, we highlight two political and economic policy features of malapportionment. First, malapportionment is associated with an increase in political concentration particularly for parties close to former non-democratic regimes. Second, overrepresented districts receive larger transfers per capita from the central government, despite the fact that they are not poorer or more unequal. This contrasts with traditional models of redistributive political economy and highlights that larger legislative representation induces greater political influence.

In future research, we plan to investigate the effects of malapportionment on economic development. The decrease in political representation or miss-allocations of public transfers we document in this paper may have negative effects on regional development. The big challenge with identifying these effects though is to find a source of exogenous variation in malapportionment at the state level. Finally, we believe that looking at specific features of
strategy were also considered in cost comparison. Alatas et al. (2012) suggest that there are synergies in targeting approaches so that combining approaches is less costly than the sum of the costs of the two approaches individually, but costs are certainly greater than that of CBT targeting alone.

Our cost analysis finally relies upon administrative data from the TUP program. The TUP program in its entirety served 7,500 households across six provinces of Afghanistan. While there is no data available on the total number of households screened by the TUP program, the portion of the program in Balkh province that was enrolled in the RCT identified 1,235 ultra-poor households out of 20,702 households screened (Bedoya et al., 2019). Assuming similar eligibility rates across Afghanistan, we estimate that the TUP program as a whole likely screened around 125,721 households. We use this value to estimate total targeting costs for the TUP program under counterfactual targeting approaches. Eligible households received benefits totaling $1,688, including a productive asset, cash transfers, a health voucher, training, biweekly social worker visits, and veterinarian visit once every two months during the year of intervention. The total benefits dispersed by the program were therefore on the order of 12.7 million (although the total program costs, including overhead, were closer to 15 million (Bedoya et al., 2019)); we use the total value of benefits to compare the costs of program targeting using our set of counterfactual targeting approaches to the direct costs of program benefits in Table S9. We find that targeting costs for a PMT or asset-based approach would represent approximately 3.97% of the total benefits delivered in the program; costs for a CBT approach would represent approximately 2.18% of the total benefits. In comparison, costs for the phone-based approach would be negligible.

When it comes to speed, in-person data collection for an asset-based (or PMT) targeting approach typically takes months or years to prepare and implement (World Bank, 2020
and identifies the constraints faced by the fiscal package in promoting equity. In essence, the paper aims to answer the following four questions: (a) what is the overall impact of fiscal policy on inequality and poverty in Turkey? (b) how do individual taxes and transfers affect distributional outcomes? (c) how does Turkey compare with

> 1 Poverty is measured using the World Bank’s absolute poverty line for upper-middle-income-countries (UMIC), set at $5.50 per-person per-day in 2011 Purchasing Power Parity (PPP) (World Bank 2017). The data are publicly available in the Poverty and Equity Data Portal (World Bank 2018). Unlike other countries, Turkey’s Institute of Statistics (TUIK) does not publish a national poverty line using cost of food and non-food basic needs. It stopped doing so in 2009.

> 2 Measured as the share of people whose income falls below the OECD relative poverty line; taken as half the median household income of the total population (OECD 2018).

2
outcomes in question. Theoretical arguments necessarily provide the basis for interpreting and evaluating empirical findings as well as for identifying evidence gaps. The availability of rigorous qualitative research tracing causal pathways and mediating factors is also considered essential for a high quality evidence base. As the evidence is scant for many interventions, there is only rarely robust quantitative as well as qualitative research, making the findings of this review tentative in many respects unfortunately.

The rest of the review is structured as follows: first, the broader background of public procurement interventions is spelled out in terms of new developments brought about by Bid Data and the data revolution. In addition, those broader groups of interventions are also briefly enlisted, which lie outside the public procurement system, but bear strong influence on procurement outcomes. Second, each of those public procurement interventions relating to a particular phase of the procurement process is reviewed. Third, public procurement interventions with a broader impact are reviewed. Fourth, conclusions are drawn, in particular by highlighting the need for further research.

# **B. Methodology**

In order to take stock of the evidence on major public procurement reforms and their impact on value for money and open access, we implemented a structured search and assessment strategy building on both standard online search and the expert knowledge of the authors and reviewers of earlier drafts of the manuscript. First, we defined the main areas of policy interventions both for those which target specific procurement phases and those which target the whole procurement process. As the choice of intervention groups defined both the search strategy and the structure of the review, we sought extensive validation of these choices at the outset. Second, we aimed at identifying as many potentially relevant academic publications as well as policy reports for each intervention area. We built on the authors’ knowledge of the field as a key starting point, while we also applied standard keyword search to each intervention group using specific keywords such as “e-procurement”, “impact”, “value for money”, etc. Once a
<!-- Start of picture text --> Partial regression coefficients conditional on<br>log GDP per capita, log population, and<br>country and year fixed effects.<br>Population Census in past 10 years<br>Agriculture Census in past 10 years<br>Household surveys: 2 or more in 10 years<br>Agriculture Surveys: 2 or more in 10 years<br>Labor Force Surveys: 2 or more in 10 years<br>Health Surveys: 2 or more in 10 years<br>Business/Establishments: 2 or more in 10 years - 7?<br>Complete Civil Registration and Vital Statistics System TT<br>-0.2 0.0 0.2 o4<br>Coefficient estimates and 95% confidence intervals<br>Data from the World Bank's World Development Indicators (WDI) and SPI.<br>Papers include all papers using data years 2000-2020.<br>Confidence Intervals at 95% significance level<br><!-- End of picture text -->
with support from the World Bank’s Living Standards Measurement Study (LSMS) team.<sup>2</sup> These six countries are part of the LSMS-Integrated Surveys on Agriculture (LSMS-ISA) project that fields longitudinal, multi-topic household surveys with focus on agriculture. Thus, the households included in the HFPS were also interviewed as part of the LSMS-ISA panel survey conducted in these countries. A uniform methodology was adopted in sampling, weighting, and implementing the HFPS across the countries, making cross-country comparison feasible. In each country, the most recent face-to-face survey prior to the start of the pandemic formed the frame for the HFPS. The HFPS was designed to be nationally representative, with rural/urban stratification.

The HFPS collects information on various indicators, including demographic information, shocks and coping strategies, economic sentiments, employment and business operation, and agriculture, among others. The current study uses data primarily from the agriculture and price modules. Given the season-specific requirements of agriculture data, the HFPS rounds that collected agriculture information were implemented either at the end of the main agriculture season or split across the post-planting and post-harvest periods in the respective countries. The agriculture module collects information on crops planted and area; harvest; fertilizer access, use and challenges with access and coping strategies; among others. While the HFPS began after the onset of the COVID-19 pandemic, the timing of implementation and number of rounds completed to date varies across countries. Table A1 gives the sample distribution across countries and the rounds of the phone survey data used for the analysis. Figures A1-A6 visualize the timeline of data collection.

# _2.2. Variable Creation_

The information collected through the HFPS that is most relevant to the objectives of this study broadly falls into three categories, described below. Not all information was collected across all rounds of the HFPS such that data availability varies by country and over time. Unless explicitly specified otherwise, all information collected was with respect to the latest agricultural season.<sup>3</sup> First
# **1. Introduction**

In most countries around the world, there are profound differences between men and women in ownership, use and control over assets and wealth. Gender gaps emerge prominently in ownership of land and housing property, which are important assets for the poor in developing countries and the primary means to store wealth in rural communities.

Does it matter if land and housing property and, by extension, overall wealth are disproportionately concentrated in the hands of men? The answer is unambiguously affirmative – from a gender equality perspective women’s ownership, use and control over resources matter for their well-being and agency (Grown et al 2005). In addition, a more equitable distribution of property and wealth may advance economic prosperity and promote human development of future generations. This is because gender differences in the ownership of land and other productive assets can induce allocative inefficiencies and foregone economic output (O’Sullivan 2017); and because gender differences in the use of productive resources, and the income derived from them, can affect development outcomes among children (World Bank 2011).

Yet, we do not have an extensive understanding of the extent of gender differences in property ownership or the factors driving these gaps. Household surveys, the primary data source for information on the possession and use of assets, traditionally collect these data for the household as a single unit, thereby obscuring gender differences. Recent efforts, mainly through specialized surveys on individual-level asset data, have started addressing this issue (see Doss et al 2020 for a review). This literature documents sizable gender gaps in asset ownership in developing countries in Africa, Asia and Latin America (e.g. ADB 2017; Deere et al 2013; Jacobs et al 2011; Kes et al 2011; Kilic and Moylan 2016). However, differences in methodology across studies impede broader comparative analysis and limit the conclusions that can be drawn for developing countries at large.

This paper contributes to the emerging literature on gender gaps in property ownership using nationally representative data
</sup><sup>**_β_**</sup><sup>_′, σ_</sup> _u_<sup>2</sup><sup>_, σ_</sup> _e_<sup>2)</sup><sup>_′_.</sup>

2. Generate, for _l_ = 1 _, . . . , L_ , a census vector **y** _d_<sup>(</sup><sup>_l_), whose elements</sup><sup>_y_</sup> _di_<sup>(</sup><sup>_l_),</sup> are generated as follows
# **V. Concluding remarks**

Even in 2012, before the current crisis, one‐fifth of the Iraqi population lived below the national poverty line; and many more lived close to the line, vulnerable precisely to the sorts of welfare shocks they are facing today. The 2014 crisis has eroded the welfare gains of the past, with poverty falling back to 2007 levels and a 20% increase in the number of the poor. While the incidence of poverty is higher among IDPs than the rest of the population everywhere except in the IS‐affected governorates, IDPs make up only a small proportion of Iraq’s 8 million poor in 2014. The rest comprise of households who already lived below the poverty line, or those who have fallen below the poverty line in the face of the massive economic disruptions the country is facing. Finally, the welfare impact of the crises varies widely across space, with the largest increases in headcount rates in KR‐I and the IS‐affected governorates. Yet, the poorest regions in the 2014 crisis scenario are the same as in 2012 – the currently IS‐affected, and the South, with poverty rates of 40 % and 30%, respectively.

Even though simulated results are not strictly comparable to ex‐post micro data estimates because of survey coverage constraints; overall the results are very much in line, particularly in Kurdistan and the South. On the one hand, the micro‐simulation estimates under‐estimated the gains in poverty reduction that were actually achieved between the first half of 2012 and the first half of 2014 prior to the crisis. Indeed, microdata estimates suggest an increasing momentum in welfare gains had the crisis not hit, relative to the 2007‐2012 period, from which the micro‐simulation derives several parameters. On the other hand, the micro‐data estimates do not include internally displaced persons, while the micro‐ simulation exercise incorporated the welfare and distributional impacts on this group as well.

24
. A digital elevation model(NASA 2015) (DEM) was used to derive the path of main river and stream channels and a raster grid created denoting distance of each grid cell from those channels. Data on reported flooding at 128 point locations across the city were obtained from the Millennium Challenge Account – Zambia (MCA-Z), and these were converted into a raster grid denoting distance to flood prone areas. Raster data were also obtained on the vulnerability to pollution of the underlying groundwater aquifers across Lusaka, based on geological characteristics. This includes taking into account infiltration characteristics including consideration of rock type, composition, tectonic lineaments, catchments or drainage basins, groundwater flow, and surface vegetation (Nick, Mweene, and Baumle 2012).

(ii) Water and sanitation infrastructure. Digitized data were obtained from the Lusaka Water and Sewerage Company (LWSC) on the piped water and sewerage infrastructure that underly the city, and
# **Acknowledgments**

This paper draws on contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Daniel Camos, Anton Eberhard, Vivien Foster, Sudeshna Ghosh Banerjee, Jean-Noel Gogua, Fatimata Ouedraogo, Orvika Rosnes, Haakon Vennemo, and Quentin Wodon. It is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in Africa Energy Department: Fabrice Bertholet, Luiz Maurer, and Fanny Missfeldt-Ringius.

2
power tariff|Residential at 100 kWh|15|<br>10.27|||
|Effective power tariff|Commercial at 100 kWh||11.73|5.0–10|.0|
|Effective power tariff|Industrial at 50,000 kWh||11.39|||

_Sources:_ Fragile and nonfragile countries’ figures as of 2005, calculated using the AICD power database (http://www.infrastructureafrica.org/aicd/tools/data), which contains household data. For the CAR, national access is for 2009 (World Bank 2009g), electricity consumption is for 2007 (CIA World Fact Book 2010 **)** ; power outages are for 2005 (Rosnes and Vennemo 2009); collection ratio is for 2010 (World Bank 2010c); cost-recovery ratio is for 2009 (World Bank 2009f); system losses are for 2009 (ENERCA 2010); effective tariff is for 2008 (World Bank 2009f).

_Note:_ Urban access for the CAR represents access to electricity in Bangui.

kWh = kilowatt-hour; MW = megawatts.

Thermal generation is curtailed by the expensive price of diesel in the CAR. The routes used to transport fuel are expensive,<sup>8</sup> increasing the price of oil in the CAR. At 144 cents per liter of diesel, in 2008 the CAR paid 40 cents more for fuel than Cameroon and 23 cents more than the Democratic Republic of Congo (GTZ 2008). This situation has affected secondary centers that rely on thermal sources for power. Further, Bangui’s diesel plant is often strapped for funds and finds itself unable to pay for fuel, limiting power availability.

The limited availability of power translates into poor access to electricity. As of 2009 only 1 percent of the CAR’s population had access to electricity compared to 33 percent in low-income countries (table 9). Urban access, at 10 percent and mainly concentrated in Bangui, is just one-ninth of
# **E.14 Results for Pre-specified Families in Ebola Sample**

**Table E.14:** Pre-specified Families in Ebola Sample

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|General utilization|0.000|0.074<br>|0.128<br>|0.036|2857|
||(1.000)|(0.040)<sup>_∗_</sup>|(0.049)<sup>_∗∗∗_</sup>|(0.042)||
|||[0.188]|[0.110]|[0.397]||
|Maternal utilization index|0.000|0.058|0.232|_−_0.055|595|
||(1.000)|(0.086)|(0.112)<sup>_∗∗_</sup>|(0.097)||
|||[0.379]|[0.143]|[0.488]||
|Health outcomes index|0.000|0.034|0.085|_−_0.006|3183|
||(1.000)|(0.062)|(0.069)|(0.074)||
|||[0.379]|[0.283]|[0.886]||
|Satisfaction index|0.000|0.108|0.106|0.109|3183|
||(1
***|1.150***|
||(1.060)|(0.0354)|(0.0369)|(0.742)|(0.0267)|(0.0258)|
|Observations|85,718|92,830|92,830|85,718|85,982|85,982|
|R-squared|0.367|0.620|0.696|0.399|0.481|0.569|

Note: Author’s calculations. Robust standard errors in parentheses, clustered at the country-pair-sector level. FDI stock is defined at bilateral, sectoral level in USD Million (Logged) in constant 2018 terms. OLS = Ordinary Least Squares. PPML = Poisson pseudo-maximum likelihood estimation. FDI = Foreign Direct Investment. IPA = Investment Promotion Agency. KM = Kilometers. GDP = Gross Domestic Product. F.E = Fixed Effects. *** p<0.01, ** p<0.05, * p<0.1 To further gauge the relative effectiveness of IPAs, we use the model from equation 6, and include an additional country-interaction effect with the IPA sectoral targeting coefficient. To maintain the anonymity of respondents in the WB-WAIPA survey, we will not make public the names of the specific countries, but instead focus on aggregate results only. Their results are summarized in Table 6 and presented in full detail in Table 7. These findings suggest that there is considerable heterogeneity between IPAs in their ability to affect FDI stocks. Overall, 10 countries have positive and statistically significant results (which we will classify as “high performing IPAs” in the next section). An additional 13 IPAs have statistically insignificant results, while 3 IPAs have negative and statistically significant results (which we will jointly classify as “other IPAs” in the next section). Finally, 10 IPAs fell out of the country-interaction equation as they either did not have sectoral targeting or exhibited