rafmacalaba/data-use-mentions-extended huggingface dataset:
Household head or spouse<br>0.73|0.76|**|
|Log household size<br>1.61|1.52|***|
|Dependency ratio<br>0.89|0.93||
|Wealth index<br>0.10|0.16||
|Observations<br>4,377<br>**Notes:**IHPS = individual interviews; IHS4 =|17,433<br>business-as-usual|survey|

**<u>Notes:</u>** IHPS = individual interviews; IHS4 = business-as-usual survey approach. Significant differences are indicated with * (p<0.1), ** (p<0.05) and *** (p<0.01). Estimates are weighted with the response weight.

Table 2 presents the descriptive statistics for labor market outcomes of interest, by gender. The estimates are coupled with the results from the tests of mean differences across the two surveys.

identifying females; a series of dichotomous variables on educational attainment; dichotomous variables identifying whether the individual is currently married, and separately, whether he/she is head/spouse of head; and individual’s number of months living away from the household over the past year; and (iii) household covariates, including household size, dependency ratio, and wealth index. The latter is a factor analysis-based index that is composed of (i) a series of dichotomous variables that capture the ownership of mortar, bed, table, chair, fan, air conditioner, radio, radio with flash drive/micro CD, TV, VCR, sewing machine, kerosene/paraffin stove, electric/gas stove, refrigerator, washing machine, bicycle, motorcycle, car, minibus, lorry, beer-brewing drum, sofa, coffee table, cupboard/drawers, lantern, desk, clock, iron, computer, satellite dish, solar panel and generator, and
the extent to which they have access to different sources of information. Opportunity structure indicators capture the distance to the nearest market, the extent to which cultural restrictions determine the nature of professions women are allowed to pursue, the amount of time women have to dedicate to household chores, and the extent to which the government provides women with job-related training.

WDIP’s impact evaluation also establishes a series of empowerment indicators for the society domain/family sub-domain, such as the percentage of women who have decisionmaking power equal to that of their husbands over the number and spacing of children, the use of contraceptives, and conjugal relations (family sub-domain). The qualitative enquiry led the study team to hypothesize that the extent to which women have a say in these matters associates with such assets as women’s education, income, and selfconfidence levels, their awareness of reproductive health issues, and their participation in women’s groups. Analysis will also test for associations among opportunity structure indicators, such as customs that influence whether or not women are allowed to disagree with their husbands, and whether or not women are expected to play a subservient role regarding sexual conduct.

- 2.2.2 Applying the Framework in Nepal: The Measuring Empowerment and Social Inclusion (MESI) Study

The Rural Water Supply and Sanitation Project I (RWSS project) was designed to build rural drinking water schemes and empower local citizens to undertake their construction and management.<sup>20</sup> In addition, it had a clear objective of empowering women, and therefore provided fertile ground for measuring empowerment. Further, this country study seeks to explore gender, caste, and ethnic dimensions of social inclusion. The MESI study is the broadest and only longitudinal study among the five cases. It is also financed independently of the other four country cases.

# _The Project_

The World Bank–funded RWSS project started in 1996. RWSS sought to promote decentralization and increase the involvement of beneficiaries and the private sector in rural water supply and sanitation service
face relatively frictionless capital markets and, thus, their R&D investment is primarily driven by technological demand (Rajan and Zingales, 1998). Specifically, we obtain the ratio of R&D expenditures to total investments for the median firm in the United States within each 2-digit SIC industry over the period

> 16 We conduct several robustness tests using a number of alternative samples and measures of R&D intensity. Notably, we obtain similar results to those reported in Table 7 if we: (a) use the continuous version of the high-R&D dummy (based on U.S. data), (b) exclude the U.S. from the sample, and (c) restrict the sample to the manufacturing industry, defining high-R&D industries as those with a SIC code in the groups 28, 35, 36, and 38.

22
We estimate equation (1) separately for each policy category. Since bank illiquidity, oil exposure, and bank size variables are standardized, the constant captures the abnormal stock 0,nn returns of the average private bank for a type of announcement in the [-1, αα _n_ ] window. The coefficients , , and capture cross-sectional differences in the response of bank 1,nn 2,nn 3,nn 4,nn stock prices. For example, αα αα αα αα> 0 would suggest that illiquid banks have higher accumulated 1,nn abnormal returns than liquid banks during the event window for a specific type of announcement. αα There are several challenges to our identification. First, owing to the high degree of global financial integration, there is extensive evidence of international spillovers from policy announcement by systemically important countries (Ait-Sahalia et al., 2012; Morais et al., 2019). To avoid contaminating the analysis from the effects of financial policy initiatives taken by core countries, we exclude data from days when the U.S. or European authorities made major announcements covering the same broad policy type category. For example, on a day when the U.S. authorities announced liquidity support measures, we keep data from stock returns of U.S. banks in the sample but exclude banks in countries that issue liquidity announcement on the same day.

Second, due to the nature and scale of the crisis, in some cases, national government authorities announced multiple financial measures during the same day. In such situations, and without intraday time stamp for each announcement category, it is difficult to disentangle the effect of each policy on bank stock prices. To deal with these confounding factors, we further condition our sample to country-dates where only policies of a particular category were announced, and report results for this restricted sample.

20
Policy Research Working Paper 7727

# **Abstract**

Household income surveys often fail to capture top incomes which leads to an underestimation of income inequality. A popular solution is to combine the household survey with data from income tax records, which has been found to result in significant upward corrections of inequality estimates. Unfortunately, tax records are unavailable in many countries, including most of the developing world. In the absence of data from tax records, this study explores the feasibility of using data on house prices to estimate the top tail of the income distribution. In an application to Egypt, where estimates of inequality based on household surveys alone are low by international standards, the study finds strong evidence that inequality is indeed being underestimated by a considerable margin. The Gini index for urban Egypt is found to increase from 36 to 47 after correcting for the missing top tail.

This paper is a product of the Poverty and Inequality Team, Development Research Group and the Office of the Chief Economist, Middle East and North Africa Region. 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://econ.worldbank.org. The authors may be contacted at rvanderweide@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
magnitude: 10 _._ 6 percent.

# **India**

The estimates of father-son schooling persistence for India are reported in columns (3) and (4) of the top panel of Table 3. The IGRC estimate for India shows that the downward bias due to coresidency is substantial; the estimate from the full sample is 29 _._ 5 percent higher than the estimate in the coresident sample. The bias in the father-son sample in India is thus significantly larger than the average bias we found earlier for the sample of all children across different measures of parental education (17 _._ 6 percent). In sharp contrast, the IGC estimate suffers from very little coresidency bias: 2 _._ 4 percent only. The estimated bias in the IGC estimate for father-son in India is thus ignorable, while the IGRC estimate suffers from strong downward bias from coresident sample selection.

The bias estimates for mother-daughter schooling persistence in India are reported in columns (3) and (4) of the lower panel of Table 4. The bias in the IGRC estimate for mother-daughter is smaller for India when compared to Bangladesh, but the magnitude of bias is still substantial 21 _._ 8 percent. The corresponding biases in IGC estimates is 9 _._ 7 percent which is less than half of the bias in the IGRC estimate.

# **Gender Differences in Intergenerational Schooling Persistence**

An important policy issue in many developing countries is whether the girls face especially strong barriers to educational mobility. If we rely on the IGRC estimates from coresident samples, the gender gap may seem smaller than it really is, because the truncation bias is usually stronger for the estimates for girls, as coresidency rates are lower (this is true in both Bangladesh and India data). But the IGC estimates from coresident samples provide a reliable measure of the gender gap. For example, consider the estimates of father-son and mother-daughter persistence in Bangladesh (Tables 3 and 5).<sup>21</sup>
To perform the analysis, we combine a large annual panel of manufacturing plants (Statistik Industri – SI henceforth) for the period from 2002 to 2014 with the village census data (PODES), which contains information on the number of preschools available per village. As village identifiers are not available in the Manufacturing Survey, we aggregate the number of preschools at the district level, taking advantage of the representativeness of firm-level data at such low level of geographic disaggregation. We use a triple-difference framework exploiting variation in preschool expansion geographically and across time, as well as sector-specific baseline reliance on female labor supply to identify the impact of preschool availability on plants. We argue that plants that relied more on female labor at baseline would benefit more from preschool expansion. This relies on the assumption – which we confirm empirically - that preschool expansion is more likely to affect female than male labor supply given women’s greater role in childcare responsibilities. This approach allows us to control for all time-varying confounders at the district and sector levels, thus addressing the main concerns to the causal interpretation of the estimates.

We find a significant positive impact of the expansion of preschools on plant-level total factor productivity – whether measured in revenue (TFPR) or quantity (TFPQ) terms. Our preferred specification suggests economically substantial impacts: an increase of 1 additional preschool per 1,000 eligible age children (approximately 1/3 of standard deviation) increases TFPR by 11 percent for plants in sectors with 41 percent female employees (which is the average in our sample). We also explore two mechanisms through which institutional childcare may impact firm productivity via the increased female labor supply: (i) improvements in allocation of talent across jobs; and (ii) increase in job stability due to reduction in turnover.

First, preschools may alleviate one of the constraints that women face when joining the labor market - childcare responsibilities - enabling them to choose between productive and reproductive work based their ability and earning potential. This is likely to improve allocative efficiency of talent across jobs,
de Luca, Loretta, Hélène Sahy, Saba Joshi, and Mayra Cortés. 2013. _Learning from Catalysts of Rural Transformation_ . International Labour Organization, Geneva.

Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar and Jake Hess. 2017. _The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution._ World Bank.

Desiere, Sam, and Valentina Costa. 2019. “Employment data in household surveys: taking stock, looking ahead.” Working Paper, Development Data Group, World Bank.

Donald, Aletheia; Gayatri Koolwal, Jeannie Annan, Kathryn Falb, and Markus Goldstein. 2017. “Measuring Women's Agency.” World Bank Policy Research Working Paper 8148.

Doss, Cheryl. 2018. “Women and agricultural productivity: Reframing the Issues.” _Development Policy Review_ 36:35-50.

Dutta, Puja, Rinku Murgai, Martin Ravallion, and Dominique van de Walle. 2014. _Right to Work? Assessing India's Employment Guarantee Scheme in Bihar._ Equity and Development; Washington, DC: World Bank.

Fletcher, Erin K., Rohini Pande and Charity Troyer Moore. 2018. “Women and Work in India: Descriptive Evidence and a Review of Potential Policies.” Faculty Research Working Paper, RWP18-004, Harvard Kennedy School.

Folbre, Nancy. 2018. _Developing care: Recent research on the care economy and economic development_ . International Development Research Centre.

FAO. 2011. _The State of Food in Agriculture 2010-11: Women in Agriculture: Closing the Gender Gap for Development._ Rome.

Fox, Louise, and Obert Pimhidzai. 2013. “Different Dreams, Same Bed: Collecting, Using, and Interpreting Employment Statistics in Sub-Saharan Africa: the Case of Uganda.” World Bank Policy Research Working Paper 6436.

Gaddis, Isis, Gbemisola Oseni, Amparo Palacios-Lopez, and Janneke Pieters. 2019. “Measuring Farm Labor: Survey Experimental Evidence from Ghana.” World Bank Policy Research working paper no. WPS 8717.

Gaddis, Isis, and Amparo
(Sendzimir et al., 2011).

# 4 Concluding Remarks

In this paper we used nationally representative household-level data to explore and compare the prevalence and economic contribution of trees on farms across five African countries. Three main findings emerge. First, trees on farms are widespread across the continent and comprise a key component of agricultural-forest landscapes. The East African countries of Tanzania, Uganda and Ethiopia had especially high incidence of trees on agricultural lands, with about a third to more than half of rural households reporting on-farm trees. Fruit trees and cash crop trees were the two most popular types of trees while trees for timber and fuelwood were much less prevalent (reported by 5% of respondents). The proximity of most households with trees on farms to forests and the high incidence of intercropping 14
This paper presents the first evidence of whether capital incentives distort recent technology diffusion - using the cloud versus own IT investment as an example. We employ novel firm-level panel data which captures the adoption of cloud, big-data analytics and AI, along with traditional investments in tangible capital, including total IT investment, as well as hardware and software.<sup>4</sup> In addition, we use matched employer-employee panel data to measure the demand for data analytics workers - as an alternative proxy for technology adoption following Corrado et al. (2022).<sup>5</sup> If the AIA policy affects firm adoption of data analytics technologies, such as cloud, big data analytics or AI, then one would expect that this would also materialise in the demand for workers performing data analytics tasks.

We study capital investment and technology diffusion in the setting of a quasi-natural experiment that exploits the introduction and adjustments to the eligibility threshold for a capital incentive policy in the UK, the Annual Investment Allowance (AIA). This scheme allowed firms to deduct the cost of investment in capital (including IT capital) against profits up to a threshold value of profits, where this threshold was adjusted over time.<sup>6</sup> The AIA impacted the marginal investment cost of some firms and not others, allowing us to use a difference-in-differences approach comparing treated versus control firms.<sup>7</sup> As firms can adjust their investment in response to the AIA, this poses a potential selection problem - firms may limit their investment in a given year to stay below the AIA incentive thresholds. To address this issue, we follow the empirical approach of Bjuggren (2018), Saez et al. (2019) and Bøler et al. (2015). These papers examine the effects of employment protection legislation or R&D tax credits in settings that also feature changes

> 4We note that software is considered an intangible asset, but was nonetheless eligible for the AIA policy in addition
in Egypt to monitor the situation of same households over extended period of time.

The study compares Growth Incidence Curves (GIC) based on a cross section of data with GICs based on the panel data. Panel data follows the same households over time (unlike the cross section which has an anonymous approach) and allows the analysis of factors that affects household’s welfare over time. It captures the dynamic aspects of poverty and growth and it is therefore an essential tool in assessing social mobility. This study finds indeed opposite results in terms of pro-poor growth between cross-section and longitudinal data. It therefore attempts to identify the main factors behind these apparently contradictory results.

This paper is divided in seven sections. The next section provides a brief review of the literature, and the subsequent (third) section contains description of data and methodologies used. Main facts about changes

> 2 These data refer to the panel component of the Household Income, Expenditure and Consumption Panel Survey (HIECPS), i.e. they are comparing data from February 2005 with February 2008.

2
Most notably, defaults are significantly less likely in fast-growing countries, and expropriations are significantly less likely under right-wing governments.

We began our data description with the observation that expropriation and default events are correlated across countries in the long run: over the period 1970-2004, countries that expropriate at least once are likely to also default at least once. In fact, the simple crosscountry correlation between the average number of default events and the average number of expropriation events per year is 0.30. This raises the following question: To what extent are the reasons for the coincidence of these two forms of sovereign theft well-captured by the parsimonious set of explanatory variables we have considered so far? One way to answer this question is to compute the correlation across countries of the residuals of the specifications in the last two columns of **Table 2** . Naturally, this correlation is lower, at 0.25, than the correlation of average expropriations and average defaults, at 0.30. However, this correlation is still far from zero, suggesting that there are other country characteristics that account for the high coincidence of default and expropriation across countries in the long run that remain to be identified.

A drawback of these cross-sectional results is that they can tell us little about the dynamics over time of the two types of events, which we have seen in the previous section to be potentially very interesting. We remedy this deficiency in **Table 3** , which reports a similar set of regressions using the full panel of 1,560 country-year observations covering the same set of 126 developing countries. In this table, the dependent variables are dummies taking the value one for the onset of default events and expropriation events, and zero otherwise for non-default and non-expropriation country-years. We report results using simple linear probability models, i.e. OLS regressions of these dummies on various explanatory variables.<sup>7</sup> One further difference with the results from the previous table is that we lag the FDI and debt
# **5. Ownership Bundles, Decision-Making, and Permission Structures by Cluster**

The previous section highlighted the importance of transfer rights for profiling landowners. The descriptive statistics in Tables 8-10 show that in all countries, those having transfer rights, either jointly or exclusively, will almost always have roles in decision making, economic ownership, and the right to make improvements. In Tanzania, for example, women in the joint and exclusive right clusters all reported having the right to make investments in/improve the parcel. Those not having transfer rights still enjoy these decision-making and management powers but to a lower extent. In some cases, there are large gaps — in Ethiopia, for example, only 27 percent of non-landowners indicated they had the right to make improvements, even though a high share in this group had economic ownership (79 of men and 62 percent of women) as well as decision-making power (89 percent of men and 82 percent of women). Despite having low levels of documented ownership, most reported landowners do have tenure security in Ethiopia and Tanzania, with higher reported rates of secure tenure among women having mostly exclusive rights.<sup>22</sup> Furthermore, the LSMS+ surveys included unique questions on whether permissions are needed for respondents to exercise each right, revealing some interesting differences across clusters. While landowners having exclusive transfer rights exhibit similar patterns on decision-making roles visà-vis those having joint transfer rights, permission structures look very different across these groups, and more likely to affect landowners in mostly joint clusters. The statistics on needing permission at the bottom of Table 8 through Table 10 are provided just for joint and exclusive clusters in columns (2) and (3), since the number of individuals with rights for the not-hold clusters (column (1)) is small. In some countries, we find that a high share of landowners need permission to exercise rights. In Ethiopia, for example, men and women with mostly joint rights reported always needing permission, as well as
.092|0.093|0.095|0.097|0.097|
|**Education**<br>Between groups|0.013|0.012|0.130|0.015|0.017|0.020|0.023|0.023|
|Share of betweengroups|13.0%|12.9%|13.4%|13.8%|15.9%|16.5%|19.5%|19.1%|
|Within groups|0.098|0.093|0.095|0.105|0.107|0.111|0.118|0.118|
|**Age**<br>Between groups|0.002|0.002|0.002|0.002|0.003|0.003|0.002|0.002|
|Share of betweengroups|1.7%|2.2%|2.2%|2.0%|2.5%|2.4%|1.5%|1.7%|

_Source_ : Authors’ calculations based on HBS.

Generally speaking, when inequality increases (if other things are kept constant), poverty normally increases as well, thus dampening the beneficial effect of growth on poverty. Fortunately, changes in the shape of the distribution of consumption are much slower than the change in the mean. Therefore, the positive growth impact should outweigh the negative effect of a widening of the distribution itself.

# **Decomposing Trends in Poverty**

Having described the trends in growth and in inequality, it is important to analyze the effects of these trends on poverty.

**Table 3:  Decomposition of Changes in Poverty, 1995-2002 (percentage points)**

|**Poverty change **|**1995**|**1996**|*