rafmacalaba/data-use-mentions-extended huggingface dataset:
more in the context of the Doha round

8 The approach here has been to take the change in the average per capita consumption of the poor, apply an estimated income-to-poverty elasticity, and assess the impacts on the poverty headcount index. We have done this by calculating the change in the real wage of unskilled workers, deflating it by a food/clothing consumer price index which is more relevant for the poor than the total price index. That real wage grows, over all developing countries, by 3.6 percent, or more than four times greater than the overall average income increase. We are assuming that the change in unskilled wages is fully passed through to households. Also, while the model closure has the loss in tariff revenues replaced by a change in direct household taxation, the poverty calculation assumes – realistically for many developing countries -- that these tax increases only affect skilled workers and high-income households. While these simple calculations are not a substitute for more -detailed individual country case study analysis using detailed household surveys as in, for example, Hertel and Winters (2005), they are able to give a broad region-wide indication of the poverty impact.

14
Impact of Fiscal Policy on Poverty and Inequality in Uganda: Fiscal Incidence Analysis Using the UNHS 2016/17 Carolina Mejia-Mantilla, Johanna Fajardo-Gonzalez, Maya Goldman, Jon Jellema and Haley Renda<sup>1</sup>

**JEL Codes:** D31, H22, I14, I24, I32

**Keywords:** Fiscal Policy and Inequality, Fiscal Incidence, Poverty, Taxation, Uganda, CEQ

> 1 Senior Economist at the Poverty and Equity Practice (corresponding author at <u>cmejiamantilla@worlbank.org),</u> Consultant at the Poverty and Equity Practice, CEQ Researcher for Africa and Asia, Deputy Director for the CEQ Project at GDN and CEQ Junior Researcher, correspondingly. We thank Tony Thompson (World Bank), Matthew WaiPoi (World Bank), and Sandra Martinez (CEQ) for their valuable comments. We obtained relevant information across the World Bank Uganda country team (both in the headquarters and in the field).
d.|2688.5|n.d.|3001.1|n.d.|<br>115.1|n.d.|3116.2|n.d.|
|University Teacher in Priv. School|2546.6|n.d.|2592.2|n.d.|2680.6|n.d.|3055.0|n.d.|<br>71.8|n.d.|3126.8|n.d.|
|**The mixed-skilled group**|**684.3**|**261.7**|**759.5**|**317.0**|**1072.8**|**492.9**|**1224.6**|**560.1**|<br>**53.9**|**34.8 **|**1278.9**|**595.4**|
|The agricultural group|177.5|91.5|184.1|92.6|744.8|270.9|817.9|323.1|<br>100.8|36.3|918.8|359.8|
|The low-skilled group|330.8|235.5|375.7|247.1|568.8|358.2|677.1|434.0|<br>25.1|17.6|702.2|451.7|

n.d.: No data in the survey. Source: Own calculations based on ENIGH96 survey Tables 6 and 7 show real mean hourly salaries and mean hourly labor earnings, respectively, for total hours in main occupation, and total hours of main occupation plus secondary occupation. The hourly salary difference
dimensions besides bonding and bridging<sup>7</sup> , and 2) that people face a trade-off in enjoying social relationships, either within their kinship and existing ties, or across new social ties.

## [TABLE 3]

# **5 Bridging, bonding and life satisfaction**

In this section, we turn to the life satisfaction equations which we use to try determining how bonding and bridging attitudes relate to happiness. We first use the pooled world sample with the disaggregated proxies for bonding and bridging attitudes, assuming these variables as two separate concepts (Table 4). We then use the constructed proxies of the locus of socializing. We assume bonding and bridging attitudes as two sides of the same variable and test these proxies on the world sample and across groups of similar countries (Tables 5, summarized in Table 6).<sup>8</sup> Table 4 shows that all bonding indicators are positively and significantly correlated with life satisfaction, and with a sizeable coefficient as compared to the standard controls. The most important indicators are ‘importance of family’, ‘importance of religion’, and ‘trust in institutions’. But also ‘importance of work’ and ‘child obedience’ are positively and significantly correlated with life satisfaction. The positive correlations of the bonding indicators used with life satisfaction are not new results with respect to those in the literature (see section 2). However, our previous analysis and the estimations in Table 4 show in more detail how different and interlinked components of bonding can affect life satisfaction.

The results for the bridging indicators are more mixed. We find ‘trust in people’ to be positive and significant, ‘homosexuality is justifiable’ negative and significant, while ‘child independence’ and ‘child imagination’ are non-significant. Whereas the finding on trust is not new, the finding on tolerance of homosexuality is rather surprising. The proportion of homosexuals in the population has been used in some studies as a proxy for bridging attitudes in the
# <u>Hypothesis 2: Respondent selection effects</u>

Our second hypothesis relates to how respondents were chosen within phone survey households. The phone surveys typically interviewed one respondent per household per survey round who was selected to be knowledgeable across the different topics covered in the survey (the “main respondent”). Often this was the household head. As a result, respondent selection was purposive, not random, and overrepresented male, older, and more educated respondents relative to the general population (Brubaker et al. 2021) . These traits, and other unobservables, may correlate with vaccine uptake and bias vaccine coverage estimates. We thus posit our second hypothesis.

**Hypothesis 2:** Purposive (non-random) respondent selection within the household overrepresents individuals that are more likely to be vaccinated.

We test this hypothesis in two ways. First, we randomly selected a respondent alongside the purposively selected (“main”) respondent during one wave of the phone surveys in three countries (Burkina Faso, Malawi, Uganda). A respondent selected at random from among the members of the household is expected to be representative of the general population of eligible individuals, that is, all adults (15+) within households with access to a phone. Random selection of any eligible household member allows for the possibility that the purposively selected main respondent and the randomly selected respondent are the same individual. In this case, we conducted a single interview and count the observation both for the main respondent estimates and for the random respondent estimates.<sup>8</sup> Second, we asked the main respondent to report the vaccination status of all household members on their behalf (‘proxy reporting’). This gives us the following information within each household: (i) the vaccination status of the purposively selected respondent; (ii) the vaccination status of a randomly selected household member (possibly, but not necessarily a different member than in (i); (iii) the vaccination status of
Financial Economics 89_ (3), 467–487.

- Betz, F. and F. Ravasan (2016). Collateral regimes and missing jobcreation in the mena region.

- Bigsten, A., P. Collier, S. Dercon, M. Fafchamps, B. Gauthier, J. W. Gunning, A. Oduro, R. Oostendorp, C. Patillo, M. S ̈oderbom, F. Teal, and A. Zeufack (2003). Credit Constraints in Manufacturing Enterprises in Africa. _Journal of African Economies 12_ (1), 104–125.

- Boneva, L., J. Cloyne, M. Weale, and T. Wieladek (2020). Firms’ price, cost and activity expectations: Evidence from micro data. _Economic Journal 130_ (627), 555–586.

- Coibion, O., Y. Gorodnichenko, and S. Kumar (2018). How do firms form their expectations? New survey evidence. _American Economic Review 108_ (9), 2671–2713.

- Coibion, O., Y. Gorodnichenko, S. Kumar, and M. Pedemonte (2020). Inflation expectations as a policy tool? _Journal of International Economics 124_ , 103297.

- Coibion, O., Y. Gorodnichenko, and T. Ropele (2020). Inflation expectations and firm decisions: New causal evidence. _Quarterly Journal of Economics 135_ (1), 165–219.

19
1.64***<br>(0.31)|56.6|
|Perc. ChangeIncome|0.059**<br>(0.029)|1.0|0.097***<br>(0.007)|1.0|0.029**<br>(0.013)|1.0|
|Class share|0.198||0.467||0.335||
|# (%) workers assigned<br>class|to<br>367<br>(18.7%)||949<br>(48.3%)||648<br>(33.0%)||

_Source_ : Authors’ analysis based on survey data.

_Note_ : WTP= willingness to pay. Results from a latent class analysis. Sampling weights are not applied. Omitted level for contract is none. Omitted level for Provident Fund is none. *, ** and *** represent statistical significance at the 10%, 5% and 1% levels, respectively.

49
to find jobs that fully value their additional education. This is so not because there are not enough firms ready to hire them, but because too many of those firms are informal and do not need more educated workers. This story has a corollary: The distortions that misallocate resources today are costlier now than 20 years ago because there are more workers with higher education now than before (and, if these distortions persist while the schooling of Mexican workers continues to improve, they will be even costlier).

We conclude with four observations. First, the analysis suggests that there is no automatic connection between increases in worker schooling and labor earnings. Rather, the translation from increased schooling to higher earnings is mediated by the degree of misallocation in the economy. If misallocation is large and persistent, earnings can stagnate even as human capital is being accumulated.

Second, our approach may help explain the fall in the returns to education observed in other countries of Latin America beyond Mexico (see Gasparini et al. 2011; Rodríguez-Castelán et al. 2016). In these countries, there has also been a steady increase in the supply of educated workers, in some cases, accompanied by mild declines in labor informality. This paper suggests that it is critical to look beyond worker and firm classifications—which may change without affecting behavior and which are not always comparable across countries. It is essential to concentrate on misallocation. Are there large distortions? Are these distortions de facto taxing firms with certain characteristics and subsidizing others? Are there systematic differences in the schooling composition of taxed and subsidized firms? While not all countries may have sufficient firm-level data to carry out computations such as the ones reported here, those presented here rely on employment surveys and may still shed important light.

47
Table 4. Impacts of Refugee Camps on Land Use Outcomes

||Dense-Op|en Forests|Land C<br>Grass|over<br>land|Crop|lands|
|---|---|---|---|---|---|---|
||_Km_|Sin*|_Km_|Sin*|_Km_|Sin*|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_A. Main Specifcation_|||||||
|SID_×_I(Post 2017)|-0.008***|-0.007***|0.008***|0.007***|0.005***|0.004***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|FDR q-values|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|R-squared|0.650|0.657|0.724|0.732|0.960|0.959|
|Observations|47,840|47,840|47,840|47,840|47,840|47,840|
|_B. Controlling for differ_|_ential linear_|_pre-trends_|||||
|SID_×_I(Post 2017)|-0.008***|-0.007***|0.010***|0.009***|0.006***|0.005***|
||(0.001)|(0.
**Table A11: Impacts of mental health on labor outcomes – Post-traumatic stress**

||**disorder**<br>||
|---|---|---|
|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
||(1)|(2)|
|Post-traumatic stress disorder|-0.035**<br>(0.017)|-0.599**<br>(0.264)|
|_First stage of 2SLS (dependent v_|_ariable is post-traum_|_atic stress disorder)_|
|Trauma experience*Year after<br>arrival|-0.018***<br>(0.005)|-0.019**<br>(0.008)|
|Kleibergen-Paap F stat.|19.070|47.439|
|AR 95-CIs|[-0.202, -0.017]|[-3.882, -0.318]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|3,687|3,621|
|Number of individuals|1,609|1,593|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and Yogo (2005)
**Figure 8: Completed Education Levels for Syrians and Jordanians in Working Age (2017-2019)**

<!-- Start of picture text --> .8<br>.6<br>Jordanian<br>.4<br>Syrians<br>.2<br>0<br>0 5 10 15<br>Education Level Completed<br>Kdensity<br><!-- End of picture text -->

**Notes** : Authors’ calculations with data from Jordan LFS. Education levels increase by one from Illiterate to PhD as shown in Figures 6 and 7.

The Elasticity of Substitution (EoS) is a measure that quantifies how easily one input can be substituted for another in the production process. In this specific context, the EoS is calculated as the percentage change in Syrian employment divided by the percentage change in Jordanian <u>a</u> S <u>S</u> employment ( **EoS** = ) . Therefore, a positive EoS implies % cchSSnn SSnn S SSnn aae eeJJ Seeaannee complements, but a negative EoS implies substitutes. If employment increases (decreases) for % cchSSnna SSnn J SSJJSSnnS nn aae eeJJSSeeaannee both groups, they are complements (EoS>0), but if employment increases (declines) for one but declines (increases) for the other, Syrians and Jordanians are substitutes (EoS<0). Table 1 shows estimates of the EoS of Jordanians and Syrians across sectors and for the economy as a whole. Although the aggregate EoS suggests Syrians and Jordanians are substitutes, this is not the case for workers engaged in the primary and manufacturing sectors.

10 | P a g e
Policy Research Working Paper 6076

# **Abstract**

Using the China Health and Retirement Longitudinal Study 2008 pilot, this paper analyzes the patterns and correlates of intergenerational transfers between elderly parents and adult children in Zhejiang and Gansu Provinces. The pilot is a unique data source from China that provides information on the direction as well as amount of transfers between parents and each of their children, and clearly distinguishes transfers between parents and children from those among other relatives or friends. The paper shows that transfers flow predominantly from children to elderly parents, with transfers from children playing an important role in elderly support. Taking advantage of the rich information available in this survey, the authors find strong evidence that transfers are significantly affected by the financial capabilities of individual children. Educated and married children have a higher tendency to provide transfers to their parents; and oldest sons are less likely to provide transfers than their younger brothers. With future continued rapid economic growth in China, the income disadvantage of the elderly will persist and upward generational transfers will likely remain the most common form of private transfers. In the absence of some other source of elderly support (such as a public pension or own savings), the dwindling number of children implies that the financial burden associated with supporting the elderly is likely to increase.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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 author may be contacted at jgiles@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
Kaufmann, Kraay, and Zoido-Lobaton (KKZ, 1999) and captures the quality of institutions in the country. CORRUPTION is a measure of the level of corruption in the government from La Porta et al. (1998) and JUDICIAL EFFICIENCY is an index of the efficiency of the judicial system, also from La Porta et al. (1998).

Our sample of countries includes those countries that experienced a systemic crisis during the last 30 years, as reported by Caprio and Klingebiel (1999), and where we also have data on FISCAL OUTLAYS and institutional characteristics (QUALITY OF INSTITUTIONS, CORRUPTION, AND JUDICIAL EFFICIENCY). Some countries experienced multiple systemic crises during our sample period (for example, Argentina) and in these cases each systemic crisis is included in our sample. Since our sample of systemic crisis cases would be biased if certain country characteristics that can explain why some countries are more prone to systemic crises than others also affect the speed of recovery from a financial crisis, we also include a control group of (a) countries that did not experience a systemic crisis during the sample period, but that did experience a nonsystemic crisis (where we use the classification in Caprio and Klingebiel (1999) to identify whether the crisis is systemic or non-systemic), and (b) countries that did not experience a crisis at all during the sample period. Our total sample of 40 countries consists of 29 countries with a total of 32 systemic crises, five countries with a nonsystemic crisis, and six countries with no crisis. Naturally, for the non-crisis countries we do not have data on fiscal outlays, accommodative policies, and output losses. As data on some variables are also not available for some crisis countries, the sample size varies on the particular regressions used. See Appendix Table 1 for the raw data by (non) crisis 19
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE As of 2008, Botswana faced efficiency issues including under-allocation of resources to the power sector, underexecution of infrastructure budgets, and misalignment of tariffs and costs in the power and water sectors. Over the period 2009/10, the government acted decisively to address these issues, and as a result of sound policy decisions the country has been able to recoup close to $68 million a year that were previously being lost to these various inefficiencies.

As a result, Botswana is perhaps the only African country that faces neither serious efficiency nor funding gaps for infrastructure; an altogether remarkable achievement.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Botswana. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Botswana, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Botswana has reached middleincome status and is a resource-dependent economy, two sets of African benchmarks will be used to evaluate Botswana’s situation: Middle-Income Countries (MICs) and resource-rich countries. Detailed comparisons will also be made with immediate regional neighbors in the Southern African Development Community (SADC) countries.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006 for countries for which data was collected in the first phase of the project. For the countries in phase two, including Botswana, data collection began later. Therefore most of the data for Botswana are from the 2004–08 period. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth
Demirguc-Kunt, Love, and Maksimovic (2006) show that incorporated firms grow comparatively faster in countries with strong financial and legal institutions than in countries with weak institutions. Beck, Demirguc-Kunt, and Levine (2005) using the same WBES database, find that incorporated and family-owned firms face the highest financing obstacles. Thus, we also examine whether firms’ perceptions of property rights are affected by their **_Organizational form_** and whether there exist interactions between organizational forms and the institutional variables.

There is also an extensive literature documenting a link between ownership structure and the institutional environment. LLSV (1997, 1998, 1999, 2000), Johnson, La Porta, Lopez-de-Silanes, and Shleifer (2000), and Burkart, Panunzi, and Shleifer (2003) show that legal protection of minority shareholders varies across countries, and this variation determines the level of ownership concentration, the existence of family firms worldwide, the patterns of separation between ownership and management, and the degree of expropriation by corporate insiders. The recent spate of privatizations in Latin America and the transition economies has also brought the performance of state-owned enterprises versus other companies into focus. La Porta and Lopez-de-Silanes (1999) using the case of Mexican privatizations provide evidence that privately owned firms outperform state owned enterprises. Hence, we also investigate to what extent **_Ownership Structure_** influences firms’ perceptions of how well property rights are protected.

Beck and Levine (2002) show that differences in legal and financial systems affect the availability of external finance and the growth of different industries in the manufacturing sector. We would expect these differences to matter as much or more when we look across different sectors. Hence, we also examine if belonging to a particular **_Industrial Sector_** impacts how firms perceive their property rights to be protected.

9
LBN PLW BRA ARG CRI ALB IRN SYC THA FJI MUS MNE ROM HUN BGR BIH CHN BHS URY ISL SWE NOR USA CHL GBR IRL NLD FRA FIN DNK BEL CHE LUX AUT MLT CZE ITA SVN CYP HRV DEU ESP POL PRT LTU EST LVA GRC SVK RUS<br><!-- End of picture text -->

_Source:_ World Bank staff based on data from household surveys, circa 2010 but spanning 2001–10. _Note_ : The sample covers 33 high-income (HIC), 35 upper-middle-income (UMC), 37 lower-middle-income (LMC), and 25 low-income countries (LIC). Classificationof households into the top 60 and bottom 40 percent are based on the income distribution.

These findings suggest that there might be important distributional effects associated with demographic transition. The labor-market implications of rising levels of education, particularly for women, influence fertility. While higher educational attainment (especially of females) and higher household income are both associated with declines in fertility, the importance of education 20