rafmacalaba/data-use-mentions-extended huggingface dataset:
- Green, Amelia Hoover and Patrick Ball. 2019. “Civilian killings and disappearances during civil war in El Salvador (1980–1992).” Demographic Research 41:781–814.

- Henderson, J Vernon, Adam Storeygard and David N Weil. 2012. “Measuring economic growth from outer space.” American economic review 102(2):994–1028.

- Hijmans, Robert J, Susan E Cameron, Juan L Parra, Peter G Jones and Andy Jarvis. 2005. “Very high resolution interpolated climate surfaces for global land areas.” International <u>Journal</u> of Climatology: A <u>Journal</u> of the Royal Meteorological Society 25(15):1965–1978.

- Jablonski, Ryan S and Brigitte Seim. 2022. “What politicians don’t know can hurt you: The effects of information on politicians’ spending decisions.” Available at SSRN .

- Lauria-Santiago, Aldo A and Jeffrey L Gould. 2008. To Rise in Darkness. Duke University Press.

- Leon, Gianmarco. 2012. “Civil conflict and human capital accumulation the long-term effects of political violence in per ́u.” <u>Journal</u> of Human Resources 47(4):991–1022.

- Liu, Shelley X. 2022. “Control, Coercion, and Cooptation: How Rebels Govern after Winning Civil War.” World Politics 74(1):37–76.

- Lowes, Sara and Eduardo Montero. 2021. “The legacy of colonial medicine in Central Africa.” American Economic Review 111(4):1284–1314.

- Loyle, Cyanne E, Kathleen Gallagher Cunningham, Reyko Huang and Danielle F Jung. 2021. “New Directions in Rebel Governance Research.” Perspectives on Politics pp. 1–13.

- Mao, Zedong. 1966. Selected military writings of Mao Tse-tung. Foreign Languages Press.

- Mart ́ın Alvarez, Alberto. 2010. From Revolutionary
)_|_(0.08)_|

_Note:_ This table presents group averages and differences in means for households matched across the two waves of the panel data. Columns (1)-(4) present average and s.d. for each variable described for ULGSP LGAs and comparator, for each of the survey waves. Columns (5)-(6) present differences in means for the specific groups and the p-value of a t-test for equality of means (clustering at LGA-level) 48
.

Regarding the category of **Barriers to competition** , the OECD STRI provides a whole list of measures under each services sector but not all of them are of interest for the TFiS framework. Although many of them are non-discriminatory in nature, some of them have a discriminatory component and therefore seem less relevant. The relevant measures vary from whether government can overrule decisions by the regulators, whether decisions of the regulators can be repealed, whether there is a review of the regulation, whether there are fee settings and / or advertising restrictions (although not in health as discussed during the expert meeting but rather in professional services). Some of these policy measures are also addressed by the World Bank’s Regulatory Governance database as previously discussed.

To that end, all the sources mentioned above do attain to this requirement, including the ones that are outlined under the sector-specific sources such as the OECD’s STRI in the majority of the cases.<sup>13</sup> 12 The STRI does not explicitly incorporate trade facilitation as a separate category of measure within the regulatory database or, by extension, the index. Part of the reason is that the rationale of the STRI is, for the most part, to collect data on formal laws and regulations only, as they are what governments typically negotiate in settings like the WTO, or for the purposes of regional trade agreements.

13 Having said that, the category of “barrier to competition” in the STRI contains some discriminatory measures and so would fall outside the scope of any TFiS coverage. For instance, for a typical sector, discriminatory measures in this category are: “When appeal procedures are available in domestic regulatory systems, they are open to affected or interested foreign parties as well”; “Foreign firms have redress when business practices are perceived to restrict competition in a given market”; and “Advertising and marketing: only locally-licensed architects are permitted to advertise and market these services”. For some services, these three measures are comprised
At the macro-level, the literature has identified several potential drivers of corruption (for an overview, see Svensson 2005; Fan et al. 2009; Dimant and Tosato 2018). Some of the drivers include GDP per capita, country size, size of the government, education level, trade openness, inflation, ethnic fractionalization and legal origin. However, except for GDP per capita, empirical evidence for most of the other determinants is somewhat mixed and inconclusive. In our baseline specification, we exploit variation in regulation at the country-industry level controlling for industry and country dummies (fixed effects). Thus, all country-specific features, such as income level (GDP per capita), ethnic fractionalization, etc., are controlled for (absorbed by the country fixed effects). In the repeated cross-section estimation, however, we exploit the variation over time in regulation defined at the country-industry level. We include dummies for country-industry pair (Country-Industry fixed effects) which control for all time invariant country-industry and therefore country characteristics that may impact corruption. However, the results here could still suffer from spurious correlation if there is a change in country characteristics over time that is correlated with the change in regulation and corruption. To guard against this possibility, we follow the literature mentioned above and include several country-level controls. The data source for all these controls is WDI unless stated otherwise. The controls include (log of) GDP per capita (PPP adjusted and at constant 2011 international dollars) as well as its annual growth rate; government size proxied by the “Freedom from Government Size” indicator from Fraser Institute’s Economic Freedom of the World database; gross enrollment rate in primary education ( _Primary Education_ ); trade openness defined as the ratio of exports plus imports to GDP; <mark>quality of bureaucracy using</mark> 13
have more assets. Low dietary diversity is identified from the number of food groups consumed by the household. Households who consumed less than 7 of the 13 food groups have low dietary diversity. The coping strategy index (CSI) is a measure of the severity in the use of coping strategies by households faced with a shock, and it is constructed by weighting strategies used to mitigate the impact of shocks by the frequency of their use—typically the number of days in a week. Higher values of the CSI indicate an intense use of coping strategies when faced with a shock, hence a higher likelihood of food insecurity.<sup>12</sup> Table 3: Estimating the Impact of Shocks on Welfare of Sudanese Households: Regression Results

|**Variables**|**Ordinary**<br>(1)<br>**Log (Consumption**<br>**per Capita)**|**Least Square**<br>(2)<br>**FCS**|**s**<br>(3)<br>**Asset**<br>**Index**|**Lo**<br>(4)<br>**Household**<br>**Poverty**|**git**<br>(5)<br>**Low Dietary**<br>**Diversity**|
|---|---|---|---|---|---|
|**Shock type**<sup>**a**</sup>**:**||||||
|Flood/drought|−0.0530***|−1.933***|−4.915**|0.276**|0.606*|
||(
(2.634)***</sup>|1.254|(2.216)|
|Obs.|548||548||548||
|No. of district|||||||
|dummies|3||3||3||
|Wald chi2|181.15||164.45||184.97||
|Spatial parameters:|||||||
|_λ_|0.801|(0.193) ***|2.010|(0.260) ***|0.783|(0.210) ***|
|_ρ_|2.772|(0.123)***|2.416|(0.054)***|2.786|(0.125)***|

# **VI. DISCUSSION**

32. One may be concerned about the robustness of the above estimation results. In particular, the definition of bus service access was an ad hoc assumption in the above. To address this issue, the accessibility to bus services was redefined using a different threshold, i.e., 500 meters. The results are shown in the first two columns of **Table 4** . The statistical significance associated with access to taxi-be routes disappeared, while the rest of the coefficients remain broadly unchanged. It can be understood to mean that the close proximity to taxi-be services is particularly important to local people. The 500-meter distance seems too long, suggesting that an extensive bus network needs to be developed to meet the people’s needs, which are reflected in the observed land values.
hardships and human rights violations experienced by refugees, highlighting that women and children are a highly vulnerable group among refugees and it is clear that women and men, boys and girls experience and respond to forced displacement differently (World Bank, 2017a). However, to date, there is a dearth of research and empirical analysis in this field, in part because there are few datasets available for this task. Hence, there is little evidence pertaining to refugee settings on how gender‐based differences affect poverty rates. Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender‐based constraints and vulnerability affect refugee women’s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty.</mark> <mark>Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender‐specific barriers, based on the premise that the experiences and potential</mark> vulnerabilities of women, men, and children are significantly different in refugee settings.

<mark>We use household‐level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household</mark>

> 2 <u>http://www.unhcr.org/globaltrends2017/, accessed August 2, 2018.</u>

> 3 <u>http://www.unhcr.org/globaltrends2017/, accessed
depending on the sample) cited “do not need insurance” as an explanation for nonpurchase. (See Section 7 of this chapter for more details.) We conclude this section by noting our view that, despite the significant body of research cited above, the literature studying the effect of monsoon quality on consumption, health, savings, labor supply, and so on is still very much incomplete. Much more needs to be understood about exactly how rural households respond to an event like a severe drought, how large the welfare consequences are, and how those costs are distributed among households. We believe that further careful, systematic research on these questions would be very valuable, especially given the potential for climate change to amplify weather variation in future years and decades.

# 4. Contract Features

In India, formal rainfall insurance contracts were first developed by the general insurer ICICI Lombard, with technical assistance from the World Bank (Hess 2003; Bryla and Syroka 2007; Hazell et al. 2010). The ICICI Lombard product was piloted in 2003 in the Mahabubnagar district of Andhra Pradesh, a semiarid region in south-central India; this pilot was expanded to also include villages in Ananthapur in 2004. These pilot areas are also the study area for field research conducted by us, which has involved a series of household surveys and field experiments conducted since 2004. Over time, rainfall insurance has become more available across many parts of India, and policies are also now underwritten by competing firms, including IFFCO-Tokio General Insurance Company and the government company Agricultural Insurance Company of India (AIC). Total amounts sold each year remain relatively modest, however (see Section 5 for a more detailed discussion of the market structure and growth).
The do files are designed to allow users to add variables according to their needs. As explained above (see “Coding Steps”), variables are created in discrete modules within each country-wave pair. They can then be stored in a user-defined location that is dedicated to temporary files. Users can therefore add their own variables by writing their own scripts to pre-process the data, and incorporate them into the final dataset by plugging their custom-made variables into the relevant dataset and country (see Figure 3 for an example). The only requirement for merging in new data is that the linking identifiers match. Users may refer to the variable directories to identify the variable name of the linking identifier in the raw data (Supplementary tables 1 to 5). The LSMS-ISA raw data, which is publicly available (see Table 1), is typically structured according to questionnaire modules (e.g. module 1 from household questionnaire). Users need to consult the survey questionnaires to identify which modules contain the variables of their interest and pull the variables from the corresponding raw data file. Once a user-made variable has been created from the raw LSMS-ISA data, and it is uniquely identified by the linking identifier, it can be merged in the appropriate dataset (either plot-crop, plot, household, or individual), within one of the do files prefixed with “Append_”. Users may follow the example of how other variables are merged into the HP data frames to do this. Once the new variable is successfully merged in the dataset, the remaining do files can be executed without further modification.

# _<mark>Performing analysis with the HP</mark>_

<mark>The LSMS-ISA surveys are designed to be nationally representative. To obtain nationally representative estimates, users need to make use of the sampling weights, EA identifiers, and strata identifiers. As mentioned above, these three variables are present in all four datasets of the HP. Statistical software typically provide packages that enable use of these variables to estimate nationally representative indicators
errors, caused by either sampling or non-sampling issues, are a problem that could arise when projecting future income growth. Given that income statistics and poverty measures depend on Census and household survey data, mistakes made when collecting this information might skew the recovered statistics, for example, Gini coefficients and income averages.

Another significant concern to model the dynamics of the income distribution is related to the causality between inequality and economic growth. When we think about the forces interacting between wealth inequality and economic growth, we might be hesitant to make conclusions regarding the magnitudes and even directions. In the specific case of wealth inequality affecting growth, there is a strand of the economic literature highlighting that inequality is suitable for incentives and therefore for growth in market-oriented economies, whereas another strand argues that inequality has a direct adverse effect on economic growth (Aghion, Caroli, and GarciaPenalosa 1999).<sup>10</sup> Recent empirical research finds that the presence of weak instruments, using a system generalized method of moments (GMM) procedure, is in detriment of robust conclusions about the effect of inequality on growth in either direction (Kraay 2015). Likewise, by making use of robust GMM estimations, Ferreira, Lakner, Lugo, and Özler (2018) find no significant result of total income inequality affecting economic growth. Lastly, Marrero and Serven (2018) develop a model where the impact of inequality on growth can be positive or negative, as it combines two types of effects—indirect and direct—that could be mutually opposing. Poverty is used to identify the indirect impact of inequality on growth. The effect of inequality on the aggregate investment of non-poor individuals can explain the direct outcome. The authors find that the sign and immediate impact of inequality on growth are fragile; this impact can take a positive or negative sign depending on the specific model used and the econometric approach employed. The authors   also find that the indirect effect of inequality and growth via poverty is negative and significant   at high but
Policy Research Working Paper 9010

# **Abstract**

This paper uses methods developed by the Commitment to Equity Institute and data from the Household Budget Survey to assess the effects of government taxation and social spending on poverty and inequality in Moldova. The paper presents the first detailed distributional analysis of the tax and expenditure sides of the fiscal system, examining in particular the contribution of different taxes and transfers to poverty and inequality reduction in Moldova, as well as the cost-effectiveness of different taxes and transfers in achieving these poverty and inequality reduction goals. The analysis finds that the tax-benefit system in Moldova is quite pro-poor and has a significant effect on poverty and inequality, with the poverty reduction effect being stronger for lower poverty thresholds. Pensions provide much of the poverty-reducing effect, which is not surprising, given that in an aging society like Moldova, pensions are the main income source for many households. Direct transfers are also quite effective in reducing poverty and are also efficient, providing a relatively high degree of poverty reduction per dollar allocated to these programs, but their overall effect on poverty is muted by their small budgetary allocations.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at acojocaru@worldbank.org, mmatytsin@worldbank.org, and prohnitchi@gmail.com.

_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
.00 0.00<br>domestic minority majority wholly domestic minority majority wholly<br>foreign foreign foreign foreign foreign foreign<br>owned owned<br><!-- End of picture text --> Source: Prowess; Own calculations The general trend across the world is that GVC trade increased rapidly until 2008 and has been declining since (World Bank 2020; Boris and Mancini 2019). Several macroeconomic reasons have been put forward to explain this. such as, lower output growth across the world, a plateau effect of the extent to which production chains can be broken up, the decelerating impact of China and Eastern European countries for

> 2 However, foreign entry is also limited by other measures which affect foreign investors. For example, there are limits/conditions to the proportion of shares that can be acquired by foreign investors in publicly controlled firms. Also, there are specific requirements for cross-border merger conditions in several sectors. Moreover, in several sectors, there are residency requirements for at least one member of the board and the manager.

> 3 As per the OECD Services Trade Restrictiveness Index, India is relatively restricted in services trade compared to other OECD and emerging economies. FDI restrictions in services in India are mainly related to equity restrictions, particularly for professional and business services, such as legal services, accounting services, real estate services, and insurance services. Several other services sectors still have some equity restrictions in place, such as retail, media services, as well as engineering and financial services. For all the services sectors in India, limitations on foreign entry, remain significantly higher than the OECD average. The sectors for which foreign entry is still somewhat burdensome include accounting, architecture; legal services; telecommunication; all types of transport services; logistics services; courier (due to postal services limitations); commercial banking; and insurance. Transparency-related barriers in services are the third most important category that explain India’s relatively high STRIs.

6
_We know of no analysis of the effects of the current crisis on urban and rural workers._ During the East Asian crisis, the initial impact of the crisis was felt particularly hard in the urban manufacturing and construction sectors.<sup>9</sup> In Indonesia, both overall and salaried employment declines were larger in urban areas, and urban women suffered substantially larger wage declines than rural women.

_To sum up, existing empirical evidence from past and present crises suggests four hypotheses:_

- Men and women experienced roughly equal falls in income and employment, although in some settings, the added worker effect may lead to smaller falls among women.

- Young workers experienced greater increases in unemployment and informal employment than adult workers.

- Impacts on less educated workers were more severe than better educated workers, but the results depend on the country context and the nature of the shock.

- The shock may have reduced employment and earnings more for urban workers than rural workers.

_Evidence on the vulnerability of different types of workers to this shock remains quite limited._ Past crises in Argentina and Indonesia may not be relevant to the current great recession, since the macroeconomic causes and consequences are very different, and most initial evidence on the current crisis has focused on unemployment and is often limited to OECD or European countries.<sup>10</sup>

# **3 Data and Methodology**

## **3.1 Data**

_This analysis draws on repeated cross sectional household data from 17 countries_ (Table 1) _._<sup>11</sup> These include five Latin American and Caribbean countries (Argentina, Brazil, Chile, Costa Rica and Mexico), seven countries in the Europe and Central Asia region (Bulgaria, Latvia, Lithuania, Macedonia, Poland, Romania and Turkey), two counties in East Asia (Indonesia and the Philippines), two countries in Middle East and North Africa countries (Egypt and Jordan) and South Africa. Countries differ in
sup> ijk<sup>P P I</sup> and Si<sup>C,P P I′</sup> j line up almost exactly along the 45-degree line with a correlation of over 98% (the analogous figure for import prices is similar). As such, I can replace βi′jkwith β<sup> ̃</sup> C Si<sup>C′</sup> j<sup>andβ</sup> i<sup>P P I′</sup> jk<sup>withβ ̃</sup> C<sup>P P I</sup> Si<sup>C,P P I′</sup> j which yields Equations 15 and 16, which can be taken to the data. The final term in each of these equations is the import-weight exchange rate for the sector, which excludes the bilateral RER for the import price Equation 15 (with weights rescaled so they add to one). This is a measure that I construct in each sector in the model and in every 6 digit NAICS manufacturing sector in the data (see Appendix B).

To generate estimates of pass-through of competitors’ exchange rates in the model (to compare to the data) I first solve the full model in steady state and calculate the set of structural β coefficients. Then I draw a sample of exchange rates, use Equation 13 and 14 to generate a vector of firm-level prices, and then estimate variants of Equations 15 and 16 on the simulated data (sampling in proportion to size) — see the online appendix for further details.<sup>17</sup> 4.1. **Calibration.** The calibration of the model is challenging due to the absence of microdata on quantities (and hence sector shares) in the BLS data on the demand side, as well as the lack of firm-specific information in the many producing countries on the supply side (for example the share