rafmacalaba/data-use-mentions-extended huggingface dataset:
28 Woolard, The Contribution of Income Components to Income Inequality in South Africa: A Decomposable Gini Analysis, Living Standards Measurement Study Working Paper No. 125 (Washington, DC: World Bank, 1996), p. 4.

° This analysis ignores feedback effects, that is, the effects that a change in any source income share might have on distribution within any source income. Of course, such an assumption might be quite unrealistic for large changes in any source income share.

1 1 This household survey was conducted by the International Food Policy Research Institute, working in collaboration with the Egyptian Ministry of Agriculture and the Egyptian Ministry of Trade and Supply. 12 For more details on this 1997 Egypt Integrated Household Survey, see Gaurav Datt, Dean Joliffe and Manohar Sharma, "A Profile of Poverty in Egypt, 1997," draft report (Washington, DC: International Food Policy Research Institute, March 1998).

> 13According to the data, 90 percent of rural households in the survey own their own house. 14 While in Jordan agriculture accounts for 6 percent of GDP and employs about 10 percent of the labor force, in Egypt it accounts for 17 percent of GDP and employs 38 percent of the labor force.

**_15_** As noted in the text, no livestock income was collected in Jordan.

**16** Only the simple correlation between transfer income and total income (0.204) is lower than that for livestock income. The simple correlations for the other source incomes are: nonfalm income, 0.362; and rental income, 0.442.

17 It should be noted that the definition of nonfarm income used here is narrower than those used in other studies. For example, Reardon includes migration (remittances) income in nonfarm income. See Thomas Reardon, "Using Evidence of Household Income Diversification to Inform Study of the Rural Nonfarm Labor Market in Africa," World Development 25 (May 1997): 735-747.

1<sup>8</sup>
<mark>While these are merely suggestive correlations (which might additionally be affected by differences in wording across countries), they do underscore that the Global Survey can uncover differences in nuanced characteristics of public administration across countries and enable scholars to explore their relevance for governance outcomes in a way that composite governance indicators cannot and do not predict (as different employee attitudes and management practices in the GSPS indicators are differentially correlated with composite governance scores</mark><sup>3</sup> <mark>).</mark>

# **<mark>Conclusions and Outlook</mark>**

<mark>How do civil service management practices differ within and across governments? How do core attitudes of public servants – such as their motivation or satisfaction – differ within and across governments? This paper introduces the Global Survey of Public Servants, which enables scholars to draw on micro-data from public servants around the world to understand these key questions in comparative public administration.</mark> <mark>The GSPS can help scholars explore stereotypes about public administrations, assess changes in civil service recruitment, and understand the internal dynamics of governments. For instance, in terms of heterogeneity across institutions, across indicators of public service management, and across demographic groups. They also enable scholars to compare public administrations around the world (with due limitations about measurement invariance and differences in languages and question wording, among others, kept in mind).</mark> <mark>Existing composite governance scores are less resource intensive to collect and will continue to have advantages in terms of cross-country and intertemporal coverage. At the same time, where the GSPS is implemented, it arguably brings greater nuance and</mark> <mark>actionability than composite governance scores by enabling more disaggregated assessments of specific management practices in specific organizations or for specific groups of public</mark> 23
To measure the quality of a paper, we use variables from the RePEc database. The database assesses both journal publications and the gray literature (working papers). In our baseline meta-regression, we use the simple impact factor. Figure 8 (right panel) displays the histogram of the simple impact factor. The figure shows that the pass-through coefficients in our sample are most often collected from studies with a simple impact factor of between five and six.

# [Figure 8 about here]

Table 2 shows the expanded descriptive statistics for author and study quality. While both _h_ -indices from ResearchGate, with and without self-citations, differ only slightly, the _h_ -index from the Scopus database shows substantially lower values. Nevertheless, the variation of the measures is comparable and the correlation coefficient between the _h_ -index from the ResearchGate and Scopus databases is 0.94. This high correlation confirms a similar assessment of author quality across the databases. Looking at the ratings of paper quality, one may notice greater differences among the various ratings, owing also to the different construction and scaling of the indices. Interestingly, for all measures of study quality the median value lies bellow the mean value, indicating similar skewness of the ratings’ distribution.

# [Table 2 about here]

To control for the difference between journal publications and the gray literature, we create a dummy variable ( _work_paper_ ) taking a value of one if the estimated coefficients are collected from working, discussion, or research papers, and zero otherwise. Figure 9 shows the distribution of the estimated coefficients between those collected from gray literature and those collected from journal publications. It reveals that our sample contains slightly more coefficients from journal publications. Using estimates from both the gray literature and journals helps us control for a possible publication bias. The problem of publication bias lies in the review process. Reviewers, especially if they are somehow involved in the topic of study, might force authors to re-estimate their models until the results match the generally known and desired ‘truth’ for
# **Appendix A: Data construction and variable description**

## **Data construction**

Our data set of reference is the WEO (October 2016 version) to capture the general government primary expenditures (GGX and GGEI series), gross domestic product, current prices (NGDP series), the GDP deflator (NGDP_D series), and the terms of trade (TT series).<sup>20</sup> We consider the period of reference by Frankel et al. (2013) from 1960-2009, extended to 2016. To select our countries, we apply the following criteria:

1. We first drop every observation with strictly fewer than one million inhabitants, following Alesina et al. (2008). The underlying idea is those tiny countries are exposed to very large shocks, making the comparison with larger countries more difficult.<sup>21</sup>

2. Then, we drop every country with strictly fewer than 16 years of data (over 1960-2016) for general government primary spending and gross domestic product series, still following Alesina et al. (2008). The underlying idea is that we need to observe at least two or three cycles in each country as we study fiscal procyclicality.

3. We drop all the countries which are simultaneously (i) classified as “high income” for the year 2017 by the World Bank (https://datahelpdesk.worldbank.org/knowledgebase/articles/906519 <u>world-bank-country-and-lending-groups); and (ii) were members to the OECD strictly before</u> the fall of the Soviet Union (year 1992). Under this double selection process, we safely remove all established developed countries, and we keep Turkey, high-income non-OECD countries (such as Saudi Arabia or Singapore) and recent OECD members (such as Israel, Mexico, and Slovenia). By keeping these countries in a first step, we are able to capture more variability in our results and eventually to remove them in a second
|**Tuberculosis testing**|3<br>3<br>3<br>3|
|---|---|
|**Tuberculosis treatment**|3<br>3<br>3<br>2|

_Source: Living Life data_

# **3.4.3. Time and Cost**

All antenatal care appointments and visits are free of charge when done through the public health system in all economies. Pregnant women do not need to be accompanied by someone else to attend an antenatal appointment. The World Health Organization (2016) recommends a minimum of eight antenatal visits to reduce perinatal mortality and to improve women’s pregnancy experiences. Even though antenatal care is widely available and free of charge, UNICEF data indicates that 87% of pregnant women in Ghana, 74% of pregnant women in Vietnam, and only 45% of pregnant women in India received at least four antenatal care visits 2014 when the World Health Organization’s recommendation was for pregnant women to receive a minimum of four antenatal visits.<sup>20</sup> Having a skilled birth attendant at the time of birth is free of charge in Chile, Ghana, and India when pregnant women opt to deliver in public health institutions. In these three economies, it is not possible to have a skilled birth attendant at the pregnant woman’s home at the time of birth, as opposed to in Vietnam where skilled birth attendants are available for home births. No data was collected for Vietnam regarding the cost of having a skilled birth attendant at the time of birth.

DTP immunization is mandatory by law in all four economies through their national expanded immunization programs. The World Health Organization recommends 3 initial doses, followed by 3 booster doses that should be administered to children and adolescents with at least 4 years between each booster dose.<sup>21</sup> Although all surveyed economies provide the first three doses of DTP immunization for free during the
financial services demand in environments like the one we study where formal legal protections are relatively weak, and household financial literacy and education is low. _D. Financial literacy and education_ The education and financial literacy statistics in Table 3 document that a significant fraction of households in our study areas are unable to answer simple mathematics or financial questions, and a smaller fraction do not understand very basic features of the rainfall insurance contracts. This provides prima facie evidence that households have only a limited understanding of the product and may make systematic mistakes about insurance purchase decisions.

19
We combine these infrastructure data with our baseline data and estimate the following specification: _Yict = φ·_ 1 ( _Submar inect ×Connectedi_ ) _+γ·_ 1 ( _Submar inect ×Connectedi ×In f ra_ ) _+β·Zi ×T +_ Γ _i +ηct +μict_ (2) where _In f ra_ is a placeholder for the (high) electrification (road) dummy. _Zi_ is a placeholder for baseline measures such as distance to the coast interacted with time trends. All else remains as previously defined. The coefficient of interest, _γ_ , measures the effect of having a high-speed Internet connection via the submarine cables in districts with high electrification (road) density. Based on the results in Tables 1 and 3, we do this analysis for total FDI and FDI in services.

Results are presented in Tables 4 and 5. Starting with Table 4, columns 1-9 show the interaction effects on FDI in all sectors. Across the various specifications, we find positive and statistically significant effects on the interaction between high-speed internet connectivity and the high electrification dummy.<sup>26</sup> Interestingly, the effect of Internet connectivity in places with low electrification is not statistically significant. This provides suggestive evidence of the complementary role that access to electricity plays in amplifying the effects of Internet connectivity. The interaction between road density and internet connectivity is largely (statistically) insignificant, albeit positive.

Next, we focus on FDI in the services sector. Once again the interaction with electrification is highly significant across all specifications. In column 10 for instance, the results show that high-speed Internet connectivity in districts with high electrification footprints increases the probability of attracting FDI into services by 8.1 pp relative to Internet connectivity in districts with low electrification. The interaction with road density is also positive and statistically significant in all cases except columns 12, 15, and 18 where we estimate the joint interaction effects together in the same specification. Nonetheless, the results here suggest that access to road networks also
” and mistakenly reported a language they knew how to speak. Further, it is possible for younger individuals living in a cosmopolitan urban setting such as Karachi not to speak the native language associated with their ethnic identity. The literature also suggests that individuals might choose to misrepresent their identity, consciously or subconsciously, for security concerns, politics, social identity, self-understanding or other reasons (see discussion in Taş et al., 2014). With these caveats in mind, most respondents in the sample, 77 percent, reported Urdu as their native language, and thus were assigned to the Muhajir ethnic group.

> 8 At the beginning of the survey, some questions were not coded as compulsory and thus, some participants did not complete all the survey questions. These observations were later dropped to maintain consistency across the samples used for the analysis.

> 9 Age was self-reported, and the facilitators did not check identity cards. It is possible for the sample age range to be wider than 18-30 years if the clustering of respondents at the two tails is caused by those younger than 18-years-old overstating their age and those older than 30-years-old understating their age to participate in the study.

> 10 World Development Indicators (WDI), World Bank, 2018.

> 11 Household Integrated Economic Survey (HIES) 2015-16.

9
22 significantly lower fertility compared to their rural counterparts. This is likely due to the fact that the impact of the type of residence is already captured by region fixed effects, which also account for differences in wealth, infrastructure, and remoteness that we are unable to pinpoint more precisely due to the lack of community-level variables recorded in the RDHS.

There is a lack of information recorded in RDHS data on migration history that would allow us to identify whether a respondent was an old or new caseload refugee. However, we know if a woman has always lived in the same community (NATIVE), thus controlling for migration and displacement. Women who never left their community of origin have significantly lower fertility compared to women who at least once in their lives shifted the place of residence. This finding supports evidence from a previous study on Rwandan refugees (Verwimp and van Bavel 2005), which finds that new caseload refugees had 1.04 times the fertility rate of Rwandan women who never migrated, after controlling for age, civil status, education, place of residence and occupation. It also fits well into the findings of Verwimp and Van Bavel (2011) from Burundi, where conflict-related displacement leads to a faster transition into motherhood.

# **b) Conflict and fertility in the long term**

Overall, the impact of conflict exposure becomes less pronounced in the long term (Table 6). The mean fertility rate in the past ten years of women who lost at least one child during the genocide (CHILDDEATH) was exp(0.12) = 1.12 times the fertility rate of women who did not lose children during the genocide. Women who lost at least one son (SONDEATH) have exp(0.13) = 1.13 more children than the reference group, while again the loss of a daughter in 1994 does not significantly influence fertility in the post-genocide period.

Unlike in the short term, sibling death in 1994 has no significant effects on fertility in the long term. These
of equation 1 using the share of the extreme poor as the dependent variable. The preferred specification is in column (3), which controls for the share of the rural population, the literacy rate, total population, and the shares of workers by sector as well as median income per capita in 2000 to account for different trends in trade and labor market outcomes along these dimensions. Illustrated in columns 1–3, exports per worker are negatively correlated with poverty at the municipal level. The results are statistically

> 7 For the export data, see “Exports of Goods and Services (% of GDP): Vietnam,” World Bank, Washington, DC, https://data.worldbank.org/indicator/NE.EXP.GNFS.ZS?end=2018&locations=VN&start=1986. For the poverty data, see Poverty and Equity Data Portal: Vietnam (database), World Bank, Washington, DC, http://povertydata.worldbank.org/poverty/country/VNM. The US$5.50-a-day poverty line is not a good reference point for comparing Mexico and Vietnam because, during this period, the vast majority of Vietnamese households were poor according to this definition.

8
of the country’s administrative regions (10 regions in total, at the time that the enterprise census was conducted). The productivity gap averaged across the five richest regions is three times larger than that for the five poorest regions. Greater Accra, the country’s richest region, has the largest productivity gap, more than four times larger than the productivity gap averaged across the other nine regions. Greater Accra’s large productivity gap is due mainly to the substantially higher average productivity in its formal sector compared to the formal sectors in other regions.

Simulated model predictions suggest a strong negative relationship between the disparity in regional intersectoral productivity gaps and both the level of national labor productivity and the contribution of structural transformation to national labor productivity. Reducing the standard deviation of regional intersectoral productivity gaps by 50 percent raises national nonfarm labor productivity, national aggregate (nonfarm and farm) labor productivity, and the contribution of structural transformation to national aggregate labor productivity by a little less than 3 percent each. Reducing the standard deviation of regional intersectoral productivity gaps by 90 percent raises national nonfarm labor productivity by 13.8 percent, national aggregate labor productivity by 11.9 percent, and the contribution of structural transformation to national aggregate labor productivity by 19.7 percent.

Our argument that the variation in regional intersectoral productivity gaps acts as a barrier to growth-enhancing structural transformation weakens if differences in the pace of structural transformation across regions naturally equalizes regional intersectoral productivity gaps over time. Using short-term net employment creation rates in informal and formal enterprises in services and industry across regions as a proxy for the differing pace of structural transformation across regions, we examine the extent to which structural transformation contributes to the change in regional intersectoral productivity gaps. We find that the differing pace of structural transformation across regions in fact has the opposite effect, increasing the disparity in regional intersectoral productivity gaps.

The rest of the paper is organized as follows. Section II provides an overview of national and regional patterns and trends in incomes, structural transformation, and informal nonfarm employment
samples. The next section (section (5)) reports evidence from a number of alternative samples, for different age ranges of children. Section (6) provides an explanation for the findings that the coresident sample bias in the IGC estimates is small, often ignorable, especially when compared to the bias in the IGRC estimates. The following section (section (7)) discusses the implications of the results for sorting out the conflicting evidence from the existing studies on intergenerational mobility in developing countries. The paper concludes with a summary of the results and their implications for the emerging literature on intergenerational mobility in developing countries.

# **2. Related Literature**

The literature on intergenerational economic mobility in developed countries is vast, but the corresponding literature on developing countries is limited at best. The economics literature on intergenerational mobility in developed countries has focused on intergenerational income correlations, with an emphasis on the link between fathers and sons (see, for example, Solon (1992, 1999), Mazumder (2005), Corak and Heisz (1999), Bowles et al. (2005)). The relative neglect of research on developing countries is evident from the fact that, in his survey for the handbook of labor economics, Solon (1999) cites only two papers: Lam and Schoeni (1993) on Brazil, and Lillard and Kilburn (1995) on Malaysia.

Research on intergenerational economic persistence in developing countries has been constrained primarily by two types of data limitations. First, the income data on parents and children are not available for more than a few years to allow reliable estimation of permanent income across generations. As shown by a substantial body of literature on developed countries, it is necessary to have good quality income data over a period of more than a decade to address the attenuation bias in the estimate of income persistence (Solon (1992), Mazumder (2005)). The household surveys available in developing countries usually provide income information only for a single year, and estimating individual income
Child mortality as the primary outcome variable of interest in this paper is generally defined as the number of children dying before the age of five per 1000 children born alive. This number can either be computed as a cohort measure, which means that a given birth cohort is followed over a five year period, or as a period measure, in which age-specific mortality rates from different birth cohorts are combined for a given time window. Even though cohort measures may seem more intuitive, they are problematic from an analytical perspective, since the estimated mortality rates reflect risk exposure over an extended period of time with potentially large changes in the underlying risk structure.

To minimize the time difference between observed water and sanitation infrastructure and mortality outcomes, we limit our analysis to mortality within the 12-months-window preceding the household interview. The dependent variable we construct is a binary variable capturing the probability of a child between the age of 0 and 48 months dying over the 12 months interval leading up to the interview. This mortality variable thus excludes children who died more than 12 months before the respective DHS survey, children older than 48 months one year prior to the interview, and children born less than 12 months prior to the interview.

Even though the Demographic and Health Surveys (DHS) standardize most variables in their published data, this is unfortunately not true for variables on water and sanitation infrastructure. Given the vast array of different technologies used across countries, the detail and structure of the collected information on water and sanitation infrastructure differs widely across the 38 surveys used in this paper. In total, we found about 150 different sanitation and 200 different water codes in our sample. To make the collected information comparable across countries, we developed a few simple coding rules, which map the various types of water and sanitation infrastructure into three broad categories. Since we want to explicitly differentiate between different types of water and sanitation technologies, we follow a coding similar to the water and sanitation ladder proposed by the WHO/UNICEF Joint Monitoring Program (JMP), which