rafmacalaba/data-use-mentions-extended huggingface dataset:
capital city’s per capita income. This has led to reported population in the Mexico DF has remaining stable over the last 20 years while the population in the state of Mexico doubled. We run the analysis both with and without this aggregation (results available on request) and, while the fundamental story does not change, the more moderate growth behavior of the aggregated capital city we find more plausible and we report those results.

> 21 In fact, the state of Chiapas also produces some very modest amounts of oil and we subtracted this off from the state product series in  1975 and 1980.

> 22 Population figures for years 1975, 1980, 1985, 1988 for Chiapas and Oaxaca were extrapolated using yearly population growth rates between 1970 and 1990. According to official figures, the mining production over GDP for Chiapas went from 7.5% in 1970 to 18% in 1975 up to 45% in 1980  and back to 7% in 1985. Clearly, years 1975 and 1980 saw arbitrary assignments of oil production to Chiapas. We have corrected for this as to allow the ratio mining production over GDP to be 7.5% for the outlier years .

8
quite low compared to in-person interviews and attempts to correct for nonresponse bias may not be perfect. Third, the scope and type of questions that can be asked is often limited compared to in-person surveys in order to keep the survey short and to minimize survey refusal.

Two main sampling strategies were used for the World Bank COVID-19 phone surveys, with important limitations intrinsic to the mode of data collection and leading to implications for the populations they were intended to represent. Surveys in 36 countries, especially those in LAC, used pure Random Digit Dialing (RDD) or assisted RDD. This method samples from all active landline and mobile phone numbers, such that RDD surveys would be representative of the population (over 18 years) with an active phone number if survey completion and response were perfect. On the other hand, surveys in 31 countries used a subsample drawn from a prepandemic nationally representative survey. The latter often collected the contact details of the household head, meaning that phone surveys using this sampling frame tend to overrepresent household heads and underrepresent members who are neither heads nor spouses. This means that individual level indicators such as employment outcomes may be biased due to respondent selection within households. The remainder of surveys use other pre-existing lists to identify the sampling frame.

To help correct for the non-representativeness of surveyed households, household sampling weights were calculated. These adjust for differential response rates among subgroups of the population, with the objective of obtaining estimates as close to nationally representative as possible.<sup>7</sup> Addressing the non-representative selection of individuals poses a greater challenge to generating statistics that are representative of individual level outcomes such as engagement in the labor market. Kugler, Viollaz, et al. (2021) examined this for five countries where surveys collected employment information for all household members and find that phone surveys overstate employment rates for the full population even once sampling weights are used. Encouragingly, their analysis suggests that phone surveys do reasonably well at tracking disparities and changes across gender, education, and urban/rural
data.

**We use data for the 2003 to 2017 period, from Eurostat, the Organisation for Economic Co-operation and Development (OECD), and** **_fDi Markets_ (Financial Times).** Given our empirical goal, and considering data availability constraints, we have collected the largest possible set of data at the NUTS-3 level from three main sources to cover the longest possible time period, namely from 2003 to 2017. On economic growth and industrial structure, we use Eurostat data from the _Regio_ database on GDP, population, employment, land area, and sectoral Gross Value Added (GVA) – for agriculture, industry (mining; electricity; manufacturing), construction, market services,<sup>13</sup> and non-market services.<sup>14</sup> Missing values in the NUTS-3 level regional series have been filled in by linearly interpolating NUTS-2 level data. On innovation, we use microdata on patents filed under the Patent Co-operation Treaty (PCT) from the _REGPAT_ database provided by the OECD, aggregated at the NUTS-3 level by priority year and inventor’s residence using the fractional count criterion. On FDI, we use data on inward ‘greenfield’ FDI from the _fDi Markets_ database provided by the Financial Times. Of particular interest for our purposes, the _fDi Markets_ database collects information on individual investment projects in terms of year, destination region at the NUTS-3 (or city) level, and business activity run in the host economy.<sup>15</sup>

**The merging and cleaning procedure of the above-mentioned data series left us with a sample of 1,321 NUTS-3 regions, which represents 98.5% of the EU-28 territory.** We also excluded _a priori_ the French Overseas Departments, as well as Ceuta and Melilla. As shown in Table

> 12 First, there are no data at all for Belgium, Cyprus, Germany, Greece, Ireland, Luxembourg, Malta, Sweden, and the United Kingdom. Second,
. . . . . . . . . . . . . . . .|39|
||5.4.2<br>Bring more transparency to loans and infrastructure projects . . . . . . . . . .|39|
||5.4.3<br>Special Economic Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|40|
||5.4.4<br>Conclusion<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|41|
|**Appen**|**dices**|**47**|
|**Appen**|**dix A Gravity model of trade**|**47**|
|A.1|Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|48|
|**Appen**|**dix B**<br>**Gravity Data**|**49**|
|**Appen**|**dix C Results**|**50**|
|**List o**<br>|**f Figures**<br>||
|1|Kenya’s overall trade balance is a bigger concern<br>Central Bank of Kenya 2015||
|2|China takes a sizeable share of total Kenyan imports (2012-2014)<br>IMF World Economic Outlook Database 2014||
|3|China and India are major sources of imports (2004-2013
**Figure 7: High school completion in Mexico, 18-24 age cohort, 2000**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Income quintile<br>Completion of high school (%)<br><!-- End of picture text --> Figures 5-7 give an impression on the accessibility of higher education per income category. Figure 5 shows higher education enrollment (as a percentage of the 18-24 age cohort) per income quintile. The inequalities are very large. University enrollment exceeds 25% in the richest quintile of the population. In contrast, university enrollment in the poorest quintile is only 3%. To study the causes of these inequalities, we look at university enrollment per income quintile among high school graduates in Figure 6. This figure gives an impression of transition probabilities from high school into tertiary education. Though the differences across income quintiles are less pronounced, there are still important inequalities in university entrance. Specifically, high school graduates from the richest income quintile have a 50% larger probability of university enrollment than high school graduates from the poorest quintile. Figure 7 shows high school completion per income quintile, and reveals that the inequalities with regard to university entrance are equally strongly caused by inequalities in high school completion. Effective government action to promote access to higher education thus not only calls for an appropriate student finance program (to reduce differences in transition probabilities from high school into tertiary education as illustrated in Figure 6), but also requires an improvement in high school completion rates among the poor and middle income groups (Figure 7).

# _5.2 Econometric model and results_

ENIGH 2000 provides information on educational attainment, age, income, and also on student support. This allows us to estimate the effect of financial support on accessibility to higher education. To that end, we can estimate the following
interpretation of transformed variables would not be possible. Omitting certain collinear variables can help, but it weakens the empirical framework and introduces omitted variables bias. Shah (1988, 1989) advocates the use of canonical analysis to form composite indices of collinear variables to overcome multicollinearity. The canonical analysis is preferable to principal component analysis, because the latter technique attempts to maximize the explained variance in these variables, ignoring correlation with the dependent variable. Canonical analysis, on the other hand, forms linear combinations of original variables in such a way that the resulting composite indexes are maximally correlated with the dependent variable. We use canonical analysis to form composite indices of competitive market structure (based upon data for state owned enterprises, state ownership of banks and price controls), democratic institutions (comprising political rights, civil rights and free media variables) and bureaucratic culture (by combining state as a helping hand, credibility of announced policies, efficiency in service delivery and informed and demanding citizenry based upon percent Protestant population). The formation of these composite indices helped us to retain the richness of a large data set while overcoming multicollinearity problems in econometric estimation.

# **Econometric Results on the Causes of Corruption**

Since reliability of corruption indices is our main concern, we weight observations by their standard deviation in our regression analysis. These standard deviations published by the Transparency International measure the dispersion of corruption ratings for each country across different polls. Although the White test rejects the existence of heterogeneity, we still use White-corrected standard errors to address country specific characteristics. The estimated model is presented in Table 1.

Table 1 shows that lack of openness of the economy is significantly related to corruption, whereas public sector size is insignificant and contrary to our a priori expectations has a negative sign. Complexity of the tax system also contributes to corruption (significant at the 10 percent level only). The lack of competitive market structure is identified as a significant source of corruption. Weakness in bureaucratic controls lessens the incidence of corruption although this coefficient is insignificant.
Household size is defined as the number of members living in the household. The mother’s primary education takes the value of 1 if the mother has at least a primary education and 0 otherwise. Similarly, the mother’s secondary education takes the value of 1 if the mother has at least a secondary education and 0 otherwise. Maternal education is included in this analysis due to its positive association with child educational outcomes as discussed in the literature. Given the evidence linking maternal education with child educational outcomes, it is reasonable to assume that it has a positive influence on a child’s primary school enrollment as well.

Household heads take the value of 1 if the head is female and 0 otherwise. The child takes the value of 1 if the child is female and 0 otherwise. The child’s age is the child’s actual age measured in months. The household’s access to improved sanitation takes the value of 1 if the household has improved sanitation and 0 otherwise.

Our data set is a pseudo-panel with children ages 6–9 sampled from three cohort pairs of independent repeated cross-sections of DHS 2000–07, 2004–11, and 2007–14, following Deaton (1985), Magadi (2016), and Ncube and Shimeles (2012), respectively. Unlike true longitudinal panel data, we replace cohort means with individual observations. If an additive individual fixed effect exists, a corresponding additive cohort fixed effect will also exist. The sample cohort means from the surveys are consistent. However, they are error-ridden estimates of the true cohort means. Such data are immune to attrition bias and can be used over long periods. For Bangladesh, the same individuals are not followed over time (unlike longitudinal studies), and histories of individuals are not available (Verbeek, 2008). The individuals sharing the year of birth are grouped into cohorts, and averages for each of the cohorts are treated as observations in a pseudo-panel. All tables and figures are presented in the Annex.

#
2000/01**|10880|15944|13536|10060|
||(250)|(779)|(487)|(273)|
|**Ratio (00/01) to (91/92)**|1.064|1.429|1.088|1.024|
|**Gini Coefficient**|||||
|**1991/92**|0.33|0.30|0.34|0.33|
||(0.01)|(0.01)|(0.02)|(0.02)|
|**2000/01**|0.34|0.34|0.35|0.32|
||(0.01)|(0.01)|(0.01)|(0.01)|

Notes: All figures shown are as calculated from Household Budget Survey data. Standard errors are given in parentheses. All figures were calculated from household-level data on a per adult equivalent basis, with weights calculated by multiplying household size by household sampling weights. 1991/92 figures were converted to 2000/01 Shillings by multiplying by 2.611811, which is the ratio of the poverty lines used to calculate the poverty levels, using nominal values in the official poverty report.

The poverty statistics calculated for this paper differ slightly from those in the official published report, _Household Budget Survey 2000/01_ (United Republic of Tanzania, National Bureau of Statistics, 2002). These are reproduced in Appendix Table 1. While the headcount rate figures are identical to those that were calculated for this paper, the published mean consumption figures differ slightly from those calculated for Table 1. This is partially because the published figures were calculated on a per capita (rather than per adult equivalent) basis.<sup>2</sup> 2 Also, for this analysis, the 1991/
# **Accountability**

While the desire for reciprocity captures taxpayers’ interest in receiving public goods and services, a demand for accountability captures the desire for an institutionalized voice in shaping how tax and spending decisions are made. Of course, the goals of reciprocity and accountability are overlapping: the goal of accountability is, often, to provide an institutionalized way for taxpayers to demand and ensure reciprocity. But they are also meaningfully distinct. Where reciprocity implies governments making potentially time-limited concessions to taxpayers, institutionalized accountability is intended to ensure that reciprocity is sustained. As importantly, accountability may have independent value to those who are granted a voice in shaping tax and spending decisions.

Evidence suggests that lower levels of corruption, as well as participatory budgeting and other forms of direct democracy, are associated with higher tax compliance. This underscores the importance of accountability in shaping trust in tax systems. For example, a recent cross-national survey experiment carried out by the World Bank finds that interventions which increase the salience of anti-corruption efforts and allow citizens to state their expenditure preferences to the government result in sizable and significant increases in tax morale relative to a control group.<sup>147</sup> Two studies from Brazil yield similar findings. In one the authors find that evidence of corruption reduces property tax receipts, and increases demands for participatory budgeting.<sup>148</sup> A World Bank study demonstrates that the presence of participatory governance institutions is closely associated with the ability of municipal governments to raise local tax revenue.<sup>149</sup> The research shows that Brazilian municipalities that voluntarily adopt participatory institutions collect significantly higher levels of taxes than other, similar municipalities without these institutions. Similarly, earlier research from Switzerland found that tax compliance is substantially higher in cantons with larger reliance on direct democracy, including fiscal referendums.<sup>150</sup> Somewhat more broadly, research has argued that tax collection and compliance often increase following the election of new governments, because of renewed popular trust following
Policy Research Working Paper 10209

# **Abstract**

Estimates of the number of people living in extreme poverty, as reported by the World Bank, figure prominently in international development dialogue and policy. An assumption underpinning these poverty counts is that there are no economies of scale in household size—a family of six needs three times as much as a family of two. This paper examines the sensitivity of global estimates of extreme poverty to changing this assumption. The analysis rests on nationally representative household surveys from 162 countries covering 98 percent of the population estimated to be in extreme poverty in 2017. The paper compares current-method estimates with a constant-elasticity scale adjustment that divides total household consumption or income not by household size but by the square root of household size. While the regional profile of extreme poverty is robust to this change, the determination of who is poor changes substantially—the poverty status of 270 million people changes. The paper then shows that the measure that accounts for economies of scale is significantly more correlated with a set of presumed poverty covariates (years of schooling, literacy, asset index, working in agriculture, access to electricity, piped drinking water, and improved sanitation).

This paper is a product of the Development Data Group, Development Economics. 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 djolliffe@worldbank.org and stettehbaah@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
_Ecological economics_ , 15, 91–95.

- Asher, Sam, Tobias Lunt, Ryunosuke Matsuura, and Paul Novosad (2019), “The Socioeconomic Highresolution Rural-Urban Geographic Dataset on India (SHRUG).” Working paper.

- Asher, Sam and Paul Novosad (2018), _Rural roads and local economic development_ . The World Bank.

28
_Brazilian spatial labor dynamics Page 2_

# **Issues in Brazilian growth**

Brazil is a country with large disparities in income between individuals and at all geographical scales. Much though not all of this inequality stems from inequalities between regions. In particular, the Northeast of the country has historically lagged behind the South and Southeast, where much of the Brazilian economy is concentrated. The income per capita ratio of the richest State and the poorest one was 8.9 in 1960 and 6.2 in 1996. (Azzoni _et al_ ., 2000). The 2000 figure was 7.7. This analysis can be extended also to social indicators such as the human development index (HDI). Out of the 10 cities with the worst HDI in Brazil, seven were in the Northeast in 1991 and eight in 2000.<sup>2</sup> Within regions, there are substantial inequalities between states and among municípios. And even within municípios there are substantial inequalities, with many indigent people living in the municipíos with the highest mean income.

Particular attention has been focused on longstanding regional inequalities, especially between the Northeast and the rest of the country. There is some evidence of convergence in incomes between states over the periods 1939-1985 (Azzoni 2001) and 1970-1985 (Ferreira and Diniz 1995). However, several papers find evidence that this convergence process stalled after 1985, with one or two groups of poor states tending towards a lower-income equilibrium than the richer part of Brazil. (Azzoni 2001; Ferreira 1998; Pontual e Porto Júnior 2000). Azzoni _et. al._ (2000) estimated traditional regressions of growth with microdata for 19 Brazilians states. They divided their dataset in cohorts and used a large list of variables as determinants of growth, including microdata from PNAD – National Household Survey. The results suggest that per capita incomes of the Brazilian states have already converged to their steady-state level and that they will remain unaltered due to huge educational and geographic differences between the states.

What accounts for these
Indian height deficit by an increasing association between sanitation and height.

# **2.3 The gap between South Asia and sub-Saharan Africa**

Although people in South Asia are, on average, richer than people in sub-Saharan Africa, children in South Asia are shorter, on average, and open defecation is much more common there. How much of the South Asia-Africa gap can sanitation statistically explain, at the level of country averages?

Table 5 estimates regressions in the form of equation 1, with the sample restricted to countries in South Asia and sub-Saharan Africa and with an indicator variable added for data from South Asia. Of the 140 DHS surveys in figure 1, 11 are from South Asia and 78 are from sub-Saharan Africa. In these data, children in South Asia are, on average, about one-third of a height-for-age standard deviation shorter.<sup>10</sup> How do further controls change the estimate of this South Asia indicator? Merely linearly controlling for open defecation reduces the gap by 30 percent from 0.360 to 0.253. Controlling, instead, for the number of people openly defecating per square kilometer (the product of population density and the open defecation rate, column 4) reduces the coefficient by 83 percent to 0.061. Column 5 verifies that this result is not merely a misleading effect of population density, controlling for which increases the gap.

Pairs of columns 6-7 and 8-9 demonstrate the statistical robustness of the explanatory power of the density of open defecation. After controlling for the log of GDP per capita, adding a further control for open defecators per square kilometer explains 73 percent of the (larger) remaining gap. The density of open defecation reduces by 92 percent the height

> 10 Jayachandran and Pande (2012), using individual-level DHS data from Africa and South Asia, suggest that first-born South Asian children are _taller_ than first-born African children. The country-level data studied here, however, show no similar reversal. If country means are computed using only first-born children,