rafmacalaba/data-use-mentions-extended huggingface dataset:
firms over time. The dataset contains detailed information on firms’ geographic location, economic classification (CNAE 5-digit), employment size, and personnel records such as monthly wage, age, gender, and education, among others.<sup>9</sup> This dataset is used to compute the key variable of interest: labor productivity at the sectoral and industry levels.

Another data source is derived from the Pesquisa Nacional por Amostra de Domicílios (PNAD).

> 8 They also report lack of convergence in poverty rates across regions because of limited labor mobility toward converging sectors and high employment of the poor in sectors exhibiting weak convergence.

> 9 National Classification of Economic Activities (CANE) is Brazil’s system of economic activity classification in the production and dissemination of economic statistics.

8
Using the standardized consumption aggregates only partly mitigates some of the disparities observed in monetary and non-monetary indicators. Table 14 shows the different non-monetary indicators of well-being of the international extreme poor when identified using the standardized consumption aggregates rather than the national consumption aggregates. Bangladesh still scores much worse in many of the dimensions when compared with India. Bangladeshi international extreme poor have lower levels of educational attainment and lower levels of physical assets compared to their Indian counterparts, even though their dependency ratios and the proportion of households engaged in agriculture are similar. This is consistent with the common view that Bangladesh is poorer than India.

While the international extreme poverty rate shows that India has a much higher poverty rate than Bangladesh, further analysis of the non-monetary dimensions of well-being show that the depth of deprivation is higher in Bangladesh than in India, strongly suggesting that the international extreme poor in Bangladesh are worse off when compared to those in India. Monetary measures of poverty show that India and Bangladesh have similar rates of poverty when imputed rent is added for urban households and would show that India has a lower poverty rate if imputed housing rent was included for rural households.

# **7. Conclusions**

The World Bank has set an ambitious target of eliminating international extreme poverty by 2030 and has taken steps to obtain proper estimates of international extreme poverty. One of the key challenges in the measurement of poverty is how to identify the poor. The World Bank uses an international poverty line to identify the international extreme poor, which is currently set at $1.90 per person per day in 2011 PPP dollars. Measuring the consumption of goods and services by households is challenging and very sensitive to survey design and data collection methods. Thus, differences in the way that household surveys are designed and collected across countries can have significant effects on poverty measurement, which are amplified when doing international poverty comparisons.

In this paper, we examine how the construction of consumption aggregates at the country-level in South Asia adds to the total error that is implicitly part of any
sector**|||||||
|Total revenue and grants|44.4|39.5|46.6|29.2|37.5|34.7|
|Oil revenue|37.9|32.1|40.1|20.7|29.6|26.8|
|Non-oil revenue|6.4|7.0|6.1|8.2|7.8|7.6|
|Total expenditure and net lending|27.9|30.0|23.4|24.6|21.5|26.0|
|Current expenditure|18.4|19.6|14.9|13.9|11.4|17.4|
|Capital expenditure|9.4|10.4|8.6|10.7|10.1|8.6|
|Non-oil primary balance (% of non-oil GDP)|-54.2|-59.0|-49.1|-39.1|-34.4|-40.1|
|Overall fiscal balance(commitment basis,incl.grants)|16.5|9.4|23.1|4.6|16.0|8.7|

_Source:_ WB staff calculation based on data from the IMF and the Congolese authorities. n/a = not available.

Social indicators remain low and are not in line with those of other lower middle-income countries. According to cross-country regressions, Congo’s social indicators should be substantially higher given its high GNI per capita. This is illustrated by Figure 1 and Figure 2, showing the under-5 and maternal mortality rate in relation to the GNI per capita. It is obvious that Congo’s performance is similar to that of low-income countries rather than middle-income countries.

> 2 See World Bank (2010c) for a detailed discussion of the risks associated
Drawing on a unique survey dataset, this study examines the existence and nature of linkages between GVC participation and the routine task intensity (RTI) of workers across 47 countries at all developmental stages. Specifically, this paper systematically assesses how the nature of GVCs mediates this relationship, accounting for differences across sectors and types of occupations, particularly offshorable and non-offshorable occupations. Moreover, we evaluate how GVCs contribute to the task structures in the domestic labor markets, and to within-country wage inequality, as measured by the Gini coefficient. We distinguish between the direct – through wages – and indirect – through RTI – contribution of GVC participation to wage inequality.

The relationship between GVC participation and RTI is depends on a country’s factor endowments which determine its type of task specialization in GVCs. In developing countries such as Indonesia, a higher backward GVC participation, i.e., the share of imported inputs used in export production, may be associated with a higher worker-level RTI. Such countries tend to have abundant low-wage labor and specialize in the production tasks of basic manufacturing GVCs, typically the final assembly stage. Thus, they rely strongly on imported inputs which they process for their semi-final or final exports. However, high backward GVC integration also characterizes countries specializing in more advanced manufacturing and services GVCs. Such countries are endowed with skilled labor and perform some routine tasks (e.g. customer service or accounting) and some non-routine tasks (e.g., IT support) (World Bank, 2020). Examples include Central Eastern European countries (Czechia, Hungary, Poland).<sup>1</sup> The type of GVC participation in East Asian and Central Eastern European countries contrasts sharply with that of many Sub-Saharan African or Latin American countries specializing in commodities – agriculture and mining (Hanson, 2017). These countries show low backward GVC participation as they predominantly export upstream GVC tasks with low reliance on imported inputs and fewer opportunities to innovate and upgrade (FernandezStark et al., 2011; Taglioni &
**Table 9:  Analysis of bundled contract attributes at company level**

||DV is log average<br>wage rate paid on<br>oDesk|DV is cumulative<br>percentage<br>differential between<br>contracts and median<br>proposals|DV is average "good<br>performance" ratings<br>over contracts from<br>feedback|DV is average "good<br>performance" ratings<br>over contracts from<br>private success survey|DV is number of<br>workers hired divided<br>by total number of<br>contracts|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||Estimates inclu|Each observation is a u<br>de fixed effects for com|nique company contact<br>pany's first year x moda|located outside of India<br>l job type x country of|<br>company contact|
|Share of contracts that are|-0.135***|-0.075***|-0.070***|-0.071***|0.021***|
|formed with workers in India|(0.008)|(0.005)|(0.007)|(0.007)|(0.002)|
|Share x ethnic Indian company|0.016|0.021|0.000|0.014|-0.002|
|contact|(0.034)|(0.015)|(0.031)|(0.030)|(0.009)|
|Ethnic Indian company contact|-0.041*|-0.025
listing exercise was then conducted in each of the randomly selected EAs, followed by the systematic random selection of 15 households (2,010 in total) to be included in the main survey. Of the 2,010 households interviewed, 1,156 of them owned and operated non-farm enterprises, with about 1 – 4 enterprises per household. This gave a total of 1,180 non-farm enterprises.

The GISMS also randomly selected up to four adults 15 years old or older in each household to provide information on personality traits and gender role attitutes, with a total of 2,652 individuals responding. Given the specific objective of examining the linkage between personality traits, gender role attitudes and business performance, we restricted the sample further to only those businesses that the owner also provided all needed information on personality traits and gender role attitudes. This led to an analytical sample of 861 businesses, which is used for most of the analyses.

# **3.2. Definition and construction of main variables**

Table 1 shows the definitions and construction of variables used in the analyses. We measure business performance in two ways: labor productivity (measured as ratio of total revenue/sale to total number of workers, including household workers) (Martins, 2021); and profits in the last month of operation (McKenzie, 2017). Each outcome variable was winsorized at the top 1% to deal with outliers. Although it may be argued that neither of these measures, taken alone, represents a perfect, universally agreed upon measure of small business success, together, they provide a fairly broad picture of business performance.

The main explanatory variables of interest are the measures of business owners gender role attitudes and personality traits, which were administered to upto four randomly selected men and women in each household. Five Likert scale response options were used – strongly disagree, disagree, neutral, agree, and strongly agree. The gender role attitudes index was constructed in four steps. First, all responses were recoded so that higher
/150 (IMF 2020). See Annex 1 for the derivation of the formulas for the interest rate calculations. For 2017, the shares of birr and fx debt at the end of 2016 (not provided in the DSA table) were inferred from the real exchange rate contribution to “automatic debt dynamics”.

Consider now the impact of moving to market determined exchange rates. IMF (2020) reports the parallel market premium dropped from 35% in November 2019 to 27.5% on April 22, 2020 (IMF 2020, p. 4). We conservatively take the premium as 20% at the end of 2019. In this case, moving to a marketdetermined exchange rate would raise the $-component of public debt from 28.3% of GDP in 2019 to 34% of GDP, so the new d would be 67.1% of GDP.<sup>13</sup> Thus adopting market interest and exchange rates as part of a strategy to attract FDI and give the domestic private sector a boost could have a large adverse effect on debt levels and dynamics. _Alternatively, adverse debt dynamics have been masked by financial repression and overvalued exchange rates, fueling complacency._ Integrating Ethiopia’s public debt sustainability with its development agenda raises questions:

1. What additional development spending does the Ethiopian Government need to make?

2. How will the loss of two implicit tax instruments—the interest savings from financial repression and the implicit tax on exports via the parallel market premium—be compensated?

3. Will the large debt financed infrastructure projects pay off in terms of exports, economic growth and taxes? IMF (2020) identifies weak exports as a key source of vulnerability, calling for a hard assessment of export potential. Past IMF country reports mentioned electricity exports of $250 million by 2019 and of $1 billion by 2023 as the country becomes a “regional energy superpower”. The question is whether this is realistic. Besides, boosting exports would require eliminating the tax
bond) markets and of the composition of the banking sector (domestic versus foreign banks). We further supplement our main databases with a variety of other sources of information on the regulation, supervision and auditing of banks (e.g., the World Bank Banking Supervision Survey), the reliance of industries on external financing, the occurrence of financial, banking or external crises, and the degree of informational and contractual complexity of export products.

To identify the causal impact of financial structures on export dynamics, we employ the difference-in-difference approach originally proposed by Rajan and Zingales (1998) and adopted by Manova (2012) to isolate the impact of financial factors on firms’ export. In particular, we test whether the impact of financial structures on firms’ export dynamics is stronger in industries in which firms are more dependent on external finance for technological reasons purely related to the production process.

The baseline results reveal that banks promote the expansion of the export sector, especially the number of exporters, better than decentralized financial markets (stock and bond markets), and this is true even after controlling for the overall size of the financial sector. This finding is consistent with the view that, through continuous monitoring and tight credit relationships, banks can garner better quality information on potential exporters than dispersed investors (Diamond, 1984; Paravisini et al., 2016). However, when we turn to break down this effect into the impact on exporters’ entry and exit, we find that the main driver of the positive impact of bank-oriented financial systems on total export is a significant reduction in the exit rate of exporters. By contrast, banks do not appear to promote entry into export more than capital markets. We next investigate whether the detected effects of banks are attributable to domestic or foreign banks. We uncover evidence that it is especially domestic banks that reduce the exit rate of exporters. In fact, a stronger foreign bank penetration in the banking system boosts exporters’ entry, exit and turnover rates.<sup>1
With increasingly stringent climate policies and green technologies – like renewable energy and electric transportation – becoming cheaper than fossil fuel equivalent, future economic growth is expected to be more energy efficient and less carbon intensive. We further show the extent to which these changes can reduce the emissions of poverty eradication<sup>24</sup> and estimate the rates of decarbonization that would be required in non-poor countries to offset these emissions.

# **Results**

**How much economic growth is needed to end extreme poverty?** We estimate the historical relationship between growth in GDP per capita and growth in consumption per capita in a random slope regression model, taking into account trends across and within countries. We use data for 168 countries from the World Bank’s 2022 Poverty and Shared Prosperity Report<sup>25</sup> , converting income distributions to consumption distributions where needed. We also winsorize daily per capita consumption at a value of $0.50 as lower values likely reflect measurement error. We find that, on average, when GDP per capita grows by 1%, consumption per capita grows by 0.7%, with variation between countries (the remainder of growth in GDP per capita is likely being allocated to savings which would imply that the savings rate converges to around 30% in the long term). Using the international extreme poverty line at $2.15 in 2017 USD PPP, we focus, in our baseline model, on the poverty target of reducing the share of people living in extreme poverty to 3% or less, the World Bank’s global poverty target and the UN’s interpretation of ending extreme poverty in the SDGs.<sup>3</sup> We estimate the growth necessary to reach this target in each country, assuming to begin with an unchanged distribution of consumption within countries and modeling population growth until 2050 according to UN forecasts.<sup>26</sup>

The per capita GDP growth needed to reduce extreme poverty to
available for three different base years (1999-00, 2004-05, and 2011-12) starting from the fiscal year 1999-2000 until the most recently available. Given the three different base years, we construct a spliced growth rate series. The spliced GDP series is constructed by using the real GDP growth rate as implied by the latest base year; for instance, if the 2011 growth rate is available under both 2004-05 and 2011-12 base year, we use growth rate based on the 2011-12 base year. We have data for approximately 550 districts (covering around 25 states and union territories) with each having, on average, 15 observations. The available district data accounts for, on average, more than 90 percent of the respective states’ GDP and based on 2015 levels, these states and union territories contribute 90 percent of India’s real GDP.

The real GDP growth rate has averaged 6.9 percent over 2000-2019 for the 550 districts covered in the sample with a standard deviation of 7.2 percent (Table 1). We winzorize our GDP growth data at the 1<sup>_st_</sup> and 99<sup>_th_</sup> percentile. This eliminates 34 observations where the absolute growth rate exceeds 50 percent.

# **3.2 Inversions**

To measure inversions we use data from the Modern-Era Retrospective analysis for Research and Applications (MERRA) database provided by NASA (Rienecker et al., 2011). This is a satellite based reanalysis product that provides data on temperature at each of 42 atmospheric levels on a 0.5<sup>_◦_</sup> _×_ 0.625<sup>_◦_</sup> grid.<sup>8</sup> We collect the daily aggregate mean of temperature for the first 19 levels of the atmosphere. This roughly corresponds to the levels beginning at the surface and extending 5km into the atmosphere.<sup>9</sup> Because MERRA indexes layers based on pressure levels
to which the variation during the Farm Bill and ARRA are closely chained. We find that results are similar using the share of R-squared explained by each variable as an alternative following Huettner and Sunder (2012).<sup>19</sup> Anticipating the discussion of controls, we assess how the results change with further inclusion of housing prices, unemployment rates, average state wage bills, average state income, as well as average state income and rent of program participants as controls. We find that, among these, the housing-price control is sufficient for meaningful precision, because the program scale also varied over this period, and housing prices and retail prices were strongly positively correlated during this period. Including the housing-price control substantially improves the first stage.<sup>20</sup> Results are robust to the inclusion or exclusion of any other combination of controls.

We show in Appendix Table N1 that our conclusions about the sources of variation in the IV remain unchanged even after controlling for this extensive list of controls, which together explain a negligible proportion of the variation in the IV. The vector of state-level changes in energy usage and prices does explain about 10%-20% of the IV variation, but the SUA continues to drive variation in the IV, suggesting that idiosyncratic prediction errors are accounting for much of the residual variation. As mentioned, our results are robust to controlling for these energy usage and price variables.

# **2.3 Price Indices**

## **2.3.1 Nielsen Retail Scanner**

We use the Nielsen Retail Scanner Dataset through a partnership between the Nielsen Company and the James M. Kilts Center for Marketing at the University of Chicago Booth School of Business.<sup>21</sup> The data consists of weekly pricing, volume, and store merchandising conditions generated by participating retail store point-of-sale systems across the US from 2006-2015.<sup>22</sup> Data is included from approximately 35,000 participating stores and include
increased attention to survey design, with those of Fafchamps et al (2012) for identifying ways to leverage an electronic survey technology beyond _ex-post_ consistency checks. This research is also closely related to focused work on improving the accuracy of consumption expenditure surveys at the household level (e.g. Beegle et al., 2012; Gibson, 2006; Deaton and Grosh, 2000). The findings from this research and the resulting survey tool are a public good that 6
many countries, especially in Africa. The situation has<sup>been</sup> aggravated by frequent revisions and introduction of new procedures. often being imposed on countries through demands for information from the international agencies. In the overall analysis of data from different sources, the value of a restricted number of indicators can be illustrated and some reforms instituted to ensure<sup>timely</sup> collection, analyses and use of these data in the future.

# 2.4 Th: desisn of country health studies

The information that is needed on patterns of health and service use in order to improve the provision of health care varies greatly between countries. Some health problems, such as infant diarrhoea, are major causes of sickness and death throughout the third world. Other diseases, such as malaria or neonatal tetanus, are of enormous significance in some countries but of little importance elsewhere. Moreover the importance of investigating any health problem depends not only on its impact on the population but also on the capacity of the health system to intervene effectively against it. Similarly, what information should be collected on the health services depends on their overall development, on the way in which health care<sup>is</sup> organised and the services that are provided and on the capacity of the system to respond to problems or issues that are revealed by new data.

Many of the specific issues of importance in particular countries can only be addressed by logistically-complex and expensive inves*igations. For example the assessment of the long-term implications of serious illness or the study of seasonal variations in nutritional status and disease incidence probably involve mounting follow up studies. Some health problems, such as parasitical infections or anaemia, can only be effectively investigated by means of medical examination and not by interview surveys. Such sophisticated, intrusive and expensive investigations can only be justified when the information that they will provide is of particular 11
# **2. Literature review**

Studies on the determinants of participation in antipoverty programs have typically focused on public works programs. There is strong empirical evidence confirming a negative relationship between household wealth and participation in these programs (see, for example, Chirwa, Zgovu, and Mvula 2002; Gaiha 1996; Jalan and Ravallion 1999). But this negative association is driven by the design of the workfare programs, which were aimed at facilitating the self-selection of unemployed workers from usually poorer families.

More recently, a number of empirical studies have argued that poverty is associated with a higher probability of participating in CCTs. Heinrich (2007) presents evidence of the positive effects of Argentina’s _Becas Estudiantiles_ CCT program on student outcomes. In the first-stage regression of this paper, the author finds that students in households with a per capita income below a certain threshold (Arg$45 per month) were significantly more likely to participate. However, after controlling for an index of basic needs, the author finds that students in households with _lower_ per capita incomes were less likely to participate in the CCT program, a result that is in line with the framework outlined in this paper.<sup>8</sup> In a related study, Oosterbeek, Ponce, and Schady (2008) analyze the impact of Ecuador’s _Bono de Desarrolo Humano_ CCT program on school enrollment. They report the results of a regression of actual treatment status on background characteristics and find that poorer people are more likely to receive the transfer. These authors incorporate a third-degree polynomial of the poverty index in the children’s outcome estimation, but they only use the linear poverty index in the regression of actual treatment.

Two studies using the Urban Evaluation Survey (Encelurb) of Mexico’s _Oportunidades_ CCT program investigate the extent to which the urban component of _Oportunidades_ affects children’s outcomes and household consumption. Angelucci and Attanasio (2009) estimate a linear probability model of program participation among eligible households in treatment areas that incorporates second-degree polynomials