rafmacalaba/data-use-mentions-extended huggingface dataset:
1996; Pesaran, 2007). However, evidence is found for convergence in per capita income growth rates in the cointegration testing framework, suggesting that income gaps do not increase over time. More recently, a factor model-framework for club convergence testing has been proposed which is less liable to make false rejections of the formation of convergence clubs than previous time-series approaches. In a dataset spanning 1970-2003 for income per capita in 152 economies, evidence was found for the existence of 5 convergence clubs, with the first dominated by advanced economies (Phillips and Sul, 2007, 2009).

# **4.1 Convergence clubs: Commonalities in productivity** **_levels_**

The first strand of the literature can identify clubs of economies well in an ex-post sense: those economies who have converged over time towards common attraction points will have similar productivity levels and thus be found to have been in a convergence club. Updating the distributional analysis literature to the post-2000 period, when EMDE productivity growth has picked up substantially relative to earlier decades, results in a similar number of clubs relative to earlier estimates (4 in the most recent period). However, relative to earlier studies, 10 faster-growing EMDEs have separated from the lowest productivity club over the past decade to join a convergence club consisting of many middle-income EMDEs (Annex D). The period of faster productivity growth in EMDEs has resulted in new convergence club dynamics—a more comprehensive examination in the following section of both the level of productivity and the trajectory of productivity over time provides greater clarity over the development of convergence clubs in recent decades.

# **4.2 Convergence clubs: Commonalities in productivity** **_trajectories_**

Common productivity trajectories. The clubs identified above capture common productivity levels at different points in time. However, these same productivity levels can be achieved along very different trajectories— a low-productivity economy may be on a growth path that is convergent with high-productivity economies in the future but may not be considered to be in a similar convergence club based
.5<br>(34.3)|62.1<br>(14.7)|34.3|41.4<br>(15.6)|33.3|6|
|South Asia|127.7<br>(41.5)|84.8<br>(29.9)|33.6|70.9<br>(22.9)|16.4|<br>4|
|Sub-Saharan Africa|179.8<br>(60.8)|139.9<br>(43.0)|22.2|87.5<br>(24.0)|37.5|22|
|All|144.4<br>(62.8)|106.5<br>(49.9)|26.2<br>.|68.4<br>(30.1)|35.8|45|

Notes: Compiled from a set of DHS Final Reports from 1987 to 1995. Where there was a choice, the value for "4completed primary" and/or "completed secondary" was chosen. In other cases the group may include mothers who have attended but not completed the educational level.

Income inequality and ethnolinguistic fractionalization are each associated with worse under-5 mortality. Flegg (1982) finds that the elasticity of infant mortality with respect to the Gini coefficient is 0.77 when female illiteracy is controlled for. Our results suggest that the 11
(3) and column (5) with (6) makes it difficult to conclude that the change in product coverage taking place between 2007 and 2009 is a substantial deviation from historical trends. First note that in columns (2) and (5), I make **bold** all realizations of the 2009 import share data that were _larger than_ the 2009 predicted import share stemming from the simple linear regression model. According to the _count_ measure, ten economies (five developing and five developed) had a larger share of 2009 imports become subject to TTBs than was predicted from the models. Only four economies (China, South Africa, Mexico and South Korea) had less product coverage in 2009 than was predicted. On the other hand, using the _value_ measure of equation (2) and comparing column (5) with (6) gives different results; only six economies (two developing and four developed) had a higher‐than‐predicted share of 2009 imports become subject to TTBs. While these economies (India, Indonesia, Australia, Canada, U.S., Japan) did experience increases in the share of imported products subject to TTBs during the economic crisis (see column (4)), the simple linear time trend model predicted this. Therefore, under such an exercise it is only the small _difference_ between the realized 22
BPC beneficiaries according to administrative data. To represent the BPC population in this fiscal incidence analysis we also note that

> 23 The minimal insurance was the minimum wage in 2019 (BRL 998.00), while the maximum could not surpass 1.74 minimum wages (BRL 1,735.29). In certain cases, we correct the data to make the unemployment insurance income to lie within this range.

> 24 PIS stands for Programa de Integração Social and PASEP for Programa de Formação do Patrimônio do Servidor Público. Both are public funds for unemployment, child benefit and allowance for formal low-paid workers. Workers can only qualify for PIS and PASEP if they are registered in the PIS/PASEP database.

15
|1,698|
|**Indonesia**|**203**|350|212|363|211|356|
|Brazil|201|345|178|292|**180**|306|
|Nigeria|153|425|163|529|163|529|

These revisions have affected the projected stationary populations dramatically. The eventual stationary world population now appears almost 10 percent greater than it did in 1980. For Africa in particular, the projected stationary population is now 21 percent larger than in the 1980 projections.

Aggregate changes since last year's projections, on the other hand, are minor. Projected world population in the year 2000 is up half of 1 percent this year as compared with last year. The largest revision for a geographic region is for Asia, but is barely above half of 1 percent.

Projections for practically all countries have changed, but in the majority of cases only minimally, and about as often upward as downward. The larger changes have been due more often to revisions in current demographic indicators rather than to revisions in assumed future trends. Current indicators may have changed, for instance, because of new data or new analysis of census or survey data. Revised current population estimates account for new projections for countries lilSe Swaziland, Costa Rica, Jamaica, Peru, and Papua New Guinea. Revised fertility estimates, often from Demographic and Health Surveys, account for new projections for countries like Nepal, Pakistan, Bolivia, Guatemala, and Nicaragua.

# ComRarisons among World Bank regions

The four World Bank regions, comprising all the borrower countries
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Africa), a smaller percentage of roads are already paved than in southern and western Africa. Nevertheless, the regional aggregates mask variation at the country level.

Some East African countries have already made the investments necessary to pave the portions of the regional network that fall within their national jurisdictions. All of the regional roads in Rwanda, almost all of the regional roads in Burundi, and 80 percent of the regional roads in Kenya are paved. In Ethiopia, Tanzania, and Uganda the share of the regional road network that is already paved is much lower, at 60 to 70 percent. The extreme case is Sudan, where only around 12 percent of the regional road network is paved.

East African countries vary substantially in their track record on maintenance of the regional road network. Overall, 77 percent of the regional network in EAC is in good or fair condition, compared with 56 percent for East Africa as a whole (table 2.3). As a general rule, all EAC members except Uganda are succeeding in maintaining their portions of the regional network in good or fair condition. In Uganda, only 61 percent of the regional road network is in good or fair condition.

Further north, the quality of roads in Ethiopia and Sudan is also very poor. Only around half the regional roads in Ethiopia and around 15 percent of the regional roads in Sudan are in good or fair condition. The large share of poorly maintained roads may be symptomatic of wider deficiencies in the funding and implementation of road maintenance works in these countries, or it may point to the low priority given to regional routes in national road plans. Significant resources are being allocated to Ethiopia and Uganda by the respective governments to improve the regional road network. More recent data will reflect this change.

**Table 2.3 . Condition of East Africa’s regional road network by member country, based on 2008 data**

|Percent||**Co**|*
response to the statement, “In general, immigrants contribute more to a country than they take away from it.” (vi) Positive Beliefs Index: Constructed using dependent variables of columns (1) to (5) following the methodology of Kling, Liebman and Katz (2007). It implies standardizing each variable within the index, calculating the average, and standardizing this average using the mean of the control group. All columns control for gender and student status, the stratification variables. Robust standard errors are reported in parentheses, with False Discovery Rate (FDR) q-values in brackets. Av. Dep. Var. (Control group) represents the mean of the dependent variable in the control group. The Diff. p-value is a Wald test of equality of coefficients between the game treatment and video treatment. YWY denotes the sharpened FDR q-values. Significance levels are indicated as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.
possibly driven by the stronger role of informality. A 10 percent increase in exports of low-tech manufactured goods in LMICs specialized in limited manufacturing is associated with a 1.3 percent rise in employment – both formal and informal – within sectors (Figure 6). Compared with the sample of HMICs (Figure 2), the elasticity is only about onethird their magnitude. This can be explained by the fact that informal workers account for a large portion of employment in lower-income countries due to the lack of unemployment benefits. Export expansion thus leads to substitution effects, where workers move from informal to formal employment (including within sectors) resulting in lower net gains for overall employment numbers. As shown in Appendix Figure 9.5, export growth of goods and services over the period 1991-2019 is indeed positively correlated with increases in the share of paid (wage and salaried workers) in total employment across a large set of countries.<sup>17</sup>

> 16 Examples for labor-saving technology in agricultural production include tomato harvesters, shake-and-catch systems to harvest tree nuts, and wine grape harvesting machines. In Asia and Africa, agricultural production also increasingly relies on outsourced services providers of agricultural machines such as tractors (Christiaensen, Rutledge, and Taylor 2020). A similar argument could be made for business services exports which increasingly depend on information technology (e.g., travel services exports now rely less on travel agents).

17 Studies assessing local labor market effects of exports for South Asia and Mexico do not find any effect on the average employment rate, but confirm a reduction in informality (e.g., Artuc et al. 2019, Robertson et al. 2020, Vazquez and Winkler 2023). Second, a large number of manufacturing jobs in SSA have been created in the informal sector. A study by Kruse, Mensah, Sen, and de Vries (2023) based on the same ETD dataset suggests that these SSA countries experienced a ‘renaissance’ of their manufacturing employment share between 2000 and 2018
# **CURRENCY EQUIVALENTS**

Currency Unit: Naira (N) Official Rate: US$1.00 = N 1.0818 (Mean 1985/1986) US$1.00 = N 22.00 (Mean 1992/93)

# **LIST OF ABBREVIATIONS AND ACRONYMS**

|CBN|=>|Central Bank of Nigeria|
|---|---|---|
|CDR|=>|Crude Death Rate|
|CEM|=>|Country Economic Memorandum|
|CES|>|Consumption Expenditure Survey|
|CPI|=|Consumer Price Index|
|CSER|=>|Center for Social and Economic Research|
|EA|=>|Enumeration Area|
|ESW|=>|Economic and Sector Work|
|FOS|z>|Federal Office of Statistics|
|FGT|=>|Foster-Greer-Thorbecke|
|GDP|=>|Gross Domestic Product|
|IFAD|z>|Institute for Food and Agricultural Development|
|ILO|=>|International Labor Organization|
|IMR|=>|Infant Mortality Rate|
|JASPA|=|Jobs and Skills Program for Africa|
|LSMS|=>|Living Standards Measurement Survey|
|NCS|=>|National Consumer Survey|
|NGO|=>|Non-Governmental Organization|
|NISER|=:'|Nigerian Institute for Scientific and Economic Research|
|NISH|=>|National Integrated Survey of Households|
|PCE|=>|Average per capita expenditure|
|SAP|=>|Structural Adjustment Program|
|SDA|=>|Social Dimensions of Adjustment|
|SSA|=>|Sub-Saharan Africa|
|UNICEF|=>|United Nations Children's Fund|
|UPE|=>|Universal Primary Education|
|WID|=>|Women in Development|

# **GOVERNMENT FISCAL YEAR**

January 1 - December 31
but 64 buyers on average, with a standard deviation of 532. Finally, the sparsity of the network increases with the narrowness when defining a product and the frequency of the data.

To further understand the composition of economic activity of the Indian state of our analysis, in Table A4, we show the types of goods firms sell and buy and what fraction crosses state and country borders. In our state, firms are mostly in the business of selling vegetables, plastics, and minerals and buying machinery, metals, and vegetables. In terms of the type of trade, firms in our state are more likely to sell to other firms in the state. This contrasts with how firms in our state buy goods, where the share of purchases that come from within the state is almost the same as from other Indian states. Finally, international exports and imports represent a non-negligible but small share of sales and purchases.

Before using the lockdown variation to understand how firm-to-firm transactions are affected, we verify the stringency of these lockdowns in Figure A2 using Google mobility data. The data shows how the number of visitors to (or the time spent in) categorized places changes in comparison to baseline days. The baseline day is the median value from the 5-week period Jan 3 – Feb 6, 2020.<sup>17</sup> Until the beginning of March 2020, there were essentially no differences in mobility trends across _Red_ , _Orange_ , or _Green_ zones. But starting at the end of March 2020, we see that there is a substantial reduction in different types of activities (time spent in retail and recreation, grocery and pharmacy, parks, commuting, and workplaces) in _Red_ zones compared to _Green_ zones; with _Orange_ zones in between. People in _Red_ zones also spent more time at home than people in either _Orange_ or _Green_ zones. We notice that starting in August 2020, a few months after the centralized lockdown was over, these differences reduced, and by December 2020, these differences, especially in
81.

- [9] BERGE.L, I.O.K.BJORVATN, A.J.G.PIRES AND B.TUNGODDEN (2015). “Competitive in the Lab, Successful in the Field?” _Journal of Economic Behavior & Organization_ , 118: 303–317.

- [10] BERTRAND.M (2010) “New Perspectives on Gender.” in _Handbook of Labor Economics_ , O. Ashenfelter, and D. Card, eds. Amsterdam: North‐Holland, 1545 – 1592.

- [11] BOOTH.A.L AND P.J.NOLEN (2012). “Choosing to Compete: How Different are Girls and Boys?” _Journal of Economic Behavior & Organization_ , 82: 542–555.

- [12] BREER.P.E AND E.A.LOCKE (1965). _Task Experience as a Source of Attitudes_ . Homewood, IL: The Dorsey Press.

- [13] BRIM.O.G (1958) "Family Structure and Sex Role Learning By Children: A Further Analysis of Helen Koch's Data." _Sociometry_ 21: 1‐16.

- [14] BUSER.T, L.GEIJTENBEEK AND E.PLUG (2015) “Do Gays Shy Away from Competition? Do Lesbians Compete Too Much?” Working Paper No. 9382. Institute for the Study of Labor (IZA).

- [15] BUSER.T, M.NIEDERLE AND H.OOSTERBEEK (2014) “Gender, Competitiveness and Career Choices.” _Quarterly Journal of Economics_ , 129 (3): 1409‐1447.

- [16] CAMERON.L, N.ERKAL, L.GANGADHARAN AND X.MENG (2013). “Little Emperors: Behavioral Impacts of China's One‐Child Policy”. _Science_ 22 339(6122): 953‐957.

- [17] CARDENAS.J.C, A.DREBER, E.VON.ESSEN AND E.
as diversify its exports to the region during the period 1990– 98.

# **Box 1: The Impact of Trade Reforms**

Trade reforms in fact helped South Asia to increase regional and extra-regional trade between 1990 and 1998.<sup>B1</sup> The reforms reduced the levels of protection, but stimulated only a moderate shift in the product

> 13 Comparison based on SITC 4-digit exports.

> 14 A further interesting observation is that India seems to be acquiring market share particularly in other developing countries beyond the South Asia region for this group of manufacturing products, conceivably emerging from typical demand attributes to suit those in the region.

22
# Figure A.1: Financial Inclusion and Tax Compliance Uruguay in a Cross-Country Comparison

<!-- Start of picture text --> A. Financial Inclusion Indicators<br>A.I: Account Ownership A.II: Credit/Debit Cards in Circulation<br>Uruguay<br>Uruguay<br>5 6 7 8 9 10 11 12 6 7 8 9 10 11 12<br>Log GDP per capita, 2011 Log GDP per capita, 2011<br>B. Size of the Informal Economy<br>Uruguay<br>7 8 9 10 11 12<br>Log GDP per capita PPP 2012<br>100.00 500<br>400<br>50.00 300<br>200<br>Account Ownership (% 15+)<br>100<br>0.00<br>Debit or Credit Cards in Circulation (% of Population) 0<br>50<br>40<br>30<br>a Share of GDP 20<br>Informal Economy as<br>10<br>0<br><!-- End of picture text --> Notes: As discussed in the Introduction and in Section 3.1, Uruguay lagged behind peer countries in terms of financial inclusion. Panel A plots the cross-country relationship between financial inclusion and GDP per capita. Panel A.I display data on account ownership, as measured by the percentage of the population (15 years +) with an account at any formal financial institution in 2011. Panel A.II display data on debit and credit card circulation, as measured by the percentage of the population (15 years +) with ownership of a debit and/or credit card in 2014. The GDP data is from the World Bank World Development Indicators Database. The account ownership data is from the World Bank Global Findex Database. The credit/debit card data is from the Global Payments System Survey. Panel B plots
Consultative Conference|(0.039)|(0.040)|(0.039)|(0.039)|(0.039)|
|Prov, Sector, year|Yes|Yes|Yes|Yes|Sector|
|Observations|11,142|11,081|11,125|11,076|11,076|

_Data sources: National Surveys of Privately Owned Enterprises in China._ Notes: Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

41
might make them less salient for treated households. Third, there is much heterogeneity in the impacts across seasons. Electricity savings start in the months following the distribution of CFLs in the spring of 2013, and continue over the summer and fall. In the winter, impacts diverge greatly from the engineering predictions, but in the spring of 2014 the electricity savings return and are similar to those predicted. The regression analysis that follows looks more closely into these impacts of the CFL treatment.

# **5.2 Basic estimates**

To assess the impacts on household electricity consumption, we first estimate a simple difference-in-differences model using the electricity utility records: where _qit_ is household _i_ ’s electricity consumption (kWh) in month _t_ , _Treati_ is an indicator of treatment status, and _Xit_ is a vector of household-level variables.<sup>47</sup> Month-by-year fixed effects, _γt_ , and household fixed effects, _λi_ , address the large seasonal variations in consumption and potential heterogeneities across households in baseline electricity consumption, as identified in the event-study graph.

The _Treati ∗ Postt_ interaction denotes the household’s assignment to treatment and if that treatment occurred in that month or prior months. The coefficient on this interaction term, _τ_ , is therefore an estimate of the average change in household monthly electricity consumption (in kWh) that resulted from random assignment to treatment.

Table 1 reports intent-to-treat estimates of _τ_ . In Table 1, Column 1, the estimated impact is identified according to the above basic regression, from variation within households over time, controlling for month-by-year shocks. This basic estimate indicates that the CFL treatment reduces household electricity consumption by 16 kWh per month. The magnitude of this impact is only half the expected size based on engineering calculations. However, in Column 1, the omitted group is comprised of all control households, regardless of whether 47These include controls for number of days in monthly billing period and whether the household uses electricity for heating. We also control for heating degree days; however, we only
Policy Research Working Paper 10770

# **Abstract**

Although access to reliable electricity enables manufacturing companies to increase their output, there have been few studies of the distribution of output growth between export and domestic markets. Although some papers have examined the impact of electricity reliability on exports (in volume terms or dummy terms), little is known about the way electricity reliability can push existing manufacturing firms more into the export market. Using the World Bank Enterprise Surveys, this paper examines a sample of 13,025 manufacturing firms surveyed in 39 Sub-Saharan African countries between 2006 and 2022. The paper employs the entropy balancing approach to examine how access to reliable electricity affects the distribution of manufacturing firms’ sales between export and domestic markets. The results show that for medium-sized manufacturing firms, electricity access increases the share of exports in total sales at the expense of the share of domestic sales. However, the results for small and large manufacturing companies are not statistically significant. Among medium-sized manufacturing enterprises, domestic companies improve their exports relative to domestic sales when they have access to electricity, with mixed results for foreign companies. Even in resource-intensive countries, electricity access enhances the share of exports relative to domestic sales. The intra-sectoral structural change induced by power access is not limited to medium-sized companies in the manufacturing sector; the same pattern is observed in the service sector with mixed findings.

This paper is a product of the Macroeconomics, Trade and Investment 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 kkaba1@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
<!-- Start of picture text --> Recent years with<br>fatalities (none omit.)<br>—————_j——_-<br>1-2 years<br>—@-—<br>3+ —_—_——_<br>——*—<br>Oo<br>0 5 1 1.5 2<br>Hazard ratio<br>® Bangladesh Chad TM Ethiopia 4 Guinea<br>* India Myanmar O Nigeria<br><!-- End of picture text -->