rafmacalaba/data-use-mentions-extended huggingface dataset:
poor governance? If 25 percent of doctors are absent from rural health centers at the time of surprise visits, does that suggest relatively strong or weak accountability for performance?

If at least a core group of indicators are measured in a standardized way across countries, it is easier to answer these questions. In the case of fund leakage, for example, comparison with the findings of the original Uganda Public Expenditure Tracking Survey (PETS)—where only around 20 percent of cash transfers made it to the school level—might suggest that the country is not doing too badly (Reinikka and Svensson 2005). Ditto with doctor absence, at least compared to the 74 percent absence rate found in small rural clinics in Bangladesh several years ago (Chaudhury and Hammer 2004). These examples are simplistic, but suggestive.

Benchmarking intra-nationally can be even more effective in sparking action, because it allows the states or regions with the most effective service delivery to set the standard for government and the public, while making it harder to make the case that poor service delivery or governance are endemic given local culture or political environment. Two illustrations of how this intra-national comparative approach has been shown to be effective include the case of Papua New Guinea, where the World Bank worked with the Health Metrics Network to strengthen collection of infant mortality data in a way that allowed comparison among parliamentarians’ districts, and India, where collection of teacher absence data that was representative at the state level increased media attention to gaps in performance among them (Kremer et al. 2005). For this intra-national comparison purpose, it is necessary to standardize measurement only at the national level, but achieving even this level of coordination and standardization may require conscious attention. Particularly in large federal countries with a 5
<mark>quantities rather than export values. The country sample is reduced to the EU countries for which export quantities (in kilograms) are readily available, i.e. it excludes Japan and the United States. The results are reported in Appendix Table 6 and broadly support our findings except for the role of</mark> an exporter’s reliance on imported inputs in a sector which remains positive but loses statistical significance.

# **4. Robustness tests and extensions**

In this section, we present two robustness tests and two extensions. First, we use an alternative measure of the Covid-19 shocks in the exporter and importer country based on industrial production. Second, we study separately the months February to April 2020 to better reflect the period of the global downturn, and the months April to June 2020 where restrictions in most countries were progressively eased. Finally, in our extensions we include China as an exporter and use mirror trade data to expand the sample to 64 developed and developing countries exporters.

## **_Industrial production as an alternative measure of the demand and supply shocks_**

Results presented in Table 2 could potentially be biased by the presence of measurement error in the work and retail mobility variables, which are used to capture respectively the demand and supply shocks induced by Covid-19. In order to control for this, equation (2) is estimated using growth in industrial production as an alternative proxy for production shocks through the GVC channel in the exporter country and in its trading partners. Interacting GVC-related sector intensities with IPI growth is more closely related to the GVC channel than the broader work and retail mobility measures. The results presented in Table 3 hold, confirming the heterogeneous impact of the demand and supply shocks across sectors.

**Table 3: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics, OLS regression results, alternative shocks**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2
3 are based on the Population Censuses. Year to year changes in employment monitored collected on an annual basis in the SAKERNAS show quite large fluctuations: they are only useful for examining medium to longer term trends in employment.

The paper starts with an examination of employment and wage growth on a national scale and on Java (compared with the Outer - Islands as a group) over the period 1987-1994. This is followed by an examination of employment and wage change by major sector in various regions of Indonesia: we first deal with longer term trends, and then examine more recent labour market developments. A final section examines inter-regional wage structure.

# **II.** CHANGING NATIONAL ECONOMIC STRUCTURE AND LABOUR MARKETS

Economic growth accelerated in the second half of the 1980s. Indonesia regained overall rates of growth of close to seven per cent, similar to those achieved in the oil boom period of the 1970s (Table 1). Growth rates increased from just over five per cent per annum in the period 1983-1987 to just under seven per cent in 1987-1993, and were slightly higher in the following year.5 Three points stand out regarding the patterns of growth from 1987 onwards.

(i) Despite the jump in non-oil exports, it was not manufacturing which accounted for the surge in economic growth rates. Although manufacturing growth rates were still very high - around 12 per cent per annum for non-oil manufacturing - they were not significantly different from those achieved in the early 1980s. Rather it was the mainly expansion in the non-traded goods sectors which accelerated.

(ii) Government administration grew much less rapidly than in previous periods. This was in contrast to the oil boom and slower growth periods when government administration grew rapidly and made a major contribution to overall growth rates (Sundrum, 1986, 1988).

(iii) The agricultural sector continued to grow steadily at just over three per cent, although its share in total GDP declined more rapidly than in the period of slower economic growth in the early 1980s.

5 The series was
Their share of all aid worldwide is undoubtedly greater, although much of what is popularly termed “aid” from China and some other new donors would not meet the DAC‟s definition of official development assistance (ODA). 5 Bilateral donors are listed in Appendix Table A2.  Multilaterals in the sample include the African, Asian and InterAmerican Development Banks, the World Bank, IMF, EU and UN.  Vertical funds include the GAVI Alliance, IFAD, and the Global Fund to Fight AIDS, Tuberculosis, and Malaria.  The sample includes all donors with sufficient data for listing in the appendix of the OECD (2011) survey report, and all donors covered by Birdsall and Kharas (2010) in their rankings of donor performance.  (The OECD tables exclude Greece, while Birdsall and Kharas exclude Turkey, the IMF and the GAVI Alliance.)

8
Spatial Distributions of Job Accessibility, Housing Rents, and Poverty in Nairobi, Kenya<sup>1</sup>

**Shohei Nakamura Paolo Avner** World Bank World Bank

Keywords: job accessibility, urban poverty, slums, urban planning, housing rent JEL Classification: R14, R21, R31, R41, O18

> 1 This paper has been prepared for the World Bank Kenya Poverty and Gender Assessment by Shohei Nakamura (snakamura2@worldbank.org) and Paolo Avner (pavner@worldbank.org). We are grateful to Nancy Lozano Gracia and Johan Mistiaen for useful comments. We thank participants in the 2018 Jobs and Development Conference at Bogota, Colombia, and the 65<sup>th</sup> Annual North American Meetings of the Regional Science Association International at San Antonio, TX. We would also like to thank Japan International Cooperation Agency (JICA) for sharing the survey data. This work was financially supported by UKAID/DfID (Department for International Development) through the Multi-donor Trust Fund on Sustainable Urbanization.
- The results of surveys of regulatory governance criteria (Section 3)--this covers both formal, legal governance criteria and surveys of how well DTE regulators operate in practice.

- The resources available to regulatory agencies in DTEs and how they compare with the demands on regulators (Section 4).

- Studies, particularly econometric studies, of the impact of regulatory effectiveness on regulatory outcomes (e.g. investment, electricity consumption growth, availability, losses)  (Section 5)--this covers not only the recent (and small) literature on electricity, but also the larger and better established literature on the impact of regulation on telecom outcomes.

- Comparison with the literature on independent central bank (ICB) governance and tests of ICB effectiveness on macro-economic outcomes (Section 6).

The paper ends with a short section of conclusions and key points arising from the literature review (Section 7).

5
” Journal of Political Economy, 122 (2), 319-368.

- [7] Altonji, Joseph, Todd Elder and Christopher R. Taber (2005). “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.” _Journal of Political Economy_ , 113 (1), 151184.

- [8] Ansolabehere, Steven and James M. Snyder. (2006). “Party Control of State Government and the Distribution of Public Expenditure.” _Scandinavian Journal of Economics_ , 108 (4), 547-69.

- [9] Banerjee, Abhijit and Lakshmi Iyer (2005). “History, Institutions, and Economic Performance: The Legacy of Colonial Land Tenure Systems in India.” _American Economic Review_ , 95(4): 1190-1213.

- 18This is based on calculation using the Database of Political Institutions 2015 (Cruz, Keefer, and Scartascini, 2016).

53
Financial Regulation and Development In this analysis, we want to verify two features: 1) the overall number of branches increases, as the model predicts; 2) new branches are more “rural”. In this case, we focus on verifying the number of branches opened in unbranched towns, as a measure of expansion into rural areas. Because our map explores all geographical features, we can see how many branches are opened in previously unbanked areas and therefore we can rely on this as the most reliably measure of bank ruralization.

For this reason, we run two tests, collapsing our branch-level database to a panel at bank level with months and year. The first test is a typical difference in difference regression where the number of branches and the number of branches in previously unbranched areas, _vity_ , are regressed over bank and month-year fixed effects and the interaction between a policy dummy taking unit value after April 2011, the introduction of the 27% rule, and the big bank dummy variable.

With the second test, we run the same variables over a series of yearly dummies, _ιy_ , which describe the branch accumulation trend for small banks and an interaction of these with the big bank dummy, which describe the differential branch expansion of big banks. Table 2 reports that in line with our expectation, after the policy each big bank opened 10 branches more than the other banks, and they also accelerated rural branching, as big banks opened 2.5 additional branches over small banks in previously unbanked areas. Comparing these values with the mean dependent variable row, these magnitudes are substantial: branches grow for big banks 20% more than smaller banks and rural branches by almost 50%.

Figure 7 further explores this and shows the coefficients for big and small banks by describing their trends. The upper panel reports the evolution of branch installation; it is clear that up until 2012 the trends are indistinguishable, but they start to diverge after the introduction of the policy. Analogously, the lower panel shows a similar story for
road transport. To identify a causal relationship, the study will use a quasi-experimental method, IV, where the treatment, defined as changes in transportation costs, will be instrumented by the distance between a firm’s location and a working station of the railroad.

In addition, the study exploits the fact that other transport corridors in Mozambique developed at different speeds and identifies two sets of control firms to match to the treated firms in the Maputo transport corridor: firms in the Beira corridor (that have access to a new port but no railroad) and firms in the Nacala corridor (that have no access to a new port or railroad). To isolate the impact of the Maputo railway rehabilitation, the study will use a matching DID estimation that assumes that the only factor making the trajectory of these three sets of firms different during the sample period is that they were exposed to different transport choice sets. The impact of the Maputo railway rehabilitation is not yet known since only the baseline survey information is available; a follow-up survey will be conducted in 2011.

Sequeira (2011) also discusses a "soft" transport infrastructure project focusing on corruption in Southern African ports.<sup>36</sup> By collecting original data on bribe payments made to customs officials and to port operators in the two competing ports of Durban and Maputo, the study is able to trace differences in bribe schedules to the organizational structure of each port. By observing how firms adapt their shipping and sourcing decisions to the type of corruption faced at each port — which enters the calculation of the overall cost of using each port — the study estimates the impact of corruption at ports on the behavior of South African firms. The estimates show that corruption imposes a distortion in terms of ―diversion‖ — i.e., firms travel on average an additional 322 kilometers, more than doubling their transport costs, just to avoid ―coercive‖ corruption at a port.

This effect is only observed for firms facing a higher probability of being coerced
7 respectively. The World Health Organization adopts US National Child Health Survey (NCHS) anthropometric data as the international reference to estimate its malnutrition indicators. To be comparable with WHO global estimates and other similar studies this paper follows the same methodology.

The three dimensions of malnutrition are interrelated. This is shown in Figures 2 and 3 which map the incidence of wasting against I/A and W/A Z-scores for Bangladesh and Brazil, respectively. Children located towards the right end along the x-axis have a high H/A score (i.e. are tall), while those towards the left end have a low H/A score (i.e. are short). Those to the left of -2Z line are classified as stunted. Children towards the top end of the y-axis have a high W/A score (i.e. are heavy), while those towards the bottom end have a low W/A score (i.e. are light). Those below -2Z line are classified as underweight. Children at the bottom-left corner (the framed area) of the figures are both stunted and underweight. Since wasting is reflected in a low W/H score, and since the axes already capture weight and height (albeit adjusted for age), we can also speculate where wasted children lie in the figures. Children who are both light and short (i.e. children in the bottom-left quadrant) will tend to have W/H scores in the normal range. These children, some of whom will be both stunted and underweight, will be most unlikely to be wasted (i.e. to have a very low W/H score). Likewise, children who are both heavy and tall (i.e. children in the top right quadrant) will tend to have W/H scores in the normal range. For the most part, wasted children will be those in the bottom right quadrant-children who are fairly tall but also fairly light in
- 27 -

last category were industrial labor force growth, high demand to sit for trades tests and requests for manpower from specific industries (Kenya V). Use of such indicators has increased, particularly in later sector work.

3.12 Benefit/cost ratios were calculated as part of the justifications for the technical colleges in Turkey II and the vocational training centers in Morocco VI.

3.13 The data available from project evaluations do not permit a comparison of the relative effectiveness of these various planning approaches. Few addressed the issue of actual manpower supply and demand, and the use of enterprise surveys and other shorter-term indicators of demand is too recent to have been evaluated. However, the SAR for Egypt IV noted that manpower planning is hampered by the rapidity of social and economic change, a general lack of useful data, a lack of coordination between ministries and agencies, and the effect of government recruitment and wage policies. Similarly, sector analyses in Jordan and Tanzania demonstrated the inaccuracy of longer-term manpower requirements forecasting. These dif'iculties have also been documented elsewhere in the literature (Dougherty, 1988 forthcoming).

3.14 Labor market factors received very little attention in the bulk of these investments. However, these became more important in later projects in middle income countries and sector-specific training operations. The occasional use of enterprise surveys is noted above (paragraph 3.11). The Sixth Morocco project was in part justified on analysis of occupational trends from census data showing increases in private sector employment of skilled workers and stagnation in government hiring. Census data on employment was also used in the Turkey II Industrial Training Project, which is also partially addressed to falling worker productivity, and includes both skill and management training. Free standing training projects designed to support one sector were justified on employment demand analyses for public and private enterprises (Sri Lanka I and II, Indonesia Public Works Manpower Development).

# Sector Work

3.15 Sector analysis has made important contributions to the development of national training systems across all regions.
specific point in time is recommended, effectively converting them into an absolute measure. These are referred to as anchored AROP poverty lines.

**In the case of Bulgaria, we construct the low-income-high-cost measure drawing from both observed expenditure and income information gathered as part of the HBS.** We estimate overall household energy expenditure and subtract this amount from the net total household income reported in the household budget survey (HBS).<sup>30</sup> We then compare the remaining household income (the net disposable household income after subtracting energy expenditures) to the official at-risk-of-poverty line (from the EUSILC survey). Income poverty lines and rates are usually estimated using the EU-SILC survey, but this survey does not collect expenditure information.  Therefore, we rely on use income and expenditures from the HBS and the official poverty line from the SILC.  Note that the at-risk-ofpoverty line differs from the official one reported by Eurostat, which relies on data from EU-SILC.<sup>31</sup>

# 2.4. Consensual Approaches

**The set of indicators often denoted as consensual approaches rely on self-reported information by households.** We next explore consensual approaches toward measuring energy poverty. Thema and Vondung (2020) define consensual measures as those that rely on self-reported assessments of housing conditions or the inability to cover basic energy needs because of a lack of access or affordability. Examples of such metrics are the share of households that cannot keep their home adequately warm/cool or those reporting leakages.

**Consensual approaches are direct and personal measures but are subjective.** Consensual approaches are easy to measure and might reflect different energy needs and demands. They also intrinsically capture some of energy poverty's negative social and psychological effects. However, they also suffer from important flaws. First, they are subjective measures, and it might be difficult to compare them across different households, regions, or countries. Next, they might also be influenced by household expectations, preferences, and social norms. These
# **Table A10. Descriptive Statistics for Students in Brazil** **_Based on Individual-Level Data for Students Matched to Surveyed Programs_**

|**Variable**|**Mean**|**Std. Dev.**|
|---|---|---|
|**Panel A. Student characteristics**|||
|Age|24.60|5.57|
|Female|0.47|0.50|
|Mother's education:|||
|Less than primary|0.11|0.31|
|Primary school|0.14|0.35|
|High school|0.19|0.39|
|Higher education|0.07|0.26|
|Unknown|0.49|0.50|
|Student administrative variables (PCA Score)|0.00|1.15|
|**Panel B. Peer(average) characteristics**|||
|Age|24.65|1.77|
|Percentage of female peers|0.47|0.28|
|Percentage of peers by mother's education:|||
|Less than primary|0.11|0.05|
|Primary school|0.14|0.06|
|High school|0.19|0.07|
|Higher education|0.07|0.06|
|Unknown|0.48|0.14|
|Peers'administrative variables (PCAScore)|-0.07|1.45|
|**Panel C. Outcomes**|||
|Graduated|0.30|0.46|

_Sources_ : Own calculations using individual-level administrative data for Brazil. For more details on data sources and variable definitions, see Section 3.3 and Appendix 1.

_Notes_ : In this table, the unit of observation is a student. Statistics
⋆_</sup> . _O_<sup>_⋆_</sup> , a _JK × JK_ input-output matrix is obtained after row-normalizing _O_<sup>ˆ</sup> = _COC_<sup>_′_</sup> , where _O_ in our case is the _S × S_ input-output matrix of the United States with sector number _S < K_ , and _C_ is a _K × S_ conversion matrix which associates products with sectors.<sup>2</sup> Assuming that an estimate of the (global country-product-pair) Leontieftype (at least pseudo-)inverse ( _IJK − H_ )<sup>_−_1</sup> exists, where _IJK_ is a ( _JK × JK_ ) identity matrix, we can define The latter is nothing else than ∆ _t_ + (<sup>�</sup><sup>_∞_</sup> _q_ =1<sup>_Hq_)∆</sup><sup>_t_,wherethefirsttermcapturestheindirect</sup> relevance of the application of policy measures in ∆ _t_ for each importer _j_ and product _k_ from other units (the same or other importers and the same or other products) in month _t_ . As ∆ _t_ is _JK ×_ 4, so is Λ _t_ , and the latter has typical row Λ _jk,t_ .

Using _ηk_ and _ξk_ for the 4 _×_ 1 parameter vectors on the columns of ∆ _jk,t_ and Λ _jk,t_ , respectively, we can define Conditional on the direct effects ∆ _jk,tηk_ , Λ _jk,tξk_ measures the indirect effects of the policy measures in ∆ _jk,tηk_ on _gjk,t_ .

We can estimate _gjk,t_ from bilateral product-level data on exports or imports _xijk,t_ , assuming a customary level-multiplicative or log-additive gravity model of product-level sales of country _i_ to _j_ in month _t_ :

> 2Conversion tables for traded products and various sector classifications are available from the United Nations Conference on Trade and Development.
represents a dummy for an active large or small mining scale operations, a dummy for active high or low value minerals extracted, and a dummy for extractive or transformative mining activities. The coefficient of interest here is ��, which captures a vector of the treatment effects post-WTO accession. The rest of the variables are interpreted in the same fashion as in equations [4] and [5].

Tables 7a–7c, in Appendix II, show the estimated results. As above, columns 1-4 present the estimates based on constant USGS mineral prices, while columns 5-8 report the estimates based on the World Bank’s real prices data.

19