rafmacalaba/data-use-mentions-extended huggingface dataset:
data, and to a lesser extent vegetation and pollution data, can provide a meaningful signal of regional economic distress and recovery, particularly during the early phases of a major crisis such as COVID-19.

This paper is a product of the Development Economics, the Poverty and Equity Global Practice, and the Human Capital Project. 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 dnewhouse@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 of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **4.3 Firm-level panel data**

We construct a firm-level panel dataset using the annual ASI data. We use firm identifiers to track firms over the years 2010-2018 and limit the sectors to manufacturing- related sectors and omit agriculture and other primary activities. As mentioned earlier, the ASI survey does not cover all firms every year and chooses a sample of small firms to be covered. Our study, however, focuses on all firms with at least 100 workers. As the ASI survey is supposed cover all such large firms in every round (as part of the “census” section of the ASI), we are less likely to lose out on firm observations because of sampling. In addition, we do not lose out on a lot of manufacturing activity because as discussed, this subset of large firms accounts for most of formal manufacturing activity.

The dataset includes information on the 3-digit industry group and the state in which the firm is located. It also includes information on output, inputs and employment. On employment, it covers labor involved in production as well as the split of workers into contract and permanent categories. For each of the labor categories, there is also information on the total wages paid out. With regard to inputs, the dataset covers expenditure on the fixed capital stock as well as variable domestic and directly imported inputs (that is, materials) sourced by the firm in the last fiscal year. We sum up the latter two expenditures to get a measure of the firm’s total expenditure on variable material inputs.

Similar to the case of the state- level panel, this dataset also drops all small states and union territories. In addition, we have to drop firms that were in the new state of Telangana as it was not possible to track the firms back in the period before the split. The dataset does not consider those firm observations that do not report any labor information. All outcomes are winsorized at the 1/99 level by year.

# **4
The empirical analysis in this paper contributes to several strands of literature. First, there is now emerging literature on trade and transport emphasizing that (i) transport prices are endogenous to trade flows, (ii) there are spillovers across routes because truckers take account of the probability of finding the next trip while planning for a trip (Brancaccio et al. 2020, Wong, 2018); and (iii) the thicker trade flows along a route allow the use of larger and more efficient trucks leading to the extent of market effect observed in our data (Allen et al.,2021; Asturias, 2020). Our empirical analysis provides suggestive evidence in favor of the presence of these externalities in the Indian setting where the trucking sector is predominantly informal and unregulated with few entry barriers. The presence of these externalities suggests that the return to the construction of a road would depend on not just the reduction in travel time but also the characteristics of the places that it is connecting.

Second, trade elasticity is a key parameter in quantitative spatial equilibrium models. We provide a causal estimate of the elasticity of trade flow with respect to trucking costs which has not been --to the best of our knowledge -- available for developing countries in the existing literature. Our estimates of elasticities imply elasticities of substitution within the range of 21-29 which are much larger in magnitude than the estimates (5-6) derived from the spatial equilibrium models estimated for India (Asturias, GarciaSantana and Ramos, 2018; van Leemput, 2021). But our estimates are very much in line with the estimates derived from direct data on freight rates by Wong (2018) (22-27). It also confirms the _a priori_ expectation that this elasticity should be higher in poorer countries where consumers tend to be more sensitive to prices (Duranton, 2014).<sup>6</sup> Third, developing countries are characterized by considerable regional inequality, where modern economies in larger cities coexist with subsistence economies in remoter regions, and improved transport connectivity
|71.69|
|Female|48.13|31.28|41.91|35.88|53.93|28.31|
|**Education**|||||||
|No education|30.05|67.29|29.35|55.37|25.01|50.00|
|Primary Incomplete|36.18|28.91|32.59|32.02|34.18|34.07|
|Primary Complete|21.22|2.93|23.52|11.28|23.52|13.52|
|Secondary Complete|6.15|0.65|6.35|1.13|8.52|2.35|
|Higher Education|6.40|0.21|8.18|0.21|8.77|0.06|
|**Labor status**|||||||
|Employed|7.56|22.91|5.13|30.92|9.31|25.93|
|Unemployed|7.88|1.30|15.24|4.63|5.83|3.64|
|Inactive|84.56|75.79|79.63|64.45|84.86|70.42|
|Source: Calculations based on ENIGH 1|992-2002. No|terural is ar|eas with less|than 2,500i|nhabitants.||

# **3.1 Poverty Depth**

Although Mexico has made substantial progress since 1996 in reducing the share of the rural population living in extreme poverty, the problem remains broad and deep. The P0 measures the proportion of people below a certain poverty line but takes no account of how far they are below that line—the degree of poverty—or whether they are becoming even
|[0.911,0.977]|0.001|0.954|[0.922,0.987]|0.007|
|Whether child is wasted|0.943|[0.911,0.977]|0.001|0.959|[0.928,0.991]|0.014|
|Shaded cells denote variables with si<br>a: This is not an OR but simply the re<br>Confidence intervals reported are ba<br>The control variables used in the ad-<br>squared, household size, the young a<br>or older than 64 and the total numbe<br>wealthquintile,a binaryvariable for|gnificant coeff<br>gression coeffi<br>sed on robust<br>hoc model incl<br>nd old depend<br>r of working a<br>rural areas an|icient estimates (P<br>cient of mother’s y<br>standard errors<br>ude: the child gen<br>ency ratios (ratio<br>ge members 15-64<br>d a set of binaryva|-value < 0.0<br>ears of edu<br>der, child ag<br>of househol<br>years old),<br>riables for r|5)<br>cation<br>e in mont<br>d members<br>binary vari<br>egion of re|hs and age in mont<br>younger than 15 y<br>ables for the house<br>sidence.|hs<br>ears old<br>hold’s|

A different picture emerges from the same analysis with data from São Tomé and Príncipe. The estimates based on the ad-hoc choice of controls (column 1 Table 4) suggest that HAZ, stunting, and maternal education are mostly significantly associated with early
change in the indicator of interest, the estimated coefficients from a quantile regression on the 25<sup>th</sup> or 67<sup>th</sup> percentile can be used to generate predicted percentiles at the 25<sup>th</sup> or 67<sup>th</sup> percentile.

Quantile regressions have been used to generate student growth percentiles (Betebenner, 2009) and conditional growth charts (Wei and He, 2006). The work on student growth percentiles has generally conditioned only on past achievement levels (and not observable characteristics of the students or their teachers), as the effort of introducing the concept into educational management in such places as the states of Colorado and Massachusetts has been to separate the description of student progress (the SGP) from the attribution of responsibility for that progress<sup>7</sup> .

Quantile regressions have been used quite extensively in economics (see Koenker and Hallock, 2001 for a review). Three applications that are most closely related to controlling for heterogeneity in looking at the performance in percentiles are: a) Essama-Nassh et al (2010), which used quantile regressions<sup>8</sup> in an extension of the growth incidence curves that have been used to look at pro-poor growth (Ravallion and Chen, 2003): b) the World Bank Poverty Assessment for Guatemala (World Bank, 2009), which used quantile regressions to look at conditional percentile distributions for a set of poverty and social indicators; and c) (Azevedo and Pizzolitto, 2009) which used quantile regressions to assess the targets of the National Development Plan of the Dominican Republic.

The approaches followed with student growth percentiles and conditional growth charts share some similarities with the approach followed with growth incidence curves. They both look at the growth of an indicator at a given percentile in the distribution. They both can control for background characteristics. However, growth incidence curves are typically calculated from repeated cross-sections. They typically cannot provide
This paper uses a synthesis of economic theory to challenge the prevailing policy regimes that focus on high-powered incentives to deliver basic health services and offers new ideas that have the potential of working more effectively to improve public health. First, the paper draws upon Khemani (2019) and World Bank (2016) to summarize the insights from a synthesis of economic theory of principal-agent relationships. Economic theory argues that optimal contracts in complex organizations that are tasked with providing public goods could use low-powered incentives (steady wages, job security), and rely on recruitment of intrinsically motivated workers, as well as professional norms among peers to achieve high performance or productivity.

Second, the paper provides evidence from a rich survey in Bihar on current health policies, management practices, and incentives, beliefs and norms of health workers in the public sector. It shows how current management practices and the environment in which health workers perform their tasks are the opposite of what economic theory would recommend.

Third, the paper discusses how this difference between economic theory and policy practice can be rationalized and explained. We use available research on institutions to argue that low trust and perverse expectations of behavior in the public sector, for rent-seeking rather than service delivery, characterize institutions in poor places like Bihar. Because of these institutions, reform leaders are reluctant to provide steady wages to new cadres of public health workers because they do not trust that the additional fiscal burden will yield significant results that are valued by the citizens who vote them into, or remove them from, office.

Fourth, the paper shows how local politics in Bihar (as in other poor places of the world, examined in World Bank, 2016) exhibit potential for change that policymakers could harness to transition to institutions of greater trust, and stronger norms of performance in the public health sector.

The paper concludes with outlining a set of policy ideas for reform leaders and international partners to immediately put to use as they respond to the COVID-19 pandemic. Over time, these ideas can contribute to addressing weaknesses in
(Gross fixed<br>capital formation (constant 2010 US$)/GDP (constant 2010 US$)).|
|Public investment‐to‐output and<br>private investment‐to‐output<br>ratios<br>�<sup>�</sup><br>�<br>,<br>�<sup>�</sup><br>�|LTGM‐PC|Individual country values are derived by applying the 2015 shares of public<br>and private investment calculated by the IMF FAD to WDI data on<br>�<br>�<br>~~.~~IMF<br>data:http://www.imf.org/external/np/fad/publicinvestment/|
|The sample of countries is guided by t<br>affected countries based on the media<br>countries was reduced to 147 to exclu|he availability o<br>n value of avail<br>de outliers that|f human capital data. For missing data in other variables, these are interpolated for the<br>able data for the corresponding country groups by income. An initial sample of 151<br>appeared in the top graphs of Graph 2(The Bahamas,Egypt,Iran and Uzbekistan).|

17
<!-- Start of picture text --> Nearest Nearest + 20km Nearest + 50km<br>2.5<br>0.0<br>−2.5<br>2001 2006 2011 2016 2020 2001 2006 2011 2016 2020 2001 2006 2011 2016 2020<br>Year<br>Refugee Presence<br><!-- End of picture text --> Figure 4: Levels of refugee presence, parishes within 150km. The figure shows that for parishes within 150km of any refugee settlement, our three alternative refugee presence measures all increase after the December 2013 start of the South Sudanese civil war (red line).

were government funded (public) or non-government funded (private).<sup>13</sup> Intersecting these points with our baseline 2001 parish shapefile gives us the number of primary schools per parish-year (and by type). We normalize these values per 1,000 primary-school-age children for our measures of _Public Primary School Access_ and _Private Primary School Access_ .

Second, we use data on secondary schools provided by the Uganda Ministry of Education that was independently verified by the World Bank. For the more than 3,600 secondary schools, we have geographic coordinates, founding year, and whether the school is public or private. We similarly construct measures for _Public Secondary School Access_ and _Private Secondary School Access_ by normalizing the number of secondary schools (by type) per 1,000 secondary-school-age children. For more details on primary and secondary school data construction, see SI Sections S1.3 and S1.4.

Third, to measure _Health Access_ , we compiled a geocoded list of approximately 6,800 health facilities in Uganda by merging the 2006 health facility list from the Uganda Bureau of Statistics and the 2012 and 2017 master facilities lists from the MoH. For this outcome we have three time periods: 2006, 2011, and 2016. Uganda has five types of health facilities, ranging from HC-I (small clinic) to HC-V (hospital).
categories, indicating no observable effect of respondent fatigue from the detailed food list on subsequent non-food categories. Clearly, at least in the context of Tanzania, the 17 most important food items (a subset of the full list of 58) performs only marginally worse than the full detailed listing.

Table 5 compares results among the three diaries. In Panel A, columns 1 and 2, we find that household diaries record significantly less total and food consumption than the personal diary, from 13 to 20 percent lower. Between the two types of household diaries, the frequency of interviewer visits makes little difference in total consumption, but food consumption is 7 percent higher in the infrequent diaries than the frequent household diary (and this difference is marginally significant at the 10 percent level; results available upon request), suggesting that the net impact of telescoping and recall error in the infrequently supervised households is positive but not especially large. Interestingly, the frequent non-food consumption exhibits the largest deviations in the household diaries from the individual diary, suggesting that a relatively large amount of these goods are consumed outside the purview of the household respondent.

In panel B of Table 5, we subdivide the household diaries on the basis of the literacy status of the household. Approximately 16 percent of households had no literate adults to fill in diaries. Households assigned to the diary with infrequent field visits had more visits if the household was deemed illiterate (see Table 1), although not as many as for households in the frequent visits subsample (module 6). The presence of an illiterate member does not affect reported consumption when households are regularly supervised (panel B columns 1-4, module 6). However, infrequent supervision results in significantly lower reported consumption among illiterate households, even for non-food non-frequent items, which are asked only in recall format. Clearly, leaving a diary with a household lacking a literate member will yield greater mis-measurement unless this difficulty can be overcome through frequent visits, thus converting the survey format to a de facto high frequency recall.

#
# **Annexes**

# **Annex I: Key Survey Dataset**

**Table A1.1** : Prices of coal and biomass fuels, and brick weight by province*

||Koshi|Madesh|Bagmati|Gandaki|Lumbini|Sudurpachim|Nepal<br>(Weighted avg.)|
|---|---|---|---|---|---|---|---|
|Coal (NRs/kg)|41.7|40.0|46.0|45.5|41.8|45.5|43.4|
|Biomass (NRs/kg)||||||||
|Traditional|8.0|8.0|8.0|8.0|8.2|-|8.04|
|Briquettes/Pellets|-|-|-|-|-|12.0||
|Brick weight (kg/brick)|3.917|3.375|2.500|2.750|2.613|2.529|2.874|

_Note_ : * Because there are only two brick kilns in Karnali province (ILO et al., 2020), we excluded this province from our study. Source: (Timilsina and Malla, 2023)

**Table A1.2** : Type of technology and fuel used by the surveyed kilns

||Technology|Fue type|
|---|---|---|
|1|CK|Coal|
|2|ZZK ID|Coal and sawdust|
|3|ZZK ID|Coal|
|4|FC BTK|Coal, sawdust and rice husk|
|5|ZZK ND|Coal|
|6|ZZK ND|Coal|
|7|HK|Coal, sawdust and bagasse|
|8|ZZK ID|Coal,
**Figure 31: Rate of Return to Capital, Togo, 1960‐2008.**

<!-- Start of picture text --> ogo: ate o etu o Cap ta : 960 008<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Capital Stock<br>Income Accruing to Capital / Estimated<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> _Source_ : Authors’ calculations. WDI data, World Bank.

# **6.3 Geography and Regional Potential**

**Togo’s economic prospects are not constrained by its geographic characteristics.** That is, when we regard geography in a narrow sense, involving the country’s physical characteristics, access to international markets, resource endowments and its degree of exposure to natural hazard phenomena. However, our opening statement needs to be made with modesty when we extend the concept to consider the economic geography dimension. That is, the one that involves the country’s current access to dynamic, high income markets with high income, growth‐enhancing products. We develop these ideas below.

**Togo is a small, modestly population‐dense country resulting in a small domestic market, but many other characteristics compensate for this.** With an area of 56,785 square km it only ranks as the 132<sup>nd</sup> biggest country in the world. This feature, however, does not impose a physical limitation to conduct business, as the country has excellent access to neighboring Ghana, Burkina Faso and Benin; is a coastal economy with a relatively well developed deep sea port in  Lomé
economies with deeper financial systems in 1960 saw faster growth in the following 30 years. This suggests that the effect of financial sector development on economic growth is significantY' Capital markets, too, have a positive effect on growth. Of 38 countries with the requisite stock market data, those with highly liquid equity markets in 1976 saw more rapid growth between then and 1990. And those with more liquid stock markets and more developed banking systems experienced the most rapid growth rates. This complementarity of banking and stock markets, which appears throughout most stages of development, likely arises because both debt and equity finance induce better accounting, auditing, and formation of a cadre of trained finance professionals. More important, as suggested by CTF, complementarity arises because efficient equity markets need to rely on efficient banking for the provision of liquidity, payment, and securities management services (OECD, 1993). It is only as countries reach the per capita income levels of OECD countries that further stock market development seems to induce a decline in firms' debt-equity ratios, as many of those services are progressively produced by non-bank financial institutions.

Convincing evidence of the relationship between finance and development also emerges from a look at how financial resources are allocated among firms before and after financial reforms. Schiantarelli _et. al._ (1994) found that, following financial reforms in Ecuador and Indonesia in the 1980s, there was an increased tendency for finance to be allocated to more efficient firms than before the reforms: with less intervention in credit allocation and pricing, intermediaries were more likely to allocate capital where it would be best used, thereby raising economic growth.1<sup>8</sup> 18
ES data for several countries around the world<sup>12</sup> and suggests that policy needs to expand the spectrum of firms that conduct R&D as a way to increase the national R&D intensity.

**Figure 13. R&D intensity, R&D incidence and GDP per capita across developing East Asian countries**

<!-- Start of picture text --> 6 25.0%<br>Vietnam Malaysia<br>Philippines<br>5<br>20.0%<br>Lao<br>Thailand<br>4 Cambodia<br>15.0%<br>3<br>10.0%<br>2<br>Indonesia,<br>5.0%<br>1<br>0 0.0%<br>3.1 3.3 3.5 3.7 3.9 4.1 4.3 4.5<br>Log GDP per capita (2011 PPP adjusted)<br>average log_Rdworker share of firms conducting R&D<br>Linear (average log_Rdworker) Linear (share of firms conducting R&D)<br>Source: Own elaboration based on ES data and WDI dataset<br>Note: R&D intensity is computed as the average logarithm of R&D per worker in US$, adjusted by sampling weights; R&D incidence<br>is computed as the share of firms doing R&D using sampling weights.<br>Share of firms<br>Average Log R&D per worker<br><!-- End of picture text --> ES data suggest that other innovation inputs, besides R&D, also play a relevant role in the innovation process. Results show that the proportion of firms that report innovation (of product or process) is much larger than reported formal R&D activities; in other words, a
|(69.20)*|(69.93)*|
|# of Regular Clients Seen||6.49<br>(7.03)|||5.77<br>(7.35)||
|# of Casual Clients Seen||-8.71<br>(4.69)*|||-10.04<br>(4.75)**||
|Activity Controls|No|No|Yes|No|No|Yes|
|Observations|12321|12321|12321|11330|11330|11330|
|Number of Women|192|192|192|192|192|192|
|_p_-value for STI today +|-|-|-|1.00|0.89|0.87|
|STIyesterday= 0|||||||

Notes: All variables are measured at the daily (rather than client) level, as we are not able to match clientspecific transfers to client-specific activities. Regressions are fixed effects regressions with controls for the day of the week and the month of the year. Sexual activity controls include vaginal sex, anal sex, oral sex, kissing, massage, stripping, whether the women gave a client company, whether the woman had unprotected sex, and whether she engaged in any other activities. All sexual activity variables measure the number of clients with whom the woman engaged in the given activity at least once, since we did not record the number of repetitions for all activities. Sexual activities were missing for some women. To avoid dropping them from the regressions, we code the activity as 0 and generate dummies for whether the activity is missing. See text for more details.

1Mean of Dependent variable is mean when all shocks equal 0. Exchange rate was roughly 70 Kenyan shillings to US $1 during the sample period.

* significant at 10%; ** significant at 5%; *** significant