rafmacalaba/datause-encoder-data huggingface dataset:
- 13 -

control variable. We take the specification including rule of law as the baseline in the rest of the paper.<sup>10</sup> Excluding advanced country banks from the sample does not change regression results much. However, when we exclude extreme observations, the coefficient of compliance becomes quite small and is no longer significant. Significance also disappears if we estimate the model using OLS instead of ordered probit.<sup>11</sup> As far as the control variables, larger banks are rated significantly higher and stateowned banks are considered more vulnerable. Foreign-owned banks are also rated more favorably and the coefficient is significant in the specification that includes the overall institutional quality of the country, but not if we restrict the sample to developing countries.<sup>12</sup> These results suggest that once we control for bank size, ownership, and institutions, the additional explanatory power of bank balance sheet variables is small.

We conclude that while there is a positive correlation between bank soundness and the overall index of BCP compliance, this result is sensitive to controlling for the institutional quality of the country and to the exclusion of outliers. An interesting question is whether different principles that make up the overall BCP index affect bank soundness differently. We turn to this issue next.

10 Using an average of the Kaufman et al. (2003) institutional indexes rather than rule of law does not change any of the results.

11 Extreme observations are identified by dropping the top one percent from the top and bottom of the distribution of each of the five bank characteristics. This results in a loss of about ten percent of the observations.

12 The baseline specification excludes liquidity variables to preserve sample size. However including liquidity throughout does not change the results.
# Figure 1. Country examples of debt accumulation episodes

<!-- Start of picture text --> A. Turkey: Government debt B. Mexico: Government debt<br>Percent of GDP Percent of GDP Rapid accumulation<br>80 Rapid accumulation 120 Non-rapid accumulation<br>Non-rapid accumulation Crises<br>Crises Associated with crises<br>60 Associated with crises 90<br>40 60<br>20 30<br>0 0<br>1970 1978 1986 1994 2002 2010 2018 1970 1978 1986 1994 2002 2010 2018<br>C. Philippines: Private debt D. Malaysia: Private debt<br>Percent of GDP Rapid accumulation Percent of GDP Rapid accumulation<br>80 Crises 180 Crises<br>Associated with crises<br>Associated with crises<br>60<br>120<br>40<br>60<br>20<br>0 0<br>1970 1978 1986 1994 2002 2010 2018 1970 1978 1986 1994 2002 2010 2018<br>Sources: International Monetary Fund, World Bank.<br><!-- End of picture text --> Note: Blue line indicates debt outside debt accumulation episodes. A period of debt accumulation is identified with the algorithm in Harding and Pagan (2002). When a change in debt-to-GDP ratios over an accumulation period is above the maximum of 10-year moving standard deviation of the ratios during the period, it is considered as a rapid debt accumulation (shown as an orange area). When it is below the threshold, it is treated as a non-rapid accumulation (shown as a light blue area). If a crisis (i.e., banking, currency, or debt crisis) occurs during a rapid debt accumulation period or within two years since the end of the period, it is regarded as an episode of rapid debt accumulation associated with a crisis (shown as a red line). An ongoing episode (e.g., the third orange area in Panel C) is also classified as either rapid or non-rapid accumulation, based on the same methodology.

18
et al, 2024). Database building algorithms were adapted to the specificities of the source websites and data repositories. The combined national public procurement dataset includes 148,637 contracts for the period 2011-2019, characterized by 129 variables. Micro-level company data was also matched to the processed public procurement dataset, using standardized company IDs and when those were missing, using company name and address. The details of data collection and processing are outlined in Annex 1.

Measuring the impact of public procurement on private sector growth requires representative firm-level data that can be linked to the procurement transaction data through unique firm identifiers. We thus employ a large firm-level panel dataset with such unique identifiers in Bulgaria from 2010-18 from Orbis which is a commercial database provided by Bureau van Dijk. The data are collected from the national offices of the Registrar of Companies. They include accounting data and information from firms’ balance sheets. For Bulgaria, the Orbis data cover all formal firms, independent of their size, in all economic activities apart from agriculture. The effective sample of joint nonmissing information for all production function variables in Bulgaria comprises over 4 million firm-year observations, implying almost 500,000 firms per year. We follow the integrated control function approach of De Loecker and Warzynski (2012) to estimate the unbiased measures for the output elasticities of inputs, allowing to compute total factor productivity. We also compute labor productivity as the log of value added per worker. We measure output as real value added. Capital, labor, and intermediate inputs are measured as real fixed tangible assets, the total number of employees, and total material costs. We also account for firms' age and firms and (partially) state-owned enterprises (SOEs).<sup>1</sup> We deflate the nominal variables using detailed 2-digit NACE code producer price indices.

# 3.2 Identifying green public procurement

Precisely identifying GPP in all its diverse forms is a key goal of this paper, which is challenging on its own. The above GPP definition encompasses a range of products, processes and considerations. It can relate to climate and sustainability goals solely; or more broadly, focusing on energy efficiency improvements, sustainable waste management or emission control, and waste and pollution reduction. Many of these considerations became part of public procurement markets long before GPP goals rose to the fore. For example, energy efficiency improvements are framed as an environmental consideration as part of GPP, yet they were pursued on purely cost-saving grounds for a very long time. In this sense, some of the GPP products we identify are not necessarily new but reclassified as being part of GPP and hence receiving far larger budget

> 1 We create a dummy variable for SOEs if the state owns at least 10 percent of the firm through direct or indirect ownership resulting from the investments of SOEs in private firms.

9
15 manufacturing sector’s share of non-agricultural tradables output is assumed to be in proportion to its share of GDP from manufacturing plus non-agricultural primary activities, shown in column 4 of Appendix Table 1. That share times the manufacturing NRA in column 2 or (after 1967-68) column 3 provides the estimated NRA for total nonagricultural tradables, shown in the final column of Appendix Table 1.

# **V. The NRA estimates and the evolution of policies**

In addition to reporting the NRA estimates, this section provides a brief history of policies up to the early 1970s and then of changes thereafter, particularly when the reforms accelerated in the mid-1980s. As there were relatively few agricultural subsidies or farm import barriers (other than quarantine restrictions) through most of the past six decades, the story is more about the _indirect_ anti-agricultural bias that resulted from the protection of manufacturing. Nonetheless, coverage of policies that directly distorted various agricultural markets post-World War II also is included.

# **_Prior to the early 1970s_**

The long history of industrial protectionism has its roots even before the formation in 1901 of the Australian Federation. Tariff revenue in the first decade of Federation accounted for one-fifth of government revenue. That is very high for what at the time was one of the world’s highest-income economies, as that share typically falls as per capita income rises. It is twice that of the Nordic countries, for example, even though their per
increase played an important role in poverty reduction achieved in the last two decades in rural Bangladesh (World Bank, 2013). We also provide point estimates of the causal e¤ect of an increase in crop yield (rice) on the outcome variables by imposing exclusion restriction on rainfall in the outcomes regressions. We use upazila and year ...xed e¤ects, and include a set of carefully chosen variables to control for time varying heterogeneity to enhance the plausibility of the exclusion restriction. The IV estimates suggest that a one percent increase in crop yield increases agricultural wage by about one percent, and reduces the share of hired labor by more than one percent.

# References

Adhvaryu, A., A. V. Chari and S. Sharma. 2013, “Firing Costs and Flexibility: Evidence from Firms’Employment Responses to Shocks in India,” Review of Economics and Statistics, vol. 95, no.3.

Bandyopadhyay, Sushenjit & Skou...as, Emmanuel, 2012. "Rainfall variability, occupational choice, and welfare in rural Bangladesh,"Policy Research Working Paper Series 6134, The World Bank.

Bhowmik, A, K, and Ana Cristina Costa (2012). A Geostatistical Approach to the Seasonal Precipitation E¤ect on Boro Rice Production in Bangladesh, International Journal of Geosciences, 2012, 3, 443-462 Brückner, Markus, 2012. "Economic growth, size of the agricultural sector, and urbanization in Africa," Journal of Urban Economics, vol. 71(1), pages 26-36.

Brückner, Markus and Antonio Ciccone, 2011."Rain and the Democratic Window of Opportunity," Econometrica, vol. 79(3), pages 923-947, 05.

Buera , Francisco J. and Joseph P. Kaboski, 2012. "The Rise of the Service Economy," 33
Policy Research Working Paper 6066

# **Abstract**

Despite their increasing prominence in policy debates, little is known about gender inequities in nonagricultural labor market outcomes in rural areas. Using matched household-enterprise-community data sets from Bangladesh, Ethiopia, Indonesia and Sri Lanka, this paper documents and analyzes gender differences in the individual portfolio choice and productivity of nonfarm entrepreneurship. Except for Ethiopia, women are less likely than men to become nonfarm entrepreneurs. Women’s nonfarm entrepreneurship isn’t strongly correlated with household composition or educational attainment, but is especially prevalent amongst women who are the head of their household. Female-led firms are much smaller and less productive on average, though gender differences in productivity vary dramatically across countries. Mean differences in log output per worker suggest that male firms are roughly 10 times as productive as female firms in Bangladesh, three times as those in Ethiopia and twice as those in Sri Lanka. By contrast, no significant differences in labor productivity were detected in Indonesia. Differences in output per worker are overwhelmingly accounted for by sorting by sector and size. They can’t be explained by differences in capital intensity, human capital or the local investment climate, nor by increasing returns to scale.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at brijkers@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
One major challenge is avoiding the double counting of conditional transfers (very likely captured in central government accounts). Transfers to local governments should be netted from central government accounts, and the use of funds recoded as part of the local government data, where the use of resources will be clearly specified.

As mentioned, template 10 should also be filled with local government fiscal data. Identification of relevant local government authorities (for example, provinces, districts, and/or municipalities) and their role in providing infrastructure services should be done in the pre-field stage. Based on this work, the user should have a good sense of whether there is a centralized repository of local government accounts and if they are reported using a common structure.

There two possible options for collection local governments data:

- When there is standardized data reporting to a central unit:

- If local governments use the same budget coding structure as the central government, the user will use the code mapping carried on from the central government to input the data in template 10. But the double counting of conditional transfers (very likely captured in central government accounts) should be avoided by netting out transfers.

- If local governments use different budget coding from the central government and make expenditures in the relevant infrastructure sectors, the user will have to carry out a new recoding exercise before filling in template 10.

- When there is no standardized data reporting to a central unit:

- If aggregate figures for local government are available but without a further economic or functional breakdown, the user should report local government accounts on template 10. As in the above case, the user should avoid double counting. The user should determine whether it can be assumed that most local government spending goes to the relevant productive infrastructure sectors.

- If aggregate figures are not available, the user should select a few of the most relevant/largest local government entities, depending on desired accuracy and resource availability. Only when this has been decided will the user proceed to produce template 10 for the selected entities.

# Fiscal data templates at the operator level

Fiscal data template 11 consists of three parts: the income statement, the cash flow statement, and the balance sheet. It is also very helpful for economic analysis to collect a copy of the complete tariff schedule in application for each power and water utility. Fiscal data template 11 should be prepared for each public operator and/or special fund with clear responsibilities in the delivery (or funding) of activities within the 4-digit COFOG sector categories: irrigation, electricity, transport, communication, wastewater management, and water supply. In the case that one enterprise covers more than one subsector, for example, electricity and water supply, template 11 (as well as the 48
Table 5 -—- Treatment Effects on Normalized Test Scores by Subject

|PanelA:2012.|AllStudents|Boys|Girls|
|---|---|---|---|
||<br>(1)|(2)|(3)|
|Treatment * Hindi|0.317***|0.323***|0.306***|
||(0.087)|(0.092)|(0.092)|
|Treatment « English|0.256**|0.244*|0.261**|
||(0.121)|(0.124)|(0.127)|
|Treatment x Math|0.369%**<br>(0.095)|0.394***<br>(0.098)|0.340***<br>(0.096)|
|Observations|17,460|8,509|8.951|
|R-squared|0.250|0.268|0.240|
|Mean ofdepvar in control|0.000|0.040|-0.039|
|P-value: T x Hindi=T x English|0.446|0.314|0.618|
|P-value:T x Hindi=T = Math|0.331|0.232|0.562|
|P-value: T = English =T<br>x Math|0.169|0.092|0.351|
|Panel B: 2013|All Students|Boys|Girls|
||(1)|2)|(3)|
|Treatment x Hindi|0.127|0.139|0.119|
||(0.099)|(0.106)|(0.096)|
|Treatment « English|0.159|0.172|0.151|
||(0.125)|(0.127)|(0.129)|
|Treatment x Math|0.136|0.159|0.118|
||(0.109)|(0.114)|(0.109)|
|Observations|15,984|8,193|7,791|
|R-squared|0.247|0.248|0.249|
|Mean ofdepvar in control|0.000|0.004|-0.004|
|P-value:TxHindi=T=English|0.652|0.646|0.679|
|<br>P-value:T x Hindi=T x Math|0.814|0.677|0.980|
|P-value:TxEnglish=T<br>xMath|0.746|0.844|0.676|

Notes: Robust standard errors in parentheses, clustered by village. *** p=<0.01, ** p=<0_05, * p=0_1. All analyses are restricted to test-takers, defined as students who were present for pre- and postprogram tests in the year the test was administered (2012 or 2013). Observations are at the studentsubject-level. All specifications include controls for 2011 (pre-program) enrollment by gender, cohort fixed effects, and subject (Math, English, and Hindi) fixed effects.
more resources to invest in its NSS. To examine this hypothesis, we plot in Figure 2 a country’s SPI against its Economic Complexity Index (ECI). This figure indicates a strongly positive relationship between the two indexes, with a 0.1 increase in a country’s ECI being associated with a 1.5 percentage point increase in its SPI score.<sup>25</sup>

# _A Regional Decomposition of SPI Scores_

We turn next to decomposing the SPI score by region in Figure 3. This figure demonstrates that among the six regions, Europe and Central Asia (ECA) and South Asia (SAS) perform above the global average, with the remaining four regions falling behind in the following decreasing order: Middle East and North Africa (MENA), East Asia and Pacific (EAP), Latin American (LAC), and Sub-Saharan Africa (SSA). Notably, ECA stands out from the other regions, which have a more similar score: the difference between its score and that of the second-strongest region SAS is 15 percentage points, which is more than the difference between the latter region’s score and that of the weakest-performing region SSA.

It can be useful to further examine whether the patterns seen in Figure 3 apply to scores in all four dimensions. Disaggregating the regional scores further by the four dimensions can help us disentangle which dimensions drive these differences and can be improved. Estimation results are shown in Figure 4, which offers several interesting observations. First, a country’s score on a certain dimension can be quite different from that of its overall SPI score. In particular, all dimensions—except for the DPO dimension—have a ranking order for country performance that is different from that with the overall score. An interesting example is the ECA region, which is consistently the best performer on all dimensions but AKI, where the SAS region is now the best

> 25 For comparison, the US and the UK both have an ECI of 1.6, while the corresponding figure for China and Mexico is 0.9.

30
# **References:**

- Anderson, R. and R. May, 1991, <u>Infectious Diseases of Humans, Oxford University</u> Press.

- Becker, G., 1964, <u>Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, New York: Columbia University Press.</u>

- Behrman, J., 1996, “The Impact of Health and Nutrition on Education”, _World Bank Research Observer_ 11, 23-37.

- Bransford, J.D., 1979, <u>Human Cognition: Learning, Understanding, and Remembering,</u> Wadsworth.

- Cebu Team, 1991, “Underlying and Proximate Determinants of Child Health: The Cebu Longitudinal Health and Nutrition Study”, _American Journal of Epidemiology_ 133, 185-201.

- Centers for Disease Control and Prevention, 1998, “Recommendations to Prevent and Control Iron Deficiency in the United States”, MMWR, 47 (No. RR-3).

- Cohen S., T. Kamarck, and R. Mermelstein, 1983, “A Global Measure of Perceived Stress”, _Journal of Health and Social Behavior_ 24, 385-396.

- Cronbach, L. J., 1951, “Coefficient alpha and the internal structure of tests”, _Psychometrika,_ 16, 297-334.

- Chay, K., and M. Greenstone, 2005, “Does Air Quality Matter? Evidence from the Housing Market”, _Journal of Political Economy_ 113, 376-424.

- Crane, R., A. Daniere, and S. Harwood, 1997, “The Contribution of Environmental Amenities to Low-Income Housing: A Comparative Study of Bangkok and Jakarta”, _Urban Studies_ 34, 1495-1512.

- Di Tella, R.. and R.. MacCulloch, 2006, “Some Uses of Happiness Data in Economics”, _Journal of Economic Perspectives_ 20, 25-46.

Dunn, L.M., 1965, Peabody Picture Vocabulary Test, American Guidance Service, Inc.

- Engle, P. L., M. M. Black, J. Behrman, M. Cabral de Mello, P. Gertler, L. Kapiriri, R. Martorell and M. E. Young, 2007, “Strategies to Avoid the Loss of Developmental Potential among 200 Million Children in the Developing World”, _The Lancet_ 329, 229-240.

- Esrey, S., J. Potash, L. Roberts, and C. Shiff, 1991, “Effects of Improved Water Supply and Sanitation on Ascariasis, Diarrhea, Dracunculiasis, Hookworm Infection, Schistosomiasis and Trachoma”, _Bulletin of the World Health Organization_ 69, 609-621.

- Fernald, L., P. Gertler, and L. Neufeld, “How Important is the Amount of Cash in Conditional Cash Transfer Programs for Child Development?”, UC Berkeley Working Paper, 2006.

25
12 separately by size category for each country.<sup>12</sup> Poverty is always higher in the smallest towns (XS), for any poverty line, in Albania, Brazil, Sri-Lanka and Thailand (up to the 90<sup>th</sup> percentile). It is generally lowest for metropolitan areas in the vicinity of the national poverty lines, but this ranking is not everywhere robust to larger changes in the poverty line. Figures 3 and 4 illustrate two polar cases: Brazil and Morocco. Figure 3 shows that metropolitan areas first-order stochastically dominate all other size categories in Brazil: poverty is lower in these large cities than in any other type of town, by any poverty line. Conversely, very small towns are first-order stochastically dominated by every other size grouping: poverty is highest in this size category than in any other, by any poverty line.

A very different picture (in terms of dominance relationships) is that of Morocco, shown in Figure 4. The poverty ranking between metropolitan areas and large towns which is observed at the country’s poverty line of Dhs 3,400 reverses at higher poverty lines (above Dhs 8,000). Similarly, there is no dominance relationship among very small, small and medium towns in Morocco: their cumulative distribution functions cross many times. Even in Morocco, however, which displays the largest number of cumulative distribution function crossings in our sample, there is still one broad regularity: taken as a group, large and very large cities (L, XL) do provide a lower envelope for the smaller towns (XS, S, M). There is no strict stochastic dominance but it is evident that, for almost every poverty line one could think of, poverty is lower in the group of larger cities than in other urban settings.

It is possible, of course, that poverty is both more widespread and deeper in smaller towns, but that population is so concentrated in large cities that the bulk of the poor live there. If this were the case, greater attention to (and resources for) metropolitan poverty might be justified on the basis that the _share of poverty_ is greatest there. But Table 2 shows that this is nowhere the case. In fact, the share of the poor is lower than the population share in every country that has a metropolitan area: the difference is relatively small in Kenya, but very substantial elsewhere. In Brazil, although 22% of the population live in cities greater than 1 million, only 9% of the country’s poor do. In Kazakhstan, 14% of the population lives in Almaty, but only 3% of the poor. In Mexico, 27% of the population resides in Mexico City and the other very large metropolitan areas of the 12 Again with the exception of Kenya, for which we do not have the disaggregated poverty mapping data.