rafmacalaba/datause-encoder-data huggingface dataset:
|**3 members**|4972|125.0048|116.0121|1|729<br>1896|124.5807|120.6218|1|729|3076|125.2663|113.0963|1<br>729|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**4 members**|4972|0.3071|0.4613|0|1<br>1896|0.2706|0.4444|0|1|3076|0.3296|0.4702|0<br>1|
|**5 members**|4972|0.2765|0.4473|0|1<br>1896|0.2595|0.4385|0|1|3076|0.2871|0.4525|0<br>1|
|**6 members**|4972|0.1237|0.3293|0|1<br>1896|0.1255|0.3314|0|1|3076|0.1226|0.3280|0<br>1|
|**7 members**|4972|0.0525|0.2230|0|1<br>1896|0.0585|0.2348|0|1|3076|0.0488|0.2154|0<br>1|
|**8-9 members**|4972|0.0422|0.2011|0|1<br>1896|0.0443|0.2058|0|1|3076|0.0410|0.1982|0<br>1|
|**Work and study**|4972|0.0447|0.2066|0|1<br>1896|0.0670|0.2501|0|1|3076|0.0309|0.1730|0<br>1|
|**Self-employed**|4972|0.0453|0.2079|0|1<br>1896|0.1187|0.3235|0|1|3076|0.0000|0.0000|0<br>0|
|**Immigrant of which**|4972|0.1267|0.3327|0|1<br>1896|0.1287|0.3349|0|1|3076|0.1255|0.3313|0<br>1|
|**With family**|4972|0.0306|0.1722|0|1<br>1896|0.0274|0.1634|0|1|3076|0.0325|0.1774|0<br>1|
|**For educational reasons**|4972|0.0034|0.0584|0|1<br>1896|0.0011|0.0325|0|1|3076|0.0049|0.0697|0<br>1|
|**Looking for a job**|4972|0.0050|0.0707|0|1<br>1896|0.0074|0.0856|0|1|3076|0.0036|0.0597|0<br>1|
|**For other reasons**|4972|0.0877|0.2829|0|1<br>1896|0.0928|0.2903|0|1|3076|0.0845|0.2782|0<br>1|
|**From rural areas**|4972|0.0696|0.2545|0|1<br>1896|0.0807|0.2724|0|1|3076|0.0627|0.2425|0<br>1|
|**From a small town**|4972|0.0356|0.1853|0|1<br>1896|0.0327|0.1779|0|1|3076|0.0374|0.1897|0<br>1|
|**From a big city**|4972|0.0135|0.1153|0|1<br>1896|0.0090|0.0943|0|1|3076|0.0163|0.1265|0<br>1|
|**From another country**|4972|0.0080|0.0893|0|1<br>1896|0.0063|0.0793|0|1|3076|0.0091|0.0950|0<br>1|
|**Found her / his job through**<br>**educational institutions**|4972|0.0129|0.1127|0|1<br>1896|0.0338|0.1806|0|1|3076|0|0|0<br>0|
|**Through job fairs**|4972|0.0078|0.0882|0|1<br>1896|0.0206|0.1420|0|1|3076|0|0|0<br>0|
|**Through Public Employment Offices**|4972|0.0145|0.1195|0|1<br>1896|0.0380|0.1912|0|1|3076|0|0|0<br>0|
|**Through labor contractors**|4972|0.0050|0.0707|0|1<br>1896|0.0132|0.1141|0|1|3076|0|0|0<br>0|
|**Through direct call by the employer**|4972|0.0658|0.2479|0|1<br>1896|0.1725|0.3779|0|1|3076|0|0|0<br>0|
|**Through public job adverts**|4972|0.0084|0.0915|0|1<br>1896|0.0222|0.1472|0|1|3076|0|0|0<br>0|
|**Through personal network of friends**<br>**and relatives**|4972|0.2019|0.4015|0|1<br>1896|0.5295|0.4993|0|1|3076|0|0|0<br>0|
|**Through other search channels**|4972|0.0529|0.2238|0|1<br>1896|0.1387|0.3457|0|1|3076|0|0|0<br>0|
|**Contract of unlimited duration**|4972|0.1400|0.3470|0|1<br>1896|0.3671|0.4821|0|1|3076|0|0|0<br>0|
|**Contract of limited duration**|4972|0.0209|0.1431|0|1<br>1896|0.0549|0.2278|0|1|3076|0|0|0<br>0|
|**Seasonal contract**|4972|0.0344|0.1823|0|1<br>1896|0.0902|0.2865|0|1|3076|0|0|0<br>0|
|**Oral contract**|4972|0.0020|0.0448|0|1<br>1896|0.0053|0.0725|0|1|3076|0|0|0<br>0|
|**Paid no tax on her / his job**|4972|0.0736|0.2612|0|1<br>1896|0.1930|0.3948|0|1|3076|0|0|0<br>0|

43
**Optimal Locational Choice for Agrobusinesses in Madagascar: An Application of Spatial Autoregressive Tobit Regression**

Atsushi Iimi<sup>¶</sup> Transport & Digital Development Global Practice World Bank Group Key words: Location theory; Agglomeration economies; Spatial autocorrelation; Censored regression.

JEL classification: H54; H41; R32; C21; C26.

> ¶ Corresponding Author.
Policy Research Working Paper 9205

# **Abstract**

To comprehend how development really happens, it is necessary to understand the evolution of its drivers and their relationship with individuals’ income. This paper analyzes the expansion of access to education and basic services in Latin America and its association with the evolution of incomes in the region. The paper focuses on the importance of access to opportunities as one of the drivers of development and highlights the role of policy making. The findings suggest that access to education and basic public services early in life are positively correlated with incomes in adulthood. The analysis also suggests that countries follow a dissimilar path to increase access to education and basic services. The paper undertakes a comprehensive analysis of historic census records to add granularity to the assessment of the development of countries, matched with detailed individual-level information from household surveys of several countries in the region. The paper widens an ongoing area of research on the long-run relationship between access to opportunities during childhood and incomes in adulthood.

This paper is a product of the Poverty and Equity 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 gcaruso@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
-46-

Table 3.10 shows value added (US census concept) as a percentage of gross value of output. These percentages were applied to the gross output figures for Brazil and the USA in table 3.7 and for Mexico and the USA in table **3.8.** The results for the two countries are presented, respectively, in table 3.11 and 3.12.

-------------------------------------------------------------------

__------

**TABLE 3. 10**

|**Value Added (US Census Conce**|**pt) as a per**|**centage**<br>**of**Gr|oss**Value of**|
|---|---|---|---|
|Output,<br>1975,|**in national**|**currencies**||
||**...-**<br>|**------------------------**<br>|**-------------------------------__----------------**<br>|
||Brazil|Mexico|USA|
|Total Manufacturing|39.21|45.79|42.57|
|Grain Mill Products<br>|25.83|28.41|24.54|
|Sugar & Sugar Products<br>|38.26|48.76|20.80|
|Maltand Malt Beverages|58.97|62.65|34.17|
|Tobacco and Tobacco Products<br>|52.49|47.22|46.17|
|Textiles|34.83|46.70|39.42|
|Footwear<br>and Leather<br>Products|46.11|51.64|51.33|
|Pulp and Paper|43.31|42.98|43.99|
|Soap and Detergents|38.04|42.68|48.34|
|Paints<br> <br>|39.28|46.16|41.29<br>|
|Agricultural<br>Fertilizers|29.94|46.48|47.43|
|Petroleum<br>Refining<br>and Products|<br>26.38|30.08|14.05|
|Tires and Inner Tubes|43.82|59.45|48.48|
|Cement|54.63|61.07|57.10|
|Bricks<br>|72.97|52.98|58.20<br>|
|Iron and Steel|35.09|41.22|37.39|
|Radio and TV Receivers|40.39|52.85|34.71|
|Motor Vehicles|24.94<br>J|36.88<br>|30.65|
|Weighted<br>average 17 industries|______________<br> <br>34-05|_____________<br>42.33|31.50|

Source: Derived from tables 3.2 and 3.9.
**Figure 4. Regional variation in air pollution, energy use, and education outcomes** _Investment in human capital is needed to preserve competitiveness. Investment is also needed to ensure that growth is sustainable._

# **A. Mean exposure to air pollution, 2015**

# **B. Energy use intensity, 2013**

<!-- Start of picture text --> Micrograms of PM2.5 per cubic meter<br>80<br>India<br>70 EAP ECA<br>60 China LAC MENA<br>SAS SSA<br>50<br>40<br>South Africa<br>30<br>Russian<br>20 Federation<br>10<br>Brazil<br>0<br>0 5 10 15 20 25<br>GDP per capita, US $ thousands<br><!-- End of picture text -->

# **C. Renewable energy consumption, 2012**

<!-- Start of picture text --> Percent of total final energy consumption<br>100<br>EAP ECA<br>80 LAC MENA<br>SAS SSA<br>60<br>Brazil<br>40 India<br>20 China Russian<br>South Africa Federation<br>0<br>0 5 10 15<br>GDP per capita, US $ thousands<br><!-- End of picture text --> <!-- Start of picture text --> Ton of oil equivalent per capita<br>15<br>EAP ECA<br>LAC MENA<br>SAS SSA Bahrain<br>10<br>Russian<br>Federation<br>5<br>South Africa<br>China<br>Brazil<br>India<br>0<br>0 5 10 15 20 25<br>GDP per capita, US $ thousands<br><!-- End of picture text -->

**D.  Quality of math and science education, 2015**

<!-- Start of picture text --> Score<br>6<br>5 China Russian<br>India Federation<br>4<br>3 Brazil<br>EAP ECA<br>2<br>South Africa LAC MENA<br>1 SAS SSA<br>BRICS<br>0<br>0 5 10 15 20 25<br>GDP per capita, US $ thousands<br><!-- End of picture text --> Source: World Economic Forum, World Bank.

Note: EAP is East Asia and Pacific, ECA is Europe and Central Asia, LAC is Latin America and the Caribbean, MENA is Middle East and North Africa, SAS is South Asia, and SSA is Sub-Saharan Africa. BRICS are Brazil, Russian Federation, India, China and South Africa.

A. Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas.

B. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.

C. Renewable energy consumption is the share of renewable energy in total final energy consumption.

D. Data are as of 2015. The score is from 1 to 7. Higher value indicates better quality. Quality of math and science education surveyed countries on the question of “In your country, how do you assess the quality of math and science education?” 9
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE infrastructure that is observed across Sub-Saharan Africa. The problem, however, is that the DRC’s infrastructure needs are substantially greater than those of other African countries.

**Table 12. Incomplete overview of spending on infrastructure in the DRC**

|Sector|O&M<br>Public sector|Public sector|$m per y<br>Ca<br>ODA|ear, average 200<br>pital expenditure<br>Non-OECD<br>financiers|1–06<br>PPI /<br>household<br>self-finance|Total CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|ICT|0|—|0|1|127|128|128|
|Power (trade)|≈50|—|4|0|0|4|54|
|Transport (basic)|300|—|55|2|0|57|357|
|WSS|—|—|0|0|62|>62|>62|
|Total|≈350|<100<sup>*</sup>|60|2|188|≈350|≈700|

Source: Derived from Foster and Briceño-Garmendia 2009 and additional data supplied by World Bank, 2010. _Note:_<sup>*</sup> A detailed breakdown of public investment in infrastructure for the period 2001–06 by sector is not available. The number given is the estimated total public investment for all sectors in 2006. PPI = private participation in infrastructure; — = data not available.

Since 2006, there has been a large upswing in external financing commitments from OECD and nonOECD partners, with commitments of around $4.1 billion secured. As the DRC has emerged from the immediate aftermath of conflict, the country has been able to capture an increasing amount of external finance for infrastructure—for example, ODA from multilateral and bilateral sources reached $1.6 billion by 2009. About half of this total was for the transport sector—mainly roads—and the other half for energy, including major rehabilitation efforts at the Inga power plant and associated high-voltage transmission line to the southeast. In addition, a major new financing agreement signed with the People’s Republic of China promises $3 billion, primarily for road and urban infrastructure projects (plus other funds for projects outside the infrastructure sectors).

Overall, public investment (including infrastructure and other areas) is projected to jump to a much higher level from 2010 onwards. From a base of around $200 million a year in 2007 and 2008, public investment is projected to reach and sustain much higher levels over the medium term, . This would be primarily attributable to the surge in external finance, though parallel increases in domestically funded national and provincial public investment budgets are also anticipated. While not all of these resources will be allocated to infrastructure, future public investment promises to move closer to requisite levels. Much less clear, however, is where DRC will find the $1 billion needed annually to sustain maintenance of infrastructure networks.

# How much more can be done within the existing resource envelope?

There is evidence that additional resources worth at least $430 million could be recovered each year by improving efficiency (table 13), in particular by increasing revenue collection and reducing distribution losses. The power sector has the highest operational inefficiencies.

26
**xxvii** Lee & Anas (1992)

- xxviii Chemow Ron (1997) _The Death of the Banker: The decline and fall of the great financial dynasties and the triumph of the small investor,_ Vintage Books, Random House Inc, New York

xxix London Economics research

- xxx New Zealand requires telecommunications and other infrastructure providers to register with the Ministry of Commerce. Registration is automatic once a small number of competitively neutral conditions (such as payment of fee to cover administrative costs and information reporting) are complied with. London Economics research

- xxxi Barron's online _Crowed Skies: Will satellite phone systems come crashing down on investors' heads_ June 15 1998
# **Outward orientation**

Exporting firms import more relative to the non-exporting ones, especially when they have foreign ownership participation. For instance, exporting firms with foreign ownership imported 45 percent of their inputs, while non-exporting firms with foreign ownership imported only 17 percent of the inputs. Both figures are lower for firms without foreign ownership, but still exporting firms import more than non-exporters (Figure 10).

Nigerian exporting firms are more productive than the non-exporting ones (Figure 11.)<sup>4</sup> The positive association between the exporting nature of firms and productivity cannot be used as a causality statement. In the empirical section, we deal with potential endogeneity issues.

Figure 10. Exporter status and foreign participation in ownership <!-- Start of picture text --> 45<br>28<br>17<br>9<br>FDI Non-FDI FDI Non-FDI<br>non-exporter exporter<br>Source: Authors' calculation based on Enterprise Surveys 2014<br>50<br>40<br>30<br>20<br>10<br>% of Material inputs or supplies of foreign origin<br>0<br><!-- End of picture text --> Figure 11. Exporter status and total factor productivity

> 4 Figure 11 uses the total factor productivity (TFP) measure of productivity, which is a residual of a regression described in the text. Hence it can be either positive or negative.

11
# **A New Cross-National Measure of Corruption**

Laarni Escresa and Lucio Picci

**JEL Codes:** C18, C43, D73, F53, F55, H11, H50, K42

**Keywords:** Bribery, corruption, judicial statistics, measures of corruption

**Sector board** : Public Sector Governance (PSM)

Laarni Escresa is an assistant professor at the School of Economics, University of the Philippines; her email is laarni.escresa@eui.eu. Lucio Picci (corresponding author) is a professor at the Department of Economics, University of Bologna, Strada Maggiore 45, I-40125 Bologna, Italy; his email is lucio.picci@unibo.it. They would like to thank, for their comments on a previous version of this paper, Rajeev Goel, Miriam Golden, Johann Graf Lambsdorff, Jerg Gutmann, Robert Klitgaard, Nicholas McLean, Angela Reitmaier, Susan Rose-Ackerman, participants to a workshop at the Inter-American Development Bank in Washington, DC, to the European Political Science Association 4<sup>th</sup> Annual Conference in Edinburgh, and to the 10<sup>th</sup> Annual Conference of the Italian Society of Law and Economics in Rome and three anonymous referees. Laarni Escresa acknowledges research funding from the Deutsche Forschungsgemeinschaft (German Research Foundation). Lucio Picci acknowledges the financial support of the Fondazione Cassa dei Risparmi di Forli. All Website contents were accessed on June 8, 2015.
Domic ́ılios (PNAD), a representative survey of the Brazilian population, are used.

The unit of observation is the household, and the individual outcome is measured as the monthly household equivalent income, expressed in 2008 Brazilian real.<sup>32</sup> Household income is computed as the sum of all household members’ individual incomes, including earnings from all jobs, and all other reported income, including income from assets, pensions, and transfers.

The population is partitioned into 15 types using the information on two circumstances: region of birth and race. Region of birth is coded in five categories (North, Northeast, Southeast, South, Center-west), and race is coded in three categories (white/east Asian, black/mixed race, and indigenous). Individuals who were born abroad and those classified as “other” for the variable race are excluded because the number of observations is too low to make appropriate inference. Hence, the sample sizes of each wave considered in this analysis are as follows: 366,388 households in 2002, 390,046 in 2005, and 372,581 in 2008.<sup>33</sup> .

The full opportunity profiles for the three waves are reported in Table 4 in the appendix.<sup>34</sup> In this table, it is clear that race is the main determinant of the disparity in opportunities. Consistent with a number of contributions on socio-economic inequality in Brazil, racial relationships appear to be the major source of outcome and opportunity inequality in Brazil (Telles 2004; Bourguignon et al. 2007; among others).

To analyze the distributional impact of growth in Brazil according to the EOp perspective, two three-year period growth processes are considered: 2002–05 and 2005–08. The choice of these particular periods is driven by the observation that during these years, Brazil experienced quite diverging economic trends. The former was a period of economic slowdown; the PNAD data record an increase in the overall mean income of only 0.26%. In contrast, the latter period was a period of pronounced growth, with an overall mean income growth of approximately 6.36%.

> 32Equivalent income is obtained by dividing total income by the square root of the household size.

> 33Again, the richest and poorest 1% of the household distribution in each wave are dropped.

> 34All estimates are based on the sample weights according to Silva et al. (2002).

28
impact of digital technologies on labor demand and on the demand for tasks depending on the degree to which industries use information technology and face regulatory enforcement.

# **3.1. Matched employer-employee administrative data**

We use data collected by the Brazilian Labor Ministry, which requires by law that all registered establishments report on their formal workforce in each year. This information has been collected in the administrative records _Relação Anual de Informações Sociais_ (RAIS) since 1986. For our analysis, we use data from RAIS for the years 1996 through 2006, when we also have complementary information on regulatory enforcement and the provision of internet services.

The main benefit of the RAIS database is that both establishments and workers are uniquely identified. The data offer information on workers’ skill levels, as defined by occupation and education, beyond many similar data sets, and also include the industry<sup>9</sup> and municipality of each establishment. We restrict observations as follows. First, we drop all data pertaining to the public sector and to the agricultural sector, as the Ministry of Labor has recognized issues of data quality pertaining to these two broad sectors of the economy. Moreover, labor adjustment in the public sector is not likely to respond to the same forces as labor adjustment in the private sector. Second, as is customary in the literature, we drop all establishments that have fewer than five employees.

At the beginning of our sample in 1996, the main employer-employee data set includes over 14 million workers employed in 2,192 different occupations in approximately 440,000 establishments, producing in 544 4-digit CNAE industries and located in 4,229 municipalities across Brazil. By the end of our sample period in 2006, Brazil’s formal labor force had grown significantly to almost 22 million workers employed in 2,217 occupations across around 750,000 establishments, producing in 529 industries and located in 4,868 municipalities.

> 9 The industrial classification available in RAIS is the 4-digit National Classification of Economic Activities (CNAE).

12
As governments everywhere in Africa are delved into natural resources extraction, our paper contributes to the growing evidence that the quality of local institutions matters for how mining activities could translate into local welfare for nearby communities. In addition, we demonstrate that inter-governmental fiscal arrangements regarding taxes and revenues are of utmost importance in the way that natural resources may affect local socio-economic outcomes. More specifically, for communities within 50-km to an active mine, the results indicate that a high level of corruption alongside a high level of decentralization constitutes the worst-case scenario. On the other hand, when the incidence of bribe payment is very low, the higher level of decentralization translates into the more positive appraisal of welfare policies.

# **References**

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- Aragón, F.M., Rud, J.P., 2013. Natural Resources and Local Communities: Evidence from a Peruvian Gold Mine. American Economic Journal: Economic Policy 5 (2), 1–25. 10.1257/pol.5.2.1.

- Badeeb, R.A., Lean, H.H., Clark, J., 2017. The evolution of the natural resource curse thesis: A critical

literature survey. Resources Policy 51, 123–134. 10.1016/j.resourpol.2016.10.015.

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Berman, N., Couttenier, M., Rohner, D., Thoenig, M., 2017. This Mine is Mine! How Minerals Fuel

- Conflicts in Africa. American Economic Review 107 (6), 1564–1610. 10.1257/aer.20150774.

- Bhattacharyya, S., Hodler, R., 2010. Natural resources, democracy and corruption. European Economic Review 54 (4), 608–621. 10.1016/j.euroecorev.2009.10.004.

- Boschini, A., Pettersson, J., Roine, J., 2013. The Resource Curse and its Potential Reversal. World Development 43, 19–41. 10.1016/j.worlddev.2012.10.007.

Caselli, F., Michaels, G., 2013. Do Oil Windfalls Improve Living Standards? Evidence from Brazil. American Economic Journal: Applied Economics 5 (1), 208–238. 10.1257/app.5.1.208.

- Chuhan-Pole, P., Dabalen, A.L., Kotsadam, A., Sanoh, A., Tolonen, A.K., 2015. The local socio-economic effects of gold mining: Evidence from Ghana. Africa Region, Office of the Chief Economist, Washington, DC.

- Cust, J., Poelhekke, S., 2015. The Local Economic Impacts of Natural Resource Extraction. ANNUAL REVIEW OF RESOURCE ECONOMICS, VOL 7 7, 251–268. 10.1146/annurev-resource-100814-125106.

- Dauvin, M., Guerreiro, D., 2017. The Paradox of Plenty: A Meta-Analysis. World Development 94, 212– 231. 10.1016/j.worlddev.2017.01.009.

- Dawson, J.F., Richter, A.W., 2006. Probing three-way interactions in moderated multiple regression. The Journal of applied psychology 91 (4), 917–926. 10.1037/0021-9010.91.4.917.

31