rafmacalaba/datause-encoder-data huggingface dataset:
Since our empirical specification derives from an aggregate discrete choice model (grouped logit model), the estimated coefficients cannot be directly interpreted as marginal effects. To find the partial effect of a change in a location characteristic on the migration probability between two states, we need to differentiate equation 4 with respect to the _X ij_ of interest, which can be written as: To evaluate the importance of the direct effect of market access on domestic migration, we replace β with the estimated coefficient of market access and _sijst_ with the observed migration probabilities. Equation 14 then tells us how the probability of migrating from state _i_ to any state _j_ in sector _s_ in year _t_ is affected by a change of 1 percent in the sectoral market access gap.

The values of the elasticities for the 4183 observations in our benchmark specification (column 2) range from 0.0003 to 0.14, with an average elasticity of 0.012. For an increase of 1 percent in the market access gap, this translates into a substantial growth of 34 percent to 57 percent in the number of migrants for each observation. Using the estimates from column 3, which consider the joint effect via both channels, this increase reaches 44 percent to 74 percent.

The last three columns of table 1 provide robustness checks. Column 4 replicates our benchmark estimation using the Poisson Pseudo-Maximum Likelihood estimator (PPML) to deal with the high number of zero-migration flows. The coefficient of our key variable of interest remains highly significant, confirming the positive impact of market access on migration rates. The large standard error in column 4 indicates that even though the coefficient is higher than in the previous estimates, the magnitude is not significantly different from the one in the benchmark equation.<sup>26</sup>

# Columns 5 and 6 address the concern that the positive coefficient on market

> 26 The data set in column 4 consists of all the 1748 sector-origin-destination combinations for which we observe a positive migration flow for at least one year. The panel is not entirely balanced since we exclude fifty-seven migration rates because i) they are constructed with less than six individual observations; or ii) we do not have wage data for the origin-sector combination.

18
8 operations and growth (15 independent questions in the survey) are ranked according to the percentage of firms that consider them a major/severe obstacle, corruption comes in the 10<sup>th</sup> place in Mauritania, but only in 2<sup>nd</sup> place, for instance, in Mali (Figure 3).<sup>5</sup>

**Figure 3: Corruption as major/severe obstacle to growth (% of firms, right axis) and**

**overall rank as an obstacle to growth (left axis), formal sector, selected countries**

<!-- Start of picture text --> 100 10<br>84<br>OECD<br>80 8<br>59 56<br>60 53 6<br>49<br>40<br>40 4<br>18 16 20 18<br>20 2<br>9 8 8<br>0 0<br>BeninBurkina FasoNiger Cameroon MaliSenegalMauritaniaSouth AfricaCzech RepublicMexico GreeceHungaryKorea<br>rank (line)<br>obstacle to growth (column) Corruption place in obstacles<br>% firms reporting major or severe<br><!-- End of picture text --> _Source: ICS database_

# **_3.2 Petty corruption_**

Petty corruption in Mauritania is pervasive.<sup>6</sup> Regardless of the firm category, the most common payment of bribes by entrepreneurs is made in order to: (a) establish a water connection; (b) obtain a construction permit; and (c) establish an electricity connection. Relatively fewer firm, by contrast, make informal payment to establish a connection to a mainline telephone and obtaining an import or operating license. On average, mediumsize enterprises operating in Nouadhibou are the most taxed by informal payment although there are notable exceptions (See Annex 5 for the complete set of data).

The extent of petty corruption associated with the provision of selected public services becomes even more startling when compared internationally. In water, the share of bribepaying firms (75 percent) in Mauritania is about the double than those of Benin,

> 5 There are 15 independent questions on 15 distinct obstacles to growth in the ICS.

> 6 This analysis is based on reported answers of whether bribes were paid in order to speed up the delivery of day-to-day services.
|Vietnam-HCFC Phase-out Project(Phase I)|QingWang|
|---|---|
|Vietnam-HCFC Phase-out Project(Phase I)|QingWang|
|Vietnam-HCFC Phase-out Project(Phase I)|QingWang|
|Vietnam-HCFC Phase-out Project(Phase I)|QingWang|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|HCFC Phase-out in the PU Foam Sector Project|Laurent Granier|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Nepal-India ElectricityTransmission and Trade Project|Rabin Shrestha|
|Sierra Leone EFA FTI Program|Susan E. Hirshberg|
|Sierra Leone EFA FTI Program|Susan E. Hirshberg|
|Sierra Leone EFA FTI Program|Susan E. Hirshberg|
|Sierra Leone EFA FTI Program|Susan E. Hirshberg|
|Sierra Leone EFA FTI Program|Susan E. Hirshberg|
|Sierra Leone EFA FTI Program|Susan E. Hirshberg|
|Sierra Leone EFA FTI Program|Susan E. Hirshberg|
|Development Marketplace for the African Diaspora in Europe Win|n Tshiya A. Subayi|
|Development Marketplace for the African Diaspora in Europe Win|n Tshiya A. Subayi|
|Development Marketplace for the African Diaspora in Europe Win|n Tshiya A. Subayi|
|Education For All - Fast Track Initiative Catalytic Fund Bridge Grant|Margo A. Hoftijzer|
|Education For All - Fast Track Initiative Catalytic Fund Bridge Grant|Margo A. Hoftijzer|
reflect individual preferences or tastes of the household’s decision maker. For example, households may place less disutility on the labor supply of women. Second, sex-typing of tasks may be important in labor supply decisions, so that a given gender might predominately perform certain jobs. In Vietnam, males and females have similar participation rates in agriculture, but males are more likely to work outside of the household for wages and females are more likely to be involved in the production of non-traded goods within the household. Even within agricultural tasks, there are differences (Desai 2000). Males tend to be stronger and are thereby more likely to perform physically intensive tasks. Females tend to spend relatively more time in livestock care and maintenance. Sex-typing may reflect individual preferences, comparative advantage, gender discrimination, or cultural norms. Regardless of the reason for sex-typing, the value of time can be affected by changes in rice prices differently by gender. Hence, men and women may experience different changes in their labor supply. One could similarly argue that rice price changes might differentially impact the labor supply of people of different ages, in particular children versus adults. As a result, our empirical work allows the relationship between changes in rice prices and an individual’s labor supply to vary across the gender and age categories.

# **3.  Data Description**

We examine the relationship between product prices in the rice sector and the economic activities of household members using two rounds of the VLSS, a multi-purpose household survey, that span the period of the rice market liberalization. The VLSS includes questions on household composition, the labor activities of adults and children, education, expenditure, land holdings, and agricultural activities. Concurrent with the household survey, an enumerator ventured to a local market to collect prices on 38 different kinds of food at the commune 9
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Lewis, C. M., and A. Mody. 1998. “Contingent Liabilities for Infrastructure Projects: Implementing A Risk Management Framework for Governments.” _Viewpoint_ .

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- Musacchio. A., and E. Pineda Ayerbe. 2019. _Fixing State-Owned Enterprises: New Policy Solutions to Old Problems_ . Washington, DC: Inter-American Development Bank.

- Musacchio, A., E. Pineda Ayerbe, and G. Garcia. 2015. “State-Owned Enterprise Reform in Latin America: Issues and Solutions.” Discussion Paper 401, Inter-American Development Bank, Washington, DC.

- Polackova Brixi, H., and A. Mody. 2002. “Dealing with Government Fiscal Risk: An Overview.” In _Government at Risk: Contingent Liabilities and Fiscal Risk_ , ed. H. Polackova Brixi and A. Schick, 21– 58. Washington, DC: World Bank.

- Polackova Brixi, H., and A. Schick, eds. 2002. _Government at Risk: Contingent Liabilities and Fiscal Risk_ . Washington, DC: World Bank.

- Roubini, N., and B. Setser. 2004. _Bailouts or Bail-Ins? Responding to Financial Crises in Emerging Economies_ . Washington, DC: Peterson Institute for International Economics.

- Ter-Minassian, T. 2018. _State-Owned Enterprises and Fiscal Risks in Peru_ . Inter-American Development Bank, Washington, DC.

- ———. 2019. “Identifying and Mitigating Fiscal Risks from State-Owned Enterprises (SOEs).”  In _Fixing State-Owned Enterprises: New Policy Solutions to Old Problems_ , ed. A. Musacchio and E. Pineda Ayerbe, 49–72. Washington, DC: Inter-American Development Bank.

- Wagner, R. A., A. Musacchio, and M. Jara-Bertin. 2016. “Does the Bond Market Discipline State Owned Enterprises?” SSRN Working Paper 2670899.

World Bank. 2019. _World Development Indicators_ . Washington, DC.

31
Policy Research Working Paper 11058

# **Abstract**

Accurate and comprehensive measurement of household livelihoods is critical for monitoring progress toward poverty alleviation and targeting social assistance programs for those who most need it. However, the high cost of traditional data collection has historically made comprehensive measurement a difficult task. This paper evaluates alternative satellite-based deep learning approaches using detailed household census extracts from four African countries to accelerate progress toward comprehensive, fine-scale, and dynamic measurement of asset wealth at scale. The results indicate that transformer architectures solve multiple open measurement problems, by providing the most accurate measurement of local-level variation in household asset wealth across countries and cities, as well as changes in household asset wealth over time. Experiments that artificially restrict data availability show the model’s ability to achieve high performance with limited data. The proposed approach demonstrates the promise of combining satellite imagery, publicly available geo-features, and new deep learning architectures for hyperlocal and dynamic measurement of wealth in data-scarce environments.

This paper is a product of the Development Data Group, Development Economics. 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 and tkilic@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
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23
rural Zambia. This suggests that the changes in the price of cotton relative to maize has been mild.<sup>10</sup> In summary, the available information prevents the econometric identification of the effects of the cotton reforms on the dynamics of cotton shares among Zambian farmers. But we have argued that the observed trends can be mostly linked to those reforms, and that the induced increase in cotton shares could have been larger. This is an interesting instance of a domestic policy that had crucial impacts on the farmers and on their ability to reap most of the benefits from further trade liberalization.

In what follows, we investigate the effects of the complementarities between domestic reforms and international agricultural liberalization on household welfare. As shown in section 1, international cotton markets are subject to strong intervention, particularly by developed countries. We begin by looking at the welfare effects of the increase in world prices that would take place if agricultural markets were liberalized. More concretely, we merge the analysis of cotton shares with increases in world cotton prices.

Define the household income as where π<sup>_h_</sup> _c_<sup>is the income (profit) from cotton sales andπ</sup><sup>_h_</sup> _j_<sup>is income from activity</sup><sup>_j_(wages,</sup> vegetables, maize, groundnuts, etc.). The change in income caused by an increase in the price of cotton _p_ is _c_

> 10See Food Security Research Project (2000).

13
# **Data Sources:**

ANP (2012). National Agency of Petroleum, Natural Gas and Biofuels, _Hydrous Consumers Price_ , data available at http://www.anp.gov.br/preco/prc/resumo_mensal_index.asp ANP (2012) National Agency of Petroleum, Natural Gas and Biofuels, Pure Gasoline Market Price, data available at http://www.anp.gov.br/?pg=55347 accessed April 2012 <mark>ANP</mark> Agência Nacional do Petróleo, Gás Natural e Biocombustíveis _<mark>Levantamento de Preços e de Margens de Comercialização de Combustíveis,</mark>_ data available at <u>http://www.anp.gov.br/preco/</u> accessed March 2012.

CEPEA (2012) Centro de Estudos Avançados em Economia Aplicad, _Indicador Mensal Açúcar_ ( _Sugar Price International Trade),_ data available at <u>http://cepea.esalq.usp.br/english/sugar/ accessed April 2012.</u> CEPEA (2012) Centro de Estudos Avançados em Economia Aplicada, _Indicador Mensal Ethanol Anhydrous and Hydrous),_ data available at <u>http://www.cepea.esalq.usp.br/etanol/?page=407</u> CONAB (2012) Companhia Nacional de Abastecimento (National Food Supply Agency). _Levantamento de Safra_ , (sugar cane, ethanol and sugar produced) data available at <u>http://www.conab.gov.br/conteudos.php?a=1253&t</u> accessed February 2012.

<mark>EIA (2012) U.S. Energy Information Administration,</mark> _<mark>New York Harbor Conventional Gasoline Regular Spot Price</mark>_ <mark>, data available at</mark> <u>http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=EER_EPMRU_PF4_Y35NY_DP G&f=M</u> accessed May 2012.

ERS (2012) Table 4. _World and U.S. Sugar and Corn Sweetener Prices_ , data available at U.S. <u>http://www.ers.usda.gov/Briefing/Sugar/Data.htm</u> accessed May 2012.

IEA 2012. _Preços Médios Mensais Recebidos pelos Agricultores Cana de Açucar (Sugarcane_ Price to Producers) data available at <u>http://ciagri.iea.sp.gov.br/bancoiea/precos_medios.aspx?cod_sis=2</u> accessed April 2012.

Nebraska Energy Office, Ethanol and Unleaded Gasoline Average Rack Prices, data available at <u>http://www.neo.ne.gov/statshtml/66.html accessed May 2012.</u> Mandate available at http://www.senado.gov.br/ http://www.senado.gov.br/noticias/jornal/cidadania/Gasolinaaditivada/not009.htm UNICA (Brazilian Sugarcane Industry Association), data available at http://english.unica.com.br/ 47