rafmacalaba/datause-encoder-data huggingface dataset:
- Denizer, C., Kaufmann, D., & Kraay, A. (2013). Good countries or good projects? Macro and micro correlates of World Bank project performance. _Journal of Development Economics_ , _105_ , 288–302.

- Dreher, A., Fuchs, A., Hodler, R., Parks, B. C., Raschky, P. A., & Tierney, M. J. (2019). African leaders and the geography of China’s foreign assistance. _Journal of Development Economics_ , _140_ , 44–71.

- Dreher, A., Sturm, J. E., & Vreeland, J. R. (2009). Development aid and international politics: Does membership on the UN Security Council influence World Bank decisions? _Journal of Development Economics_ , _88_ (1), 1–18. https://doi.org/10.1016/j.jdeveco.2008.02.003

- Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. _American Political Science Review_ , _97_ (1), 75–90.

- Galiani, S., Knack, S., Xu, L. C., & Zou, B. (2017). The effect of aid on growth: Evidence from a quasi-experiment. _Journal of Economic Growth_ , _22_ (1), 1–33. https://doi.org/10.1007/s10887-016-9137-4

- Gallup, J. L., Sachs, J. D., & Mellinger, A. D. (1999). Geography and economic development. _International Regional Science Review_ , _22_ (2), 179–232. https://doi.org/10.1177/016001799761012334

- Galor, O., & Özak, Ö. (2016). The agricultural origins of time preference. _American Economic Review_ , _106_ (10), 3064–3103.

- Gennaioli, N., & Rainer, I. (2007). The modern impact of precolonial centralization in Africa. _Journal of Economic Growth_ , _12_ (3), 185–234. https://doi.org/10.1007/s10887-007-9017-z

- Imbens, G. W., & Rubin, D. B. (2015). _Causal inference for statistics, social, and biomedical sciences: An introduction_ . Cambridge, England, Cambridge University Press.

- Jablonski, R. S. (2014). How aid targets votes: The impact of electoral incentives on foreign aid distribution. _World Politics_ , _66_ (2), 293–330.

- Jedwab, R., Kerby, E., & Moradi, A. (2017). History, path dependence and development: Evidence from colonial railroads, settlers and cities in Kenya. _The Economic Journal_ , _137_ (603), 1467–1494. https://doi. org/10.1111/ecoj.123

- Jedwab, R., Meier zu Selhausen, F., & Moradi, A. (2018). _The Economics of Missionary Expansion: Evidence from Africa and Implications for Development_ (Working paper No. 7). Centre for the Study if African Economies.

- Jedwab, R., & Moradi, A. (2016). The permanent effects of transportation revolutions in poor countries: Evidence from Africa. _The Review of_

43
# **Social Protection in Niger:**

**What Have Shocks and Time Got to Say?**

Annan, Francis & Sanoh, Aly<sup>1</sup> Poverty & Equity Global Practice World Bank Group Key words: social interventions; shocks & vulnerability; incentives; household consumption; poverty JEL classification: H53; O12; I38; I32; O19

> 1 **Annan** : `fa2316@columbia.edu` & `fannan@worldbank.org` . **Sanoh** : `asanoh@worldbank.org` . For helpful discussions and suggestions, we thank Andrew Dabalen, Johannes Hoogeveen and numerous colleagues at the AFR GPV07. We also thank reviewers from the Social Protection and Labor GP. All remaining errors are ours.
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# **<mark>References</mark>**

<mark>Acemoglu, Daron. "Why not a political Coase theorem? Social conflict, commitment, and politics."</mark> _<mark>Journal of comparative economics</mark>_ <mark>31, no. 4 (2003): 620-652.</mark> <mark>Acemoglu, Daron, Ufuk Akcigit, Douglas Hanley, and William Kerr. "Transition to clean technology."</mark> _<mark>Journal of political economy</mark>_ <mark>124, no. 1 (2016): 52-104.</mark> <mark>Akerlof, George A., and Rachel E. Kranton. "Economics and identity."</mark> _<mark>The quarterly journal of economics</mark>_ <mark>115, no. 3 (2000): 715-753.</mark> <mark>Anderson, Soren T., Ioana Marinescu, and Boris Shor.</mark> _<mark>Can Pigou at the polls stop us melting the poles?</mark>_ <mark>. No. w26146. National Bureau of Economic Research, 2019.</mark> <mark>Barrows, Geoffrey, Teevrat Garg, and Akshaya Jha. "The health costs of coal-fired power plants in India."</mark> _<mark>Available at SSRN 3281904</mark>_ <mark>(2019).</mark> <mark>Becker, Gary S., and Casey B. Mulligan. "The endogenous determination of time preference."</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>112, no. 3 (1997): 729-758.</mark> <mark>Besley, Timothy, and Torsten Persson. "JEEA-FBBVA lecture 2017: The dynamics of environmental politics and values."</mark> _<mark>Journal of the European Economic Association</mark>_ <mark>17, no. 4 (2019): 993-1024.</mark> <mark>Bhandary, Rishikesh Ram, and Kelly Sims Gallagher. "What drives Pakistan’s coal-fired power plant construction boom? Understanding the China-Pakistan Economic Corridor’s energy portfolio."</mark> _<mark>World Development Perspectives</mark>_ <mark>25 (2022): 100396.</mark> <mark>Biardeau, Léopold T., Lucas W. Davis, Paul Gertler, and Catherine Wolfram. "Heat exposure and global air conditioning."</mark> _<mark>Nature Sustainability</mark>_ <mark>3, no. 1 (2020): 25-28.</mark> <mark>Bistline, John. Household Environmental Kuznets Vurves: Evidence from Passenger Transport Emissions. EPRI Working Paper 2022.</mark> <mark>Bruckner, Benedikt, Klaus Hubacek, Yuli Shan, Honglin Zhong, and Kuishuang Feng. "Impacts of poverty alleviation on national and global carbon emissions."</mark> _<mark>Nature Sustainability</mark>_ <mark>(2022): 1- 10.</mark> 16
above to being below the expected share of labor in exports, and in 2011 it was also below most comparator countries. In minerals on the other hand, South Africa’s labor intensity of exports has markedly increased and by 2011 it had the highest share of labor in exports not only among the comparator countries but vis-a-vis most other countries in the world. This change was due entirely to an increase in the intensity of labor directly supporting minerals exports, rather than labor supporting exports via their backward linkages. There was a significant increase in the direct LVAX share, more than doubling from $13 to $30 per $100 of gross exports, while the indirect LVAX share declined by almost half, from $22 to $12 per $100 of gross exports.

This finding is consistent with micro evidence based on annual reports from listed mining firms, which suggests that between 2001 and 2011, wages as a percent of total costs in South Africa’s mining sector almost doubled, from 20% to 35% (Gwatidzo and Benhura, 2013). The mining sector also experienced increases in the number of jobs by over 25% between 2001 and 2011 according to official statistics. South Africa’s top minerals exports in 2011 were unwrought gold (HS 710812, accounting for 10% of total goods exports) and unwrought platinum (HS 711011, accounting for 7% of total goods exports). South African gold and platinum production is highly unionized, which helped drive these wage increases.

**Figure 15:  Total labor content per $100 of gross exports across sectors and GDP per capita 2001 vs. 2011**

<!-- Start of picture text --> Machinery and Equipment nec, 2001 Machinery and Equipment nec, 2011<br>ZAF PER<br>COL BRARUS<br>RUSBRA CHNZAF<br>CHN CHL COL CHL<br>TUR IND<br>IND<br>TUR<br>THA<br>THA<br>PER<br>4 6 8 10 12 4 6 8 10 12<br>Log GDP per capita (current USD) Log GDP per capita (current USD)<br>Metals nec, 2001 Metals nec, 2011<br>COL PER<br>CHN RUSZAFBRA CHN BRA<br>RUS<br>IND PER CHL COL CHL<br>TUR IND ZAF<br>THA TUR<br>THA<br>4 6 8 10 12 4 6 8 10 12<br>Log GDP per capita (current USD) Log GDP per capita (current USD)<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>Total LVAX share (%) Total LVAX share (%)<br>10 10<br>0 0<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>Total LVAX share (%) Total LVAX share (%)<br>10 10<br>0 0<br><!-- End of picture text --> 25
sea level.<sup>18</sup> This storm surge-prone area is composed of 432,068 cells (approximately 18.70 percent of all cells). For each of these cells, we also calculate the shortest path—that is, Euclidean distance—from the centroid of the cell to the coast. The average distance to the coast is 23.44 kilometers.

# **3.2.Mangroves for coastal protection**

As described in Section 2.3, our mangrove distribution data come from two sources: we used Spalding, Blasco and Field (1997) to identify areas that have historically supported mangrove habitats, and Giri et al. (2011) to precisely measure the presence of mangrove at the beginning of our sample period (2000– 12).

Mangrove protection benefits can be derived from their location in areas that are naturally more protected, such as on a continental shelf, on a rugged coastal topography with elevations rising up to 200 meters, or between the shoreline and the continental slope (Jackson 1997). Hence, we began by excluding from our analysis any areas that have not historically supported mangrove habitats. Specifically, for every cell in the storm surge-prone area, we excluded cells that Spalding, Blasco and Field (1997) define as having no mangrove on their shortest path to the coast. We found that 20,504 cells (4.74 percent of all cells in storm surge-prone areas) have mangrove on their path to the coast.

For each of the remaining cells, we then calculated the mangrove width in two steps. First, we identified the line segments along the shortest path to the coast that overlap mangrove forests as defined by Giri et al. (2011). Second, we summed the line segments to measure cumulative mangrove width on the shortest path to the coast. Figure 4 provides a visual representation of the mangrove width calculations on the coast. We found that in our sample, the average mangrove width is 0.25 kilometers, with a minimum of 0 and a maximum of 20.67 kilometers.

> 18 Nordhaus (2006) considers areas with elevation of less than 8 meters as vulnerable to storm surge. We use a less stringent definition because Shuttle Radar Topography Mission elevation estimates under 10 meters are not considered reliable (McGranahan, Balk and Anderson 2007).

14
**<u>Table 16. Distribution of Center and State Tax Revenues</u>**

|**FY 2000/2001**<br>|**FY 2001/2002**|
|---|---|
|Percentof Total Revenues||
|CenterOwn<br>45%|46%|
|Center Devolved<br>17%|16%|
|State Own<br>39%|38%|
|Total<br>100%|100%|
|Total RevenuesasPercentofGDP<br>16%|17%|

Source: _Economic Review 2002-2003_ , Tables 2.10-2.11.

We can consider a rough order of magnitude estimate of the impacts of underdeclaration of property values using the following simple model normalized for one rupee of actual real estate transaction. Define λU as the percent of underdeclaration of real estate values and t as the stamp duty rate. Then the amount of stamp duty collected per rupee of actual real estate transactions equals (1- λU ) t. Also, define λU as the amount of money that is circulated in the black economy. In principle, we can trace the impact of this injection of money into the black economy through a money demand equation, or where MB = the supply of black money VB = the velocity of black money PB = the price level YB = the real income added to the black economy.

Normalizing PB to 1 and substituting for λU, we get YB= λU*VB. Now let γT equal the ratio of total tax collections to GDP. Then the total tax revenue lost on this additional black income equals (γT * λU * VB). It is possible to calculate the total tax revenue lost under different assumptions about the values of these parameters.

Table 17 provides us with the most recent values for γT. For this illustrative calculation, we use the historical average round number of 15. Estimates of the velocity of black money are difficult to come by for the obvious reason that market participants make every effort to avoid disclosure of these values to official sources. Establishing a robust empirical relationship between the two is even more daunting. However, for the sake of discussion, the velocity of currency in circulation may be used as a proxy initial value. Over the last thirty years, this value 27
occurs because more children imply fewer resources per child and credit constraints limit the family's ability to borrow against future returns. Sibling rivalry occurs because all children benefit from having fewer educated siblings with comparatively higher returns on investment.

Resource dilution and sibling rivalry in educational investments in poor countries is well documented. In addition to the Taiwan and Ghana studies, the list includes: Binder (1998) for Mexico, Morduch (2000) for Tanzania, Edmonds (2007) for Nepal, Ota and Moffatt (2007) for India, and Dammert (2010) for Guatemala and Nicaragua. While Garg and Morduch focus on credit constraints and differences in relative returns to education as the cause for sibling rivalry, Edmonds (2007) emphasizes that comparative advantage in home production can lead to similar implications when girls have comparative advantage and it is not possible to hire labor for home production. Both Edmonds (2007) and Dammert (2010) find evidence consistent with this sibling rivalry interpretation.

Such models of sibling rivalry neglect that parents have additional knowledge about their children’s capabilities and use this information to make school investment decisions. A literature embedded in testing the one-period consensus parental preferences model of human capital investment of Becker and Tomes (1976) and Behrman, Pollack, and Taubman (1982) uses child endowments in modeling the investment decision.<sup>3</sup> Most studies that examine the investment decision process have to work around the fact that actual child ability or endowment is typically not observed (Rosenzweig and Schultz 1982; Behrman, Pollack and Taubman 1982; Behrman, Rosenzweig and Taubman 1994). Some recent studies are able to use direct measures of child ability. Kim (2005) uses an IQ test administered to Wisconsin high school juniors and finds that higher ability children receive more parent transfers. Glick and Sahn (2010) use achievement test scores from Senegalese children taken in Grade 2 to explain school outcomes seven years later.

> 3 See Behrman (1997) for an overview of the consensus parental preferences models.

5
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maintained (CGAP, 2010). Among others, these requirements include the disclosure of the minimum balance requirement, early withdrawal penalties, and the account balance.

We also include two indices measuring the enforceability of consumer protection laws: the _Monitoring Index_ and the _Enforcement Index_ , which refer to the number of monitoring and enforcement actions available to the regulator, respectively (CGAP, 2010). Examples of these actions are mystery shopping and onsite inspection of financial institutions for the monitoring index, and the ability to issue public notices of violations and impose fines and penalties for the enforcement index. While we expect greater consumer protection to be correlated with greater use of bank accounts, it is hard a priori to assess the impact of information disclosure. It is possible that greater awareness and information on the costs and requirements of using bank accounts might discourage individuals from using bank accounts.

We also speculate that the use of accounts will be affected by the extent to which individuals feel that their rights as creditors are legally protected. We include two variables to capture these effects. The _Legal Rights Index_ variable measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending (World Bank, 2011; Djankov et al., 2007). _Political Risk Rating_ comes from the International Country Risk Guide (ICRG) of the Political Risk Services Group (2010) and assesses the political stability of a country. Among others, components of this rating are government stability, investment profile (which tries to capture expropriation risk), and corruption.

In addition, we include a variable that measures the scope of explicit deposit insurance, which might be designed to build trust among consumers that their deposits are safe with the banks. The _Share of Member Banks’ Deposits Covered_ is the share of deposits of member 23
Figure 1: _Histograms of household residual variances and skewness in clusters_ <!-- Start of picture text --> Residual variances in clusters Residual skewness in clusters<br>0 1 2 3 −3 −2 −1 0 1 2<br>80 50<br>40<br>60<br>30<br>40<br>Frequency Frequency<br>20<br>20<br>10<br>0 0<br><!-- End of picture text --> Table 4: _State level headcount ratio at household-level_ New estimator ELL estimator Survey est. True value Bias RMSE Cov. Bias RMSE Cov. Bias RMSE W0(5.5) 0.1076 0.0098 0.0138 0.8900 0.1020 0.1038 0.0000 -0.0015 0.0137 on the survey yields a comparable, even slightly superior performance of the latter in terms of the root mean squared error. Since the distribution of the explanatory variables has changed from 2000 to 2010 (e.g. the share of households owning a phone increased from 67% to 70%), the ELL estimator is severely biased.

So far, the poverty measures have been calculated at the household-level, while one is typically also interested in poverty measures at the individual-level, e.g. the percentage of poor people and not households in a small area. In principle, one could conduct the first-stage regression at the individual level which is equivalent to replicating the household entries in the data sets by the respective household sizes.<sup>14</sup> However, when calculating an overall poverty measure from the simulated income values in the second stage, one then needs to know the number of individuals in each cluster at the time of the recent survey. The required information may be available from a previous listing exercise.

A second option starts with the first-stage regression on the household-level as described above. The smallest unit to match between the census and the survey are the municipalities. In fact, the same value of consumption is predicted on average for all households in the same municipality. For a single bootstrap simulation, they only differ by the simulated household error. Since a relationship between household size and income is assumed on the household level, typically that bigger households are poorer, one cannot randomly assign household sizes to the households. Hence, one possible remedy is to save the household sizes from the survey households and residuals from the first-stage regressions and draw them together in the bootstrap procedure in the second stage.

> 14This is due to the fact that both the household equivalent income and all explanatory variables are the same for all household members.

15
We find that an increase in enforcement leads to higher unemployment but lower inequality. We also document that: i) employment flows between the formal and self-employment sectors, but informal wage earners are in a segmented sector of the economy; ii) mandated benefits are borne by workers in the form of lower wages at the top of the formal wage distribution, but not at the bottom where downward wage rigidity may be important; iii) as a result, formal jobs at the bottom of the wage distribution become more attractive, inducing those who are low skilled and self-employed to search for employment in the formal sector.

# **8. References**

Acemoglu, Daron, 2001. Good Jobs versus Bad Jobs. Journal of Labor Economics, 19 (1), 1-21.

Aghion, Philippe and Peter Howitt, 1992. A Model of Growth through Creative Destruction. Econometrica, Econometric Society, vol. 60(2), pages 323-51.

Albrecht, James, Lucas Navarro and Susan Vroman, 2006. The Effects of Labor Market Policies in an Economy with an Informal Sector. IZA Discussion Paper 2141.

Almeida, Rita and Francois Bourguignon, 2006. Minimum Wages and Poverty in Brazil. World Bank, mimeo.

Almeida, Rita and Pedro Carneiro, 2008. Enforcement of Labor Regulation and Firm Performance . Journal of Comparative Economics (forthcoming).

Amaral, Pedro and Erwan Quintin, 2005. A competitive model of the informal sector. Journal of Monetary Economics, forthcoming.

Ahsan, A. and Carmen Pages, 2007. Are all labor regulations equal? Assessing the effects of job security, labor dispute, and contract labor laws in India. World Bank Policy Research Working Paper 4259.

Alvarez, Fernando and Marcelo Veracierto, 2001. Severance Payments in an Economy with Frictions, Journal of Monetary Economics, 47, pp. 477-498.

Barros, Ricardo P. and Carlos Henrique Corseuil, 2001. The impact of regulations on Brazilian Labor Market Performance. IADB Research Paper 427.

Barros, Ricardo P., Cruz, Luis, Foguel, Miguel and Rosane Mendonça, 1997. Uma avaliação empírica do grau de flexibilidade alocativa do mercado de trabalho brasileiro. IPEA Texto para discussão 499. Rio de Janeiro: Brasil.

28
**Figure 1. Portfolio and Other Capital Flows (Median flows for all emerging markets in % of GDP)**

<!-- Start of picture text -->
1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>Portfolio Flows Other Flows<br>1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1<br><!-- End of picture text -->

**Figure 2. Magnitude of FDI and non-FDI flows (Median flows for all emerging markets in % of GDP)**

<!-- Start of picture text --> 2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>‐0.5<br>‐1<br>FDI Flows Portfolio and Other Flows<br>1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1<br><!-- End of picture text --> 16