rafmacalaba/datause-encoder-data huggingface dataset:
Resource Rents, Coercion, and Local Development: Evidence from Post-Apartheid South Africa<sup>_∗_</sup> Paulo Bastos<sup>_†_</sup> Nicolas Bottan<sup>_‡_</sup> _Keywords_ : Natural resources trade, coercion, wage bargaining, historical development.

_JEL Codes_ : F16, Q02, J5, J6, N3, N4, N5, O18.

> _∗_ We thank Aaditya Mattoo, Ben Faber, Dan Bernhardt, David Atkin, Remi Jedwab, Sandra Sequeira, participants at the Trade, Poverty and Shared Prosperity conference of the World Bank, the the Washington Area international Trade Symposium, and seminars at the World Bank and the University of Illinois-Urbana Champaign for very helpful comments and discussions. Research for this paper was supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development and through the Strategic Research Program on Economic Development.

> _†_ Development Research Group, The World Bank, 1818 18th Street NW, Washington DC, United States. E-mail: pbastos@worldbank.org.

> _‡_ Department of Economics, University of Illinois at Urbana-Champaign, Illinois 61801, United States. Email: bottan2@illinois.edu.
Fig. 3 Innovation rates (% of firms) Fig. 4 Investment in intangibles (% of firms) <!-- Start of picture text --> 50 16<br>14<br>40<br>12<br>10<br>30<br>8<br>20 6<br>4<br>10<br>2<br>0<br>0<br><!-- End of picture text --> _Source:_ the authors’ calculations based on EBRD-EIB-WB Enterprise Survey. Innovative firms are defined as those introducing new or improved products, services or processes, or spending on research and development activities.

_Source:_ the authors’ calculations based on EBRD-EIB-WB Enterprise Survey. Intangible assets include trademarks, copyrights, patents, licenses, service contracts and franchise agreements.

Private sector development is lagging in MENA. On average, during the 2014-2019 period, the contribution to GDP growth of manufacturing and selected services sectors – which proxy the private sector in the Enterprise Surveys – was 44% in the region, ranging from 37% in Lebanon to 50% in the West Bank and Gaza (Figure 5).<sup>6</sup> This is in line with the average of lower-middle income and upper-middle incomes economies (44% and 45%). For example, although Morocco registered the highest total growth in the region for the 2017-2019 period, more than half of it was driven by other sectors. The employment dynamics using Enterprise Surveys are in line with value added growth at the macroeconomic level, especially in Egypt, Morocco and Tunisia (Figure 6). In Lebanon, the private sector’s contraction is reflected in the decrease in the employment at the firm level.

The significant presence of SOEs in MENA discourages the entry of private companies and hinders innovation. SOEs are typically subject to limited competition, weak oversight and soft budget constraints (Olugbade et al., 2021). As governments try to protect incumbents in strategic sectors, SOEs can become a drag on the economy by imposing fiscal burdens on the state, increasing the costs of doing business and depriving the private sector from resources that could have been used more productively. SOEs’ lack of management skills is also the cause for their lower performance (Estrin et al., 2009). In addition, SOEs frequently provide goods and services to downstream markets and purchase inputs from upstream markets, affecting the whole value chain of the sectors they operate in (Arezki et al., 2019).

> 6 In Figure 5, manufacturing and selected services (construction, wholesale, trade, retail, restaurants, hotels. transport. storage and communication) correspond to the sectors covered by Enterprise Surveys. "Other sectors" include agriculture, hunting, forestry, fishing, mining, utilities, financial intermediation, real estate and renting, public administration and defense, education, health, social and personal service activities and activities of private households as employers and undifferentiated production activities of private households.

6
- Cortez, E., A. Orre, B. Fael, B. Nhamirre, C. Banze, I. Mapisse, K. Harnack, and T. Reite (2021). _Costs and consequences of the hidden debt scandal of Mozambique_ . Centro de Integridade Pública (CIP), Moçambique.

- Cruces, J. J., M. Buscaglia, and J. Alonso (2002). The term structure of country risk and valuation in emerging markets. Universidad de San Andres Working Paper 46.

- Cruces, J. J. and C. Trebesch (2013). Sovereign defaults: The price of haircuts. _American Economic Journal: Macroeconomics 5_ (3), 85–117.

- D’Erasmo, P. (2011). Government reputation and debt repayment. Mimeo.

- Dias, D. A. and C. Richmond (2007). Duration of capital market exclusion: An empirical investigation. Mimeo.

- Diaz-Alejandro, C. (1983). Good-bye financial repression, hello financial crash. _Journal of Development Economics 19_ (1-2), 1–24.

- Eaton, J. and M. Gersovitz (1981). Debt with potential repudiation: theoretical and empirical analysis. _The Review of Economic Studies 48_ (2), 289–309.

- Estefania-Flores, J., D. Furceri, S. Kothari, and J. D. Ostry (2023). Worse than you think: Public debt forecast errors in advanced and developing economies. _Journal of Forecasting 42_ (3), 685––714.

- Estevão, M. (2021). It’s time to get a better handle on debt in developing economies. Available online: `/https://www.barrons.com/articles/its-time-to-get-a-betterhandle-on-debt-in-developing-economies-51640204237` . Barron’s.

- Fang, X., B. Hardy, and K. Lewis (2023). Who holds sovereign debt and why it matters. NBER Working Paper No. 30087.

- Fourakis, S. (2021). Sovereign default and government reputation. Mimeo.

- Frankel, J. (2011). Over-optimism in forecasts by official budget agencies and its implications. _Oxford Review of Economic Policy 27_ (4), 536–562.

- Gamboa, J. (2023). Hidden chinese lending. Mimeo.

- Gelpern, A., S. Horn, S. Morris, B. Parks, and C. Trebesch (2023). How China lends: A rare look into 100 debt contracts with foreign governments. _Economic Policy 38_ (114), 345––416.

- Goes, I. (2024). Measurement uncertainty in international statistics. Mimeo.

- Gorton, G. and G. Ordonez (2014). Collateral crises. _American Economic Review 104_ (2), 343–378.

- Gorton, G. and G. Ordonez (2023). _Macroeconomics and Financial Crises: Bound Together by Information Dynamics_ . Princeton University Press.

43
<!-- Start of picture text --> Trade between US. and South Africa<br>(US $ Millian)<br>10,000 sl<br>T5E5<br>Sada (5,885 5518<br>5,000 4.458 4,05 £6e4 S06 4462<br>b ad 25DE 21a 3,172 :<br>o<br>ats<br>. . STS<br>“5,000<br>200) oe Sd ST<br>BUS.Exports U.S. Imports Trade Balance<br>wiAGO,info<br><!-- End of picture text -->
Table A11: Relationship between general management practices and energy management practices (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|0.403<sup>_∗∗∗_</sup>|0.496<sup>_∗∗∗_</sup>|0.503<sup>_∗∗∗_</sup>|0.311<sup>_∗∗∗_</sup>|0.350<sup>_∗∗∗_</sup>|
||(0.057)|(0.037)|(0.062)|(0.035)|(0.051)|
|No. Employees|0.006|0.076<sup>_∗∗∗_</sup>|0.037|0.023|0.050<sup>_∗_</sup>|
||(0.036)|(0.024)|(0.039)|(0.023)|(0.030)|
|Capital Stock|0.049<sup>_∗∗∗_</sup>|0.022<sup>_∗∗_</sup>|0.049<sup>_∗∗∗_</sup>|||
||(0.014)|(0.010)|(0.017)|||
|Log(Sales)|0.031|0.026<sup>_∗_</sup>|0.002|0.038<sup>_∗∗_</sup>|0.024|
||(0.025)|(0.015)|(0.031)|(0.015)|(0.018)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|621|1790|694|1509|854|
|Adjusted _R_<sup>2</sup>|0.362|0.295|0.287|0.181|0.166|
|df<br>m|43|46|53|43|53|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 53
26

# **4.3 Measuring the Level of Infrastructure in Information and Communications Technologies**

Empirical assessments of the effects of ICTs on aggregate output and economic growth typically entails distinguishing the economy-wide capital stock into ICT and nonICT capital stock. However, due to the current non-availability of data to construct figures for the ICT capital stock, it will be seen in the empirical methodology section that we take an alternative approach. The approach entails including as a separate regressor a proxy for the stock of ICTs currently available in the economy, in addition to the aggregate capital stock variable as dictated by the assumption of the aggregate CobbDouglas production function. By this estimation methodology, we are implicitly testing for additional effects of ICTs on economic growth over and beyond that of its contribution as a “normal” component of the economy-wide capital stock. We will now consider the various indicators that we will use as proxies for the stock of ICTs available in the economy.

## **Number of Computers (per 1,000 persons)**

Personal computers per 1,000 population refers to the number of self-contained computers designed to be used by a single individual and is an indicator of personal computer penetration and use of relatively new technology for information processing. The data series was obtained from the World Bank World Development Indicators.

## **Number of Internet Users (per 1,000 persons)**

Internet users are people with access to the worldwide network. The data series was obtained from the World Bank World Development Indicators.

## **Number of Internet Hosts (per 10,000 persons)**

Internet hosts per 10,000 population refers to the number of computers with active Internet Protocol (IP) addresses connected to the Internet. All hosts without a country code identification are assumed to be located in the United States. It is used as an indication of how well a population has advanced to the level of adapting and using advanced communication channels (Internet) to serve its priorities. The data series was obtained from the World Bank World Development Indicators.
CCRIF 2013. _Understanding CCRIF, Answers to Frequently Asked Questions_ Crush, J., Frayne, B. and Pendleton, W. 2012. _The Crisis of Food Insecurity in African Cities_ , Journal of Hunger and Environmental Nutrition, 7:271‐292, 2012 Clarke, D. J. and Hill, R.V. 2013. _Cost Benefit Analysis of the African Risk Capacity_ , IFPRI Discussion Paper 01292 EARS 2010. _FESA Micro‐Insurance: Methodology, Validation, Contract Design_ FAO 2006. _An assessment of food security early warning systems in sub‐Saharan Africa_ FAO 2008. _An Introduction to the Basic Concepts of Food Security_ FAO 1998. _Rome Declaration on World Food Security and World Food Summit Plan of Action_ , 13‐17 November 1996 available at http://www.fao.org/docrep/003/w3613e/w3613e00.HTM FAO 2011 _. Crops statistics, Concepts, definitions and classifications_ , available at <u>http://www.fao.org/fileadmin/templates/ess/ess_test_folder/documents/Production_trade/definiti ons/Crops_statistics_concepts_definitions_classifications.doc</u> FAO, WFP and IFAD. 2012. _The State of Food Insecurity in the World 2012 Economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition._ Rome, FAO.

FAO, IFAD and WFP. 2013. _The State of Food Insecurity in the World 2013. The multiple dimensions of food security._ Rome, FAO.

GFDRR. _Economic Vulnerability and Disaster Risk Assessment of Malawi and Mozambique_ , available at http://www.preventionweb.net/files/15520_gfdrrecon.vulnerabilitydrrmalawimoz.pdf Gommes, R. and Kayitakire, F. (eds.) 2012. _The challenges of index‐based insurance for food security in developing countries,_ JRC.

Gonzalez, A. V. 2009. _Index Insurance Applied to Agriculture: The Mexican Case, IFPRI_ , Focus 17, Brief 15, 2009 Grossi, P. and Kunreuther, H. (eds.) 2005 _. Catastrophe Modeling: A New Approach to Managing Risk_ , Huebner International Series on Risk, Insurance and Economic Security Hazell, P., Anderson, J., Balzer, N., Hastrup Clemmensen, A., Hess, U., Rispoli, F. (2010). _The Potential for Scale and Sustainability in Weather Index Insurance for Agriculture and Rural Livelihoods_ , IFAD and WFP, Rome, 151 pp.

Hellmuth, M. E., Osgood, D. E., Hess, U., Moorhead, A., Bhojwani, H. (eds) (2009), _Index insurance and climate risk: Prospects for development and disaster management_ , Climate and Society No 2, IRI, Columbia University, NY, United States, 112 pp.

Hoefsloot, P., Ines, A., van Dam, J., Duveiller, G, Kayitakire, F. and Hansen, J. 2012. _Combining Crop Models with Remote Sensing for Yield Prediction: Concepts, applications and challenges for_ _heterogeneous, smallholder environments_ , Report of CCFAS‐JRC Workshop at JRC, Ispra, Italy, June 13‐14 2012 IFAD (2011). _Weather index‐based insurance in Agricultural Development: A Technical Guide_ , IFAD and WFP, Rome, 66 pp.

22
# **The Social Discount Rate: Estimates for Nine Latin American Countries**

## **Humberto Lopez**<sup>*****</sup> **The World Bank**

Keywords: Social discount rate, cost-benefit analysis, Latin America JEL codes: D61 H43

> *I would like to thank Veronica Alaimo. All the data are available from the author upon request. Views expressed in this paper are those of the author and should not be attributed to the World Bank, its Executive Directors or the countries they represent.  Address for correspondence: MS I8-801, The World Bank, 1818 H Street, Washington, DC 20433. Email: Hlopez@worldbank.org. Tel: +1 (202) 473-4909. Fax: +1 (202) 522-7528
clouded in uncertainty, with scientists disagreeing on their predictions of just how long the pandemic could last (Kissler et al., 2020).

We estimate a seasonal difference-in-differences (SDID) specification where we compare differences within-year (across seasons) and across years. This approach allows us to identify COVID-19 impacts from normal seasonal fluctuations in labor market demand and supply, where employment rates in Indonesia tend to be lower in August (post-harvest) than in February (preharvest). We leverage multiple survey years prior to the COVID-19 pandemic to establish a historical trend of a pre-pandemic normal and to ensure that our estimates are based on this prepandemic normal, instead of potentially year-specific anomalies. Therefore, we are able to attribute any remaining difference to the causal effect of the COVID-19 pandemic on Indonesia’s labor market outcomes.

Specifically, we estimate the following regression: for the pooled male and female samples, and separately for each gender sample. yipst is the labor market outcome of individual _i_ in province _p_ in month _s_ in year _t_ . Sakernas is a biannual survey conducted in February and August of each year, hence, the month _s_ can only take two values: February or August. COVIDst is a binary indicator varying at the season-year level, which takes the value of 1 for periods after March 2020, and 0 if otherwise (from February 2016 to February 2020). Our study is limited to one disseminated survey round after the initial onset of the pandemic: August 2020. The coefficient, β, is the SDID estimate. γp, θs, and δt are province, season, and year fixed effects, respectively, which control for common characteristics within province, season, and year. Xipst is a vector of control variables, including age, squared age terms, marital status dummies, household size, education level dummies, training experience dummy, and rural/urban 20
be equal to -0.0063 as is obtained from the fertility equation using the conversion method described below.

Thus, the number of “missing girls” increased by 305,496 per year due to ultrasound access. The estimated decline in EFM, on the other hand, implies that the annual number of postnatal female deaths by age 5 fell by 60,879. The ratio of increase in sex-selective abortions and the decrease in EFM is 5.02, i.e., for every 5.02 girls aborted, one girl survived due to access to ultrasound technology.

Table: Decomposition and Simulation

|Pre<br>(Pre-ultrasound)<br>(1)|Post<br>(Late difusion)<br>(2)|∆<br>(Post - Pre)<br>(3)|
|---|---|---|
|A. Regression Estimates|||
|_EFMFG_<br>0.0277|0.0077|-0.020|
|_FFG_<br>0.4790|0.4605|-0.0185|
|_NF G_<br>_N_<br>0.3340|||
|B. Decomposition|||
|_N_<br>27,300,000|||
|_m_ (#mothers)<br>_≈_207,557,211|||
|_mFG_ (#mothers with a frstborn girl)<br>_≈_0.3340*_m_|||
|_NFG_<br>0.3340*(_N_)<br>= 9,118,200|0.3340*(_N_)-0.0063*_mFG_<br>= 8,681,458|-0.0063*_mFG_<br>= -436,742|
|∆**number of excess postnatal female deaths:**|||
|(1) _NFG,post ∗FFG,post ∗_(∆_EFMFG_)|_≈_-79,956||
|(2) _NFG,post ∗_(∆_FFG_)_∗EFMFG,pre_|_≈_-4,449||
|(3) (∆_NFG_)_∗FFG,pre ∗EFMFG,pre_<br>(4) = (1)+(2)+(3)|_≈_-5,795<br>= -90,200||
|∆**number of missing girls:**<br>|||
|(5) ∆_NFG ∗_(<sup>0</sup><sup>_._49</sup><br>0_._51 <sup>_−FF G,pre_</sup><br>0_._51<br>)|_≈_-9,420||
|(6) _NFG,post ∗_∆_FFG ∗_<sup>_−_1</sup><br>0_._51|_≈_314,916||
|(7) = (5)+(6)|= 305,496||
|∆**number of missing girls /** ∆**EFM:**|||
|(8) = (7)/(4)|= -3.39||

**– Conversion of treatment effect from the fertility specification (2) to expected number of children born in a year.**

75
Given the budget constraint, where _p_ denotes the relative price of manufactures, the static first-order conditions for utility maximization yield with the solutions for consumption given by In a closed economy, demand for each good must equal its supply so that (given that population is normalized to one) _c_<sup>_M_</sup> _t_ = _Yt_<sup>_M_</sup> and _c_<sup>_A_</sup> _t_<sup>=</sup><sup>_Y_</sup> _t_<sup>_A._Thuswehave</sup> Using this with (4) yields which, in turn, solves for a unique labor allocation across sectors _nt_ such that _nt_ = _φ_ ( _T, A, β, γ_ ) with where _σ_ denotes the elasticity of substitution between labor and land and _ς_ is the value of manufacturing output relative to that of agriculture.

In this framework, a decrease in the land endowment have three effects on the allocation of labor, _n._ First, it reduces the marginal productivity of labor, MPL, in agriculture, lowering wages there and pushing labor towards manufactures, i.e., an increase in _n_ . Second, the decrease in land lowers agricultural output, creating excess demand for the agricultural good, raising its relative price and decreasing the relative price, _p_ , of the manufacturing good and pulling labor into agriculture. Third, with lower land, total income decreases, shifting demand composition in favor of the agricultural output and reinforcing the second effect. The net effect of a decrease in land endowment on agricultural employment thus depends on the relative strength of these three channels.

5
**Table 5.** Sexual Activity and Condom Demand

||**Sexually active last**<br>**six months**<br>**Six months post**<br>**intervention**|**Frequency of sex**<br>**last six months**<br>**Six months post**<br>**intervention**|**Number of partners**<br>**last six months**<br>**Six months post**<br>**intervention**|**STI presence**<br>**Six months post**<br>**intervention**|**Pregnancy**<br>**Six months post**<br>**intervention**|**Redeemed Voucher**<br>**for Free Condoms**<sup>**§**</sup><br>**Six months post**<br>**intervention**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Treatment students|−0.003|0.212|−0.009|−0.005|0.000|0.099*|
||(0.029)|(0.259)|(0.031)|(0.004)|(0.005)|(0.055)|
|Spillover students|0.023|0.278|0.043|−0.001|0.007|0.048|
||(0.031)|(0.270)|(0.031)|(0.004)|(0.006)|(0.048)|
|Control for baseline value of dep. var.|No|No|Yes|Yes|Yes|No|
|Mean of dep. var. control group|0.26|1.57|0.37|0.01|0.02|0.18|
|Observations|4,364|3,857|3,881|3,774|4,252|3,358|

_Source_ : Authors’ analysis from student surveys.

_Note:_ Dependent variables not standardized. All outcome variables are assessed six months after treatment. Standard errors clustered at the school level in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Column (1) includes students attrited for written survey but later tracked over the phone. Table does not control for baseline value of the dependent variable, because outcome was not measured at baseline (except for column (3)).<sup>§</sup> 3,358 students of the full sample agreed to be contacted for this part of the study. Specification controls for whether individual had a cellphone.
Table 11A. Workers by education levels (absolute numbers and percentages)

|||Children<br>(ages 10 to<br>14)|No formal<br>education|Not<br>finished<br>primary<br>school|Not finished<br>secondary<br>school|Secondary or<br>higher<br>education|TOTAL|Children<br>(ages 10<br>to 14)|No formal<br>education|Not<br>finished<br>primary<br>school|Not finished<br>secondary<br>school|Secondary<br>or higher<br>education|TOTAL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|COUNTR|Female|1403358|1,527,131|672,474|2,344,897|143,315|4,687,817|1341|14.59|6.43|22.41|1.37|44.79|
|Y TOTAL|Male|,,|788,193|928,912|2,407,857|249,685|4,374,647|.|7.53|8.88|23.01|2.39|41.80|

Source: Table 5.2 of Thurlow and Wobst (2003).

Table 12A. Workers by education levels (absolute numbers and percentages)

|||No formal<br>education|Not<br>finished<br>primary<br>school|Not<br>finished<br>secondary<br>school|Secondary<br>or higher<br>education|TOTAL|No formal<br>education|Not<br>finished<br>primary<br>school|Not<br>finished<br>secondary<br>school|Secondary<br>or higher<br>education|TOTAL|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Public sector|Female|1004|54412|71697|1257|128370|0.01|0.32|0.42|0.01|0.76|
||Male|2844|130410|144805|16680|294739|0.02|0.77|0.86|0.10|1.74|
|Pit tditil ilt|Female|2591190|4456276|113626|618|7161710|15.32|26.35|0.67|0.00|42.34|
|rvae raona agrcuure|Male|1560635|4796128|174841|1622|6533226|9.23|28.35|1.03|0.01|38.62|
|Pitifl t|Female|95379|524455|58132|0|677966|0.56|3.10|0.34|0.00|4.01|
|rvae-norma secor|Male|70292|615318|73883|2389|761882|0.42|3.64|0.44|0.01|4.50|
|NGO/Party or religion & Private|Female|18447|134887|47039|707|201080|0.11|0.80|0.28|0.00|1.19|
|other|Male|59835|400758|87581|6792|554966|0.35|2.37|0.52|0.04|3.28|
|Hk dti|Female|69330|316375|8682|0|394387|0.41|1.87|0.05|0.00|2.33|
|ousewor ues|Male|20397|181656|4426|0|206479|0.12|1.07|0.03|0.00|1.22|
|TOTAL|Female|2775320|5486408|299203|2582|8563513|16.41|32.44|1.77|0.02|50.63|
||Male|1714003|6124270|485532|27486|8351291|10.13|36.21|2.87|0.16|49.37|

Source: Author's own elaboration based on the ILFS (NBS, 2002a). Table 10A.

43