rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table1 : Descriptive statistics</u>**

||**Ethiopia**|**Malawi**|**Niger**|**Nigeria**|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|
|**Basic household characteristics**|||||||
|<br>Share owning land|0.98|0.95|0.98|0.92|0.98|0.95|
|<br>Share agric. cultivators|0.99|1.00|0.99|1.00|0.94|0.98|
|<br>Household size|5.15|4.75|6.82|6.02|5.69|6.04|
|adults|2.57|2.36|3.06|3.09|2.96|3.18|
|children|1.52|2.15|3.52|2.58|2.35|2.86|
|old people|0.24|0.24|0.24|0.35|0.37|0.33|
|Head’s age|44.58|43.04|44.90|50.60|48.88|47.35|
|Head female|0.21|0.25|0.08|0.12|0.23|0.30|
|Head is illiterate|0.66|0.23|0.50|0.40|0.29|0.16|
|Head has primary|0.27|0.57|0.44|0.29|0.61|0.57|
|Head has more than primary|0.04|0.19|0.06|0.30|0.10|0.22|
|**Assets**|||||||
|Value of agricultural assets (USD)||323.61|483.73|340.46|415.95|24.49|
|<br>Value of animals (USD)|499.70|85.98|489.79|185.67|266.45|369.41|
|Value of durable goods (USD)||1718.56|4402.32|1301.66||2948.10|
|**Income and its sources**|||||||
|Gross household income (USD)||663.50|1395.64|1307.00|1159.81|1201.75|
|share from crops||0.51|0.44|0.70|0.51|0.46|
|share from livestock||0.10|0.17|0.09|0.10|0.19|
|share from ag wage||0.15|0.02|0.01|0.05|0.06|
|share from non-age wage||0.09|0.06|0.08|0.11|0.10|
|<br>share from self employment||0.06|0.24|0.11|0.17|0.13|
|<br>share from transfer||0.08|0.07|0.01|0.07|0.06|
|**Details of agricultural production**|||||||
|<br>Gross inc.from crops (USD/ha)||515.57|267.09|2233.80|509.88|545.39|
|Net inc.from crops (USD/ha)||164.11|226.13|1196.67|289.16|279.06|
|Gross inc.from crops (USD)||266.48|517.01|543.06|434.02|406.27|
|# of days by family members|126.82|74.28|151.07|108.76|183.03|234.95|
|<br>Imputed return to family labor/day||2.21|1.50|11.00|1.58|1.19|
|Value of hired labor (USD)|20.47|3.56|41.93|27.05|17.79|30.35|
|Value of fertilizer (USD)||71.85|45.61|53.90|21.85|8.57|
|**Land**|||||||
|No. of plots owned|3.02|1.74|2.74|1.75|2.22|1.88|
|<br>Owned area ha|1.05|0.68|5.17|0.73|2.50|1.44|
|Operated area ha|1.16|0.72|5.17|0.79|2.12|1.40|
|Owned area/adult (14-60)|0.44|0.32|1.93|0.28|0.90|0.53|
|Operated area/adult (14-60)|0.49|0.33|1.92|0.30|0.78|0.51|
|<br>Has any type of document|0.38|0.01|0.11||0.14|0.19|
|Gender of manager reported|0.98|0.99|0.96|0.97|0.93|0.96|
|male manager|0.80|0.71|0.53|0.81|0.27|0.09|
|joint manager|||0.36|0.01|0.45|0.52|
|female manager|0.19|0.29|0.10|0.18|0.24|0.38|
|<br>Gender of owner reported|0.30|0.77|0.79|0.07|0.81|0.98|
|<br>male owner|0.44|0.35|0.58|0.75|0.40|0.25|
|joint owner|0.25|0.17|0.26||0.31|0.46|
|female owner|0.18|0.45|0.09|0.05|0.22|0.28|
|**Geo variables**|||||||
|Distance to the nearest major road (km)|17.38|9.46|12.92|17.20|22.09|8.06|
|No. of households|3,099|10,123|2,263|3,029|2,625|2,129|
|Exploded|10,527,872|2,563,476|1,938,052|15,540,444|6,620,836|4,468,866|

_Source:_ Own computation from LSMS-ISA household surveys.

14
government policy delay on pandemic outcomes, and sometimes amplify the positive link between pandemic outcomes and some vulnerabilities (such as a high share of elderly and of obesity prevalence).

Our research is subject to the caveat that all the conclusions are just correlations. This being said, our research offers suggestive evidence that decisive government policies are important where the country suffers from strong preexisting vulnerabilities and where the citizens respond to government actions more responsively such as in democracies. It is also suggestive that it is not individualistic culture, trust, and policy delay themselves per se that are decisive in explaining adverse pandemic outcomes; rather, it is likely their interactions with preexisting vulnerabilities. At the time of the pandemic, some advantages during normal times, such as democracy, or a hightrust culture, when combined with preexisting vulnerabilities and/or policy delay, may have extra adverse effects. In conclusion, key institutional and cultural traits have quite different effects during normal times and crises, and in different contexts such as different levels of preexisting demographic vulnerabilities.

# **Reference**

- Aassve, A., G. Alfani, F. Gandolfi, M. Le Moglie (2020). “Epidemics and trust: the case of the Spanish flu.” IGIER Working Paper No. 661.

- Acemoglu, D., T. Reed, J. Robinson. 2014. “Chiefs: Economic development and elite control of civil society in Sierra Leone.” _Journal of Political Economy_ 122(2), 319-368.

- Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2020). “Inequality in the impact of the Coronavirus shock: New survey evidence for the UK.” <u>https://doi.org/10.17863/CAM.52477.</u>

- Aksoy, C.G., B. Eichengreen, O. Saka. “The political scar of epidemics.” NBER Working Paper No. 27401.

- Alfano, V., S. Ercolano (2020). “The efficacy of lockdown against COVID-19: A cross-country panel analysis.” _Applied Health Economics and Health Policy_ 18, 509–517.

- Alfaro, L., E. Faia, N. Lamersdorf, F. Saidi (2020). “Social interactions in pandemics: Fear, altruism, and reciprocity.” NBER Working Paper No. 27134.

- Alon, T.M., M. Doepke, J. Olmstead-Rumsey, M. Tertilt (2020). “This time it’s different: The role of women’s employment in a pandemic recession.” NBER Working Paper 27660.

- Baker, S. R., N. Bloom, SJ. Davis, K. Kost, M. Sammon, and T. Viratyosin. 2020. “The unprecedented stock market reaction to COVID-19.” Hoover Institute Economics Working Paper 20112.

- Barro, R.J., J.F. Ursúa, J. Weng (2020). “The “Spanish flu” for the coronavirus's potential effects on mortality and economic activity.” NBER Working Paper 26866.

- Barrios, J.M., Y. Hochberg. 2020. “Risk perception through the lens of politics in the time of the COVID19 Pandemic.” NBER Working Paper No. 27088.

- Bartik, A.W., M. Bertrand, Z.B. Cullen, E.L. Glaeser, M. Luca, C.T. Stanton. 2020a. “How are small businesses adjusting to COVID-19? Early evidence from a survey,” NBER Working Paper No. 26989.

21
their validity as selection variables in Table A4. Moreover, their inclusion in equation 2.3 has little effect on results for donor aid share and most of the recipient-level variables. The coefficient for aid dependence declines somewhat when HIPC indicators are included, as the HIPC countries tend to receive more aid than most others.

Equation 2.4 adds the percentage of aid for each donor-recipient pair that is in the form of direct budget support (DBS), as reported in the PDMS. Budget support by definition uses country PFM systems. When this variable is included, the regression in effect is testing for relationships between the regressors and use of PFM systems for project aid and other non-DBS aid. Coefficients that were significant in equation 1.1 remain significant (although only at the .10 level for Control of Corruption) in equation 1.4, but are reduced in magnitude.

If using country systems strengthens them while bypassing them weakens them (OECD, 2011; 2008a), then quality of country systems is endogenous. We address this potential problem in several ways. First, any beneficial effects on quality from using PFM systems will be on the PFM systems themselves: budget execution, financial reporting, and auditing procedures. Our system quality indicators - Control of Corruption, supplemented by Civil Liberties – are defined far more broadly, and are likely affected only with a lag. Second, we lag the system quality indicators by a year in our tests. Third, we test for the presence of heterogeneous effects among donors that should be present – or absent - if coefficients on Control of Corruption are biased upward to a significant degree by reverse causation.

Our basic identification strategy is premised on differences among donors. First, we distinguish donors by size. The impact of a percentage point increase in a donor‟s use of country systems should vary with a donor‟s aid share. Each $1 increase should have the same impact, so 1 percentage point will imply a larger impact for larger donors. A sufficiently small donor could 23
2007 and those of rural Upper Egypt almost 75 points below those in urban Lower Egypt.

**Table 6: OLS Regressions of Standardized Math Test Scores on Circumstances**

||**2003**<br>**urban/rural**|**urban/rural**|**2007**<br>**six regions**|**governorate**|
|---|---|---|---|---|
|female|-0.1312|11.2214***|<br>7.5360***|<br>3.0034|
||(2.324)|(2.756)|(2.730)|(2.672)|
|father w/ basic education|<br>3.7598|<br>9.8207**|<br>6.4850|<br>3.0275|
||(4237)|(4772)|(4630)|(4461)|
|father w/ secondary|.<br>8.0609*|.<br>24.8174***|.<br>23.8173***|.<br>20.0579***|
||(4.319)|(5.862)|(5.777)|(5.578)|
|father w/ post-secondary|28.9035***|37.7932***|35.9247***|34.2455***|
||(6.137)|(5.915)|(5.803)|(5.623)|
|father w/ college|<br>44.1157***|<br>14.5372**|<br>12.9275**|<br>10.9477*|
||(5.745)|(6.452)|(6.465)|(6.357)|
|father edu. missing|-13.9200***<br>(4.769)|-5.7182<br>(5.657)|-6.0043<br>(5.591)|-8.3223<br>(5.429)|
|mother w/ basic education|<br>-6.2337|<br>-6.1142|<br>-6.3941|<br>-7.0917*|
||(3.886)|(4.331)|(4.235)|(4.134)|
|mother w/ secondary|<br>-5.5929|<br>12.5279**|<br>9.4317*|<br>7.9994|
||(4.223)|(5.491)|(5.417)|(5.268)|
|mother w/ post-secondary|21.976***|16.6585***|16.914***|12.757**|
||(5.639)|(5.935)|(5.875)|(5.791)|
|mother w/ college|<br>35.1450***|<br>-9.9665|<br>-8.7409|<br>-9.2952|
||(5.750)|(6.319)|(6.377)|(6.238)|
|mother edu. missing|-17.7730***|-12.4057**|-11.0279**|-10.9768**|
||(4.346)|(5.010)|(5.082)|(4.963)|
|parent(s) born abroad|-24.7708***|-42.2089***|-42.0816***|-46.6665***|
||(2.996)|(3.799)|(3.733)|(3.578)|
|arabic spoken: almost always|13.8799***|27.9687***|27.1901***|24.7760***|
||<br>(3.516)|<br>(4.206)|<br>(4.109)|<br>(4.091)|
|arabic spoken: sometimes|<br>82022***|<br>142392***|<br>166867***|<br>157405***|
||.<br>(2.950)|.<br>(3.761)|.<br>(3.722)|.<br>(3.718)|
|arabic spoken: never|-14.3242**|-7.5273|-3.8754|-0.0855|
||(7.076)|(7.928)|(8.352)|(7.913)|
|books at home: 11-25|<br>-1.1348|<br>-3.0248|<br>-1.3487|<br>-0.5250|
||(2.791)|(3.384)|(3.313)|(3.218)|
|books at home: 26-100|<br>9.0014**|<br>6.3848|<br>9.0688**|<br>9.0645**|
||(3.746)|(4.045)|(3.967)|(3.921)|
|books at home: >100|4.9763|1.4673|6.3031|7.1206|
||(4.466)|(5.062)|(5.006)|(4.917)|
|tv owned|45.9552***|26.5948***|22.6609***|24.5407***|
||(4.640)|(5.245)|(5.167)|(5.069)|
|satelite owned|-13.6241***|6.0568**|7.9091***|6.9546**|
||(2.395)|(2.950)|(2.896)|(2.825)|
|telephone owned|<br>67064**|<br>102867***|<br>92801**|<br>89792**|
||.<br>(2.726)|.<br>(3.816)|.<br>(3.729)|.<br>(3.677)|
|urban area|14.2031***|35.3493***||37.8641***|
||(2.424)|(2.949)||(3.549)|
|Alx Sz Said|||3.6656||
|, ,|||<br>(4.929)||
|Urban lower|||<br>19.0216***<br>||
|Ub|||(4.933)<br>49274||
|ran upper|||-.||
||||(4.878)<br>||
|Rural lower|||-7.0962<br>(4353)||
|Rural upper|||.<br>-55.3107***||
||||(4378)||
|Governorates|No|No|.<br>No|Yes|
|Constant|350.0780***|325.2088***|358.6597***|269.8665***|
||(5.502)|(6.256)|(7.098)|(13.278)|
|Number of Obs|7095|6582|6582|6582|
|<br>R-squared|<br>0.210|<br>0.181|<br>0.210|<br>0.239|

_Source: TIMSS 2003 and 2007 surveys._ 21
vulnerable group between the poor and the middle class. Income thresholds are estimated using the _Quality of life of the population_ module in the 2013 survey in which respondents were asked to report their social class. Once the thresholds were estimated for 2013, we calibrated the corresponding lower and upper bounds for the years for which this information was not available.

The subjective module of the questionnaire asked heads of household to place themselves in ordered classes: Poor; Not poor, but not middle class; Middle class; Top middle class, and Rich<sup>6</sup> (see Table 4).

# **Table 4: Responses to the Question on Self-Perceived Social Status,** **<u>2013</u>**

|**What social group would you refer the household you head to?**|**Responses**<br>**(%)**|
|---|---|
|Low-income (do not have enough funds for food, clothes and footwear)|3.7|
|Not poor, but not middle-class (enough funds to buy food-products, clothes<br>and footwear, pay for housing and utility services, however we encounter<br>difficulties with purchase of durable goods)|49.8|
|Middle class (tier, level): we do not encounter any difficulties with purchase of<br>food-products, main non-food products and services, but have insufficient<br>funds to purchase additional dwelling (apartments, houses, summer house),<br>an expensive car and etc.|44.3|
|Top-middle class (tier, level): we consume high-quality products, live in<br>comfortable conditions, have work, our own income-generating business<br>and/or property, but we do not have enough free time for recreation|2.2|
|Prosperous (rich): have enough resources (knowledge, health, finance,<br>property, time) for comfortable life||

Figure 1 shows correspondences between self-declared social class and per capita income as recorded in the survey Household Income and Expenditure Module. Not surprisingly, relatively few respondents (5.9%) placed their households in the extreme classes 1 (Poor, 3.7%) and 4/5 (Prosperous, 2.2%). The majority declared their households “Not poor but not middle class”—i.e., vulnerable—(49.8%), followed by those who assigned themselves to the middle group (44.3%). Although

> 6 Since few identified their households as “Top middle class” or “Rich,” ECA-POV merged them into a single “Prosperous” category.

8
household enterprise (for these three questions the response is yes or no). These questions were asked with respect to the last 7 days (the reference period for identifying those who are “employed” and the set of detailed questions on that employment) and, if reported to not work in the last 7 days, then asked for the last 12 months. In the shorter module, there was only one question to determine employment status with respect to the last 7 days: whether s/he did any type of work (response also yes or no). As in the detailed module, the question was asked twice – for the last 7 days and the last 12 months. The short and detailed employment modules are reported in Appendix 1.

In the second dimension of the experiment, we varied whether questions are asked directly to the subject or asked to a proxy respondent. Response by proxy rather than individuals themselves reflects the common practice to interview an informed household member (often the household head), rather than each individual him or herself. In practice proxy respondents are often used when individuals are away from the household or otherwise unavailable to interview in the time allotted in an enumeration area to conduct interviews. In the survey experiment, the proxy respondent was randomly chosen among household members at least 15 years old.<sup>11</sup> The person selected to be the proxy respondent then reports on up to two other randomly selected household members age 10+. Of course, not all persons selected to be a proxy respondent could actually be interviewed. Also discussed in the next section, in our experiment we find that the proxy respondents selected did not significantly differ from the pool of potential proxy respondents with respect to gender or education. The proxy respondent could be any adult: the head of household,

> 11 The Tanzanian CWIQ 2006 data indicate that the average Tanzanian household has between two to three adults who could serve as a proxy with a minimum age of 15. This informed the design of our survey, and, in fact, our sample households had about 2.7 members 15 years and older.

12
|||Publishing, audiovisual and broadcasting<br>activities<br>Telecommunications<br>IT and other information services||
|---|---|---|---|
|Hotels and restaurants|-|Accommodation and food services|Hotels and restaurants|
|Financial<br>intermediation|-|Financial and insurance activities|Financial intermediation|
|Real estate, renting and<br>business activities|-|Real estate activities<br>Other business sector services|Real estate, renting and<br>business activities|
|Public<br>administration<br>and defense|-|Public<br>administration<br>and<br>defense;<br>compulsorysocial security|Public administration and<br>defense|
|Education|-|Education|Education|
|Health and social work|-|Human health and social work|Health and social work|
|Other<br>community,<br>social<br>and<br>personal<br>service activities|Energy – Other Fuel<br>Combustion|Arts, entertainment, recreation, and other<br>service activities|Other community, social<br>and<br>personal<br>service<br>activities|
|Activities<br>of<br>private<br>households<br>as<br>employers|-|Private households with employed persons|Activities<br>of<br>private<br>households as employers|

Source: CAIT, OECD, SEDLAC As a quality check for our own estimations of emissions, we compare them to the corresponding emissions calculated using other sources of information. This check is necessary because there is a concern that our process of assigning emissions using IOT all sectors may result in country-level measures of emissions (when adding up the assigned GHGE of all sectors) that are very different from official measures of country-level emissions. In general, the country-level measures are very similar across sources (Table 6). Estimations are slightly higher when using our method, which is expected due to the choice of excluding imports in the calculations.

**Table 6 – Comparison of estimated GHG emissions (Megatons) with other sources (2015 data)**

|**Country**|**Own**<br>**Estimation**|**WDI**|**CAIT**|**OECD**|
|---|---|---|---|---|
|ARG<sup>a</sup>|385.3|372.4|362.6|328.9|
|BRA|1228.7|1105.5|1082.7|1041.7|
|CHL|136.4|102.2|104.0|103.0|
|COL|214.9|175.0|177.0|170.6|
|CRI|19.2|14.3|14.6|13.6|
|MEX|869.9|674.3|670.1|745.8|
|PER|116.1|96.6|94.9|102.0|

_Notes:_ a OECD data for Argentina compiles its GHGs emission data according to the IPCCC 2006 guidelines, using the Agriculture, Forestry and Other Land Use (AFOLU) category instead of Agriculture and Land Use, Land Use Change and Forestry (LULUCF) categories.

12
hide significant variation across countries, as Iran for example employs more than 40% of the country’s workforce in the construction and infrastructure sectors, while Egypt and Yemen employ just around 11%.

**Table 6 Employment shares by sector (% of all employed in 2009)**

|Total<br>Con<br> <br>|struction <br>|Infrastructure<br>|
|---|---|---|
|**GCC oil exporters**<br>**18**|**12**|**6**|
|UAE<br>19|13|6|
|Saudi Arabia<br>18|7|11|
|Qatar<br>18|10|8|
|Oman<br>21|11|10|
|Kuwait<br>19|13|6|
|Bahrain<br>18|15|3|
|**Developing oil exporters**<br>**23**|**13**|**10**|
|Yemen<br>11|7|5|
|Iraq<br>15|9|6|
|Iran<br>42|37|5|
|Algeria<br>15|9|6|
|Syria<br>15|8|7|
|**Oil importers**<br>**17**|**9**|**7**|
|Tunisia<br>16|11|5|
|WB&G<br>23|15|8|
|Morocco<br>25|14|11|
|Jordan<br>21|13|8|
|Egypt<br>11|3|8|
|**MENA**<br>**19**|**11**|**8**|
|**World Average**<br>**16**|**8**|**8**|
|**Developed countries**<br>**15**|**8**|**7**|
|**Developing countries**<br>**16**|**9**|**8**|

Source: ILO Developing oil exporting countries have a higher share of jobs in the infrastructure and construction sectors, not only relative to other groups in the region, but also relative to international benchmarks. In contrast, oil importers’ employment shares are more in line with international averages, although their employment in infrastructure is below various international benchmarks (Table 6). It is worth noting that many of the infrastructure and construction jobs in the GCC economies are performed by migrant workers, although lack of data precludes separating out the impact of infrastructure investments on employment of migrant and national workers.

Assuming that world benchmarks gauge the normal size of employment in the infrastructure sector (Table 6), we conclude that in general the scope for increasing the relative size of employment in the sector is limited. The same is the case for employment in construction in all sub-regions and the region as a whole, although there is some scope to expand the relative 9
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- Jonathan B Baker and Steven C Salop. Antitrust, competition policy, and inequality. _Geo. LJ Online_ , 104:1, 2015.

- Simcha Barkai. Declining labor and capital shares. _Stigler Center for the Study of the Economy and the State New Working Paper Series_ , 2, 2016.

- Thomas W Bates, Kathleen M Kahle, and Ren ́e M Stulz. Why do us firms hold so much more cash than they used to? _The Journal of Finance_ , 64(5):1985–2021, 2009.

- Itzhak Ben-David, John R Graham, and Campbell R Harvey. Managerial miscalibration. _The Quarterly Journal of Economics_ , 128(4):1547–1584, 2013.

- Bruce A Blonigen and Justin R Pierce. Evidence for the effects of mergers on market power and efficiency. NBER Working Papers 22750, National Bureau of Economic Research, Inc, 2016.

- Nicholas Bloom and John Van Reenen. Measuring and explaining management practices across firms and countries. _The Quarterly Journal of Economics_ , 122(4):1351–1408, 2007.

- Glenn W Boyle and Graeme A Guthrie. Investment, uncertainty, and liquidity. _The Journal of Finance_ , 58(5):2143–2166, 2003.

- Carol A Corrado and Charles R Hulten. How do you measure a technological revolution? _American Economic Review_ , 100(2):99–104, 2010.

- Arnaud Costinot, Lindsay Oldenski, and James Rauch. Adaptation and the boundary of multinational firms. _The Review of Economics and Statistics_ , 93(1):298–308, 2011.

- Council of Economic Advisors. Benefits of competition and indicators of market power. _Issue Brief_ , 2016.

- Nicolas Crouzet and Janice Eberly. Intangibles, investment, and efficiency. In _AEA Papers and Proceedings_ , volume 108, pages 426–31, 2018.

- Colleen Cunningham, Florian Ederer, and Song Ma. Killer acquisitions. _Mimeo_ , 2018.

- Aswath Damodaran. Dealing with cash, cross holdings and other non-operating assets: approaches and implications. _SSRN Working Paper Series_ , 2005. Available at SSRN: https://ssrn.com/abstract=841485.

31
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# **References:**

Christofides, L. and Pashardes, P. (2002), “Self/paid employment, public/private sector selection, and wage differentials”, _Labor Economics_ , vol. 9: 737-762 Glinskaya, E. and Lokshin, M. (2005), “Wage Differentials Between the Public and Private Sectors in India”, World Bank Policy Research Paper 3574.

Heckman, J. (1979), “Sample selection bias as a specification error”, _Econometrica,_ vol. 47: 53161.

Lindauer, D.L. and Sabot, R.H. (1983), “The Public-Private Wage Differential in a Poor Urban Economy”, _Journal of Development Economics_ , vol. 12(3):137-157.

Psacharopoulos, George (1994) "Returns to Investment in Education: A Global Update." _World Development_ 22(9): 1325-1343.

Psacharopoulos, George (1996) “RR Manual: A Program to Estimate the Rate of Return to Investment in Education.” The World Bank. Washington, D.C.

Schultz, P. (2003), “Evidence of Returns to Schooling in Africa From Household Surveys : Monitoring and Restructuring the Market for Education”, Yale Economic Growth Center Discussion Paper No. 875.

Stelcner, M., van der Gaag, J. and Vijverberg, W. (1989), “A Switching Regression Model of Public-Private Sector Wage Differentials in Peru: 1985-1986”, _Journal of Human Resources_ , Vol. 24(3): 545-559.

Tansil, A. (1999), “Public-Private Employment Choice, Wage Differentials and Gender in Turkey”, Yale Economic Growth Center Discussion Paper No. 797.

Terrell, K. (1993), “Public-private wage differential in Haiti”, Journal _of Development Economics_ , vol. 42: 293-314.