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**<mark>Andrabi, T., Das, J., & Khwaja, A. I. (2013).</mark>** <mark>Students Today, Teachers Tomorrow: Identifying constraints on the provision of education.</mark> _<mark>Journal of Public Economics</mark>_ <mark>.</mark>

**ASER India.** Annual Status of Education Report India (2012). **ASER Pakistan.** Annual Status of Education Report Pakistan (2012). **<mark>Bagwell, K., & Riordan, M. H.</mark>** <mark>(1991). High and declining prices signal product quality.</mark> _<mark>The American Economic Review</mark>_ <mark>, 224-239.</mark>

**Banerjee, A.V., S. Cole, E. Duflo & L. Linden** (2007). “Remedying Education: Evidence from Two Randomized Experiments in India”. _The Quarterly Journal of Economics_ 122 (3): 1235-1264.

**Banerjee, A. V., Banerji, R., Duflo, E., Glennerster, R., & Khemani, S.** (2010). Pitfalls of Participatory Programs: Evidence from a randomized evaluation in education in India. _American Economic Journal: Economic Policy_ , _2_ (1), 1-30.

**Björkman, M., & Svensson, J.** (2009). Power to the people: evidence from a randomized field experiment on community-based monitoring in Uganda. _The Quarterly Journal of Economics_ , _124_ (2), 735-769.

**Burde, D., & Linden, L. L.** (2012). The effect of village-based schools: Evidence from a randomized controlled trial in Afghanistan (No. w18039). National Bureau of Economic Research _._

**Carneiro, P., Das, J., & Reis, H.** (2013). Parental valuation of school attributes in developing countries: Evidence from Pakistan. Working Paper. Retrieved from: http://ipl.econ.duke.edu/bread/papers/1013conf/carneiro.pdf

**<mark>Chan, Y. S., & Leland, H.</mark>** <mark>(1982). Prices and qualities in markets with costly information.</mark> _<mark>The Review of Economic Studies</mark>_ <mark>, 49(4), 499-516.</mark>

**Chaudhury, N. and D. Parjuli** (2010). “Conditional cash transfers and female schooling: the impact of the female school stipend programme on public school enrolments in Punjab, Pakistan”. _Applied Economics_ , Volume 42, Issue 28 **Chiang, H.** (2009). How accountability pressure on failing schools affects student achievement. _Journal of Public Economics_ , _93_ (9), 1045-1057.

**<mark>Das, J., & Zajonc, T.</mark>** <mark>(2010). India shining and Bharat drowning: Comparing two Indian states to the worldwide distribution in mathematics achievement.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>92</mark>_ <mark>(2), 175-187.</mark>

**Das, J., Dercon, S., Habyarimana, J., Krishnan, P., Muralidharan, K., & Sundararaman, V.** (2013). School inputs, household substitution, and test scores. _American Economic Journal: Applied Economics,_ Vol. 5, Issue 2. **De Janvry, A. and B. Sadoulet** (2006). “Making Conditional Cash Transfer Programs More Efficient: Designing for Maximum Effect of the Conditionality”. _World Bank Economic Review_ , 20 (1): 1-29.

31
-43-

- (a) inhiRni - established practice is to incorporate by reference all definitions from the PL into the Regs and then to add, as necessary, any additional ones which may be required;

- (b) Records and Reports - the rightsholder is always required under the Regs and in its PA both to keep fuDl and accurate technical, geological and geophysical records of all petroleum operations conducted for review by the CA, on demand, and to report regularly and periodically to the CA on such operations at the intervals and in the format(s) required by the Regs;

- (c) Accounts - the rightsholder is also required under the Rep to maintain and to submit periodically to the CA, pursuant to the agreed schedule in the PA, full and accurate accounts of all quantities and qualities of petroleum produced from the Contract Area and the ultimate disposition of same3;

- (d) Production Rates - it is generaly discouraged for the CA to set or mandate production rates for reservoirs and Contract Areas. Rather, the international standard which the Regs should recognize instead is maximum efficient rate (MER), which is the rate of production from a reservoir which achieves the maxinum, ultimate economic recovery of petroleum;

That **is,** eter **sold** or refined, In the **cass** of **crude oil;** or, In the case of natural gas, **reinjected,** treated **(eg** removal of ntral **ga** liquki) or Baod, _Ifthe_ hiter I permitted by **the law** of th host Stats. In **additin,** a cetahi amount of paduction may be validly conaumed by Contractor In conducting petroleum opeations.
summary of the value and sources of these elasticities of substitution used in the version of the model in this study.

# **Table B2. Elasticities**

|**Parameter**|**Source**<br>**Values**|
|---|---|
||**Production elasticities**|
||Solar: 0.27|
|σrr<br>|Calibrated<br>Wind: 0.28|
||Hydro, Nuclear: 0|
|σxrx|GTAP, EPPA, RTI-ADAGE, DIEM<br>0|
||Electricity:<br>LUSC: 0.229<br>SUSC: 0.219<br>LSC: 0.259<br>SSC: 0.253|
||LSUB: 0.299|
|σrx|Calibrated<br>SSUB: 0.295|
||LCFB: 0.373|
||SCFB: 0.340|
||OTHC: 0.361|
||<br>HPG: 0.041<br>LPG: 0.161|
||Other sectors: 0.4|
|σr<br><br>|Calibrated<br>Other sectors: 0.50; Electricity: 0.01|
|σh<br>|Hu_et al._(2019)<br>0.30|
|σf|Cossa (2004), RTI-ADAGE<br>Other sectors: 1.00; Electricity: 0.10|
||Agriculture: 0.24|
||<br>Coal, Crude oil, Natural gas, Mining: 0.20|
|σx|Jomini_et al._(1991)<br>Food: 1.12<br>Services: 1.36|
||Transportation: 1.48|
||<br>Other sectors: 1.26|
|σx<br><br>|GTAP, EPPA, DIEM<br>0|
|σn<br>|Set to zero to suppress international leakage 0|
|σdr<br>|GTAP<br>0|
||**Consumption elasticities**|
|σxr<br><br>|GTAP<br>0|
|σr<br><br>|Calibrated<br>0.55|
|σf<br>|DIEM<br>0.50|
||<br>Household consumption: 1.00|
|σx<br>|GTAP<br> <br>Government consumption investment: 0|
||,<br>**Transformation elasticities**<sup>1</sup>|
|σw<br><br>|GTAP<br>1.50 for capital, +∞ for labor<br>|
|σxk<br>|GTAP<br>3.00 for capital, +∞ for labor<br>|
|Note: σwreprese<br>elasticities betwe|nts the factor transformation elasticities between sectors;σwkrepresents the factor transformation<br>en subsectors within a sector.|

57
households at risk of becoming poor recognizes that safety net expansion to nonpoor households is crucial to sustaining progress in poverty reduction even during economic downturns and shocks.

In practice, using vulnerability rates boosts eligibility among households in urban and peri-urban communes. While rural communes are, on average, poorer, urban and peri-urban communes exhibit lower than average poverty rates, but account for a relatively larger share of all nonpoor households at risk of poverty. Thus, most rural communes exhibit poverty rates that are higher than the national median poverty rate, that is, they are more likely to count among the chronically poor (table 4, panel a). Meanwhile, urban communes in Dakar show poverty rates below the national median, indicating a lower incidence of poverty. Similarly, other urban areas generally show low poverty rates, but risk-induced vulnerability rates that surpass the national median. This also has implications on the type of benefits for different localities, where a stronger focus on social insurance versus social protection should be the case for areas with higher risk-induced vulnerability.

**Table 4. Commune vulnerability rates, by urban or rural location**

||_Dakar urban_|_Other urban_|_Rural_|_National_|
|---|---|---|---|---|
|_Communes, by vulnera_|_bility rate, number_||||
|Low poverty|47|35|28|110|
|High vulnerability|1|63|101|165|
|Chronically poor|0|20|256|276|
|_Census predictions: pop_|_ulation averages, %_||||
|Poverty-induced|8.2|22.5|56.8|38.2|
|Risk -induced|6.9|18.8|21.5|17.5|
|Risk/poverty ratio|83.9|83.2|37.9|45.8|
|_Census predictions: com_|_mune average, %_||||
|Poverty-induced|9.5|32.6|63.0|51.8|
|Risk -induced|7.3|24.4|20.5|20.2|
|Risk/poverty ratio|77.6|75.0|32.5|39.0|
|_Estimated population,_|_2018_||||
|Total, million|3.3|3.6|8.7|15.5|
|Share of total,%|21.3|23.2|56.1|100|

_Sources:_ Calculations using commune vulnerability rates from the poverty mapping exercise; RGPHAE 2013 (2013 Recensement Général de la Population et de l'Habitat, de l'Agriculture et de l'Elevage; Population and Housing Census, 2013) (dashboard), National Agency of Statistics and Demography, Dakar, Senegal, https://www.ansd.sn/enquete-et-etude/recensement-generalde-la-population-et-de-lhabitat-de-lagriculture-et-de-lelevage.

To evaluate the effect of vulnerability rates on the allocation of eligibility quotas to urban and peri-urban communes, we computed the ratio of risk-induced vulnerability to poverty-induced vulnerability. A higher ratio indicates that risk-induced vulnerability is more important in the total quota. This ratio is highest in urban Dakar and other urban areas (see table 4). For instance, urban Dakar shows poverty- and riskinduced vulnerability rates of 8.2 and 6.9 percent. This produces a ratio of risk-induced vulnerability rate 19
-42 -

study, has been transferred, at the country's request, to the National Board of Revenue. The China model, in addition to being used for tax analysis, will be extended in an ongoing study on price reform. The developmer.t of the India model is at an early stage compared to the other two, but is expected to be adapted for an investigation into related sets of issues.

# VI. CONCLUSIONS

This paper has described three sets of applications of tax policy models developed by the World Bank's Public Economics Division during the course of economic work on Bangladesh, China and India.

The Bangladesh model was used to highlight the role of shifting assumptions in influencing the relative attractiveness of different options for raising revenue. To that end, revenue and incidence effects of tax changes were combined and summarized in a single measure that allowed different sectors to be ranked with respect to the efficiency-cumequity cost of raising revenue. Those rankings were used to compare traditional versus general equilibrium based approaches to incidence analysis. This was done by ascertaining what would happen to the rankings first if, as is common in partial equilibrium incidence analysis, it were assumed that all tax increases would be fully forward shifted into user prices, and if second, as is sometimes the case in partial equilibrium analysis, it were assumed that all tax increases would be fully backward shifted into factor returns. Incidence judgments were more heavily influenced in the first case by the characteristics of the socioeconomic groups that consume the product on which the tax is to be increased and in the second case by the characteristics of those employed in its production. The change in the rankings in the three cases: full forward shifting, full backward shifting and the "central" case of a combination
- **Dao, Thu Hien, Fr ́ed ́eric Docquier, Chris Parsons, and Giovanni Peri.** 2018. “Migration and development: Dissecting the anatomy of the mobility transition.” _Journal of Development Economics_ , 132: 88–101.

- **de Chaisemartin, Cl ́ement, and Xavier d’Haultfœuille.** 2020. “Two-way fixed effects estimators with heterogeneous treatment effects.” _American Economic Review_ , 110(9): 2964– 2996.

- **de Chaisemartin, Cl ́ement, and Xavier d’Haultfœuille.** 2022. “Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: A survey.” _The Econometrics Journal_ , 26(3): C1–C30.

- **de Chaisemartin, Cl ́ement, and Xavier d’Haultfœuille.** 2024. “Difference-in-differences estimators of intertemporal treatment effects.” _Review of Economics and Statistics_ , 1–45.

- **Dettmann, Eva, Alexander Giebler, and Antje Weyh.** 2020. “Flexpaneldid: A Stata toolbox for causal analysis with varying treatment time and duration.” _Available at SSRN 3692458_ .

- **Di Iasio, Valentina, and Jackline Wahba.** 2023. “Expecting Brexit and UK migration: Should I go?” _European Economic Review_ , 157: 104484.

- **Feigenberg, Benjamin.** 2020. “Fenced out: The impact of border construction on US-Mexico migration.” _American Economic Journal: Applied Economics_ , 12(3): 106–139.

- **Gaikwad, Nikhar, Kolby Hanson, and Aliz T ́oth.** 2023. “Exit Options: How International Migration Opportunities Shape Economic Standing and Political Preferences.” _Working paper_ .

- **Gazeaud, Jules, Eric Mvukiyehe, and Olivier Sterck.** 2023. “Cash transfers and migration: Theory and evidence from a randomized controlled trial.” _Review of Economics and Statistics_ , 105(1): 143–157.

- **Gibson, John, David McKenzie, Halahingano Rohorua, and Steven Stillman.** 2018. “The long-term impacts of international migration: Evidence from a lottery.” _The World Bank Economic Review_ , 32(1): 127–147.

27
# **Are Women More Credit Constrained? Experimental Evidence on Gender and Microenterprise Returns**<sup>**#**</sup>

Suresh de Mel, University of Peradeniya David McKenzie, World Bank Christopher Woodruff, UCSD _JEL codes:_ O12, O16, C93. _Keywords:_ Microenterprises; Gender; Microfinance; Randomized Experiment.

> # We thank Alejandrina Salcedo-Cisneros, Michael Callen, Susantha Kumara, and Jayantha Wickramasiri for excellent research assistance, and participants at several seminars for helpful comments. AC Nielsen Lanka administered the surveys on which the data are based. Financial support from NSF grants numbers SES-0523167 and SES-0617424 is gratefully acknowledged, along with additional support from the World Bank’s Gender Action Plan.
# **7 References**

Andolfatto, D., 1996. "Business Cycles and Labor‐Market Search," American Economic Review, American Economic Association, vol. 86(1), pages 112‐32, March Albrecht, J., Navarro, L., and Vroman, S. (2008) “The effects of labor market policies in an economy with informal sector” Forthcoming Economic Journal.

Djankov, S., R. La Porta, F. López-de-Silanes, A. Shleifer, and J. Botero. 2003. The Regulation of Labor. NBER Working Paper no. 9756. National Bureau of Economic Research, Cambridge, MA.

Bravo‐Ortega C., and Lederman, D., ESTUDIOS DE ECONOMIA 2004, Vol 31, numero 002 Gutierrez, C., Paci, P., and Park, B. (2009) Quantifying the Impact of the Financial Crisis on Employment and Wages in a Multi‐sector Labor Market. Mimeo World Bank Gutierrez C., Paci P. and Park B. (2009) “Simulating the impact of the financial crisis on Indonesia’s labor market” mimeo World Bank Maloney W., and Bosch, M., (2008) “Cyclical Movements in Unemployment and Informality in Developing Countries” World Bank Policy Research Working Papers No. 4648, June 2008. Washington DC.

Mortensen and Pissarides (1994). Technological Progress, Job Creation and Job Destruction.

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Paci, Revenga and Rijkers 2009, “Coping with crises, why and how to protect employment and earnings” Policy research working paper No. 5094, World Bank Washington DC.

Pissarides, C. (2000). Equilibrium Unemployment Theory. Cambridge, MIT Press Satchi M., and Temple, J. (2006). “Growth and labour markets in developing countries” Discussion Paper Series No. 5515, CEPR.
A serious data limitation is that households with migrants can only be identified if they reported receiving remittances in the previous year. Three groups of households could be misclassified under this definition. The first group consists of households with migrants who send no remittances. These could be households with a migrant who has just departed and is in the process of establishing him or herself, or households where a migrant brings the remittances home rather than sending them. The second group comprises households that receive remittances but do not report them. Such households might be afraid of revealing information on remittances because of the tax consequences or simply due to concerns for personal safety. Finally, some households could receive remittances from individuals who are not household members. Classifying the households in these three groups as having no migrants would result in biased estimates of the impact of work-related migration on household consumption. Although the direction of the bias is unclear _a priori_ , the size of the bias is proportional to the sizes of these three groups of households.

To assess the extent of such misclassifications, we compare the proportion of migrants in the total population from the 2001 Nepal Census with the proportion of households with remittances in the NLSS data. The proportion of domestic migrants in the 2001 Census (4.8 percent) is statistically close to the proportion of migrants from households receiving domestic remittances in the NLSS (5 percent). The censuscalculated proportion of households with international migrants (14 percent) is lower than the reported NLSS proportion of household receiving remittances from abroad (18 percent). The official statistics report about 1,000,000 prime-age male expatriates working outside Nepal. The equivalent NLSS figure is about 900,000. These relatively small discrepancies indicate that the bias resulting from misclassified households would most likely also be small. More importantly, these results make it feasible to extrapolate inferences about the effects of work-related migration on income distribution from sample households receiving remittances to those in the general Nepali population.

6
potential new trade barriers to other complementary databases (e.g., on trade flows) to determine how economically important any given policy action would be.

Such an approach was also adopted by a new initiative launched in June 2009 called the _Global Trade Alert_ .<sup>16</sup> The Global Trade Alert (GTA) was designed to go beyond the trade remedy data provided in the Global Antidumping Database to catalogue and detail dozens of other types of trade and trade-related measures – e.g., changes in tariffs, import licensing, government procurement, imposition of subsidies, and changes in technical and sanitary or phytosanitary standards among others. Many interpreted the GTA’s contributions throughout the global economic crisis as being intended to ‘name and shame’ government policymakers by raising the public profile of trade policy actions. Perhaps government officials would think twice about how they structured new policy initiatives if they were concerned that the GTA would flag their policies with a red triangle that indicated ‘the measure has been implemented and almost certainly discriminates against foreign commercial interests.’ Nevertheless, the publicity around the GTA has resulted in critics of its own. Some focused on the utility of the information being provided for understanding the scale of the spread of protectionism taking hold during the global crisis, given the lack of comparable pre-crisis data available to use as a benchmark. Others argued that the GTA was overstating the incidence of protectionism which could provide fodder and cover for other policymakers interested in increasing 16 For a discussion see Evenett (2009). According to the Global Trade Alert website, it is coordinated by the Centre for Economic Policy Research (London), and its funders include The World Bank, UK’s Trade Policy Unit (combining the Department for International Development and the Department for Business, Innovation and Skills), the German Marshall Fund of the United States, Canada’s Centre for International Governance Innovation, and Canada’s International Development Research Centre.

16
imports of goods and services would fall by 35 percent or US$604 billion relative to the baseline after three years. Among the other regions, most affected are South Asia and Sub-Saharan Africa where imports decline by 33 percent and 19 percent, amounting to a decline in real imports by US$459 billion and US$161 billion relative to the baseline by 2020, respectively. These results are not surprising, as these are the regions for which the increase in tariffs is the most pronounced. Imports of goods and services decline the least in the European Union and China by 2.4 percent and 3.5 percent relative to the baseline after three years. At the sectoral level, imports of food and agricultural commodities are disproportionately affected. For instance, imports of food decline by 82 percent in South Asia and by 52 percent in Sub-Saharan Africa. For countries that have limited possibilities to substitute more expensive imports with domestic production of food and agricultural commodities, the increase in tariffs will lead to significant negative spillovers in terms of nutrition, poverty and inequality.

Table 7: Impact on regional trade

||**Ex**|**ports**|**Im**|**ports**|
|---|---|---|---|---|
||Percent|US$ billion|Percent|US$ billion|
|Latin America and the Caribbean|-26.4|-415.7|-35.2|-604.4|
|Sub-Saharan Africa|-21.9|-158.6|-19.1|-161.0|
|South Asia|-15.9|-151.5|-32.6|-458.7|
|Middle East and North Africa|-13.7|-242.0|-13.9|-261.9|
|Europe and Central Asia|-12.8|-179.3|-9.3|-133.8|
|East Asia and Pacifc|-12.0|-322.5|-17.6|-540.6|
|European Union|-11.9|-369.8|-2.4|-113.7|
|Other Advanced Economies|-8.3|-340.2|-5.6|-231.1|
|United States|-7.9|-258.0|-4.4|-143.1|
|China|-3.9|-179.8|-3.5|-97.8|
|**Total**|**-9.0**|**-2,570**|**-9.0**|**-2,657**|

Source: Authors’ simulations.

Note: Results capture changes in the volume of trade relative to the baseline in 2020. Changes is the value of trade amount to a global decline by 9.4 percent or US$2.7 trillion.

Regarding external competitiveness, the regions most affected by a decline in exports are Latin America and the Caribbean by a decline of 26 percent, Sub-Sahara Africa by 22 percent and South Asia by 16 percent relative to the baseline by 2020. In LAC, for instance, 23
Table 9: Regression results for CDM projects cooperation level (Dependent variable is Volume of investment)

|Equation|1<sup>a</sup>|2<sup>b</sup>|3|4|5|
|---|---|---|---|---|---|
|Estimationprocedure|Tobit|Tobit|Tobit|Tobit|Tobit|
|Dependet variable|_Total Volume_<br>_of Investment_|_Annual Volume_<br>_of Investment_|_Ln(Total_<br>_Volume of_<br>_Investment)_|_Ln(Annual_<br>_Volume of_<br>_Investment)_|_Ln(Total_<br>_Volume of_<br>_Investment)_|
|_hst_Averag GDP_|||||6.30E-05**<br>(2.56)|
|_inv_Averag GDP_|||||2.09E-05***<br>(8.25)|
|_hst_inv_Averag GDP_|||||1.99E-11<br>(1.07)|
|_hst_Average Annual Energy Use_|2.92E06***|3.90E05***|0.034***|0.033***||
||(6.22)|(6.25)|(2.52)|(2.63)||
|_inv_Average Annual Energy Use_|5.43E06***|7.22E05***|0.152***|0.139***||
||(8.69)|(8.68)|(8.41)|(8.32)||
|_hst_Ease of Doing Business_|-5.26E06***|-7.05E05***|-0.152***|-0.145***|-0.179***|
||(-2.90)|(-2.93)|(-3.06)|(-3.20)|(-2.70)|
|_inv_Ease of Doing Business_|3.32E06|4.46E05|0.116|0.134|0.108|
||(0.67)|(0.68)|(0.89)|(1.13)|(0.46)|
|_hst_inv_Ease of Doing Business_|||||1.81E-03<br>(0.87)|
|_hst_Governance_|3.91E07**|5.10E06**|1.30***|1.22***|1.43|
||(2.22)|(2.18)|(2.73)|(2.82)|(0.79)|
|_inv_Governance_|2.19E08***|2.90E07***|6.29***|6.04***|6.97***|
||(5.62)|(5.62)|(5.86)|(6.09)|(5.93)|
|_host_inv_Governance_|||||-0.034<br>|
||||||(-0.21)|
|_hst_Renewable Energy_|5.63E05***|7.52E04***|0.019***|0.017***|0.012*|
||(2.87)|(2.88)|(3.55)|(3.55)|(1.83))|
|_inv_Renewable Energy_|-7.55E05***|-1.00E05***|-0.019***|-0.017***|-0.004|
||(-3.12)|(-3.11)|(-3.01)|(-2.92)|(0.54)|
|_host_inv_Renewable Energy_|||||1.05E-05<br>(0.76)|
|_hst_Impact Vulnerability_|1.76E07***|2.3026***|0.626***|0.561***|0.763***|
||(4.37)|(4.33)|(5.59)|(5.51)|(2.59)|
|_inv_Impact Vulnerability_|2.51E07***|3.33E07***|0.700***|0.663***|0.907**|
||(5.09)|(5.09)|(5.17)|(5.37)|(2.02)|
|_host_inv_Impact Vulnerability_|||||-4.77E-03<br>(-0.58)|
|_Trade1_|||3105.00**<br>(2.35)||2106.44<br>(1.40)|
|_Trade2_||||657.55**<br>(1.98)||
|_Constant_|-5.99E9***|-7.95E7***|-178.73***|-166.58***|-191.54|
||(-8.79)|(-8.78)|(-9.11)|(-9.19)|(-7.48)|
|Observations|2771|2771|2771|2771|2771|
|Uncensored obs.|182|182|182|182|182|
|LogLikelihood|-4306.05|-3938.14|-1207.96|-1191.85|-1207.47|
|LR|423.80|424.20|420.42***|420.13***|421.38***|
|Pseudo R<sup>2</sup>|0.047|0.051|0.148|0.149|0.148|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

Note: See Appendix 3, equation 3 for regression results of 2856 observations; See Appendix 3, equation 4 for regression results of 2856 observations.

33