rafmacalaba/datause-encoder-data huggingface dataset:
de Melo and Winters, 1992; Yang et al., 1997; Ianchovichina and Martin, 2004; Walmsley et al. 2006; Anderson et al. 2006); and on natural disasters and wars (Grobar and Gnanselvam, 1993; Collier, 1999; Rose and Liao, 2005; Okuyama, 2007). Grobar and Gnanselvam (1993) use a case study approach relying on national accounts data to examine the economic effects of the Sri Lankan civil war and the potential future costs associated with a continuation of the conflict. Collier (1999), who provides an ex post assessment of all civil wars between 1960 and 1990, finds that war affects not only the level but also the composition of economic activity, especially for manufacturing and some services sectors in Uganda. Input-output (IO) models, as in Rose et al. (1997), are the most widely used modeling tools for ex-ante assessments of the higher-order effects of both natural and man-made disasters. The popularity of these models is based mainly on their ability to reflect the interdependencies within a regional economy and their simplicity, but they have rigid structure with respect to substitution among inputs and imports. These models also lack explicit resource constraints and responsiveness to price changes (Rose, 2004).

Unlike these widely used approaches for evaluation of disasters, this paper relies on a global computable general equilibrium (CGE) framework, documented in Hertel (1997). The model has been widely used in global, regional, and country-specific trade liberalization assessments and offers advantages in terms of ensuring consistency through explicit constraints while including important sectoral detail, such as input and import substitutability and price responsiveness. The paper demonstrates the advantages of the GE approach over these simpler conventional frameworks by assessing the “pure” general equilibrium (GE) effects of conflict, defined as the difference between the non-linear and linear solutions to the model, where by linear we mean a first-order approximation of the solution with data coefficients kept constant at initial levels. The pure GE effects differ in sign and size and are significant for Syria and Iraq, the two countries 3
||Employed|Durables|Tuition|Input:<br>Livelihood|Politics|Bank|Mobility:<br>Taluk Office|Police<br>Station|
|---|---|---|---|---|---|---|---|---|
|BC: Others x exp|-0.0142<br>(-0.95)|0.000136<br>(0.01)|0.00290<br>(0.20)|0.00559<br>(0.43)|0.00259<br>(0.25)|0.0148<br>(0.98)|0.0410*<br>(2.05)|0.0116<br>(0.99)|
|Observations|5190|3284|3280|3283|3279|3299|3299|3299|
|Adjusted R-squared|0.278|0.115|0.117|0.156|0.076|0.136|0.157|0.050|

Notes: (1) Source: Authors' calculations based on data collected by Social Observatory, World Bank and Government of Tamil Nadu. . Notes (2) - (5) of Table 8 apply..

# **Table 11: Percentage of pairwise differences that are significantly different**

|||Employment|Input in intra-<br>household decision<br>making|Mobility|
|---|---|---|---|---|
|Bihar|SC-ST<br>Others|47.6<br>66.7|28.6<br>45.8|25.0<br>58.3|
|Odisha|SC-ST|61.9|14.3|19.0|
||Others|38.2|16.8|9.5|
|TN|SC-ST|10.0|15.0|13.3|
||Others|31.4|7.6|9.8|

Source: Authors' calculations based on data collected by Social Observatory, World Bank and state Government of Bihar, Odisha and Tamil Nadu, respectively.

38
in rural areas. The trainees were responsible for finding their own accommodations near the workshop, but received a small stipend (about 4300 MWK, approximately US$28) to cover meals and accommodation.

# **2.2 Experimental Design**

The evaluation used an experimental phase-in design. Participants were randomly assigned to two cohorts, a treatment group that started the program immediately, and a control group that started the program around 4 months later on average, right around the time of the follow-up survey. We are therefore able to report short-run effects of training. Two thirds of the 1,900 eligible youth were assigned to treatment and the remaining third to the control group.

The baseline survey was collected in March-April 2010 on a random subset of the youth selected. We surveyed 1,122 individuals of the original 1,900, of whom 363 were in the control group and 759 were in the treatment group (see Figure 1). Summary statistics from the baseline survey indicate that randomization was successful in achieving balance across treatment and control groups (Table 1).

Trainees reported to training between August 2010 and May 2011; the specific start date varied by district and by MC. Training lasted for three months on average, but varied depending on the type of skill being taught. Table 2 provides the breakdown of occupations by gender of trainees.

The follow-up survey was conducted in June-August, 2011. The follow-up survey included questions on time use, employment, psychological well-being, risky sexual behavior, and trainee assessments of training quality. In order to increase the sample size, we returned to the original pool of 1,900 youth who had been selected to participate in the study. The sample at follow-up is composed of the 755 baseline respondents who we were able to find at the time of follow-up, plus 274 new participants (181 treatment, 93 control), for a total of 1,029 respondents.

In addition, we surveyed all MCs regarding their experience as trainers and their perception of each of the trainees’ skills, diligence, effort, attendance, and so on. Finally, we also conducted a brief qualitative survey with the implementing agency’s desk officers regarding their experience with the intervention to inform future program design.

7
**The World Bank Washington D.C.**

# **A Diagnostic Framework for Assessing Public Investment Management**

**Anand Rajaram Tuan Minh Le Nataliya Biletska Jim Brumby**

This paper is an output from work undertaken in 2005-07 on Fiscal Policy for Growth and Development and part of an on-going work program by the Public Sector and Governance Unit of the Poverty Reduction and Economic Management (PREM) network to assist clients in strengthening Public Investment Management (PIM).
services employment leads to a 4.23% increase in non-tradable employment. Such an increase in tradable employment increases the number of firms in non-tradable services by 2.85%, as shown in column (5), although the coefficient is now only significant at the 10% level. Given the smaller coefficient and lower significance of the effect on the number of firms, it seems likely that the positive employment spillovers are more due to the expansion of existing firms (the intensive margin), rather than new firm creation (the extensive margin). Notably, the IV coefficients are larger than OLS, possibly due to measurement error related attenuation bias in the OLS regressions.

Furthermore, Goldsmith-Pinkham et al. (2020) have recently raised concerns that Bartik instruments may suffer from endogeneity of the lagged shares and recommend using control variables that help ensure that the initial distributions of tradable and non-tradable services are not biased. A natural candidate in our case is the level of education by district, which we proxy by the literacy rate. These results are shown in columns (3) and (6). Overall, the coefficients remain rather similar in statistical significance and magnitude compared to the baseline, but the effect on the number of firms is no longer significant.

Our employment estimate of 0.42 is larger, but comparable to Moretti (2010), who finds a coefficient of 0.33 in the US, but includes only manufacturing in the tradable sector. Our estimates are also higher than Frocrain and Giraud (2017), however, who find an elasticity of 0.23 for France for tradable services on non-tradable services.

Table 3: Impact of tradable services on non-tradable services

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||E|mploymen|t|No|. of Firm|s|
||OLS|IV|IV|OLS|IV|IV|
|Log tradable services|0.098***|0.423**|0.418**|0.079***|0.285*|0.279|
||(0.017)|(0.190)|(0.203)|(0.017)|(0.170)|(0.181)|
|Education|||0.001<br>(0.007)|||0.001<br>(0.005)|
|Observations|1,173|1,173|1,173|1,173|1,173|1,173|
|District FE|yes|yes|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|yes|yes|

Standard errors (in parentheses) are clustered at the district level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1.

13
would otherwise be unemployed, under-employed, in school, or enjoying leisure during the training period.

Turning our attention to the effects of treatment assignment on time use _after_ the training is completed, we see that the most important consequence of the training program is continued investment in human capital. This is promising, because this may have significant and lasting implications for labor market opportunities in the long run. In some cases, this is because the trainee forms a longer-term relationship with the master craftsmen, something we will explore below.

Training participation increases total hours spent on skill development (through school, or other job training) by 6 hours (ITT) or 14-24 hours (IV) one month after the training. This is large relative to how the control group spends their time, and relative to how all individuals spent time prior to the start of training. In the week preceding the survey, those who completed the training program continued to spend 3-6 hours per week in additional human capital development activities. We do not observe strong significant effects in hours worked in the period after training, except for some effect on self-employment that is sensitive to the inclusion of control variables, and therefore not robust.

In Table 8 we see that all this extra time spent on training and on further human capital development post-training comes at a financial cost to the trainees. Trainees have to draw down their savings by 1600-3000 Kwatcha (US$10-20)<sup>8</sup> , which is a substantial amount in this sample. Importantly, we will see below that this effect is largely driven by female trainees, who face even more drastic decreases in savings (of about US$38) in the corresponding specification. Data we will present below on the training experience indicates that the stipend provided for the participants (of 4300 MWK, or US$28 on average) was not sufficient to cover transportation and lodging costs.

Given that we do not find any significant changes in hours worked in the short run, it is not surprising that we also do not find a discernible impact of training on the total earnings (last week) and on total monthly expenditure. The estimated effects are negative, but generally not statistically significant. Consistent with the human capital investment results both during and post training, we also see that training participants were significantly less likely to start a

> 8 The exchange rate used is MWK 1 = 0.0065 USD (from http://www.oanda.com/currency/historical-rates/).

15
_viii_

**_Index_**

|_WPS#_|<br>_Author_|_Working_<br>_Paper Title_|_Page_|
|---|---|---|---|
|Public<br>|**Sector**Management**and Private Secto**<br>|**r Development**<br>||
|80|Samuel Paul|Emerging Issues of Privatization and the Public Sector|27|
|113|Barbara Nunberg|Public Sector Pay and Employment Reform|40|
|118|John Nellis|Contract Plans and Public Enterprise Performance|41|
|126|Samuel Paul, John C. Ickis,<br>and Jacob Levitsky|Educating Managers for Business and Government|44|
|160|Mary M. Shirley|Evaluating the Performance of Public Enterprises<br>in Pakistan|56|
|199|Samuel Paul|Private Sector Assessment: A Pilot Exercise in Ghana|71|
|**Rese**|**arch Administration**|||
|97|Richard H. Snape|Subsidies|33|
|**Socio**|**economic Data**|||
|5|Angus Maddison and Bart van Ark|Comparisons of Real Output in Manufacturing|4|
|22|Jean-Paul Azam, Patrick Guillaumont,<br>d Sli Gillt|Methodological Problems in Cross-Country Analyses of<br>Ei Gth|10|
||an yvane uaumon|conomc row||
|**Trade**|**and Finance**|||
|104|Manuel Hinds|Economic Effects of Financial Crises|38|
|**Trade**|**, Finance, and Industry**|||
|183|Alberto R. Musalem|Private Investment in Mexico: An Empirical Analysis|64|
|**Trade**|**Policy**|||
|1|Cristian Moran|Imports Under a Foreign Exchange Constraint|3|
|2|Vinod Thomas|Issues in Adjustment Lending|3|
|3|Jaime de Melo|CGE Models for the Analysis of Trade Policy in<br>Developing Countries|4|
|20|Ram6n L6pez and Vinod Thomas|Imports and Growth in Africa|10|
|21|Jaime de Melo and Patrick Messerlin|Effects of European VERs on Japanese Autos|10|
|83|Jaime de Melo and David Tarr|Welfare Costs of U.S. Quotas on Textiles, Steel,<br>and Autos|29|
|84|Brian Pinto|Black Markets for Foreign Exchange, Real Exchange<br>Rates, and Inflation: Overnight versus Gradual Reform<br>in Sub-Saharan Africa|29|
Table 4: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Percent Direct Exports

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.249|-0.171|-0.011|0.031|0.18|0.193|
||(0.075)|(0.086)|(0.045)|(0.054)|(0.068)|(0.072)|
|Log(Rev. per worker)|-0.156|-0.123|-0.002|0.017|0.121|0.13|
||(0.044)|(0.049)|(0.026)|(0.03)|(0.038)|(0.04)|
|Invest (binary)|-0.003|-0.005|-0.003|-0.004|-0.003|-0.004|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.098|-0.158|-0.076|-0.084|-0.058|-0.024|
||(0.122)|(0.131)|(0.067)|(0.074)|(0.095)|(0.098)|
|Log(Avg. wage)|-0.099|-0.084|-0.02|-0.005|0.043|0.059|
||(0.035)|(0.04)|(0.021)|(0.024)|(0.033)|(0.034)|
|Log(Capacity Util)|0.007|0.043|-0.001|0.021|-0.008|0.002|
||(0.017)|(0.018)|(0.01)|(0.011)|(0.015)|(0.016)|
|Log(Energy Intensity)|0.053|0.093|0.029|0.06|0.01|0.033|
||(0.051)|(0.057)|(0.035)|(0.039)|(0.054)|(0.056)|
|Innovation|-0.022|-0.013|-0.01|-0.001|0|0.009|
||(0.013)|(0.014)|(0.007)|(0.008)|(0.011)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and the percentage of sales that are direct exports. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the percentage of direct exports and columns labeled “high” evaluate the estimated model at the 90th percentile of the percentage of direct exports within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

39
[12] Skidmore, M. and H. Toya, 2002, “Do natural disasters promote long-run growth?,” _Economic Inquiry_ , 40: 664-687.

[13] Tol, R. and F. Leek, 1999, “Economic analysis of natural disasters,” in _Climate Change and Risk_ eds by T.E. Downing, A.J. Olsthoorn and R.S.T. Tol. London: Routledge.

[14] Jacoby, H.G. and E. Skoufias, 1997, “Risk, financial markets and human capital in a developing country,” _Review of Economic Studies_ , 64: 311-335.

[15] Kimball, M.S., 1990, “Precautionary saving in the small and in the large,” _Econometrica_ , 58: 53-73. [16] Ligon, E., 1998, “Risk sharing and information in village economies,” _Review of Economic Studies_ , 65: 847-864.

[17] Ligon, E., J.P. Thomas and T. Worrall, 1997, “Informal insurance arrangements in village economies,” CUDARE Working Paper 825, University of California at Berkeley, Department of Agricultural and Resource Economics.

[18] Morduch, J., 1999, “Between the state and the market: Can informal insurance patch the safety net?,” _World Bank Research Observer_ , 14: 187-207.

[19] Noy, I., 2008, “The macroeconomic consequences of disasters,” _Journal of Development Economics_ , 88: 221-231.

[20] Owens, T. and J. Hoddinott, 2003, “Ex-ante actions and ex-post public responses to drought shocks: Evidence and simulations from Zimbabwe,” _World Development_ , 31: 1239-1255.

[21] Porter, C.C., 2008, “Gone with the Wind?: Hurricane Risk, Fertility and Education,” Manuscript, University of Washington.

[22] Quisumbing, A., 2005a, “Targeting and fairness of emergency food assistance: The 1998 floods in Bangladesh,” Manuscript, International Food Policy Research Institute.

[23] ————–, 2005b, “A drop in the bucket? The impact of food assistance after the 1998 floods in Bangladesh,” Manuscript, International Food Policy Research Institute.

[24] Rosenzweig, M.R., 1988, “Risk, implicit contracts and the family in rural areas of low-income countries,” _Economic Journal_ , 98: 1148-1170.

[25] Rosenzweig, M.R. and O. Stark, 1989, “Consumption smoothing, migration, and marriage: Evidence from rural India,” Journal of Political Economy, 97: 905-x.

[26] Rosenzweig, M.R. and K. Wolpin, 1993, “Credit market constraints, consumption smoothing and the accumulation of durable production assets in loq-income countries,” _Journal of Political Economy_ , 101: 223-244.

[27] Sharma, M. 2005. The Impact of Food Aid on Consumption and Asset Levels in Rural Malawi. Unpublished paper, International Food Policy Research Institute.

[28] Skoufias, E., 2003, “Economic crisises and natural disasters: coping strategies and policy implications,” _World Development_ , 31: 1087-1102.

[29] Townsend, M., 1994, “Risk and insurance in village India,” _Econometrica_ , 62: 539-591.

20
# The Effectiveness of Environmental Provisions in Regional Trade Agreements<sup>_∗_</sup>

Ryan Abman<sup>_†_</sup> , Clark Lundberg<sup>_‡§_</sup> , and Michele Ruta<sup>_¶_</sup>

**Keywords:** Deforestation, Regional Trade Agreements, Trade Liberalization, Environmental Policy, Climate Change

**JEL Classification Codes:** F14, F18, Q23, Q27, Q28, Q56, Q17

> _∗_ We are grateful for discussion and helpful comments from Richard Damania, Gabriel Felbermayr, Jose-Antonio Monteiro, Alex Pfaff, Urvashi Narain, and Joel Trachtman. We also appreciate comments and discussion from participants in the World Bank Seminar Series on Deep Trade Agreements and the World Bank Trade and Climate Change Seminar Series. This paper has benefited from support from the World Bank’s Umbrella Facility for Trade trust fund financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. The findings and conclusions in this article are those of the authors and should not be construed to represent any official USDA or US Government determination or policy. All errors are our own.

> _†_ Department of Economics, San Diego State University. 5500 Campanile Drive, San Diego, CA 92182. E-mail: rabman@sdsu.edu.

> _‡_ Department of Economics, San Diego State University. 5500 Campanile Drive, San Diego, CA 92182. E-mail: clundberg@sdsu.edu.

> _§_ Economic Research Service, United States Department of Agriculture. E-mail: clark.lundberg@usda.gov.

> _¶_ World Bank. 1818 H St NW, Washington, DC 20433. Email: mruta@worldbank.org
as these will need some measurement of the externalities and net benefits associated with export promotion. Moreover, higher returns may be obtained by investing those resources in improving the overall business climate (e.g., infrastructure, education, etc.). The analyses discussed herein, however, do provide guidance about EPA’s institutional design, objectives and activities. Finally, the evidence of diminishing returns to scale in EPA budgets suggests that small is beautiful when it comes to EPAs.

# **References**

- [1] Djankov, Simeon, Caroline Freund, and Cong S. Pham (2009), “Trading on time”, _Review of Economics and Statistics_ , forthcoming.

- [2] Frankel, Jeffrey and David Romer (1999), “Does trade cause growth?”, _American Economic Review_ 89(3), 379-399.

- [3] Greenaway, David, and Richard Kneller (2005), “Firm Heterogeneity, Exporting and Foreign Direct Investment: a survey”, Research Paper Series Globalization, Productivity and Technology, Nottingham University.

- [4] Hausmann, Ricardo, and Dani Rodrik (2003), “Economic Development as Self Discovery”, _Journal of Development Economics_ 72(2), 603-633.

- [5] Heckman, James (1979), “Sample Selection as a Specification Error”. _Econometrica_ 47(1), 53-161.

- [6] Helleiner, Gerald K. (2002), _Non-traditional Export Promotion in Africa: Experience and Issues_ , Palgrave MacMillan.

- [7] Hogan, Paul (1991), “Some Institutional Aspects of Export Promotion in Developing Countries”, in Paul Hogan, Donald Keesing, and Andrew Singer eds., _The Role of Support Services In Expanding Manufactured Exports in Developing Countries_ , Economic Development Institute, World Bank.

19
**Table 14** : Composition of population by religion and rural-urban division of each group, 2009-10 (percent)

|Religion|Rural|Urban|Population<br>(million)|
|---|---|---|---|
|Hinduism|74|26|856|
|Islam|66|34|133|
|Christianity|70|30|24|
|Sikhism|75|25|18|
|Buddhism|60|40|7|
|Jainism|13|87|3|
|Zoroastrianism|3|97|0.16|
|Others|79|21|3|
|Total|73|27|1043|

Given their small shares in the total population and geographical concentration, random sampling of households in the expenditure surveys yields less than 100 observations for minority religious communities, other than Muslims, in the vast majority of the states. Indeed, as Table 15 indicates, only 13 of the 17 largest states had a sufficiently large number of households even for Muslims to allow poverty to be reliably estimated. Each of Orissa, Haryana, Punjab and Chhattisgarh had fewer than 100 Muslim households in the survey. Thus, we attempt poverty estimates by religious groups in the states separately for Hindus and Muslims only. We do provide estimates for the catch-all “other” category, but caution that in many cases these estimates are based on fewer than 100 observations and therefore subject to large statistical errors.

**Table 15** : Number of households sampled by religious groups in the states, 2009-10

|State|Rural|Hindus<br>Urban|Total|Rural|Muslims<br>Urban|Total|Rural|Others<br>Urban|Total|
|---|---|---|---|---|---|---|---|---|---|
|Uttar Pradesh|5079|2155|7234|812|894|1706|15|38|53|
|Maharashtra|3599|2971|6570|188|600|788|228|409|637|
|Bihar|2789|1098|3887|498|164|662|12|9|21|
|Andhra Pradesh|3540|2380|5920|254|468|722|134|116|250|
|West Bengal|2425|2405|4830|1102|322|1424|49|22|71|
|Tamil Nadu|3068|2817|5885|83|271|354|169|230|399|
|Madhya Pradesh|2611|1662|4273|92|248|340|28|56|84|
|Rajasthan|2395|1205|3600|129|267|396|59|81|140|
|Gujarat|1584|1406|2990|130|251|381|5|48|53|
|Karnataka|1825|1648|3473|189|304|493|22|82|104|
|Orissa|2880|991|3871|39|44|83|56|20|76|
|Kerala|1389|1078|2467|614|423|1037|603|345|948|
|Assam|1749|719|2468|779|97|876|88|15|103|

28
- 20 -

# 6. Implications for Future Poverty Alleviation Prospects

Naturally we cannot say anything with similar confidence about what has happened to poverty in Indonesia since early 1987 (the last available SUSENAS survey), or what is likely to happen in the future. But some aspects of the methodology we have used here can throw light on the issue. Our 1987 results also allow us to estimate the elasticities of poverty to any future distributionally neutral growth in mean consumption, in a similar manner to those we have used in the 1984-87 analyses. We find that the 1987 growth elasticities are also high; and, indeed, higher than those for 1984. For example, our estimates are -4.1 for the 1987 elasticity of the poverty gap measure with respect to mean consumption using the lower poverty line (as compared to -2.9 for 1984 from Table 4); similarly, for our preferred measure we obtain an elasticity of -4.8 (-3.4 for 1984). Furthermore, from national accounts for 1987 and 1988 it appears that growth rates of real consumption per capita have increased. Both these factors - the higher growth elasticities of poverty and the higher growth rates - imply increasing poverty alleviation through any distributionally neutral growth since early 1987.

Sustainable future consumption growth in Indonesia will clearly require that investment is revived, after the cuts of the mid 1980s. But it is possible for the poverty alleviation benefits of even substantial consumption growth to be lost entirely through a sufficient contemporaneous deterioration in overall equity. As we have seen, there was a modest decrease in inequality over 1984-1987. Is this likely to have abated since then?