rafmacalaba/datause-encoder-data huggingface dataset:
of the impacts of irrigation are the impacts of irrigating one of a farmers’ plots, gross of any input reallocations made by the farmers across plots in response to that irrigation. We would be particularly concerned about the bias generated by these reallocations if inputs were being shifted out of production on non-irrigated plots: in that case, our estimated impacts of access to irrigation would include reduced farming intensity on non-irrigated plots. However, the substitution of inputs we estimate from most important plots outside the command area is generally not significantly different from zero, and the largest point estimate implies it is 37% as large as substitution away from command area plots.<sup>43</sup> We therefore conclude that the dominant within-household spillover is a reduced intensity of cultivation on irrigated plots. This suggests any bias in our estimates caused by spillovers onto plots outside the command area is likely to be small, and that spillovers onto plots inside the command area decreases our estimates in Section 3.

# **5.2.2 Impacts of separation failures on adoption of irrigation**

We now quantify the impact of separation failures on adoption of irrigation. We ask what would happen to adoption of irrigation if all households with two or more plots in the command area only had one plot in the command area. This counterfactual follows naturally from our estimates of the effect of the sample plot shock on adoption of irrigation on the most important plot, which we can interpret as the effect of a household’s second plot (the sample plot) being moved to the command area on adoption of irrigation on its first plot in the command area (the most important plot).

we calculate Specifically, First, ( _β_ 1 + _β_ 3 _,_ CA) (from Equation 5) is the total effect of the sample plot shock on adoption of irrigation on most important plots in the command area. Second, in the denominator, we count the total number of command area plots among households’ sample plots and most important plots.<sup>44</sup> Third, in the numerator, we apply the

> 43We calculate this using _β_ 1 _/_ ( _β_ 1 + _β_ 3 _,_ CA) from Equation 5, the impact of the sample plot shock on input allocations on most important plots outside the command area divided by the impact on most important plots inside the command area. Estimates come from Table 8.

> 44We implicitly ignores households’ other plots; we do so because our research design has little to

38
- Beaman, L. and J. Magruder (2012): “Who Gets the Job Referral? Evidence from a Social Networks Experiment.” _American Economic Review_ , 102, 3574–3593.

- Belot, M., P. Kircher, and P. Muller (2018): “Providing Advice to Jobseekers at Low Cost: An Experimental Study on Online Advice,” _Review of Economic Studies_ , 86, 1411–1447.

- Benjamini, Y., A. Krieger, and D. Yekutieli (2006): “Adaptive Linear Step-Up Procedures That Control the False Discovery Rate,” _Biometrika_ , 93, 491–507.

- Bertrand, M. and B. Crépon (2019): “Teaching Labor Laws: Evidence from a Randomized Trial in South Africa,” Manuscript, University of Chicago and CREST.

- Bhorat, H., T. Caetano, B. Jourdan, R. Kanbur, C. Rooney, B. Stanwix, I. Woolard, L. Patel, Z. Khan, L. Graham, K. Baldry, and T. Mqehe (2016): “Investigating the Feasibility of a National Minimum Wage for South Africa,” Manuscript, University of Johannesburg: Centre for Social Development in Africa and University of Cape Town: Development Policy Research Unit.

- Bhorat, H. and H. Cheadle (2009): “Labour Reform in South Africa: Measuring Regulation and a Synthesis of Policy Suggestions,” Development Policy Research Unit Working Paper 09/139, University of Cape Town.

- Blundell, R., M. C. Dias, C. Meghir, and J. Van Reenen (2004): “Evaluating the Employment Impact of a Mandatory Job Search Program,” _Journal of the European Economic Association_ , 2, 569–606.

- Botero, J., S. Djankov, R. L. Porta, F. Lopez-de Silanes, and A. Shleifer (2004): “The Regulation of Labor,” _Quarterly Journal of Economics_ , 119, 1339–1382.

- Bryan, G., S. Choudhury, and A. M. Mobarak (2014): “Under-Investment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh,” _Econometrica_ , 82, 1671–1748.

- Card, D., J. Kluve, and A. Weber (2018): “What Works? A Meta-Analysis of Recent Active Labor Market Program Evaluations,” _Journal of the European Economic Association_ , 16, 894–931.

- Chandrasekhar, A. G., M. Morten, and A. Peter (2020): “Network-Based Hiring: Local Benefits; Global Costs,” Working Paper 26806, National Bureau of Economic Research.

- Conlon, J., L. Pilossoph, M. Wiswall, and B. Zafar (2018): “Labor Market Search with Imperfect Information and Learning,” Working paper 24988, National Bureau of Economic Research.

- Crépon, B., E. Duflo, M. Gurgand, R. Rathelot, and P. Zamora (2013): “Do Labor Market Policies Have Displacement Effects? Evidence from a Clustered Randomized Experiment,” _Quarterly Journal of Economics_ , 128, 531–580.

- Donovan, K., J. Lu, T. Schoellman, et al. (2018): “Labor Market Flows and Development,” in _2018 Meeting Papers_ , Society for Economic Dynamics, vol. 976.

- Falk, A., D. Huffman, and U. Sunde (2006): “Self-Confidence and Search,” IZA Discussion Paper 2525.

- Farber, H. and R. Gibbons (1996): “Learning and Wage Dynamics,” _Quarterly Journal of Economics_ , 111, 1007–1047.

34
**TABLE 12. Indicators of job turnover after investment in ICT**

||Replaced|Workers|Elimi<br>Occup|nated<br>ations|Cre<br>Occup|ated<br>ations|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Probability|0.056|2***|0.10|1***|0.31|8***|
|(no controls)|(0.00|563)|(0.00|728)|(0.0|111)|
|Observations|1,6|72|1,7|19|1,|750|
|High Productivity|0.0014|0.00089|‐0.015|‐0.013|‐0.039|‐0.041*|
||(0.012)|(0.012)|(0.016)|(0.016)|(0.024)|(0.024)|
|Skills|0.0018|0.00057|0.032*|0.033*|‐0.015|‐0.016|
||(0.013)|(0.013)|(0.018)|(0.018)|(0.026)|(0.026)|
|Internet|0.044***|0.034**|0.11***|0.13***|0.22**|0.19*|
||(0.012)|(0.017)|(0.020)|(0.031)|(0.097)|(0.099)|
|Computers|0.027**|0.024*|‐0.027*|‐0.020|0.028|0.019|
||(0.012)|(0.012)|(0.016)|(0.017)|(0.024)|(0.025)|
|Observations|1,629|1,626|1,673|1,670|1,706|1,703|
|Industry effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Trends||Yes||Yes||Yes|

Dependent variable: dummy indicating whether workers were replaced (columns 1 and 2), whether occupations were eliminated (columns 3 and 4), and whether occupations were created (columns 5 and 6). Regressors: raw average (first panel), and firm‐level indicator variables defined as in Table 5. All regressions in first differences. Robust standard errors in parenthesis.

36
# Easterly, W. and L. Serven., editors,  (2003), **The Limits of Stabilization: Infrastructure, Public Deficits and Growth in Latin America** , Stanford University Press

- Estache, A. (2004),  “Utilities reform in Africa: efficiency vs. equity of performance outcomes”, mimeo, The World Bank

- Estache, A. and K. Gassner (2004), “Recent Economic Development in Energy and Water in Eastern Europe”,  mimeo, The World Bank.

- Estache, A. , A. Goicoechea and M. Manacorda (2004), ‘The access, quality and price outcomes of telecoms reform and governance”, mimeo, The World Bank

- Estache, A. and M.E. Pinglo (2004), “What happened to the private sector returns in developing countries since the Asian crisis?”, World Bank Policy Research Working Paper, No3373

- Estache, A.  and M. Rossi (2004), “ _Do Regulation and Ownership Drive the Efficiency of Electricity Distribution? Evidence from Latin America_ ", **_Economic Letters,_** _(forthcoming)_

- Estache, A. and T. Serebrisky (2004), Where Do We Stand on Transport Infrastructure Deregulation and Public-Private Partnership?”, World Bank Policy Research Working paper No. 3356

- Estache, A, V. Foster, and Q. Wodon (2002) **Accounting for Poverty in Infrastructure Reform** _._ Washington, D.C. The World Bank Institute of Development Studies

- Estache, A. and M. Rossi. (2002) _“How different is the efficiency of Public and Private Water Companies in Asia”,_ **The World Bank Economic Review** _,_ Vol. 16,  No1, 139-148

- Guasch, Jose-Luis. (2004) **Granting and Renegotiating Infrastructure Concessions** **_._** Washington, D.C., The World Bank Institute of Development Studies.

- Kessides, I. (2004), Reforming Infrastructure: Privatization, Regulation and Competition, Washington, D.C., World Bank, Policy Research Report Series

- McKenzie, David and Dilip Mookherjee. (2003).  “Distributive Impact of Privatization in Latin America:  An Overview of Evidence from Four Countries.” **_Economia,_** vol. 3, no. 2, Spring, pp. 161-218.

- Nellis, J. and N. Birdsall, N. (forthcoming in 2004), **Glass Half Full: Assessing the Distributional Impact of Privatization** , The Center for Global Development, Washington, D.C.

- Stern, J. and J. Cubbin (2003), Regulatory effectiveness: The impact of regulation and regulatory governance arrangements on electricity industry outcomes: a review paper”, mimeo, London Business School, Regulation Imitative Series, December

19
<!-- Start of picture text --> Figure 1: Survival Curves for Unemployed Males<br><!-- End of picture text --> <!-- Start of picture text --> 9Owv) ProbsbIDty<br>12<br>~.....................<br>0 .0 .. . . . ... .. . .......... ... .. .<br>O A .. ...... .................. . . . ....<br>Age >26<br>04 L A1 :<br>0 1 2 0 4 6 7 8 10 n 2 U<br>Duaton In months<br>Tlhl_u -4- Con"#<br>Ago 4- 26<br>Wlth Prior Work Experbnce<br>8uwhdva Probbility<br>......................... ....................................................<br>0F~~~~~............ .. .................................................<br>0 2 .=. :.=. ............ ............ .......<br>OA ... . .. . .. _ ... , _ . . . .. . . . . .. .. . .. . . .<br>O 1 2 8 4 6 O r a 9 in 12<br>Duration In months<br>-lhl_e -4- Controblnn<br><!-- End of picture text --> <!-- Start of picture text --> Survival Prbgbillty<br>1 t2<br>Ag " 2<br>0 x ... .. .. . ...... ......<br>o A ..o ........ .. ..... .... . .....<br>Age'<-26<br>0 1 2 j- 8 4 5 6 7 0 9 ni<br>Duration In months<br>Das. Contros<br>N Age 26<br>With No Prlor Work Expertm<br>Sulv 8w12 Pooltyl<br>.......... ......................................................<br>... .. .............................. .......... ...............<br>.. .. . .... .. . .. . . . . .. . . . . .. .. .. . .. . . . . . .. . . . . . .. ................<br>OA . .. ......................<br>0 1 2 8 4 8 0 7 e 9 10 n 12<br>Duration In months<br>L-r cn tu dw son<br>Not: oontrol sapl very smelt<br><!-- End of picture text -->
**Figure 3.4: Amount of Wage Increases for Those Employed Both at Baseline and End-line Surveys (in Bangladeshi taka)**

<!-- Start of picture text --> Treatment Control<br>1,610<br>1,547 1,560<br>1,335<br>1,233<br>646<br>Male Female Total<br><!-- End of picture text --> _Source:_ Original figure for this publication.

_Note:_ The number of observations with wage data available at baseline and end-line is 275 for male and 57 for female. Outliers (above 10,000 taka increase) were excluded since such huge increases are likely due to some drastic circumstantial changes unrelated to certificates and employment. The differences for _total_ and _females_ are statistically significant at the 10 percent and 5 percent levels, respectively. These wage increase data are from those who were in wage employment at both baseline and end-line points. The majority of wage-employed workers at baseline remained wage employed at end-line (75 percent and 70 percent in the treatment and control groups, respectively).

# **Formality of Employment**

Further, the study explored whether the RPL program had any effects on the types of employers. Workers with certificates would be in a better position to apply for jobs at formal companies. The survey results seem to support this expectation (figure 3.5). At the baseline, only 21 percent and 18 percent of the wage employed participants in the treatment group and control group, respectively, were working at formally registered private companies or government entities. The rest was all in companies owned by family, friends, or other individuals: in other words, informal small and medium-sized enterprises. However, at the end-line, a significantly larger share (33 percent) of wage-employed workers in the treatment group work at formally registered private companies and government. The percentage for the control group remains constant at 21 percent at the end-line. The difference between the two groups at the end-line is large and statistically significant. Probit regression suggests (not reported here) that the RPL is associated with around a 13 percentage point increase in the likelihood of working at formal companies.

20
significantly higher for firms located in larger (higher-population) regions, and in regions where information on public procurement was more transparent.<sup>6</sup> Kenny (2009) uses Enterprise Surveys for ECA (Eastern Europe and Central Asia) countries to examine bribe-paying by construction firms. He reports that kickbacks are lower on average in wealthier countries, and for larger firms. Ownership of firms (state, private foreign, or private domestic) and city size made no significant difference in kickbacks.

We add to this literature by analyzing the impact of procurement institutions on kickbacks and on bidding rates. This study is the first one to analyze the relationship between procurement outcomes, as measured by the Enterprise Surveys, and procurement rules and practices, as measured by question 19 in the PEFA assessments.<sup>7</sup> Although PEFA assessments have been conducted since 2005, the procurement question was changed substantially beginning in the 2011 assessments, and again in 2016. Of the three versions, the largest number of assessments were implemented using the second version, between 2011 and 2015. The quality of assessments is also believed to be more consistent in recent years than in PEFA’s earlier years (PEFA Secretariat, 2016). We therefore limit our use of PEFA procurement indicators to this period.<sup>8</sup> The PEFA indicators are measured for the central government level. In some countries a substantial share of public procurement occurs at sub-national levels, using systems that are likely weaker (Transparency International, 2014). The quality of procurement systems at the central government and sub-national levels are plausibly correlated across countries, but not perfectly. For this reason, and because the share of procurement occurring at sub-national levels varies across countries, our ability to measure system quality only at the central government level introduces some measurement error.<sup>9</sup> The PEFA indicators have been used in a limited number of cross-country research studies, and most of these do not single out the procurement question for emphasis. In regression analyses of 57

> 6 The transparency measure is from Balsevich et al. (2011), who examined how well 83 regional public procurement websites complied in 2007 with the recently-passed Federal Law on Public Procurement.

> 7 Detailed information can be found in PEFA Secretariat (2011), available at https://pefa.org/sites/default/files/PMFEngfinalSZreprint04-12_1.pdf.

> 8 Three of the four sub-indicators from the second version (“PEFA 2011’) are changed only slightly in the third version (“PEFA 2016”), so those could be treated as comparable.  To date, however, very few assessments have been implemented using PEFA 2016 and no cross-country data set has been compiled for it.

> 9 PEFA assessments have been conducted at the sub-national level but only for a limited number of countries and subnational units in each of them.  Note the procurement questions in the Enterprise Surveys do not ascertain from which public entities firms are trying to obtain contracts.

8
exemptions of duty on import of equipment for power projects with capacity greater than 500 MW (PWC, 2014).<sup>8</sup> The role of hydropower in balancing the power system in India has increased significantly due to the rapid expansion of intermittent renewable resources (solar and wind). At present, intermittent renewable resources account for almost 20% of the total installed capacity. Having a larger share of intermittent resources would mean that a power system would need more flexible or quick ramping technologies, such as storage hydro, to ensure the system balance. This situation further demands hydropower power capacity, particularly storage type in India. Since, solar power is likely to face more interruptions during the monsoon season, enhanced cross-border transmission interconnection would help India to access hydropower generation from Nepal and Bhutan, where hydropower generation from run of the river capacities would be much higher in the monsoon season as compared to the drier season. Like India, Bangladesh and Pakistan would also benefit if the share of hydropower in their capacity mix is increased. Thus, the critical role that hydropower plays in power system balancing will increase the demand for hydropower in South Asia, particularly from countries where surplus potential exists.

# **3.3. Hydropower for Climate Change Mitigation**

South Asia makes an important contribution to global greenhouse gas (GHG) emissions. The region accounts for about 7% of global greenhouse gas (GHG) emissions from fossil fuel 8 In Bangladesh and Pakistan, demand for hydropower to provide flexibility in their power systems may not be as critical as in India because hydropower supplies sufficient share for balancing the system in Pakistan, and both systems have enough gas fired generation capacities which can provide flexibility to the systems due to its quick ramping up-down characteristics. Therefore, we have not discussed here the importance of hydropower for system flexibility in Bangladesh and Pakistan. In future, however, as the size of their systems increases, importance of hydropower to improve their system flexibility also increases.

19
Kozak, Marta, (2007). “Micro, Small, and Medium Enterprises: A Collection of Published Data,” IFC.

Levine, Ross (2005). “Finance and Growth: Theory and Evidence,” _Handbook of Economic Growth_ .

Loayza, N. (1996). “The Economics of the Informal Sector: A Simple Model and Some Evidence from Latin America,” _Carnegie-Rochester Conference Series on Public Policy_ , vol. 45, 129-62.

Loayza, N. and J. Rigolini (2006). “Informality Trends and Cycles,” _World Bank Policy Research Working Paper 4078,_ Washington, DC. _._ Loayza, N., A. M. Oviedo, and L. Serven (2005). “The Impact of Regulation on Growth and Informality – Cross-Country Evidence,” _World Bank Policy Research Working Paper 3263_ , Washington, DC.

Maloney, W. F. (2004). “Informality Revisited,” _World Development,_ vol. 32(7), 1159-1178.

Parisi, M., F. Schiantarelli, and A. Sembenelli (2006) "Productivity, Innovation and R&D: Micro Evidence for Italy," _European Economic Review_ , vol. 50,8.

Petersen M. and R. Rajan (1994). "The Benefits of Lending Relationships: Evidence from Small Business Data," _Journal of Finance_ , _49_ (1), 3-37.

Rajan, R.G. and L. Zingales (1998). “Financial Dependence and Growth,” _American Economic Review_ , vol. 88(3), 559-87.

Schneider, F. (2002). “The Size and Development of the Shadow Economies of 22 Transition and 21 OECD Countries,” _Institute for the Study of Labor_ , Discussion Paper Series, IZA DP-514.

Schneider, F. (2005). “Shadow Economies Around the World: What do we Really Know?,” _European Journal of Political Economy_ , vol. 21(3), 598-642.

Straub, S. (2005). “Informal Sector: the Credit Market Channel,” _Journal of Development Economics,_ vol. 78, 299-321.

Tanzi, V. (1980). “The Underground Economy in the United States: Estimates and Implications,” _Banca Nazionale del Lavoro_ , 135 (4), 427-453.

World Bank (2007). _Informality: Exit and Exclusion._ 18
The correlation between electrification and welfare outcomes does not indicate the causal effect of electricity access because of potential non-random adoption of electricity at both household and village level. To address the potential endogeneity issue, we use the following model to estimate the causal effects of electrification: where _Wi_ denotes the outcome variables of interest, including women’s monthly employment hours, labor force participation, health outcomes, and media exposure at home; _Xi_ is a vector of household- and village-level characteristics, as in equation (6); _Ei_ is a dummy variable measuring access to electricity; _T_ is common yearly shocks; ηi represents unobserved household- and village-level determinants of outcome variables; _ε i_ is an idiosyncratic error term.

To control for unobserved variable ηi , we rely again on propensity score matching and exploit the correlation between initial characteristics and unobserved heterogeneity. Specifically, we estimate a two-stage propensity score-weighted model. In the first stage, a weight variable is created based on the probability of a household adopting electricity given household and village-level characteristics in 2005. In the second stage, the effect of electrification is estimated using _p_ -weighted OLS model using 2012 data.

Table 8 reports estimation results of equation (7). For comparison, we also report findings from a simple OLS estimation. Based on our preferred specification, the p-weighted model, electrification is associated with almost one hour increase in girls’ study time each week. In addition, with access to electricity, girls’ grade attainment on average increases by 0.48. Women’s labor force participation increased 3.8 percentage points, and women’s employment hours increased 36.3 percent because of electrification. Electrification also brought positive health benefits for women. The probability of having a fever, cough, or diarrhea for women falls when a household is connected to the grid. Also, the number of sick days of women falls by 12.5 percent because of grid connectivity. Electrification also increased women’s exposure to electronic media—radio and television. The probability of listening to radio and watching 16