rafmacalaba/datause-encoder-data huggingface dataset:
pendix Table A2 shows that these individuals who remain have significantly lower monthly expenditures at baseline.

The primary analysis therefore focuses on the endline survey, conducted entirely after the intervention ended in November-December 2014. Table 2 shows there was no statistically significant difference in attrition, nor any selective attrition by particular characteristics. The raw levels of attrition, similar to the control group from the midline survey, indicate that the difference observed in the first wave was driven by particularly low attrition in the cash group. This is consistent with the conditionality of the cash transfer driving the low level of attrition. The 2,855 households interviewed at baseline that were interviewed again at endline will make up our main sample.

At endline, we also interviewed new households with a newly pregnant woman or infant born since baseline. This was done primarily to measure the spillovers associated with the information intervention, given that these women would not have been eligible for the cash intervention,<sup>8</sup> but may have elected to participate in the information sessions.

The baseline data indicate that the women and households in our sample are particularly disadvantaged. More than 70% of women interviewed never attended any type of formal school, and about the same number are illiterate. This is somewhat different from data from the 2011 Nepal DHS, where only 44% of women in rural areas who would be considered eligible (by nature of being pregnant or having a child under 24 months old) never attended any school, and only 38% are illiterate. PAF in general targets especially poor areas with little economic development, and this likely explains the difference between women in our sample and women in rural areas from the nationally representative DHS survey.

The level of knowledge among eligible women at baseline indicates that there is substantial room for improvement from the information intervention. Only about half of women indicated that a newborn infant should be fed breast milk exclusively for exactly six months, and about half answered that a pregnant woman should eat more food compared to before getting pregnant. Out of the total respondents, 40% responded that a baby should be breastfed more than usual during an episode of diarrhea, and 40% knew that a recently delivered woman should begin vitamin A supplementation within 45 days of delivery. This relative lack of

> 8 Newly eligible women were not eligible for the cash transfer given that this may have led to the perverse incentive to increase fertility.

9
- Donald, A. (forthcoming): “Household-level Impacts of Women’s Financial Control: Experimental Evidence from Cote d’Ivoire,” _Unpublished Manuscript_ .

- Dupas, P., A. Keats, and J. Robinson (2017): “The Effect of Savings Accounts on Interpersonal Financial Relationships: Evidence from a Field Experiment in Rural Kenya,” _The Economic Journal_ , 129, 273–310.

- Dupas, P. and J. Robinson (2013): “Why Don’t the Poor Save More? Evidence from Health Savings Experiments,” _The American Economic Review_ , 103, 1138–1171.

- Fafchamps, M. (2011): “Risk Sharing Between Households,” in _Handbook of Social Economics_ , ed. by J. Benhabib, A. Bisin, and M. O. Jackson, North-Holland, vol. 1, 1255–1279.

- Fafchamps, M. and S. Lund (2003): “Risk-sharing networks in rural Philippines,” _Journal of Development Economics_ , 71, 261–287.

- Feldhaus, C. and J. Mans (2014): “Who do you lie to? Social identity and the cost of lying,” Working Paper Series in Economics 76, University of Cologne, Department of Economics.

- Fiala, N. (2018): “Business Is Tough, but Family Is Worse: Household Bargaining and Investment Decisions in Uganda,” Tech. rep., Working paper, University of Connecticut.

- Field, E., R. Pande, N. Rigol, S. Schaner, and C. Troyer Moore (2021): “On her own account: How strengthening women’s financial control impacts labor supply and gender norms,” _American Economic Review_ , 111, 2342–75.

- Gneezy, U. (2005): “Deception: The Role of Consequences,” _American Economic Review_ , 95, 384–394.

- Goldberg, J. (2016): “Kwacha Gonna Do? Experimental Evidence about Labor Supply in Rural Malawi,” _American Economic Journal: Applied Economics_ , 8, 129–149.

- ——— (2017): “The effect of social pressure on expenditures in Malawi,” _Journal of Eco-_

- _nomic Behavior & Organization_ , 143, 173–185.

- Grimard, F. (1997): “Household consumption smoothing through ethnic ties: evidence from Cote d’Ivoire,” _Journal of Development Economics_ , 53, 391–422.

- Grimm, M., F. Gubert, O. Koriko, J. Lay, and C. J. Nordman (2013): “Kinship ties and entrepreneurship in Western Africa,” _International Journal of Entrepreneurship & Small Business_ , 26, 125–150.

- Hausman, J. A. (1985): “Taxes and labor supply,” New York: North-Holland Publishers, vol. 1 of _Handbook of Public Economics_ , chap. 4, 213–263.

- Heidhues, P. and B. Kőszegi (2009): “Futile attempts at self-control,” _Journal of the European Economic Association_ , 7, 423–434.

43
10 Table 2. Some Statistics for Net<sup>Equity Flows and Gross Bond Flows</sup> (millions of dollars, on annual basis,<sup>1988 -</sup><sup>**1992)**</sup>

||||Latin America||Asia||
|---|---|---|---|---|---|---|
|||Net Equity|Gross Bonds|Gross Bonds<br>(without Brady|Net<sup>Equity</sup><br>)|Gross Bonds|
|1988|Sum|177.000|658.000<br>|388.000<br>|132.000<br>|2239.000<br>|
||Average|19.667|73.111|43.111|14.667|2d8.778|
|1989|Sum|376.000|567.000<br>|567.%00<br>|61.000<br>|2920.000<br>|
||Average|41.778|63.000|63.000|6.778|324.444|
|1990|Sum|1206.000<br>|9673.000<br>|829.000<br>|192.000<br>|2890.000<br>|
||Average|134.000|1074.778|92.111|21.333|321.111|
|1991|Sum|2343.000|2870.000<br>|2306.000<br>|220.000<br>|3985.000<br>|
||Average|260.333|318.889|256.222|24.444|442.778|
|1992|Sum|3170.000|4011.000<br>|4011.000<br>|740.000<br>|3837.000<br>|
||Average|352.222|445.667|445.667|82.222|426.000|
|Adjusted|Sum|4226.667<br>|5348.000<br>|5348.000<br>|986.667<br>|5116.000<br>|
|1992|Average|469.630|594.222|594.222|109.630|568.444|
||sUM|7272.000|17779.00|8101.000|1345.000|15871.00|
||MEAN|1530.947|3742.947|1705.474|283.158|3341.263|
||St. D|1328.517|2956.418|1589.646<br>|279.622<br>|775.669<br>|
||CV|0.868|0.790|0.932|0.987|0.232|

Note: Fourth Column: Gross bond<sup>flows without Brady; Sum: annual sum for all the countries for a particular year;</sup> Average: Sum divided by the number<sup>of countries; Adjusted 1992: the nine months of 1992 grossed to a yearly basis;</sup> SUM: the sum for the whole period;<sup>MEAN = (SUM*12)/57; CV: Coefficient of Variation = standard deviation/</sup> MEAN.

Country-Specific Variables. The<sup>study employs several domestic equity, credit, and</sup> exchange market variables as explanatory<sup>factors. The two equity market variables-- price</sup> earnings ratios and rates of return<sup>on domestic stock markets--are from the IFC's Emerging</sup> Markets Data Base. The country credit<sup>variable was constructed by using Institutional Investor's</sup> semi-annual country credit rating. Secondary<sup>market debt prices are from Salomon Brothers</sup>
# **Table 17: Investment and Labor Growth**

||**Invest**|**ment**|**Labour**|**Growth**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|**_Initial conditions_**|||||
|log initial capital|0.202***|0.190***|0.046**|0.039**|
||(0.053)|(0.053)|(0.019)|(0.020)|
|log labor days at startup|0.123|0.072|-0.251***|-0.261***|
||(0.076)|(0.073)|(0.062)|(0.066)|
|Age of the firm in years|0.022**|0.019**|0.000|0.000|
||(0.009)|(0.008)|(0.004)|(0.004)|
|-Food and beverages, brewing/distilling|0.255|0.380|0.163*|0.135|
||(0.288)|(0.254)|(0.092)|(0.100)|
|-Grain milling|0.539|0.647|0.012|0.065|
||(0.670)|(0.685)|(0.166)|(0.174)|
|-Hotels and restaurants|0.815**|0.867***|0.183|0.164|
||(0.353)|(0.315)|(0.145)|(0.150)|
|-Retail trade via stalls and markets|0.444|0.565|0.011|-0.013|
||(0.412)|(0.460)|(0.116)|(0.115)|
|-Services|0.260|0.437|0.008|0.009|
||(0.403)|(0.402)|(0.074)|(0.085)|
|-Whole sale trade|-0.363|-0.431|-0.029|-0.027|
||(0.367)|(0.361)|(0.103)|(0.107)|
|-Transport services|0.116|0.041|-0.245*|-0.200|
||(0.511)|(0.562)|(0.124)|(0.125)|
|-Manufacturing (excl: grain milling,<br>|0.281|0.622**|0.180|0.188|
|food and|(0.296)|(0.298)|(0.109)|(0.120)|
|**_Characteristics of the manager_**|||||
|Manager's Age|0.006|0.001|0.014*|0.017*|
||(0.022)|(0.023)|(0.008)|(0.009)|
|Manager's Age Squared/1000|-0.142|-0.055|-0.140|-0.172*|
||(0.247)|(0.250)|(0.091)|(0.098)|
|Manager is a male|0.102|0.006|-0.096|-0.079|
||(0.180)|(0.186)|(0.068)|(0.069)|
|Years of schooling|0.016|-0.003|0.026|0.022|
||(0.073)|(0.071)|(0.027)|(0.028)|
|Years of schooling squared / 1000|1.918|3.601|-0.374|-0.478|
||(8.505)|(9.053)|(2.565)|(2.751)|
|**_Local Demand_**|||||
|WRSI- 2006|-0.022|-0.044|0.009|0.009|
||(0.038)|(0.036)|(0.008)|(0.008)|
|WRSI- 2005|0.080***|0.102***|-0.002|-0.001|
||(0.023)|(0.026)|(0.003)|(0.003)|
|**_Geography_**|||||
|Remote location||-0.040||0.058|
|||(0.345)||(0.081)|
|Location in rural town||<br>-0.058||<br>0.108|
|||(0.373)||(0.080)|
|Distance to nearest financial institution||<br>-0.084||<br>0.007|
|||(0.129)||(0.035)|
|Distance to nearest all weather road||-0.098||-0.012|

-38-
-58-

------------------------------------------------------------------------

__---__----------

|Gross Value<br>in national<br>cur|TA<br>of Output I<br>rencies<br>and<br>|BLE**41.1**<br>n Brazil and<br>at the officia<br>...-------<br>|Mexico in**197**<br>l<br>exchange<br>---------------------<br>|**5.**<br>rates<br>-----__-------------<br>|-------------_<br>|
|---|---|---|---|---|---|
||Mex|ico|Br|azil|Mexico/|
||(million<br>pesos)|(million<br>cruzeiros)|(million<br> <br>pesos)|(million<br>cruzeiros)|Brazil<br> <br>(X)|
|Total Manufacturing Output|480<sup>.</sup>048<sup>.2ab</sup>|<sup>312.226.5ab</sup>|<sup>1,203,398.9</sup>|782,698.5|39.89|
|GrainMillProducts|9,123.1<br>|5,933.7<br>|11,420.8<br>|7,428.1<br>|79.88<br>|
|Sugar & Sugar Products|6,596.3|4,290.3|18,668.9|12,142.4|35.33|
|Halt and Malt Beverages|10,973.8|7,137.4a|<br>5,272.0|3.429.0|208.15|
|Tobacco and Tobacco Products|<br>3,847.4a|2,502.48|<br>9,407.0|6,118.4|40.89<br>|
|Textiles<br>|17,909.9<br>|11,648.7<br>|45,234.0<br>|29,420.5<br>|39.59<br>|
|Footwear<br>and Leather Products|6,447.2<br>|4,193.3<br>|15,823.7<br>|10,291.8<br>|40.74<br>|
|Pulp and Paper|14,607.1|9,500.6|16,499.0|10,731.1|88.53|
|Soap and Detergents|6.335.6|4,120.7|5.576.4|3,626.9|113.61|
|Paints|3,460.2|2,250.5|11,164.1|7,261.2|30.99|
|Agricultural<br>Fertilizers|4,865.7|3,164.7|18,597.7<br>|12,096.1<br>|26.16<br>|
|Petroleum<br>Refining and Produc|ts<br>28<sup>,</sup>463 <sup>.</sup>4<sup>ab</sup><br>|18<sup>,</sup>512<sup>.8ab</sup>|<br>89,212.9|58,024.6<br>|31.90<br>|
|Tires and Inner Tubes|4,969.8|3,232.4|11,083.9|7,209.0|44.83|
|Cement|5,648.6|3,673.9|8,745.8|5,688.3|64.58|
|Bricks|1,636.9|1,064.6|9,288.2|6,041.1|17.62|
|Iron and Steel|32,836.9|21,357.3|79,220.1|51,525.3|41.45|
|Radio and TV Receivers|4,854.5|3,157.4|13,843.0|9,003.6|35.06|
|Motor Vehicles|39,425.6<br>___<br>-----------------------|25,642.7<br>------------------------------|88,854.4|57.791.5|44.37|
|Total in our sample<br>|202,002.0|131,383.4|457,911.9|297,828.9|44.14|
|as%of Total Manufacturing||||||
|Output|42.|08|38|.05||

a) indirect taxes and subsidies are deducted (see table 2.3).

b) includes 25,008.3 million pesos (excl. indirect taxes and subsidies) for petroleum refining which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico.

Note: Figures are converted at the exchange rate of 1.5375 pesos to the cruzeiro. Source: Figures for Brazil from Censo Industrial, and for Mexico from Resumen General (except figures mentioned under footnotes a) and b)).
||Education<br>Primary<br>school vs.<br>illiterate|s.e.|Pearlin<br>2 - 1|s.e.|Pearlin<br>3 – 1|s.e.|Caste<br>2 - 1|s.e.|Political<br>2- 1|s.e.|Male -<br>female|s.e.|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|NREG days post movie|-5.098**|2.138|-0.23|2.098|1.293|2.297|-0.546|1.873|-1.529|1.371|-1.118|1.535|
|NREG wages post movie|-487.30**|204.172|35.65|207.564|169.881|220.525|-37.958|183.366|-113.54|135.939|-35.462|147.347|
|NREG wages received<br>post movie|-106.035|148.744|107.668|192.594|237.912|210.289|168.739|157.837|-106.74|130.428|43.038|133.216|
|Desiredparticipation R2|-0.016|0.061|0.027|0.063|-0.085|0.084|0.026|0.059|0.016|0.139|0.023|0.047|

_Note_ : Estimated using a single difference on R2 data. Estimates give the difference in estimates between the specified groups. Definitions of the perception variables are given in the box below. The stratifications are as follows: education: 1= illiterate, 2 =literate and up to class5 pass, 3=more than primary; caste: 1 = Mahadalit, 2 =all others; Pearlin: 1=scale<3, 2=scale=3, 4 or 5, 3 = scale>5;political: 1=person voted, if close to Mukhiya or close to ward member, 2= none of those is true.

|Codes for Perceptions|
|---|

- 1     Can influence wages

- 2     Benefit of participating in Gram Sabha

- 3     Women participate in Gram Sabha

- 4     Gets work under BREGS when demand

- 5     BREGS projects have increased employment 6     BREGS decreased migration of labor

- 7     BREGS work will be available next year in village

- 8    Women have the right to chose BREGS projects

- 9    Assets useful to women created under BREGS 10  Women work in BREGS projects 11  Women treated well at BREGS worksite

- 12   Women get work if they bring children to worksite 13   Distance women would be willing work on BREGS worksite 14   Women paid equal wages as men 15   Women of HH would like to work on BREGS 16   Women of HH would be allowed to work on BREGS 17   Knowledge of BREGS increased in last year in your family 18   BREGS work opportunities improved in last year in your family 19   Infrastructure improved in village 20   Work increased in village 21   Wage increased in village 22   Migration decreased in village

26
- 26 -

<!-- Start of picture text --> Fig 19<br>Pussic and Moscow Rezion Nominal Wc-es<br>July 1991 - OC:_e- 199_<br>10<br>9<br>2 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~ "11<br>1<br>July91 I Feb. | Apr. JunO | Aug. | Oct92<br>Jon92 Mar. Mcy July Sept.<br>0 Russia + Moscow 0 Moscow Oblost<br>Fig 16<br>Russia and Moscow Region Wage Levels<br>Jon 92 v:ces<br><!-- End of picture text -->

<!-- Start of picture text --> 2.9<br>2.<br>2.5-<br>2.3<br>2.2-<br>2.<br>2-a -<br>1.9<br>1 L<br>1.75<br>1.2 -<br>1.5<br>0.9<br>Fet9, Moc A;' May .- e Ju;y Aug Cc:92 o<br>C Russia + Moscow 0 Moscow oblost<br><!-- End of picture text -->
grounds. The advantage of looking at measures such as import refusals is that they are implemented on a country- and product-specific basis, rather than being the same de jure for all exporters. Most standards and regulations are effectively most-favored nation (MFN) measures, which makes it difficult to identify their effects on exporters by exploiting cross-country variation in outcome measures, such as trade flows. Focusing on a country- and product-specific measure, such as import refusals, provides a potentially much richer source of data in which identification can be based on cross-country as well as cross-product and through-time variation.

In addition to exploiting new data on US import refusals, this paper makes a number of contributions to the existing literature. First, we provide some of the first explicit evidence of reputation effects in the enforcement of sanitary and phyto-sanitary (SPS) measures. Specifically, we show that even after controlling for the size of import flows and other factors, a history of compliance is associated with fewer current import refusals. Second, we show that it is not just reputation for a particular product that matters, but sector-wide reputation (i.e., there are cross-product spillovers in enforcement): a product tends to suffer from more import refusals if closely related products are also subject to refusals. Third, we investigate, and find evidence to support, the hypothesis that the reputation of neighboring countries also matters for SPS enforcement. Our results suggest that imports are more likely to be refused if the same product from neighboring countries has also been subject to refusals. We interpret this finding as evidence in favor of geographical spillovers in enforcement of refusals.

To our knowledge, the three reputation effects we are investigating have not been explicitly considered before in the literature. Baylis et al. (2009) use similar data to ours to analyze the determinants of US import refusals over the period 1998-2004. Their core hypotheses are that: countries with greater experience exporting food to the US experience fewer refusals (the “learning curve” effect); and import refusals are subject to political pressure (the “standards for sale” effect; cf. Grossman and Helpman, 4
_Climate adaptation and energy efficiency._ ECA is an energy-intensive region that relies heavily on non-renewable energy. Belarus, Bosnia and Herzegovina, and Turkey are implementing policy reforms (such as cost-based energy pricing) and investments in both public infrastructure and private industry, including renewable energy and energy efficiency, in partnership with the World Bank. Efforts to adapt to climate change include improved water resource management (flood protection, water loss reduction, irrigation efficiency) in Kazakhstan; climate-smart agriculture (switching to more resilient crops) in Tajikistan; and better weather forecasting and climate change monitoring in Russia.

_Education and health._ The region has made significant advances in the area of human development, including reductions in child mortality rates. Many countries in the region have achieved universal primary enrollment and gender parity in both primary and secondary education, and literacy rates are high (Figure 13). On average, the ECA region scores above average among EMDE regions in several education and health indicators. Nevertheless, shortcomings remain. Levels of learning achievement are low in several countries, and socio-economic and ethnic disparities in education persist. Among the basic education indicators, regional gaps are most apparent for math and science education. The region scores well below the EMDE average on attracting and retaining talent. Building a highly skilled workforce will require improving the quality of education, investing in on-the-job training, and using talent more effectively.

# **III.3. Which policies can help address investment needs in the ECA region?**

Unmet investment needs, along with governance, financial, and labor market obstacles, limit output growth in the region (World Bank 2015d-g; World Bank and Vietnam 2016; World Bank 2016c; EBDR 2015a). While policy priorities depend on country circumstances, appropriate cyclical and structural policies are needed in all cases to raise investment growth. Fiscal policy could help most directly by expanding public investment while monetary policy could boost activity by lowering financing costs. Structural reforms could address factors holding back private investment, including by boosting productivity and aggregate growth prospects and improving the business climate.

This constrains their ability to finance public investment and places a premium on reforms that encourage private investment. Only a few regional economies can tap debt markets to finance infrastructure, while weak domestic banking systems and underdeveloped capital markets restrict the ability of governments to borrow domestically.

With weak growth, limited fiscal resources, and net capital outflows, the gap between infrastructure needs and the ability of governments to meet those needs may widen. This places a premium on measures to improve investment efficiency and to obtain funding from multilateral sources or the private sector.

25
According to NEPA, Beijing's current abatement rate for industrial S02 is 2.5%. For a conservative benefit-cost assessment, we adopt the lowest rate of abatement -- 15% -- which has been included in Figure 28. At 15% abatement, we estimate the incremental cost of 100 tons of S02 abatement to be $2,860. Taking this to be the incremental cost of saving a life, we use our estimated incremental lifesaving benefit ($8,000) to calculate a social rate of return to abatement of approximately 180%.

**Table 5.2: Industrial S02 Abatement in Five Cities**

||. **S02**<br>**Produced**|**S02**<br>**Emitted**|**Abatement**<br>**%**|
|---|---|---|---|
|**Shenyang**|**196,100**|**144,700**|<br>**26.2**|
|**Shanghai**|**393,700**|**356,700**|<br>**9.4**|
|**Chongqing**|**544,800**|**494,800**|<br>L<br>**9.2**|
|**Guangzhou**|<br>**165,100**|**151,500**|<br>**8.2**|
|**Beijing**|**208,900**|**203,700**|<br>**2.5**|

We should stress that this estimated rate of return, while clearly attractive, is extremely conservative as a guide to the lifesaving value of air pollution abatement in Beijing. For a large, non-SOE plant, our econometric results imply a MAC of $332 for 100 tons of S02 abated when the abatement rate is 10%. The equivalent figure for 100 tons of particulates abated is around $120. If (as seems likely, given overall abatement rate of 2.5%) there are large non-SOE plants in Beijing which are abating at less than 10%, **then failure to enforce greater abatement is equivalent to valuing a Beijing worker's life at less than $500. Continued inaction means ignoring a public investment with whose social rate of return is likely to be in excess of 3,000%! We believe that the opportunities for such high returns may be quite rare in China's urban health sector.** We conclude that it would make extremely good economic sense to tighten air pollution control in Beijing. Our abatement cost estimates show that it would also be sensible to focus on particulate emissions from large facilities.

# **6.2 Case 2: Zhengzhou**

Our results for Beijing do not necessarily yield appropriate conclusions for other urban areas in China. In the first place, our cost analysis reflects only microeconometric evidence on marginal abatement costs (MAC) for end-of-pipe treatment. These almost certainly overstate overall MAC, because process changes often provide the least-cost abatement opportunities for industrial facilities. Secondly, Beijing is not a representative Chinese city for environmental analysis. It is extremely large, and considerably more polluted than the average large urban area in China.

Our second case is designed to address both of these problems. For the abatement cost analysis, we use our cross-provincial econometric results on pollution intensities because they incorporate all abatement options available to Chinese factories. Our focal city is 34