rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2: Convergence in Poverty Estimates Based on Subsample Imputation and Subsample Direct Estimation**

<!-- Start of picture text --> National Rural (200 households) Urban (120 households)<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>30 30 30<br>25 25 25<br>20 20 20<br>1 4 7 10 13 16 19 22 25 28 1 4 7 10131619222528 1 4 7 10 13 16 19 22 25 28<br>Replication Replication Replication<br>Imputation Imputation Imputation<br>Direct Direct Direct<br>Full Sample (mean) Full Sample (mean) Full Sample (mean)<br>Full Sample (95% CI) Full Sample (95% CI) Full Sample (95% CI)<br> headcount ratio, %<br>mean<br>Cumulative<br><!-- End of picture text --> _Source_ : Authors’ estimations using 2005 HIES data.

Figure 2 shows the convergence of the cumulative means of poverty rates as the number of replications increases. The cumulative means of both the Subsample Imputation and the Subsample Direct Estimation appear to converge even with only 30 replications. This gives us a certain level of confidence about the results.

# **Robustness Check for Subsample Imputation and Subsample Direct Estimation for 1,000 Replications**

In the above analysis, even with only 30 replications, the cumulative means appear to converge. This is encouraging. Nevertheless, there is no full guarantee that the above results would hold even if the number of replications increased. As a robustness check, we examined whether results would change dramatically after increasing the number of replications.

However, as mentioned before, increasing the number of replications is not a simple task. Implementing ELL’s methodology is simplified dramatically thanks to development of PovMap 2 software. But, each replication needs to be implemented manually. For example, users of PovMap2 need to select models and run simulations for each replication separately. It is not possible to repeat the selection of subsamples and estimation of poverty headcount rates 1,000 times. Therefore, we simplified the estimation and simulation process significantly so that we can repeat the process 1,000 times (section II provided an exact description of the simplified process).

12
75% in 1999 to 85% in 2000. The most significant changes were in the extremely-noncompliant black group, whose percentage dropped from 11% in 1999 to 2% in 2000, and a major shift from the partially-compliant yellow group (44% to 22%) to the fully-compliant blue group (27% to 61%).

Evidence for the Green Watch program throughout Jiangsu Province indicates both increasing participation by firms and improvement in their compliance rates. As shown in Table 1, the number of rated firms increased more than tenfold, from 1,059 in 2001 to 11,215 in 2006; and the percentage of firms with positive ratings (green, blue, and yellow) increased from 83% in 2001 to 90% in 2006. Furthermore, Table 1 suggests that Green Watch ratings provide a strong improvement incentive for noncompliant (red and black) firms, with stronger effects on firms with red ratings (moderate noncompliance) than those with black ratings (extreme noncompliance).

# **4.  Survey Data and Descriptive Statistics**

The results reported in this paper are drawn from a 2002 survey of managers of firms rated by Green Watch in four cities in Jiangsu Province of China: Huaian, Wuxi, Yangzhou, and Zhenjiang. Following the success of the pilot program in Zhenjiang, Huaian, Wuxi and Yangzhou adopted the same program in 2001. Table 2 provides information on socioeconomic and environmental conditions in the four cities, as well as polluting emissions in 2001. Wuxi has the largest population as well as the highest GDP per capita, while Huaian is the poorest. Wuxi and Yangzhou have the lowest readings for air quality, measured by SO2 (sulfur dioxide) and NO2 (nitrogen dioxide), and water quality measured by TSS (total suspended solids) and regulatory compliance percentage.

7
response to international prices; huge stockpiles of foodgrains were subsequently accumulated through government procurement (World Bank, 2010c; Himanshu and Sen, 2011).

The upshot of these interventions is that output prices faced by Indian agricultural producers do not always perfectly track those in international markets.<sup>20</sup> Moreover, since domestic market integration is somewhat limited (especially in the case of rice), there is considerable variability across states in crop price movements. On the one hand, this variation may reflect differential transmission of exogenous price pressure (e.g., because of varying levels of state procurement or exposure to trade, both with other countries and with other states); on the other hand, it may reflect localized supply or demand shocks, which can also drive rural wages directly.

# **3.2 Crop prices**

Wholesale crop price data averaged at the state level from observations at several district markets per state (and weighted by district production), are compiled by the Ministry of Agriculture, as are production and area data at the district level. So as to focus on a period of substantial price movement, as well as to match the NSS wage data (see below), I consider state-level price changes between the 2004-05 and 2009-10 crop marketing seasons. Given the relative ease of moving produce across district (as opposed to state) lines, state-level wholesale prices seem the appropriate measure of farmer production incentives.<sup>21</sup> I base the crop value shares, the _sd,j_ in equation (7), on production data from the 2003-04 crop-year, which has the best district/crop coverage for the pre-2004-05 period. Value of production is calculated at 2004-05 state-level prices. Note, however, that I do not take the value-weighted sum of price changes across every single agricultural product grown in India. Price data for many of the minor field crops and the tree crops are incomplete or not reliable.

> 20This is true for the principal intermediate input in agriculture as well. Despite a substantial upsurge in the international prices of chemical fertilizers beginning in 2007, retail prices in India, which are set by the central government, remained uniform and unchanged over the 2004-09 period (Sharma, 2012).

> 21Since sugarcane is sold mostly to mills and not in wholesale markets, I use the national MSP or, when relevant, “State Advised Prices,” which tend to be much higher and, hence, closer to international cane pricing standards (see Gulati, 2012).

12
||**Table 5: Child**|**Test Scores and Maternal Educ**|**ation**|
|---|---|---|---|
||(1)|(2)|(3)|
||OLS|Heckman Selection|Control Function|
|**English**|0.34|0.35|0.35|
||[0.10]a|[0.09]a|[0.11]a|
|**Urdu**|0.33|0.35|0.35|
||[0.11]a|[0.09]a|[0.11]a|
|**Math**|0.24|0.25|0.24|
||[0.12]b|[0.10]b|[0.12]b|
|**Observations**|676|4218|663|

_Notes:_ Robust standard errors, clustered at the village level in parentheses; (a): significant at p<0.01, (b): significant at p<0.05, (c): significant at p<0.1. The Heckman selection and control function estimates use the distance to school as an additional excluded variable in the determination of testing results. The results are presented as the causal impacts of maternal education on standarddeviation changes in test-scores. All regressions control for a full set of child age indicator variables, child gender and birth_tehsil fixed effects.

35
- Bourguignon, François, Francisco H.G. Ferreira and Marta Menéndez (2007): “Inequality of Opportunity in Brazil”, _Review of Income Wealth,_ **53** (4): 585-618.

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Cohen, Gerry A., (1989). “On the Currency of Egalitarian Justice”, _Ethics_ , **99** : 906-944.

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32
both countries as a robustness check<sup>27</sup> . Results were significantly different than 0 (at a 5% level) with this wider age range in both countries, suggesting the importance of age in some behaviors and outcomes.

As suggested by the ecological framework, the opposite results are observed when considering the correlation between risk factors and positive behaviors (Figures 3a and 3b) for 18-24 year old youth. The relationships are significant in Mexico at a 10% level and not significant in Chile at the 10 percent level. The relationship is strengthened (1% level) in Mexico when grouping all clusters together rather than breaking up the different age groups. This is partially due to few risk factors in the Mexican data as compared to protective factors. The Chilean dataset, which is richer in risk factors than in protective, shows a negative and significant relationship at 1% between risk factors and positive behaviors in the 15 to 17 year old age group, but only at 15% in the 18 to 24 year olds<sup>28</sup> .

# C **.  Proxies for At-Risk**

Due to the nature of the data used in this analysis, we can use a large set of risk and protective factors to predict the level of risky behavior that will be displayed by youth. Some of our variables are easily observable characteristics and are regularly found in most household data sets, but most of these data are unique and not available for most countries and youth. Thus, it would be useful to identify a youth’s risk level by a single or small set of easily observable characteristics. We test a set of observable variables to determine their usefulness in predicting risky behavior: poverty, living in a rural area, ethnicity, gender, and age.

_Poverty_ is a fairly good proxy to identify youth at-risk. Tables 3a and 3b summarize the information from the clusters and reveal that as the risk type rating of a group increases, the average level of parental education falls. Plotting the clusters against wealth proxies and estimating the slope of the line confirms this relationship.<sup>29</sup> Figure 4a shows that in Mexico, the more at-risk clusters have lower parental education, and the relationship is statistically significant at the 1% level. The same result emerges with each Mexico age group as well as when merging the Mexico age groups together, with a negative and significant coefficient on

> 27 Results are available upon request.

> 28 The relationship was significant at 10% and negative for the 18 to 29 year old cohort.

> 29 Since poverty level is an exogenous variable, we test these relationships for all poverty indicators: education level of household head, household ownership of luxury or durable goods, or monthly earnings of heads of households.  Since there are no significant differences between the various proxies for poverty, we chose parental education level since it is a widely available and observable indicator.

19
Rating Company Limited and currently has loans from both commercial financial institutions and the Government of Pakistan. LESCO does not have a credit rating. A summary of financial indicators is shown in Table 15.

**Table 15: Summary of financial indicators for LESCO and KE, 2015**

||**LESCO**|**KE**|**Global**<br>**comparator ***|
|---|---|---|---|
|Net profit (loss) margin|-5<br>percent|17<br>percent|-12 percent|
|EBITDA margin|-9<br>percent|23<br>percent|6 percent|
|Current ratio|3.16|0.98|0.79|
|Debt service coverage ratio|-9.96|1.25|-4.56|
|External Financing Index Ratio<sup>43</sup>|-2.79|-0.26|6.92|
|Investment as a percent of revenues|-8<br>percent|15<br>percent|18 percent|
|Government transfers as a percent of utility<br>revenue|10<br>percent|21<br>percent|6 percent|
|Net capital cost recovery ratio<sup>44</sup>|68 pe|rcent|16 percent|

* Note: Sample average is derived from the Rethinking Power Sector Reform Project data observatory and includes data from Tanzania, Senegal, Peru, Colombia, Pakistan, the Philippines, Vietnam, Kenya, Uganda, Tajikistan, the Indian states of Rajasthan, Odisha, and Andhra Pradesh, and Egypt

# _3.4.2 Institutions_

The regulator, **National Electric Power Regulatory Authority (NEPRA)** , was created in 1997 through Act N°40 and its main responsibilities are to issue licenses for generation, transmission and distribution of electric power; to establish, and enforce standards to ensure quality and safety of operation and supply of electric power to consumers; to approve investment and power acquisition programs of the utility companies; and to determine tariffs for electricity generation, transmission, and distribution.

The performance of the regulatory system is evaluated from two standpoints. First, an evaluation is made of the de jure performance by comparing the laws, rules, and regulations in place with various criteria that would be desirable in an ideal system. Second, a de facto evaluation incorporates actual behavior. The various performance tables, as well as including the score for the Pakistan regulatory system, include the average score across all the countries included in the

> 43 _External financing index ratio_ measured as net cash flow from financing divided by the net cash flow from operations.

> 44 _Net capital cost recovery ratio_ is the percent of full capital costs that can be recovered through tariff revenues net of operating costs.

48
States (score of 4.33).<sup>15</sup> Also, results from quantile regression show that the score of the United States is consistently higher than China and Vietnam at all relevant deciles; more specifically, the (negative) difference in managerial quality in relation to the United States appears greatest among the better managed (higher deciles) firms for both Vietnam and China (Figure 18).

**Figure 17. Dispersion in managerial quality: China and Vietnam vs United States**

<!-- Start of picture text --> 1 2 3 4 5 1 2 3 4 5<br>management management<br>United States China United States Vietnam<br>1<br>.6<br>.8<br>.4<br>.6<br>Density D ensity<br>.4<br>.2<br>.2<br>0 0<br><!-- End of picture text --> Combined K-S tests: p value= 0.000 Combined K-S tests: p value= 0.000 Source: Own elaboration based on WMS data

**Figure 18. Coefficients of quantile regression (China and Vietnam against United States) across deciles**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Quantile Quantile<br>-0.30<br>-0.20<br>-0.40<br>-0.40<br>-0.50<br>country=China country=Vietnam -0.60<br>-0.60<br>-0.70<br>-0.80 -0.80<br><!-- End of picture text --> Source: Own elaboration based on WMS data

> 15 According to World Bank (2017), Malaysian firms drawing from different waves of WMS data showed that the spread of management practices within Malaysia is wide, with a smaller tail of badly managed firms than China.

20
# **References**

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# Table IV: Firm Size, Management Mode and Regional Trust

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dependent|ln_L_|ln_L_|ln_L_|ln_L_|ln_L_|
|Trust|0.236<sup>_∗_</sup><br>(0.1428)|0.047<br>(0.1259)|0.197<br>(0.1332)|0.454<sup>_∗∗∗_</sup><br>(0.1421)|-0.044<br>(0.3242)|
|FOFM||-0.392<sup>_∗∗∗_</sup><br>(0.0404)|-0.214<sup>_∗∗∗_</sup><br>(0.0779)|-0.002<br>(0.0914)|-0.338<sup>_∗∗_</sup><br>(0.1172)|
|Trust_×_FOFM|||-0.506<sup>_∗∗_</sup><br>(0.2212)|-1.074<sup>_∗∗∗_</sup><br>(0.2741)|0.183<br>(0.4220)|
|Complex_×_FOFM||||-0.114<sup>_∗∗_</sup><br>(0.0573)||
|Complex_×_Trust||||-0.210<sup>_∗∗_</sup><br>(0.0947)||
|HighRol_×_FOFM|||||-1.522<sup>_∗∗∗_</sup><br>(0.2405)|
|HighRol_×_Trust|||||-0.341<br>(0.3605)|
|Trust_×_FOFM_×_Complex||||0.218<br>(0.1476)||
|Trust_×_FOFM_×_HighRol|||||2.178<sup>_∗∗∗_</sup><br>(0.5490)|
|Controls||X|X|X|X|
|Clustering|Region|Region|Region|Region|Region|
|No. Clusters|134|133|133|133|133|
|_R_<sup>2</sup>|0.28|0.33|0.33|0.38|0.33|
|_N_|119477|112954|112954|41778|112954|

Note: All specifications include 3 digit SIC and country fixed effects. Controls include log firm age, log region gdp per capita and the region’s college share of residents. Complex is complexity measure defined in text. HighRol is a dummy for high rule of law countries Germany, Netherlands, Finland and Switzerland.

30
That is, they result not only in stronger bilateral trade flows, but also an increased probability that countries will trade at all.

We extend this recent work in three ways. First, “unbundling” institutions and focusing on one important aspect—transparency in the trading environment—allows us to bring additional focus and clarity to what is potentially a very wide-ranging area. In this sense, our approach is complementary to the recent work on contract enforcement referred to above: it too concentrates on just one aspect of the broader links between institutions and trade.

Second, we develop a comprehensive set of indicators that measure the transparency of a country’s trading environment from a number of different perspectives. Using APEC member economies for our empirical work, we examine both perceptions-based and “objective” data taken from sources such as the _Global Competitiveness Report_ , the _Doing Business_ dataset, and a new _Logistics Perception Index_ developed by the World Bank. These data cover issues such as the prevalence of trade-related corruption, political favoritism, efficiency of customs and border agencies, the extent of hidden trade barriers, and uncertainty surrounding trade policy settings, logistics performance, and corruption. We then use factor analysis to combine these indicators into two composite measures of transparency: the Importer Transparency Index (ITI) and the Exporter Transparency Index (ETI).

Third, our empirical work using a gravity model of intra-APEC trade attempts to account for the possible endogeneity of transparency using an instrumental variables strategy. In our APEC sample, we find that British colonization prior to the 20<sup>th</sup> Century is closely associated with higher transparency today. We therefore exploit variation in the pattern of British colonization to identify exogenous changes in transparency. That our core result is robust to instrumental variables estimation is an important finding, since existing work on institutions and trade assumes—but does not test for—exogeneity.

The paper proceeds as follows. In the next section, we discuss in greater detail the links between transparency and trade. Our analysis takes a transaction cost perspective, in which predictability and simplification of the trading environment are associated with lower cost burdens facing exporters and importers. Section 3 introduces our dataset, and 4