rafmacalaba/datause-encoder-data huggingface dataset:
countries, although Colombia, Costa Rica, Uruguay, and Argentina appear to fare relatively better than Mexico and Chile.<sup>9</sup> This tale of several groups of countries also characterizes the regional sanitation panorama, although the composition of these groups is slightly different, with Costa Rica, the Dominican Republic and Paraguay now the strong underperformers, Brazil, Mexico, and Uruguay in an intermediate and rather weak position, and Bolivia, Ecuador, Peru and Colombia performing well.

Finally, for electricity, the region displays overall a much smoother concave picture, with the notable exception of Peru, whose poor performance makes it an outlier.

In addition, the use of household survey micro data allows for an unprecedented window into the relative treatment of households at different levels of the income distribution within countries. The comparison of within-country premia for each decile tells us whether the current access situation is progressive, meaning that a given country does relatively better delivering infrastructure to the lower rather than the upper part of the distribution, or regressive (Figure 3).

For water, there is again a clear dichotomy, between countries that display a rather regressive pattern (El Salvador, the Dominican Republic, Peru, Bolivia, and Paraguay) and those that display the opposite pattern, especially Colombia, Costa Rica, Chile, and Argentina. Note that to a large extent, these two groups overlap with the ones that emerged from the comparison of per capita GDP and average premium in the preceding figure. What this means is that for water, given the convergence to high access levels at the top of the income distribution, overall underperformance mostly stems from a failure to address service shortfalls for the lowest income quantiles.

This situation contrasts with that of sewerage. Indeed, in this case, a few countries fare badly overall, as witnessed by their negative premium in figure 2 above and the fact that the entire set of decile-level premiums are negative in Figure 3, despite boasting a rather progressive pattern. This is the case of Paraguay, Costa Rica, and the Dominican Republic. The only country with a clearly regressive pattern is Peru, whose premium goes from -1% at the first decile, to +27% at the tenth decile.

> 9 No official figure is available for Argentina’s per capita GDP around 2012. We set it at 20,000$.

10
<!-- Start of picture text --> Chart 1 World Real Interest Rate<br>and Growth in the HICs<br>15<br>10-<br>- .1973 1975 1977 1979 1981 1983 1985 1987<br>1974 1976 1978 1980 1982 1984 1986<br>Years<br>-- w Growth Rate ofHICs - Real InterestRate<br><!-- End of picture text -->
urgent need for active promotion of suitable averting behavior and hygienic practices among pesticide traders in Bangladesh. As one in five pesticide shipments arrive at the shop damaged, unsealed or broken, the packaging of pesticides should also be carefully reexamined, along with the adoption of improved international standards such as airtightness. In addition, since many of the serious pesticide-related health effects can be avoided if exposure is minimized, it is important to monitor traders with regular checkups and blood tests before the level of contamination leads to any serious illness (Extension Toxicology Network, 2004).

Our analysis indicates that the formal training and information on use and safe handling received when combined with pesticide toxicity (an interaction effect) has a positive impact on health impairment. This may provide evidence as to the effectiveness of the training received among the sample of traders surveyed. As 90% of the traders indicated the need for further instructions on pesticide use and handling, this result is not surprising. The information content of current training programs should be critically reviewed and/or revised to reflect any current informational gaps. Potential areas of improvement include more specific information on the health hazards of pesticides and averting behavior. In the design of training, it would also be advisable to include both farmers and traders to better reflect the needs of the final users. In light of limited resources available for such efforts, targeting such information programs at the traderlevel would perhaps be advantageous for two reasons. First, retailers would benefit from this information and take appropriate precautions to reduce their own health risk. Secondly, they may pass this information along to the farmer, and thus improve the farmers’ understanding of pesticide handling and precautions.

# **References**

Ai, C. and E. C. Norton (2003), ‘Interaction Terms in Logit and Probit Models’, Economics Letters 80: 123-129.

18
state capacity to deliver goods and services should also be measured based on the ability to provide equal access to all citizens.

# **Figure 1: Gaps in Outcomes**

# **_1A: Gap Between Spending and Outcome          1B: Gap Between Outcomes by Gender_**

<!-- Start of picture text --> 5000 100 100<br>90 90<br>4000<br>80 80<br>3000<br>70 70<br>2000<br>60 60<br>1000<br>50 50<br>0 40 40<br>Per Capita Expenditure on Education, 2015<br>Literacy Rate, Total Male Literacy Rate Female Literacy Rate<br>Per-Capita Expenditure (in INR) Literacy Rate in Percentage<br><!-- End of picture text --> _Note_ : Literacy data is from the Planning Commission (India); Expenditure Data is from Reserve Bank of India.

This paper seeks to improve the measurement of a state’s capacity to deliver goods and services by broadening the definition from the ‘ability to deliver goods and services’ to the ‘ability to deliver quality goods and services while ensuring equal access to all citizens — men/women, rich/poor. By doing so, the measure will not only account for state-by-state variations but also differences in outcomes within a state.

6
|**Top**<br>**manager's**<br>**experience**|0.0033|0.0006|0.0094|0.0014|‐0.0023|0.0118|
|---|---|---|---|---|---|---|
||(0.0029)|(0.0045)|(0.0120)|(0.0030)|(0.0050)|(0.0180)|
|**Female managed**|0.0002|‐0.0028**|‐0.0015|0.0009|0.0013|‐0.0070**|
||(0.0011)|(0.0012)|(0.0077)|(0.0013)|(0.0013)|(0.0031)|
|**Legal status(ref cat = s**<br> <br>|**oleproprietor**<br>|**ship): **<br>|||||
|**Publicly**<br>**listed**<br>**company**|0.0051**|0.0041|0.0387***|0.0011|0.0030||
||(0.0024)|(0.0037)|(0.0039)|(0.0020)|(0.0034)||
|**Limited**<br>**Liability**<br>**Company**|0.0074***|0.0027|0.0055|0.0005|0.0019|0.0006|
||(0.0015)|(0.0019)|(0.0055)|(0.0014)|(0.0023)|(0.0025)|
|**Partnership**|0.0111***|0.0064|0.0028|‐0.0075**|‐0.0001|0.0073|
||(0.0031)|(0.0080)|(0.0164)|(0.0038)|(0.0059)|(0.0044)|
|**Limited Partnership**|0.0076***|0.0045***|0.0035|‐0.0013|0.0003||
||(0.0017)|(0.0017)|(0.0058)|(0.0013)|(0.0024)||
|**Other**|0.0053|0.0023||0.0061***|‐0.0046||
||(0.0049)|(0.0027)||(0.0020)|(0.0032)||
|**Constant**|9.0352***|11.7948***|8.2938***|1.5757***|3.1828***|1.4354|
||(0.2662)|(0.3490)|(0.8951)|(0.1908)|(0.4745)|(1.0406)|
|**Observations**|2,821|633|165|702|348|39|
|**R‐squared**|0.3904|0.4198|0.6559|0.8672|0.7710|0.7186|
|**Standard errors inpare**|**ntheses ***p**|**<0.01, **p<0.05**|**, *p<0.1**||||

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions.

Another important driver of productivity is innovation, which may be related to management. Indeed, it has been shown by Sadun et al. (2017) that well‐managed firms invest more in R&D and hold more patents. In terms of whether management practices may be more or less effective than technological innovations, Giorcelli (forthcoming) shows that in a sample of Italian manufacturing firms, the positive impact of improved management practices was longer‐lasting than that of more technologically‐advanced equipment, and that improved management and better equipment were in fact complementary. We can compare the relative impacts of improved management practices and innovation by examining both variables in a single model. The results displayed in panel A of table 13 below show that, on average across all firms, both have a positive and significant impact on labor productivity. However, for larger enterprises (those employing 50 or more workers), when we control for management practices and innovation together, only management is significantly linked to higher output per worker. Looking at the impact of both management practices and innovation on total factor productivity, panel B of table 13 shows that, on average across all firms, more innovation is related to higher total factor productivity, but better management is not. However, for larger firms the pattern flips, and it is better management, and not innovation, that is associated with higher total factor productivity.

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**_Figure 1: Discriminatory behavior by firms in the DRC inside and outside the capital city_**

<!-- Start of picture text --> 79%<br>45% 45% 43%<br>35%<br>31%<br>25% 25% 26%<br>19% 17%<br>13%<br>10% 8%8%<br>0% 3% 5% 5% 2% 3%<br><!-- End of picture text --> While it is difficult to ascertain the relative roles played by discriminatory laws versus social norms in holding women back in the DRC from reaching their full economic potential, the data summarized in Figure 1 do provide suggestive evidence that social norms may matter. The legislation set out in the labor and family code refers to the entire economy, and not just to Kinshasa – this suggests that something other than laws is driving the differences we see in discriminatory behavior. An alternative explanation may be that there is a lack of enforcement in laws in Kinshasa, but it is quite likely that the level of enforcement is endogenous to prevailing social norms. We posit that social norms are a likely driver, in line with a wellestablished literature that shows that women with more traditional gender roles or living in a more traditional context are less likely to be employed than women with less traditional gender roles or living in less traditional contexts (Fernández, 2007; Alesina et al., 2013). These findings are also in line with interesting insights about the role of culture or social norms in the DRC provided by the Demographic and Health Survey (DHS) data. During the 2013-2014 DHS, questions concerning the decisions about the use of the salary were asked to married women who had worked during the previous 12 months. In Kinshasa, 63 percent of married women replied that they are the main person who takes decisions about the use of their salary versus an average of 26 percent outside Kinshasa. The same pattern is observed when questions about women’s autonomy in making decisions on other aspects of life are asked, such as decisions on health care for women, purchases for the household, and visits to women’s family. A higher percentage of women 13
# **Table 2: Revenue Implications of a COMESA Common External Tariff**

in millions of USD

|**Country**|**Mala**|**wi**|**Zamb**|**ia**|**Keny**|**a**|
|---|---|---|---|---|---|---|
|Elasticities|default|high|default|high|default|high|
|Old imports|1416.0|1416.0|3930.9|3930.9|9909.1|9909.1|
|New Import|1416.6|1419.0|3949.5|3972.5|9947.9|9965.6|
|Change|0.6|2.9|18.6|41.7|38.9|56.5|
|% Change|0.0%|0.2%|0.5%|1.1%|0.4%|0.6%|
|Old tariff revenue|75.9|75.9|245.8|245.8|436.4|436.4|
|New tariff revenue|72.2|69.3|203.0|196.0|330.3|319.1|
|Change|‐3.6|‐6.6|‐42.8|‐49.8|‐106.2|‐117.3|
|% Change|‐4.8%|‐8.7%|‐17.4%|‐20.3%|‐24.3%|‐26.9%|
|Old excise revenue|78.8|78.8|98.5|98.5|517.6|517.6|
|New excise revenue|78.4|79.0|98.0|98.5|517.6|517.6|
|Change|‐0.4|0.1|‐0.5|‐0.1|0.0|0.0|
|% Change|‐0.6%|0.2%|‐0.5%|‐0.1%|0.0%|0.0%|
|Old VAT revenue|117.0|117.0|684.7|684.7|798.4|798.4|
|New VAT revenue|115.9|116.9|680.1|683.5|794.2|796.3|
|Change|‐1.2|‐0.1|‐4.5|‐1.1|‐4.1|‐2.0|
|% Change|‐1.0%|‐0.1%|‐0.7%|‐0.2%|‐0.5%|‐0.3%|
|Old total revenue|271.8|271.8|1029.0|1029.0|1752.4|1752.4|
|New total revenue|266.5|265.2|981.2|977.9|1642.1|1633.0|
|Change|‐5.2|‐6.6|‐47.8|‐51.0|‐110.3|‐119.3|
|% Change|‐1.9%|‐2.4%|‐4.6%|‐5.0%|‐6.3%|‐6.8%|
|Old collected tariff rate|5.4%|5.4%|6.3%|6.3%|4.4%|4.4%|
|New collected tariff rate|5.1%|4.9%|5.1%|4.9%|3.3%|3.2%|

1) The COMESA CET scenario assumes zero tariffs among COMESA partners, unchanged tariffs for SADC partners. For all other trading partners, the new CET rate is applied but deflated by the current ratio of collection efficiency (collected tariff rate / statutory tariff rate) to account for tariff exemptions. In the few cases of tariff lines for which no final agreement has been reached yet the lowest tariff rate under consideration is used. 2) The default scenario assumes 0.5 for import demand elasticity, 1.5 for exporter substitution elasticity. High scenario assumes 1 for import demand elasiticity, 5 for exporter subsitution elasticity.

Table 2 presents results of revenue losses from the above defined COMESA CET scenario. We choose two sets of elasticity values: the standard default values and higher values to assess sensitivity.<sup>19</sup> In Malawi, the implementation of the CET is projected to 19 Default (high) choices for elasticities: Exporter Substitution Elasticity: 1.5 (5), Demand Elasticity: 0.5 (1) 13
The identification of the PAF impact is based on a design that involves a randomized phasein approach, in which certain localities were randomly assigned for earlier intervention than others. Not surprisingly, the actual PAF intervention does not perfectly comply with the random assignment. However, there is a strong correlation of planned and actual intervention status. Using two rounds of survey data (pre and post-intervention) and difference-in-difference combined with instrumental variable estimation method, we find significant PAF effect on key welfare indicators. The treatment-on-the-treated impact is 19 percent growth on real per capita consumption is, 19 percentage points decline on incidence of food insecurity (defined as food sufficiency for six months or less) and 15 percentage points increase in school enrollment rate among 6-15 year olds. Impacts (positive or negative) are yet to be detected on indicators associated with child malnutrition, social capital and female empowerment. The policy implications of these results should be of interest to the government and to development partners in determining what may be effective instruments to deliver services to marginalized communities in what remains a fragile and difficult political environment.

The study contributes to evidence on social funds evaluation in three ways. We use an experimental design to evaluate the impact of a large-scale intervention. Moreover, we estimate the causal impact even when there is no perfect compliance between the actual intervention and the randomized assignment. Imperfect compliance is a reasonable real world setting and allowance for such can be deemed to maintain external validity of the impact estimates. Lastly, in addition to using per capita household consumption, we also report impact on food security, an important indicator of vulnerability in conflict settings.

Like most evaluations, our study has its limitations. First, the treatment-on-the-treated instrumental variable estimate does not necessarily represent the average treatment effect for the entire population, but only of the sub-population that was actually treated because of random assignment. This is a well-documented qualification of the IV method. Second, our analysis has focused on the outcome indicators only but there are important process related questions that this impact evaluation does not yet answer. For example, while we do not find any statistically significant impact on social capital and female empowerment, project documents including socioeconomic surveys of assisted households at baseline and currently suggest that women have greater control over their earnings and make more diversified investments than men. Seventyfour percent of all PAF members are women. Future evaluation should carefully explore these relationships.

12
households with per capita consumption or income below the international poverty line. The primary data come in various forms, ranging from micro data (the most common) to specially designed grouped tabulations from the raw data, constructed following our guidelines.

We draw on 850 surveys for 125 countries. Taking the most recent survey for each country, 2.1 million households were interviewed in the surveys used for 2008. The surveys were mostly done by governmental statistics offices as part of their routine operations. Not all available surveys were included. A survey was dropped if there were known to be serious comparability problems with the rest of the data set. As in past work, we have tried to eliminate obvious comparability problems, either by re-estimating the consumption/income aggregates or the more radical step of dropping a survey. However, there are problems that we cannot deal with. For example, it is known that differences in survey methods (such as questionnaire design) can create non-negligible differences in the estimates obtained for consumption or income.

Following past practice, poverty is assessed using household per capita expenditure on consumption or household income per capita as measured from the national sample surveys. When there is a choice, we use consumption in preference to income, on the grounds that consumption is likely to be the better measure of current welfare on both theoretical and practical grounds.<sup>19</sup> Of the 850 surveys, 521 allow us to estimate the distribution of consumption expenditures; this is true of all the surveys used in the Middle East and North Africa, South Asia and Sub-Saharan Africa, though income surveys are more common in Latin America.<sup>20</sup> Our data are national for almost all countries. The exceptions are China, India and Indonesia, for which we do an urban-rural split. (Given that Method 2 allows different lines for urban and rural areas we plan to do urban-rural splits for more countries in future applications.) The measures of consumption (or income, when consumption is unavailable) in our survey data set are reasonably comprehensive, including both cash spending and imputed values for consumption from own production. But we acknowledge that even the best consumption data need not adequately reflect certain ―non-market‖ dimensions of welfare, such as access to certain public services, or intra-household inequalities. For these reasons, our poverty measures need to

> 19 For further discussion see Ravallion (1994) and Deaton and Zaidi (2002).

> 20 For a few cases we do not have consumption distributions but we still have survey-based estimates of mean consumption. Then we replace the income mean by the consumption mean leaving the Lorenz curve the same (i.e., all incomes are scaled up by the ratio of the consumption mean to the income mean).  There is, however, no obvious basis for adjusting the Lorenz curve.

11
report an increase.<sup>25</sup> The average change is a 9.2 percent increase in late or nonpaying customers.

The economy in Syria during the crisis is heavily cash based. Most firms, 72 percent of them, pay their suppliers in cash. This is particularly common in Aleppo where all firms reported that they paid in cash. It is also more prevalent among small and medium enterprises than among large enterprises where only 50 percent reported doing so.<sup>26</sup> Similarly, 91 percent of firms reported paying their employees in cash. Nevertheless, the vast majority of firms report having access to a bank branch although the response rate was very low on this question (for reasons we cannot explain).

The impact on employment The crisis has also resulted in reduced employment levels in firms and difficulty for employees in reaching work. Three‐quarters of respondents reported that the size of their workforce has decreased since the start of the crisis while 12 percent said that it had not changed and 12 percent reported an increase. Temporary workers do not appear to have filled the gap with only 10 percent of firms reporting they had increased their use of temporary workers. About 22 percent of firms report experiencing work stoppages in the last six months due to employees being unable to reach their place of work. Given the high number of firms reporting loss of workers as a major or severe obstacle, we may conclude that the reduction in employment in firms is to at least some degree, and possibly to a large degree, due to the human impact of the conflict, with loss of life and displacement severely affecting the supply of labor.

The number of workers in firms can be compared for each firm between 2017 and 2009 (as this same question was asked in both surveys). For the 160 firms that were surveyed in 2017, the average number of permanent full‐time employees was 50.2 workers _in 2009_ . By 2017 this had dropped to 23.2, a reduction of 54 percent. The reduction was more severe in Damascus with a 62 percent reduction and less severe in Latakia with a reduction of 44 percent.<sup>27</sup> Manufacturing firms saw a reduction of 54 percent while services firms were slightly lower at 51 percent.<sup>28</sup> Medium sized firms had the smallest reductions at 41 percent while small firms reduced by 61 percent and large firms had the largest reduction at 64 percent.<sup>29</sup>

> 25 Statistically significant at the 5% and 10% level, respectively.

> 26 Significant at the 5% level.

> 27 Both significant at the 1% level.

> 28 Statistically significant at the 5% level.

> 29 Statistically significant at the 1% level (small and medium firms) and 5% level (large firms).

15