rafmacalaba/datause-encoder-data huggingface dataset:
magnitude of 20-30 percent. Such differences are not merely of academic interest but can affect policy interventions guided by survey results.

The correction of sampling bias also affects the time series analysis of the CILSS data. Original results observed a gradually rising trend in poverty in C6te d'Ivoire between 1985-87. In fact, poverty did not change (or even fell marginally) between 1985 and 1986, followed by a rapid increase in 1987. This clearly affects the assessment of macro-economic policies conducted over the period, especially since in 1987 the Government of C6te d'Ivoire abandoned a previously sustained adjustment program. The overestimation of poverty in 1985 obviously also meant that the total increase in poverty between 1985 and 1988 was underestimated. Taking the head-count ratio as an example, poverty was recorded as increasing from 32.4 percent in 1985 to 45.9 percent in 1988 (an increase of 41.7%). However, the weighted head-count for 1985 is only 30.0 percent, so that poverty in fact increased by 53 percent over the period. The underestimation of the trend in poverty is even clearer for regional estimates. In West Forest, for example, the unweighted data record an increase of 164.6 percent, whilst the weighted data indicate that the increase was 210.7 percent. Without applying the H-S weights, the time series analyses of CILSS data are certain to be subject to errors of this order.

The CILSS data have been widely used to research welfare and pove.ty issues, especially using the 1035-86 data. To our knowledge, none of this work has applied any corrections for the sampling bias noted in this paper. This casts serious doubts on the robustness of many of the findings reported in the earlier work, particularly since the results in this paper have shown that the order of magnitude of the bias is not trivial and will affect welfare and poverty measures regardless of the poverty index and poverty line chosen. More importantly, the policy recommendations in the earlier work may need to be revised.

As a practical matter, what can users of household survey data do to detect possible sampling problems? Our experience suggests that the first line of defense is a thorough knowledge of the country being researched. This alerted us to the surprisingly large 35
<!-- Start of picture text --> East Asia & South Sub-Saharan Europe & Latin America Middle East &<br>Pacific Asia Africa Central Asia & the North Africa<br>Caribbean<br>Women’s and girls’ ioe<br>Internet access leve!<br>Men's and boys’<br>Mereraeesies | 37 | om | ome | am | aoe om<br>|Gendergap(weisnes)| 20% | 33% | ame |e | tom |<br>Globa! gender gap<br>(144 developing<br>countries)<br><!-- End of picture text -->
# **Table 3: Alternative identification strategies: disentangling the mechanisms**

||**Overcoming **<br>OLS|**MA barriers**<br>Heckman|**Overcoming **<br>Conservative<sup>_b_</sup>|**heterogeneity**<br>Liberal<sup>_b_</sup>|
|---|---|---|---|---|
|Log of Budget per capita|0.491<sup>_⋆⋆_</sup>|0.550<sup>_⋆⋆_</sup>|0.054<sup>_⋆⋆_</sup>|0.055<sup>_⋆⋆_</sup>|
|(ln _Bud/pop_)<sup>_a_</sup>|( 0.158 )|(0.133)|( 0.003 )|( 0.003 )|
|Log of GDP per capita<br>|0.764<sup>_⋆⋆_</sup><br>|0.917<sup>_⋆⋆_</sup><br>|0.775<sup>_⋆⋆_</sup><br>|0.775<sup>_⋆⋆_</sup><br>|
|(ln _GDP/pop_)|( 0.148 )|( 0.141 )|( 0.031 )|( 0.031 )|
|Log of Trade restrictiveness|-0.136|-0.234<sup>_⋆_</sup>|- 0.469<sup>_⋆⋆_</sup>|-0.469<sup>_⋆⋆_</sup>|
|(ln _T_)|( 0.119 )|( 0.111 )|( 0.037 )|( 0.037 )|
|Log of Trade restrictiveness in ROW|-1.180<sup>_⋆⋆_</sup>|-1.180<sup>_⋆⋆_</sup>|-2.600<sup>_⋆⋆_</sup>|-2.599<sup>_⋆⋆_</sup>|
|(ln _MA_)|( 0.319 )|( 0.270 )|( 0.068 )|( 0.068 )|
|Log of Forex volatility|-0.192|-0.188<sup>_⋆_</sup>|0.090<sup>_⋆⋆_</sup>|0.090<sup>_⋆⋆_</sup>|
|(ln _V ol_)|( 0.123 )|( 0.109 )|( 0.008 )|( 0.008 )|
|Days to comply with export|-0.008|-0.003|-0.008<sup>_⋆⋆_</sup>|-0.008<sup>_⋆⋆_</sup>|
|regulation (ln _Reg_)|( 0.008 )|( 0.007 )|( 0.002 )|( 0.002 )|
|Log of geo-trade/GDP ratio|0.524<sup>_⋆⋆_</sup>|0.420<sup>_⋆⋆_</sup>|0.407<sup>_⋆⋆_</sup>|0.407<sup>_⋆⋆_</sup>|
|(ln _F_&_R_)|( 0.093 )|( 0.082 )|( 0.022 )|( 0.022 )|
|Interaction Budget and|0.164<sup>_⋆⋆_</sup>|0.188<sup>_⋆⋆_</sup>|||
|Trade Restrictiveness in ROW|( 0.069 )|( 0.060 )|||
|Interaction Budget and<br>|||-0.015<sup>_⋆⋆_</sup>|-0.016<sup>_⋆⋆_</sup>|
|Homogeneous good dummy<sup>_b_</sup>|||( 0.004 )|( 0.004 )|
|Constant|-3.996<sup>_⋆⋆_</sup>|-5.476<sup>_⋆⋆_</sup>|-24.68<sup>_⋆⋆_</sup>|-24.68<sup>_⋆⋆_</sup>|
||( 1.213 )|( 1.109 )|( 0.327 )|( 0.327 )|
|Regional dummies<sup>_c_</sup>|Yes|Yes|Yes|Yes|
|_P −value_ of F or Chi-squared Wald-test.<sup>_d_</sup>|0.000|0.000|0.000|0.000|
|Number of observations|78|103|58540|58540|
|Number of uncensored|78|77|58540|58540|
|R-squared|0.957|0.965|0.373|0.374|
|_λ_<sup>_e_</sup>|NA|0.832<sup>_⋆⋆_</sup>|NA|NA|
|||( 0.230 )|||

> _a_ White robust standard errors are in parenthesis; _⋆⋆_ stands for significance at the 5 percent level; and _⋆_ stands for significance at the 10 percent level.

> _b_ This is Rauch dummy for homogeneous goods (see Rauch, 1999). The third column Rauch conservative definition of homogeneous goods and the fourth column the more liberal definition.

> _c_ The regional dummies are LAC, OECD, EEA, MENA, and SSA.

> _d_ For OLS estimates we report the F-test and for Heckman estimates we report the Wald test on the joint significance of all coefficients.

> _e_ The selection parameter _λ_ (Mills ratio) captures the extent to which selection is a problem in the sample. When statistically different from zero, this suggest that there is a sample bias that needed to be corrected.
PROVISION OF WATER TO THE POOR IN AFRICA

# **Figure 3.5   Price by water service provider**

<!-- Start of picture text --> 14,00<br>12,00 Cape Verde<br>10,00<br>8,00<br>6,00<br>4,67<br>4,00 4,00<br>2,00 1,93<br>1,63<br>1,04<br>0,49<br>0,00<br>HH connect. Small Piped HH reseller Stdp-kiosk Water vendor Water tanker<br>Network<br>US$/m3<br><!-- End of picture text --> Source: AICD WSS database 2007; other.

The growing importance of the informal water market is forcing policymakers to assess the impact of their policies on this market segment both in terms of its coverage, but also in terms of how higher prices influence informal market prices and access to improved water. In countries where the formal sector water market is unable to keep pace with urban growth yet has low tariffs, and the informal market thrives, one might find a large distortion between the formal and the informal water price. At the same time, if a utility is not recovering costs because its formal tariffs are too low, it may be less able to keep pace with service expansion and one would expect the distortion to grow. In order to measure both the weight of the informal sector, and the degree to which its prices exceed those in the formal sector, one can use the weighted average informal overprice (WAIO), calculated as follows: Where: Pi: End-user price of each water service provider (US$/m<sup>3</sup> ) Ci: coverage of each water service provider (percent) Pc: end-user price of residential customer 10m<sup>3</sup> (US$/m<sup>3</sup> ) i: type of water service provider Since the overall WAIO applies to all water service providers, including the household connections provided by the utility, the calculation indicates the total disruption at the city or urban level. It can be 33
are now turning to deliberation in more challenging contexts. Trough detailed qualitative and ethnographic work, scholars have shown how deliberative forums can be used as a space to make dignity claims for underprivileged groups (Rao and Sanyal, 2010), and even as a tool to solve social problems such as female genital mutilation (Mackie, 2015). Other work has expanded the scope of deliberation to include everyday communication outside the context of formal forums (Swidler and Watkins, 2015). Our work builds upon this scholarship by examining deliberative outcomes in a challenging rural context, but departs methodologically from this earlier literature by _quantifying_ inequalities in participation.

Beyond these observational studies, there is a growing literature that tests hypotheses derived from deliberative theory in the context of lab experiments (Fishkin and Luskin, 2005; List et al., 2013; Goeree and Yariv, 2011; Karpowitz et al., 2012; Karpowitz and Mendelberg, 2014). While such work has helped inform our understanding of how institutional contexts (e.g. decision rules, moderators, etc.) affect deliberative quality, studying systematic variation in the real world has been much more challenging. A notable exception has been the study of parliamentary debates, where the availability of data has enabled scholars to leverage temporal variation to study paterns in deliberative quality (Clayton et al., 2016); unfortunately, this strand of work has ofen been limited in its ability to test the effects of _institutional_ variation due to the focus on any single deliberative body. By contrast, our study provides a new source of transcript data from numerous local deliberative bodies — enabling us to correlate deliberative outcomes with local institutional variation, including the gender of the local politicians moderating discussion. Moreover, the focus on _local_ , rather than national-level institutions, allows us to examine citizen voice rather than official debate; in doing so, we are able to address normative questions about whether and how citizens are able to participate in their own governance.

Tis paper also provides an important bridge between empirical work and normative theories of deliberation by generating a clear set of metrics that can be coded using automated methods. Given the considerable debate within the normative literature both about deliberative standards as well as the more basic question of what constitutes deliberation, there has been meaningful 5
Wahba, Jacqueline. 2006. “International Migration, Education and Market Failure in MENA”, paper prepared for a World Bank Regional report on Education, mimeo.

Walmsley, Terrie and L. Alan Winters. 2005. “Relaxing the Restrictions on the Temporary Movement of Natural Persons: A simulation Analysis,” _Journal of Economic Integration_ , 20(4): 688-726.

Winters, L. Alan. 2005. “Developing Country Proposals for the Liberalization of Movements of Natural Service Suppliers,” Working Paper T8, Sussex Centre for Migration Research. Winters, L. Alan, Terrie Walmsley, Zhen Kun Wang and Roman Grynberg. 2003. “Liberalising Temporary Movement of Natural Persons: An Agenda for the Development Round,” _World Economy_ 26(8): 1137-61.

24
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report attending life skills training at least once a week and the majority can recall at least four of the topics covered in these courses); (ii) 25% have received financial literacy training; (iii) 34% have participated in vocational skills training (in T2/T3 treatment arms where such training was offered); (iv) only 13% report having received a microfinance loan from BRAC. The overall pattern of participation in activities is thus very much in line with what was reported earlier in Figure 3 in terms of club offerings.

As the remaining Columns show, these patterns of awareness and participation are very similar for those aged 12-17 at baseline, and those aged 18-25 at baseline (although the older cohort is more likely to have received microfinance). Hence the demand for ELA clubs comes both from girls enrolled full time in education pre-crisis, as well as older girls who were predominantly engaged in income generation pre-crisis.

# **4.5 Research Design**

We use a 2 _×_ 2 factorial research design, where one dimension is the quasi-random assignment of villages to high and low Ebola-related disruption, and the other dimension is the random assignment of villages to ELA. We study how outcomes are impacted by the intensity of the crisis, and how the ELA intervention mitigates these impacts in low and high disruption intensity villages. For outcome _y_ for individual _i_ in village _v_ in district _d_ we estimate the following ANCOVA specification: where _yi_ 0 is the outcome at baseline (if available), _Tvd_ is a dummy equal to one if village _v_ is assigned to treatment, _Evd_ is a dummy equal to one if village _v_ is exposed to high disruption, _Xivd_ and _Xvd_ are characteristics of _i_ and her village _v_ , _λd_ are district fixed effects (the randomization strata), and _uivd_ is an error term.<sup>29</sup> In the Appendix we examine the robustness of our core findings to: (i) using randomization inference to test the null of no treatment effects; (ii) adjusting for multiple hypothesis testing; (iii) only controlling for district fixed effects.

## **4.5.1 Localized Intensity of Ebola-Related Disruption**

To operationalize this design we need to construct a localized measure of village level Ebola-related disruption, _Evd_ . As shown formally below, to establish causal impacts of the severity of disruption,

> 29The village controls include those shown in Panel A of Table 1, on baseline balance, and the individual controls (measured at baseline) are age, the household poverty score, household size and whether the individual is illiterate (where this last control is dropped for some specifications related to skills).

18
with the bulk of the literature, our dependent variable is log total consumption per capita.<sup>39</sup> Consumption is measured in local currency units. Spatially deflated consumption values are available for all countries except Burkina Faso.<sup>40</sup> We use two poverty lines, corresponding to _H jt_  2.0 and 0.4 for all ( _j_ , _t_ ). The 40% figure coincides fairly closely with the overall poverty rate found for the Africa region using the World Bank’s international line.<sup>41</sup> The 20% rate allows us to focus on how well the method does at identifying those who can be considered extremely poor. When comparing the actual values and the PMT scores one can chose to either fix the poverty rate (at 0.2 or 0.4) or fix the poverty line in the  1  1 consumption space (i.e., fixing _z jt_  _F jt_ (02.) or _F jt_ (4.0) ). This choice makes a difference and practice varies so we present results for both options.

# **4. Results for Basic PMT**

The “Basic PMT” follows our reading of prevailing practice. Variables used include the type of toilet a household has; floor, wall and roofing material; type of fuel used for cooking; certain characteristics of the head, including gender, education and occupation; the household’s religion and demographic size and composition. All regressions have dummy variables for categories of household size, age of head, month of survey and region of residence; the latter is measured at an aggregate level (typically a state or province) for which the surveys can be considered representative. The <u>Statistical Addendum</u> gives the OLS regression results for the Basic PMT. The simple average R<sup>2</sup> is 0.53, with a range from 0.32 (for Ethiopia) to 0.64 (Burkina Faso); Table 3 provides summary statistics for the Basic PMT (as well as for the extended version discussed below). This explanatory power is typical of past studies.<sup>42</sup> We did not try to prune this model, by either _ad hoc_ or more systematic methods (such as stepwise regression). This would reduce the number of predictors but (of course) also reduce R<sup>2</sup> and probably increase targeting errors. However, we do consider stepwise regression as an option in Section 5 when using a much larger set of explanatory variables.

> 39 We also considered the option of using log consumption per equivalent single adult using the scales provided by the LSMS. We focus on the “per capita” case in this paper although the Addendum also gives regressions and key results using scales.

> 40 We use nominal consumption for Burkina Faso.

> 41 Using the World Bank’s international line of $1.90 a day at 2011 purchasing power parity, 43% of the population of SubSaharan Africa are found to be poor in 2013 (based on <u>PovcalNet).</u>

> 42 A seemingly representative set of studies is Grosh and Baker (1995) (R2 from 0.3 to 0.4), Ahmed and Bouis (2002) (R<sup>2</sup> =0.43), Narayan and Yoshida (2005) (R<sup>2</sup> =0.59), Sharif (2009) (R<sup>2</sup> =0.57), Stoeffler et al. (2015) (R<sup>2</sup> =0.62), Pop (2015) (R<sup>2</sup> =0.54) and Cnobloch et al. (2015) (R<sup>2</sup> =0.5 to 0.7). The simple average is 0.52.

16
GDPSUM and the square of GDP differences would change to account for ASEAN GDP. For skill differences interacted with GDP differences, we would use ASEAN GDP interacted with skill differences between Singapore and its investment partners. Meanwhile all other variables remain the same as the basic specification. Here, we focus only on inbound investment from industrialized countries.

Annual real GDP figures for ASEAN<sup>9</sup> in local currencies were converted into dollars using the market exchange rate. Both ASEAN GDP and exchange rates are from the _International Financial Statistics_ of the IMF.

# **4. Econometric Results**

We first apply the basic framework to the inbound and outbound FEI data for all sectors (manufacturing plus services) 10 and countries. We then repeat the analysis for manufacturing separately. The available data series on FEI stocks in manufacturing only begins in 1994, and hence those regressions cover just the latter half of the period.

Our instrumental-variables procedure is to run the first-stage regressions of relative skill differences on the instrumental variables discussed above for each sample and use the predicted values to estimate second-stage tobit regressions. With few exceptions, the Sargan test11 indicated that the variables are uncorrelated with the residuals and serve as appropriate instruments. The first-stage equations are available upon request.

> 9 Sum of the GDPs  of Singapore, Malaysia, Indonesia, Thailand, the Philippines, Vietnam, Cambodia and Laos. Time series data for Myanmar and Brunei Darrusalam were not available for all years and hence were excluded fromt the ASEAN GDP.

> 10 Mining and agriculture are extremely small sectors in Singapore and no FEI data are reported for them.

> 11 A Sargan test for over-identifying restrictions is used to test for the validity of instruments. The instruments adopted are valid if the p value is greater than 0.1.

20
Policy ReseaRch WoRking PaPeR 4857

# **Abstract**

Formal and informal commercial sex work is a way of life for many poor women in developing countries. Though sex workers have long been identified as crucial in affecting the spread of HIV/AIDS, particularly in SubSaharan Africa, the nature of sex-for-money transactions remains poorly understood. Using a unique panel dataset constructed from 192 self-reported sex worker diaries which include detailed information on sexual behavior, labor supply, and health shocks, the authors find that sex workers adjust their supply of risky, better compensated sex to cope with unexpected health shocks, exposing themselves to increased risk of HIV infection.

In particular, women are 3.1 percent more likely to see a client, 21.2 percent more likely to have anal sex, and 19.1 percent more likely to have unprotected sex on days in which a household member falls ill. Women also increase their supply of risky sex on days after missing work due to symptoms from a sexually transmitted infection. Given that HIV prevalence has been estimated at 9.8 percent in this part of Kenya, these behavioral responses entail significant health risks for sex workers and their partners, and suggest that sex workers are unable to cope with risk through other formal or informal consumption smoothing mechanisms.

This paper—a product of the Knowledge Strategy Group—is part of a larger effort to understand the issues related to risk, behavior, and the prevention of HIV/AIDS. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at jmrtwo@ucsc.edu and eyeh@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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