rafmacalaba/datause-encoder-data huggingface dataset:
Hejoj, I. (2007). ‘A Profile of Poverty for Palestinian Refugees in Jordan: the Case of Zarqa and Suhkneh Camps’, _Journal of Refugee Studies_ , 20(1): 120-145.

<mark>Horrell, S., & Krishnan, P. (2007). Poverty and productivity in female-headed households in Zimbabwe.</mark> _<mark>Journal of Development Studies</mark>_ <mark>,</mark> _<mark>43</mark>_ <mark>(8), 1351-1380.</mark> Human Rights Watch. (2014). “Those Terrible Weeks in their Camp”: Boko Haram Violence against Women and Girls in Northeast Nigeria. Retrieved from: https://www.hrw.org/sites/default/files/reports/nigeria1014web.pdf.

Internal Displacement Monitoring Centre. (2020). Global Report on Internal Displacement - - (2020). Retrieved from https://www.internal <u>displacement.org/global report/grid2020/.</u> International Labour Organization. (2009). Report VI: Gender equality at the heart of decent work. Geneva: International Labour Office. Retrieved from https://www.ohchr.org/Documents/Issues/Racism/IWG/Session7/GenderEquality.pdf.

International Organization for Migration. (2020). World Migration Report 2020. Geneva: International Organization for Migration. Retrieved from https://publications.iom.int/system/files/pdf/wmr_2020.pdf.

International Organization for Migration. (2013). Internal Displacement in Iraq: Barriers to Integration. Baghdad: International Organization for Migration.

Kelly, J. (2021). The Risk that Travels with You: The Links between Forced Displacement, Conflict and Intimate Partner Violence in Colombia and Liberia. World Bank and Policy Research Working Paper, forthcoming. World Bank Group.

Kelly, J, Holmes, M., & Voors, M. (2021). Gender-based Violence among Displaced and Host Populations in Eastern Democratic Republic of Congo. World Bank and Policy Research Working Paper, forthcoming. World Bank Group.

<mark>King, E. M., Klasen, S., & Porter, M. (2007).</mark> _<mark>Women and development</mark>_ <mark>. Copenhagen Consensus Center.</mark> <mark>King, E., & Mason, A. (2001). Engendering development: Through gender equality in rights, resources, and voice.</mark> <mark>Klasen, S., & Lahoti, R. (2020). ‘How Serious Is the Neglect of Intra-Household Inequality in Multi-dimensional Poverty and Inequality Analysis?’,</mark> _<mark>The Review of Income and Wealth</mark>_ <mark>, 67(3), 705-731.</mark> <mark>Klasen, S., Lechtenfeld, T., & Povel, F. (2015). A feminization of vulnerability? Female headship, poverty, and vulnerability in Thailand and Vietnam.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>71</mark>_ <mark>, 3653.</mark> <mark>Klasen, S., & Povel, F. (2013). Defining and measuring vulnerability: State of the art and new proposals.</mark> _<mark>Vulnerability to poverty</mark>_ <mark>, 17-49.</mark> 31
Census and both reference populations (see subsection 5.1).<sup>25</sup> These measures incorporate households from all census villages that match to outlets on the head, tail, _and_ middle 40% of a given channel. In Table 5, we add these variables one-by-one to the specifications in column (2) of Table 4. In each case, the coefficient on _τ_<sup>1</sup> _×_ ( _L_<sup> ̄</sup> _H − L_<sup> ̄</sup> _T_ ) is virtually unaffected.

# **6 Conclusion**

How a shift in control from centralized bureaucracy to local government affects resource allocation has been empirical terra incognita up until now. It is worth reiterating why this is so: natural experiments in decentralization are extremely rare, rarer still in contexts where rent-seeking outcomes can be objectively measured. The devolution of irrigation management in Pakistan’s Indus Basin provides just such a felicitous combination.

We have compared changes in water discharge along channels whose management was taken over by locally elected farmer organizations (FOs) to changes that occurred in channels that remained centrally managed. Water theft increased by more in the former case, leading to a large redistribution of wealth. That decentralization also increased water theft by more along channels with a greater preponderance of large landowners at the head suggests that investment in de facto political power (borrowing the terminology of Acemoglu and Robinson 2008) can sometimes _more_ than offset changes in de jure political power brought about by institutional reform. Here, as our theoretical model indicates, decentralization shifts the lobbying arena from the upper-tier of the bureaucratic hierarchy to the communal governance structure, which leads to greater rent-seeking.

While our evidence is not favorable to the decentralization effort in the Indus Basin inasmuch as it did not deliver on its promise of a more equitable (and efficient) distribution of canal water, it would be premature to throw out the reform baby with the bathwater. 25For the calculation of the Gini coefficient, landholdings of landless households are set to 10 _−_ 8 acres.

35
# **IV. Firm-level Tests on Access to External Finance**

As mentioned earlier, while cross-country studies focusing on country-level variables are highly useful for summarizing patterns in the data and identifying relationships between variables, they are subject to methodological issues. For example, the observed results may be driven by measurement errors or unobservable factors. In an attempt to overcome these potential problems, we next turn to firm-level data drawn from the Investment Climate Surveys (ICS) conducted by the World Bank over the past decade.<sup>21</sup> Our specific reasons for augumenting the cross-country study with the firm-level analysis are as follows: First, while country level indicators of banking development might suffer from measurement errors, firm-level responses about their use of financial services should not. Second, our cross-country level regressions involve one observation per country and thus have fewer degrees of freedom than what are available in firm-level regressions. Third, the firm-level data sets allow us to control for a number of potentially relevant firm characteristics (e.g., size, sector) that could explain substantial variation in the demand for, and use of, financial services. Thus, overall the firm-level regressions can provide more precise estimates of the relationships between financial development and our explanatory variables than the country-level regressions. Furthermore, to the extent that we derive similar qualitative results for the firm- and country-level regression, it provides additional, and even stronger, support for the conclusions that we have drawn to this point based only on the country-level regressions.

Table 7 presents summary statistics on our firm-level sample. Firm characteristics (all dummy variables) include size (small, medium, and large based on the number of employees), industry (manufacturing, services, and others), exporter status and foreign ownership status. It appears that there are more small firms and non-exporting firms in the African (excluding South Africa) sample than in other developing countries, while the fraction of firms with foreign

> 21  These surveys have been used frequently in recent cross-country, firm-level studies on law, institutions, finance and growth (e.g., Beck, Demirgüç-Kunt, and Levine, 2005; Beck, Demirgüç-Kunt, and Maksimovic, 2005).

28
We contribute to the literature by being the first to examine the extent to which selective attention shapes the impact of public information campaigns and to compare the effects of addressing information access (La Ferrara, 2016) versus psychological distortions in information- gathering, attention, and processing (Handel and Schwartzstein, 2018). In doing so, we combine two strands of the literature. We find that the latter is crucial for the success of information campaigns. Our results are also of interest for a larger literature on the determinants of learning and information diffusion (e.g., Banerjee et al., forthcoming; Liverpool-Tasie et al., 2019; Paul and Dillon, 2019). They are of particular relevance for studies concerned with improving our understanding of the process of technology adoption and diffusion (e.g., Nelson and Phelps, 1966; Foster and Rosenzweig, 1995; Jovanovic and Nyarko 1996). This is an important area of research because heterogeneity in technology levels can explain significant differences in the performance between economies and among economic agents within the same country (e.g., Caselli and Coleman 2001, Syverson 2011).

In terms of methodological contribution, our study presents a low-cost experimental approach to estimate the effects of two components of a nationwide mass-media information campaign. We conducted a symmetric randomized encouragement design together with automated voice telephone surveys. This approach has two important features. First, it generates exogeneous variation in campaign exposure without randomizing the broadcasting itself (e.g., radio airwaves). This makes the approach relatively straightforward to reproduce. We arguably improve upon Berg and Zia (2011) by delinking monetary rewards from remembering of broadcasting content with the objective of mitigating the concern that people are artificially induced to pay more attention to the information than they would normally do. Second, inexpensive mobile phone surveys compensate for the fact that encouragement designs usually require a relatively large sample size which are difficult to achieve within a standard personal interviewing-based data collection budget. We hope that this low-cost evaluation design can serve as a blueprint for future research and encourages more studies of media broadcasts to reduce the dearth of knowledge in this area (La Ferrara, 2016).

We see an important avenue for future research in generally increasing the evidence base of nationwide mass media campaigns. Also, a more complete understanding is desirable as to what information content is important for campaigns to consider for greater and more predictable impacts on the adoption and diffusion of technologies. This may include generating more knowledge on the implications of selective inattention for individual decision-making and the design of public programs.

27
**Electricity.** The average tariff for electricity has risen by around 20% in real terms since the reform (Figure 4.1), continuing a trend of gradual price increase throughout the 1990s. Although Electropaz’s tariffs have risen more steeply during the course of the 1990s than those in Cochabamba and Santa Cruz, they are currently at very similar levels to those charged in Bolivia’s other two major cities (Barja and Urquiola, 2001).

While, the electricity tariff structure barely changed as a result of the reform (Figure 4.2). Residential customers continue to be classified into four groups according to the quality of service they receive, in particular the level of voltage and the type of transmission (monofasic or trifasic). Higher quality of service is reflected in a greater fixed charge ranging from $3-$10. However, all types of customers face similar unit charges for actual use of electricity, following a rising block schedule.

**Figure 4.2: Electricity tariff structure**

<!-- Start of picture text --> 90<br>80<br>70<br>60 B2<br>50 B3<br>40 B4<br>30 B5<br>20<br>10<br>0<br>0 50 140 300 500 1000<br>Monthly consumption (kWh)<br>Monthly bill (US$)<br><!-- End of picture text --> _Source:_ Electropaz, 2000

**Water and sewerage.** The average tariff for water and sewerage has risen by around 20% in real terms since the reform (Figure 4.1). This took the form of a step change brought in immediately prior to the reform in order to ensure the financial sustainability of the concession (Uzin, 1996). This is modest when compared to increases of 50% between 1994-97 in Cochabamba, and 75% between 1995-99 in Santa Cruz (Barja and Urquiola, 2001). Indeed, water tariffs in La Paz and El Alto are currently similar to those charged in Cochabamba and barely a third of those charged in Santa Cruz.

The principal reform of the tariff structure for water was a radical simplification from 150 different tariff structures (which differentiatedin a far from transparent mannerbetween customers according to geographic area, property value, diameter of connection and socioeconomic level) to a single tariff structure for domestic customers. Furthermore, the reform eliminated the fixed charge which corresponded to a minimum consumption block of 10 cubic meters per month. This policy change was based on the desire to eliminate an implicit cross-subsidy between large and small consumers. The reason is that average measured consumption for customers falling below this threshold of 10 cubic meters per month was only 5.2 cubic meters, so that these small consumersmany of them in low income neighborhoodswere being forced to pay for 22
coming from richer countries have higher returns to both US and foreign experience. In a separate paper, they document that a similar gap exists when looking at non-migrants across a smaller set of rich and poor countries (Lagakos et al., 2018). They interpret these results as evidence for the fact that workers born in rich countries accumulate more human capital over their life-cycle, possibly because of differences in the quantity and quality of their education.

I follow Lagakos et al. (2016) in implementing a development accounting exercise based on their country-specific estimates for the returns to experience. I assume that human capital per worker is where _θc_ is the average experience and _φc_ p _._ q is a country-specific function, reflecting different returns across countries. Since most countries in Lagakos et al. (2016)’s sample have between 15 and 19 years of average experience, I simply use the estimated returns for this category. I focus on returns to foreign experience from their baseline specification.<sup>13</sup> Table 4 shows the result. I report the results for the specification using educational attainment only for the 66 countries for which returns to experience are available. Since the estimates are relative to foreign experience for US immigrants, the sample does not include the United States. I use instead France (90<sup>_th_</sup> percentile of the income distribution) as a benchmark country for the computation of both income and human capital gaps.

Accounting for different returns to experience substantially boosts the contribution of human capital. While human capital in France is 47% larger than in Ghana when measured by educational attainment, the gap is of 366% when experience is taken into account. Overall, the success measure more than triplicates, from 9% to 29%. The accumulation of human capital during workers’ career appears to be an important source of cross-country differences in economic performance.

# **4.4 Development Accounting with Migration Data**

In this section I discuss further recent work using migration data to investigate cross-country differences in human capital. The premise of this approach is that when individuals migrate they bring the human capital accumulated in their country of origin, but then face a common technological and institutional environment in the new host country. Therefore, comparing earnings across migrants’ nationalities is informative on the cross-country variation in human capital, since it allows to keep constant the impact of other factors of production. Of course, these exercises need to take into account that migrants are not randomly selected

> 13Lagakos et al. (2016) emphasize that this approach ignores that life-cycle wage growth also depends on the time allocated to human capital investment. They incorporate this aspect in an alternative exercise, disciplined by a Ben-Porath model of human capital accumulation. They find that the resulting contribution of experience to development accounting is marginally lower.

16
# <u>Qualitative information</u>

Communities selected for the qualitative work were all part of the ENAHO sample; they were selected after a systematic stratification of the water users’ associations in the three coastal regions (north, center, and south) to ensure coverage of a variety of contexts. The thematic areas of potential impact of the project on agricultural performance are presented in Table 1. The qualitative strategy relied on individual interviews and focus group exercises; the instruments were semi-structured and modularized around the themes in the ENAHO survey. The instruments aimed to capture associations between the project intervention and changes in agricultural performance from various angles, particularly where the quantitative data were weakest; for example, it includes information on the quality of natural endowments (such as the soil and types of cultivation), other activities affecting agricultural production active in the region, farmer attitudes and labor activities with relation to the project (Gutierrez and Velez-Vega 2009).

# [INSERT TABLE 1]

# **IV.Empirical strategy**

Estimating treatment effects using non-experimental data is a common challenge faced by evaluators (Lokshin and Yemtsov, 2005). Generally, one seeks to estimate the difference between households located in treatment and control localities along several outcomes of interest. The assumption is that at baseline the treated were on average similar to the control along most relevant dimensions. However, given that the project did not have an experimental design and beneficiaries were not randomly assigned to 9
Policy Research Working Paper 10423

# **Abstract**

Access to reliable electricity is a Sustainable Development Goal, and key for both economic growth and individual wellbeing. Yet, in the absence of sophisticated monitoring systems, policy makers in developing countries commonly rely on surveys to measure electricity reliability and prioritize investments. The accuracy of such survey-based methods is unclear. This study built a low-cost national electricity outage monitoring network, using off-the-shelf components in Tajikistan—a country with severe electricity service constraints. The system was introduced alongside a monthly household survey called Listening to Tajikistan, which allowed benchmarking the survey summary statistics against unbiased measures. The results show that although the two measures were well correlated, the survey data suffered from significant and systematic bias. Survey respondents (i) systematically underreported the incidence and severity of electricity outages on average, but (ii) systematically overreported the incidence of outages during a period of abnormally widespread service disruption of long duration. These findings suggest that bias in survey-based measures is sensitive to the salience of outages to the respondent, and that, where feasible, automated electricity monitoring can provide more accurate quality measurement. For survey settings, the results also suggest that estimates are more accurate over short (daily) reference periods.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at wseitz@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
<!-- Start of picture text --> 1 Agriculture<br>0.9<br>08<br>207<br>§ 06<br>2<br>0.5<br>Soa<br>5E<br>003<br>0.2<br>04<br>2 4 6 8 10 12<br><!-- End of picture text --> <!-- Start of picture text --> 1 Manufacturing<br>0.9<br>0.8<br>207<br>806<br>f a<br>@ 0.5<br>Soa<br>35 |<br>003 |<br>0.2<br>0.1<br>2 4 6 8 10 12 14 16<br><!-- End of picture text -->

<!-- Start of picture text --> 1 Services _<br>0.9<br>08<br>207<br>8 06<br>2<br>@ 0.5<br>So<br>55 |<br>003<br>0.2<br>0.1<br>5 10 15 20<br><!-- End of picture text -->
same weights were employed for all countries). Sectoral coverage indicators rise substantially. 'HIC' coverage increases by 10.6 percentage points (or 26 percent) to 51.2 percent; developing country coverage rises by 1.8 percentage points (or some 17 percent). Thus, commitments were made in activities that are of 'above average' importance in GDP terms. The third row in Table 5 relates the specific commnitments, weighted by sectoral contributions to GDP, to the global market for services, as measured by global GDP. 'HIC' commitments account for 49 percent of the global services market. Those by all developing countries represent only 4.8 percent of the world market, reflecting the low share of developing countries in global GDP (estimated at 13.7 percent).'

**Table 5: Sectoral Coverage of Specific Comnmitments on Market Access, Weighted by Sectoral Contributions to GDP and Country Shares in Global GDP**

||**'HIC'**|Developing Group|
|---|---|---|
|1. Sectoral coverage, weighted by'restrictiveness'|40.6|9.4|
|2. Sectoral coverage scaled by 'restrictiveness' and GDP weights|51.2|11.2|
|3. Sectoral coverage weighted by restrictivenes**s**, ectoral<br>contributions<br>to GDP and country share in world GDP|49.4|4.8|
|Memo: GDP weights|82.0|13.7|

Table 6 reports the results of coverage ratio calculations incorporating an effort to take into account the relative importance of modes of supply across service sectors as well as the share of service sectors in GDP. For this purpose each Member's commitments were first aggregated to the '2-digit' GNS level (see Annex 1), and divided by the maximum number of possible commitments for each of these sectors. Each of the four modes was then given a weight, reflecting a subjective assessment of the relative importance of the modes for each of the 2-digit sectors. Adjustments were also made to reflect the fact that country offers for some service sectors almost invariably exclude certain activities. Thus, R&D service offers tend to be limited to social science activity; primary and secondary level education services to specialized activities; insurance commnitments usually exclude life insurance; maritime transport excludes cabotage and liner traffic; air transport commitments are limited to certain support services; and road transport is usually limited to international shipment. Annex 4 reports the weights applied. Table 6 shows that incorporating mode-of-supply weights and adjusting for sector-specific exclusions has little impact on the coverage ratios reported in Table 5. The 'HIC' coverage measure declines somewhat, falling from 51.2 percent to 48.5 percent. In contrast, the coverage ratio for developing countries rises slightly, increasing from 11.2 to 11.4 percent. Coverage indicators therefore do not appear to be very sensitive to incorporating 'mode of supply weights'.

5 Sources for GDP data were the World Development Report and the World Bank STARS database.

19