rafmacalaba/datause-encoder-data huggingface dataset:
period in 2008 (see figure 1). It now appears likely that remittances will be at least one third lower in 2009 than in the previous year, which implies a fall in remittance inflows of about $750 million, or about one quarter of the value of imports. Remittances to the Kyrgyz Republic are also projected to fall by 30 percent in 2009 from their 2008 levels.

# **Figure 1**

# **Monthly Remittances to Tajikistan, Millions of US Dollars: Jan 2005 to April 2009**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Jan 2005MarMay JulSepNovJan 2006MarMay JulSepNovJan 2007MarMay JulSepNovJan 2008MarMay JulSepNovJan 2009March<br><!-- End of picture text --> Source: National Bank of Tajikistan Export earnings will be reduced in 2009 because of a fall in world market prices for metals, which are major exports of Armenia, the Kyrgyz Republic and Tajikistan and lower demand, especially in Russia which is the most important destination for exports.<sup>4</sup>

> 4 Tajikistan’s main export is aluminum. In the 12 months since April 2008, world market aluminum prices have fallen by more than 50 percent.

7
# **_3.2. The micro module: linking household surveys to the CGE model_**

The poverty and distributional effects of the Canal expansion are estimated using a top-down approach. The top CGE-generated prices and labor reallocation are used to ̳shock‘ the bottom micro module so that a counterfactual income distribution can be estimated. No feedback from the micro module to the macro model is explicitly accounted for in this model. The following equations represent the core of the micro module: Income of household _h_ , _Yh_ , is defined as the sum of labor remunerations, (  _h_  , _l_<sup>_w_</sup> _l_ ) , and an exogenous, non-labor income ( _Yho_ ) . Welfare effects are _l_ approximated by changes in real household incomes which, in turn, depend on: 1) changes in the prices of purchased goods (  _pg_ ) and the initial share of expenditure on each good (  _hc_ , _g_ )<sup>11</sup> ; 2) changes in the returns to skilled and unskilled labor in the different labor market segments ( <sup>_w_</sup> _l_ ) ; and 3) changes in the allocation of workers in the different labor market segments, i.e. agricultural and non-agricultural sectors as well as formal and informal activities within urban areas (  _h_  , _l_ ) . A new household welfare aggregate is computed based on the sum of the exogenous income plus the _simulated_ labor income for each member of the household given his or her skill endowments and sector of employment. Based on the simulated welfare aggregate, a counterfactual distribution of income is generated and compared with the initial distribution.

A key issue in this modeling framework is the connection between the macro CGE part and the micro module and therefore a major difficulty consists of satisfactorily mapping the sources of incomes from the CGE model to the micro model. For example, in the CGE model it is possible to clearly distinguish labor remunerations from capital earnings; however, in the micro data, for the large group of self-employed people, incomes are a mix of labor and capital returns. For this group, an imputed wage is estimated and the remaining amount is classified as capital income (see Annex 2 for details). Furthermore, the micro-simulation module defines an exogenous household income ( _Yho_ ) as all non-labor income components like transfers, imputed rents, capital remuneration, etc. This exogenous income is not modified during the simulations. Thence, although consistency between macro and micro is always pursued, the changes in capital remunerations predicted by the CGE are not reflected in the micro data. The

> 11 For simplicity we only distinguish between food and non-food products.

9
# **INSTITUTIONAL AND POLICY ANALYSIS OF RIVER BASIN MANAGEMENT**

# **_The Murray Darling River Basin, Australia_**<sup>1</sup>

**William Blomquist Brian Haisman Ariel Dinar Anjali Bhat**

World Bank Policy Research Working Paper 3527, February 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 This paper is a product of the study, “Integrated River Basin Management and the Principle of Managing Water Resources at the Lowest Appropriate Level – When and Why Does It (Not) Work in Practice?” The Research Support Budget of the World Bank provided major funding. The project was carried out by the Agriculture and Rural Development Department at the World Bank. The Water Resources Management Group and the South Asia Social and Environment Unit at the World Bank have provided additional support. The study core team includes Karin Kemper and Ariel Dinar (Co-Task Team Leaders, World Bank), William Blomquist and Anjali Bhat (consultants, Indiana University), and Michele Diez (World Bank), William Fru (consultant), and Gisèle Sine (International Network of Basin Organizations). Basin case study consultants include Maureen Ballestero (Tárcoles - Costa Rica), Ken Calbick and David Marshall (Fraser - Canada), Rosa Formiga (Alto Tietê and Jaguaribe - Brazil), Consuelo Giansante (Guadalquivir - Spain), Brian Haisman (Murray Darling - Australia), Kikkeri Ramu and Trie Mulat Sunaryo (Brantas - Indonesia), and Andrzej Tonderski (Warta - Poland). Useful comments on this paper by Geoffrey Spencer, Staff from the Water Policy and Murray Darling section of the Department of Agriculture, Fisheries and Forestry, and staff from the Murray Darling Basin Commission are appreciated. We are grateful to the individuals whom we interviewed in the course of this research. None of those individuals is responsible for the findings and conclusions in this paper.
Barrera-Osorio and Raju (2010) show that within two years the program generated large gains in enrollment and school inputs (a gain of approximately 40%) and student achievement (a gain of 0.3–0.5 standard deviation). Interestingly, the group-based teacher incentive is found to have no impact on student achievement. This coincides with a finding by Muralidharan and Sundararaman (2011) that group-based teacher incentives are less effective than individual teacher incentives in an experiment in India. The examples above indicate the importance of incentive scheme design, which should be highlighted given the recent strong interest in creating incentives in the region and around the world to improve learning quality<sup>7</sup> .

7 Group-based incentives are much less complex to implement than individual incentives, and therefore more common. The few recent studies that have found significant impacts of group-based teacher incentives tend to have particular features. For example, Glewwe et al. (2010) study schools in Kenya that typically have only 200 students and 12 teachers in a school, hence a very low student-teacher ratio. Fryer et al. (2012) find small and statistically insignificant results from a standard group-based incentive scheme, but find large and statistically significant results when applying a loss aversion mechanism where teachers are paid in advance and asked to give back the money if their students do not improve sufficiently. On the other hand, Duflo et al. (2012), the other study that we are aware of that is able to implement individual teacher incentives, finds significant improvements in student learning. Although their estimates may not be directly relevant to developing countries, Imberman and Lovenheim (2012) use U.S. data to show that small groups provide productivity gains over large groups in response to group-based teacher pay incentives, with the optimal group size being roughly 3 to 5 teachers. Small groups may be better at keeping the “free riding” and “award salience” problems in check while benefiting from group cooperation. Free-riding is the problem of relying on others’ efforts in a group without exerting enough own efforts, and award salience is the problem of seeing own efforts as atomic and inconsequential in affecting the aggregate outcome. Indeed, besides the above mentioned studies, most other recent research on group-based teacher incentives given to schools or relatively large groups has also shown small or negligible effects on student achievement (see, for example, Springer et al., 2010; Goodman and Turner, 2012; Fryer, 2013). Studies vary greatly in the context of the intervention and it is important to design an effective incentive scheme in accordance with the specific context; however, the discussion above highlights some key design elements for both the government and private sector to take into account.

22
funds and keep books, but it is noteworthy that 17% of VF committees had no member reporting any of these skills.

On average, VF committees meet 5.8 times annually, but there is considerable variation from village to village, as Table 6 shows; 27% of committees meet just once or twice a year, while 18% meet monthly. There is also a good deal of variation in the usual length of meetings; while the median length is 90 minutes, 49% last for an hour or less, and 38% last for two hours or more. Surprisingly, there is no association between the length and number of meetings: the correlation coefficient is 0.012 and is not statistically significant.

**<mark>Table 5.  Educational and Professional Experience of Village Fund Committee Members</mark>**

||No. of VF com<br>|mittee members<br>||
|---|---|---|---|
||Full sample|Restricted sample|# of VF reporting|
|**Educational level:**||||
|No diploma|8.3|8.8|2,472|
|Up to two years of university|0.8|2.0|1,084|
|Two or more years of higher education (but no degree)|1.2|2.0|1,507|
|University degree|0.1|1.2|286|
|Memo: Committees with no higher-educated member|30.2%|||
|**Professional experience:**||||
|.. in accounting|1.5|1.9|2,036|
|.. in management|2.4|3.8|1,660|
|.. in banking|1.1|2.3|1,280|
|.. other|0.7|2.4|488/213**|
|Memo: Committees with no member reporting<br>professional expertise|16.5%|||

Note: “Full sample” results assume that missing values mean that there is nobody with that skill or education on the committee; “Restricted sample” shows the means for cases that reported, with the sample size in the final column. Source: Village Fund Survey, 2010.

**<mark>Table 6.  Frequency and Length of Village Fund Meetings</mark>**

|Number of VF committee<br>meetingsperyear|% breakdown|Length of a typical VF<br>committee meeting,minutes|% breakdown|
|---|---|---|---|
|1|7.6|About 30 *≤ 30+|10.0|
|2|19.8|About 45 *>30, ≤45+|3.6|
|3|16.2|About 60 [> 45, ≤60+|25.2|
|4|12.4|About 90 *>60, ≤90+|12.6|
|5|6.4|About 120 *>90, ≤120+|24.8|
|6|8.0|About 180 *>120, ≤180+|10.9|
|7 – 11|7.7|About 240 [>180]|2.5|
|12|18.1|||
|> 12|3.7|||
|Memo: mean|5.8|Memo: mean|91.8|
|Memo: median|4.0|Memo: median|90.0|

Source: Village Fund Survey, 2010.

In most cases, some VF committee members participate in training courses or seminars. In 2009, an average of three members attended training sessions, which lasted for an average of two days. In addition to training 9
market size measured by its income level and some non-linear transformation of the distance to that market center. Second, the extent of the market is instrumented using geographic characteristics to allay any concerns regarding potential reverse causation. Finally, in addition to parametric regression analysis, this paper utilizes semi-parametric techniques to uncover the nonlinearity in the e ff ects of the extent of market on agricultural specialization.

The parametric regression results show that the extent of the market has a statistically signi fi cant (at 1 percent level) positive e ff ect on land allocated to non-cereal crops and on crop sales. In the case of Her fi ndahl index of concentration of cropland use, the e ff ect of a larger market is negative, but lacks statistical signi fi cance (not signi fi cant at 5 percent level). The semi-parametric estimation indicates that the relationship between Her fi ndahl index and market size is U-shaped. In contrast, the relationship is monotonically upward sloping in the case of share of land allocated to non-cereal crops as a function of the market size. The relationship between sales of non-cereal (non-staple) crops and market size is monotonically increasing with no signi fi cant evidence of nonlinearity. These results imply that when the farmers have access only to small urban markets, the production structure in a village economy tends to be specialized in subsistence agriculture with most of the land devoted to a single subsistence crop (rice in case of Nepal) and only a limited degree of commercialization. As the extent (size) of the market increases, the portfolio of crops in a village economy becomes more diversi fi ed initially. However, after the market size reaches a threshold, the production structure starts to specialize again. This evidence on the stages of agricultural diversi fi cation thus mirrors the stages of diversi fi cation identi fi ed in the recent literature for the economy as a whole and also for the manufacturing sector (Imbs and Wacziarg, 2003). Our results also show that there is a monotonically increasing relationship between the degree of market participation (commercialization) among the households in a village and the extent of the relevant urban market. To the best of our knowledge, this paper provides the fi rst evidence on the stages of agricultural specialization as it relates to the extent of the (urban) market<sup>5</sup> .

> 5 Although there is a large literature on the role of urban market in agricultural specialization, the focus of that literature is on the access to urban markets (see, for example, Jacoby, 2000; Fafchamps and Shilpi 2003, and 2005). For more general discussion on the role of urban markets in agricultural diversi fi cation and

4
We test this hypothesis by exploiting a sample expansion of the phone survey in Nigeria after the first 12 rounds of data collection.<sup>11</sup> Initially, the sample of households selected for the phone survey had constituted of a randomly selected subset of all households for which a phone number was available from the latest in-person LSMS-ISA survey: 4,934 households had a phone contact, 3,000 of whom were randomly selected and 1,950 were successfully interviewed in rounds 1-12 of the phone survey from April 2020 to April 2021 (1,050 were not interviewed due to nonresponse and failed contact). Starting from round 13 of data collection in November 2021, the remaining 1,934 households with an available phone number were contacted as well to expand the sample. This setup provides us with a (randomly selected) sample of respondents who had been previously interviewed and a (randomly selected) sample of respondents who were interviewed for the first time in round 13, and we can compare their reported vaccination status. We make the following prediction.

**Prediction 4:** Respondents interviewed for the first time, as opposed to repeat respondents, will display lower rates of vaccine uptake.

To test our prediction, we run the following OLS regression.

where _Di,s_ is a dummy variable denoting whether individual _i_ has been previously interviewed. In line with our prediction, we expect _β_ to be significant and positive.

# <u>Hypothesis 5: Proxy reporting biases</u>

When testing for respondent selection biases, one approach we explore is asking the purposively selected (main) respondent to report on the vaccination status of the remaining household members. However, such proxy reporting may be inaccurate (Davin et al. 2019; Li et al. 2015; Triplett 2010; Mosely and Wolinsky 1986). For errors in proxy reporting to drive the gap between survey estimates and administrative records, proxy reports would need to be systematically biased upwards, that is, main respondents would need to overstate vaccine uptake among the members of their household. We thus formulate the following hypothesis.

**Hypothesis 5:** Proxy reporting of other household members’ vaccination status by the interviewed respondent overestimates true vaccine uptake.

We test this hypothesis by comparing two separate reports of the vaccination status for the same person: (i) as self-reported by the randomly selected respondent; and (ii) as proxy-reported by the purposively selected (main) respondent (see Hypothesis 2). This allows us to check the alignment of proxy reports with self-reports.<sup>12</sup> Our prediction is as follows.

> 11 Out of these 12 rounds, three had collected data on COVID-19 vaccination.

> 12 This excludes cases where the randomly selected respondent was identical to the purposively selected (main) respondent. In these cases, self-reported information equals “proxy-reported” information by definition.

13
<u>Table 2: Impacts on maternal labor market outcomes from RCT studies</u>

|||Work||Labor force|participation|
|---|---|---|---|---|---|
||**Brazil**|**Kenya**|**Chile**|**Brazil**|**Chile**|
|ITT|0.04|0.10|0.03|0.03|0.03|
|LATE/ToT*|0.10|0.31|0.12|0.06|0.15|
|Control group participation|0.41|0.58|0.60|0.77|0.71|
|||Percent increas|e from baselin|e/control group||
|ITT|10%|17%|6%|3%|5%|
|LATE/ToT*|23%|53%|19%|7%|21%|

Notes on differences in definitions of variables: Work is defined as: (i) mother is working at the time of the survey in Brazil; (ii) mother is engaged in at least one incomegenerating activity in Kenya; (iii) mother works at least one month during May-December (the school year) in Chile. Labor force participation is defined as: (i) mother works or is looking for work in Brazil; (ii) mother participates always from May to December (the school year) in Chile.

We report specifications which include control variables; all estimates are statistically significant at conventional levels (between 0.01 and 0.1) Studies in Brazil and Chile report ToT, study in Kenya reports LATE, which we consider equivalent to ToT due to low <u>likelihood of crossing over from control group: researchers controlled supply of vouchers.</u> 5
such as education and caste. We also construct measures of social proximity between a migrant’s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion.

We also investigate a number of factors that may in fl uence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations in fl uence the choice of migration destination. Additional controls include distance and prices.

The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries. There are also many non-mountainous countries that nevertheless su ff er from serious geographical isolation because of the lack of roads. This applies, for instance, to much of sub-Saharan Africa. Many of the same factors are likely to a ff ect migration patterns in these countries as well.

It has long been observed that migrants often are better educated than non-migrants.<sup>2</sup> Migrants may di ff er from non-migrants in terms of unobservables as well. A number of recent studies have sought to estimate returns to migration that are immune to selection on unob-

> 2 A related strand of work points out that migration prospects raise investment in education (de Brauw and Giles, 2006; Batista and Vicente, 2008).

2
Policy Research Working Paper 7564

# **Abstract**

This paper examines the effects of market integration on household consumption using data on seven food and two energy markets across South Sudan. The analysis reveals that markets in South Sudan are highly segmented. Price differences for narrowly defined products, across cities exceed in some cases 100 percent. In addition, price volatility increased substantially following the imposition of the trade restrictions with Sudan. This increase tends to hurt disproportionately the poor, who cannot smooth purchasing decisions over time because of liquidity constraints. Transportation costs explain almost half of the variation in food prices across space, and improving the quality of roads has a large potential to reduce prices in the most expensive towns. On the basis of this price effect, the simulations suggest that bringing all road quality across states to that of primary roads can yield a reduction in poverty from the rate of 51.7 percent in 2009 to between 42.8 and 46.9 percent. These estimates have to be interpreted as conservative, as they do not take into account the second-order effects of road construction from increased trade that will result from better road connectivity.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at gvarela@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