rafmacalaba/datause-encoder-data huggingface dataset:
where the outcome of interest �� ��� is an indicator variable for the decision of household h to send a work migrant at time _t+1_ (i.e., in the following survey wave). We define Χ as the vector of other observed household characteristics that influence a household’s decision to migrate. These covariates thus serve as lagged explanatory variables that predate the migration event and thus address the simultaneity problem associated with regressing migration decisions on contemporaneous outcomes.<sup>7</sup> Meanwhile, �� � represents a set of exogenous factors, such as price and conflict shocks, which may drive a household’s decision to send a work migrant. The model also includes region and year (to account for time trends) dummies for time _t_ , and clusters standard errors at the enumeration area level. Despite the reliance on lagged predictors, this specification cannot account for unobserved factors that determine a household’s migration behavior, nor can it control for the possible anticipatory changes in behavior that a household might adopt in advance of sending out a work migrant.

To partially mitigate the concern of endogeneity, we also specify a model as follows: where ��� represents the time-invariant household factors that shape a household’s migration decisions. By applying a household fixed-effects approach in Equation (2), we are accounting for those household characteristics – observed or unobserved – that do not vary across the survey waves and are correlated with the error term in Equation (1). In addition, the parameter �� captures the within-household variation of observed characteristics across time. While this non-experimental framework will not fully erase the bias from our estimates, it will offer insights into the factors associated with a household’s decision to send a migrant.

Before presenting the econometric results, we provide the household-level means for sending a permanent work migrant (Table 2). While only 3 to 5 percent of households reported sending out a work migrant during the first two survey waves, the corresponding share in the latter two waves is markedly higher. This jump may be tied to a change in the way the household roster module was administered, since 2010/11 was the first year in which the UNPS employed computer-assisted personal interviewing methods for data collection. The increase may also be tied to survey fatigue among third-wave respondent households, since the remainder of the roster would not be administered for that individual. To account for this possible discrepancy, we include year dummies for all regressions that rely on Equation (1) and Equation (2).

> 7 Note that the reliance on lagged explanatory variables obliges us to ignore all household-level observations from the 2011/12 UNPS data set, aside from the panel household’s decision to send a work migrant in the preceding 12 months.

7
Table B5: Effect of Extended Provider Training on Patient Satisfaction by Accuracy of Pre-Consultation Malaria Beliefs - No Additional Controls

||(1)<br>Full Sample|(2)<br>Has Valid Ho|(3)<br>me-Based RDT|
|---|---|---|---|
||Overall<br>Satisfaction<br>Index|Overall<br>Satisfaction<br>Index|Overall<br>Satisfaction<br>Index|
|Extended Training|-0.156<sup>_∗∗_</sup>|-0.170<sup>_∗∗_</sup>|-0.138<sup>_∗∗_</sup>|
||(0.0634)|(0.0730)|(0.0622)|
|Extended Training _×_ Prior-Risk Match|0.127<sup>_∗_</sup><br>(0.0652)|0.164<sup>_∗∗_</sup><br>(0.0759)|0.0885<br>(0.0579)|
|Match: Malaria Prior and Malaria Risk|-0.116<sup>_∗∗_</sup><br>(0.0467)|-0.0934<br>(0.0580)|-0.0480<br>(0.0455)|
|P-value: ET + ET _×_ Prior Match = 0|0.420|0.883|0.189|
|Prior Match Measure|Expected<br>Prior Match|Expected<br>Prior Match|Actual Prior<br>Match<br>(Home RDT)|
|Mean (Basic Training, No Pat. Info.)|0.00|-0.02|-0.02|
|N|1429|1092|1092|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. Standard errors for regressions that include a derivative of predicted malaria risk in the set of independent variables are bootstrapped using 500 replications to account for the fact that predicted malaria risk is a generated regressor. All regressions include strata and survey date fixed effects. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

B3
# **4.1. Identification using department and year of birth: Age-cohorts from ENV-2008**

According to the ENV-2008 survey data, for more than 90 percent of the individuals who earned the CEPE (completed six years of primary education), it took between 6 to 10 years. This suggests the majority of the students in the primary school are in the 6 to 16 age group with the plausible assumption that primary education normally starts at the age of six. To identify the potential victims of war, we construct a young cohort including all primary school goers who were exposed to the conflict between 2002 and 2006. Based on this, the individuals aged between 10 and 22 years constitute the young cohort in the ENV-2008 survey. Using ENV-2008, we compare average years of education for individuals in the young cohort against an older cohort, aged between 23 and 32. The individuals in the old cohort are likely to be over the age of primary school goers between 2002 and 2006. We use the year of birth and the department of birth to identify an individual‟s exposure to war. To begin with, a straight forward difference-indifference of average years of education is calculated based on year and department of birth.

Table 4.1.1 Means of Years of Education by Cohort and War Prevalence

||Ye|ars of educ|ation|
|---|---|---|---|
||No War|War|Difference|
|Old Ch Ad 23 32 i 2008|7.84|9.18|-1.34|
|oort (ge to n )|(0.14)|(0.08)|(0.18)|
|Y ht Ad 10 t 22 i 2008|6.32|6.46|-0.14|
|oung coor (ge o n )|(0.06)|(0.04)|(0.07)|
|Diff|1.52|2.71|-1.20|
|erence|(0.13)|(0.08)|(0.16)|

Note: Standard errors are in parenthesis, all estimated coefficients are statistically significant at 1 percent Table 4.1.1 reports average years of education for both age-cohorts and a war prevalence dummy, which takes the value of one if a department (of birth) experienced at least one conflict event, zero otherwise. The war prevalence of a department reflects the total number of conflict events between 2002 and 2006. For both age-cohorts, the average years of education in conflictaffected departments is higher compared to the rest. However, the gap in average years of education is negligible for the young cohort. Two possible explanations can be offered. First, the war zones (departments that experienced conflict) traditionally had higher average years of education and this could be due to better educational facilities or better job prospects. Second, 11
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of activity in the Late groups during RR and R2, no sizeable business activity in them until R3. In general, as we would expect based on a simple duration-from-treatment model, the Late group at R3 has outcomes that are intermediate between the R2 and R3 outcomes for the Early group.

The average disbursement to groups was $5,867. Within the first six months of disbursements, groups spend nearly half of total available funds on set-up costs and working capital, ending up with combined assets plus working capital at about 83% of the grant. Figure 3 shows how this number then evolves over time: group value climbs quickly between 6 and 12 months, peaks at about 96% of the originally disbursed value 12 months subsequent to disbursement. This increase comes from some of the investments paying off (e.g., piglets are born; pigs fatten). At the 36-month follow-up, the total value of the stock of assets at the enterprise level in Early treatment groups that received no UCT has fallen to $2,660, or 45% of the disbursed amount. Hence, in the absence depreciation or profit in the group enterprise, we would expect just over half of the disbursed money to have shown up in household balance sheets within three years simply through consumption of the principal.

What then is the evolution of returns to group members? To examine this, we use questions asked of each group member about individual investment and profit-taking. During the enterprise survey, we ask every individual in the group to report time inputs in managing the group activity and the value of cash and kind investments in the group, as well the income taken out of the group by individuals. By calculating the difference between income and investment at the individual level we have a net income per individual in the group, and by then dividing this by the average time input we can directly calculate an effective wage rate. Note that because we cannot distinguish between realized profit and consumption of business principal, this wage rate is inclusive of the pace at which overall group value is being drawn down. Wage changes over time in the UCT 18
Figure 12: Percentage of population agreeing, disagreeing with “I have done better in life than my <u>parents”</u> <!-- Start of picture text --> 100<br>90<br>26<br>80 34<br>43<br>70<br>9<br>60 43<br>11<br>50<br>40 26<br>30 62 14<br>52<br>20<br>10 24 27<br>0<br>Armenia Azerbaijan Georgia Other CIS<br>Disagree Neutral Agree<br><!-- End of picture text --> _Source_ : LiTS (2016) _Note_ : The bars for each country may not add up to 100 percent because responses that were “Don’t know” and “Not applicable” are not plotted here. Strong agreement and agreement are grouped into a single agreement category In the case of South Caucasus, living standards have broadly improved in both Georgia and Armenia, as discussed in the earlier sections. So how do perceptions of mobility look? The question of who people generally consider appropriate benchmarks to evaluate their current economic circumstances is very illustrative. The LITS survey asked this question to all respondents in the 2016 round of the survey and the responses for the three South Caucasus countries, together with the average response across all other transition countries is presented in Figure 11. Around 27 percent of the population in Armenia, and 24 percent in Georgia reported to have their own family pre-transition in mind as comparators when they evaluate their life circumstances today. This is significantly higher than the average of 9 percent of the population across all transition countries. The number for Azerbaijan too is at 9 percent, but Azerbaijan stands out for its unusually high non-response rates (responses of “Don’t know”) so it is difficult to definitively interpret its results. Likewise, roughly one in five individuals in each of the countries in the South Caucasus looks at their parents, and the life they had when they were of similar age as the appropriate comparison. This is also slightly higher than the average for other transition countries. Western Europe and how the people there live is a used as a benchmark by a roughly a fifth of the population in Georgia and smaller shares in Armenia and Azerbaijan.

So irrespective of what the short run fluctuations in welfare and living standards may have been, one thing this suggests is that a non-trivial proportion of people in the South Caucasus still 25
PRE Working Paper Series

|||LAtho||Contact<br>for pape|
|---|---|---|---|---|
|WPS469|Modeling Economic Behavior in<br>Peru's Informal Urban Retail Sector|J. Barry Smith<br>Morton Steloner|August 1990|M. Abundo<br>36820|
|WPS470|What Do Alternative Measures of<br>Comparative Advantage Reveal<br>About the Composition of Developing<br>Countries' Exports?|Alexander J. Yeats<br>|August 1990|J. Epps<br>33710|
|WPS471|The Determinants<br>of Farm<br>Investment and Residential<br>Construction in Post-Reform China|Gsrshon Feder<br>Lawrence J. Lau<br>Justin Lin<br>Xiaopeng Luo|August 1990|C. Spooner<br>30464|
|WPS472|Gains in the Education of Peruvian<br>Women, 1940 to 1980|Elizabeth M. King<br>Rosemary Bellew|August 1990|C. Cristobal<br>33640|
|WPS473|Adjustment, Investment, and the<br>Real Exchange Rate in Developing<br>Countries|Riccardo Faini<br>Jaime de Melo|August 1990|R. Sugui<br>37951|
|WPS474|Methods for Measuring the Effect<br>of Adjustmsnt Policies on Income<br>Distribution|Anne Maasland|August 1990|P. Dixon<br>39175|
|!PS475|Does Divestiture Matter? A<br>Framework for Learning from Experie|Ahmed Galal<br>nce|August 1990|G. Orraca-Tetteh<br>37646|
|WPS476|Health Insurance in Sub-Saharan<br>Africa: A Survey and Analysis|Ronald J. Vogel|August 1990|K. Brown<br>35073|
|WPS477|Private Participatian in the Delivery<br>of Guinea's Water Supply Services|Thelma A. Triche|August 1990|M. Dhokai<br>33970|
|WPS478|Interrelations Among Child Mortality,<br>Breastfeeding, and Fertility in<br>Egypi, 1975-80|John Marcotte<br>John B. Casterline|August 1990|S. Cochrane<br>33222|
|WPS479|Conversion Factors: A Discussion<br>of Alternate Rates and Correspondin<br>Weights|Michael Hee<br>g|August 1990|E. Zamora<br>33706|
|WPS480|An Evaluation of Neutral Trade Policy<br>Incentives Under Increasing Returns<br>to Scale|Jaime de Melo<br>David Roland-Holst|August 1990|R. Sugui<br>37951|
|WPS481|The Etfects of Trade Reforms on<br>Scale and Technical Efficiency:<br>New Evidence from Chile|James Tybout<br>Jaime de Melo<br>Vittorio Corbo|August 1990|R. Sugui<br>37951|
|WPS482|Membership in the CFA Zone:<br>Odyssean Journey or Trojan Horse?|Shantayanan Devarajan<br>Jaime de Melo|August 1990|R. Sugu;<br>37951|
# Introduction

The Russian Federation is a major producer and exporter of natural gas and crude oil. A number of authors (see Oomes and Kalcheva, 2007 and World Bank, 2005 among others) argued that – following the more than 300 percent increase in the oil price (from about 25 USD to 110 USD per barrel) between 2002 and 2008 – the economy had been showing symptoms of the Dutch disease. Increased revenues from oil exports pushed up the real exchange rate and hurt the competitiveness of the tradable sectors; manufacturing and other exportables shrunk, while services and non‐tradables expanded.<sup>2</sup> More recently the Russian economy has been facing the opposite situation; between July and December 2014 the oil price halved from around 110 USD to less than 60 USD per barrel (World Bank, 2015). The consensus is that the oil price will stabilize at the current lower levels (the average price in 2015 was 50 USD per barrel, and 42 USD in 2016) reflecting structural changes in the global oil markets rather than cyclical and short term adjustments.

The main objective of this paper is to assess the distributional impact of this terms of trade shock. Recent macroeconomic data suggest that a reverse Dutch Disease may be at work. In 2015, the exchange rate depreciated by more than 30% and terms of trade losses were large, equivalent to about 7% of GDP. Assuming that these income losses will be putting pressure on prices of non‐traded goods and services and will require related adjustments in the labor markets, this paper tries to identify who are the winners and losers, and to estimate the changes in income distribution and poverty.

Two difficulties arise in the estimation of the distributive impact. The first is that a household survey for the period after the shock is not yet available. However, and this is even more important, even if survey data were available, they may not provide an adequate counterfactual given that, simultaneously to the oil price drop, the Russian economy has been influenced by other shocks, for example, economic sanctions. For these reasons, the analysis is performed using a macro‐micro simulation model which allows to generate a counterfactual scenario in which the oil price change is the only shock affecting the economy.

> 2 The economic effects of a natural resource boom were modeled by Corden and Neary (1984) who distinguished between a _resource movement effect_ – related to the reallocation of labor and capital from the tradable sectors to the booming natural resource sector – and a _spending effect_ – which brings a real exchange rate appreciation and further de‐industrialization. The real exchange rate appreciation is caused by the fact that higher incomes from natural resources raise demand for both tradable and non‐tradable goods, but while domestic prices of tradables are linked to international prices and thus remain constant, domestic prices of non‐tradables go up.

2
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_2 Railway Reform in_ **_the_** _Cetral and Eastern European_ **_(CEE)_** _Economies_ provide door-to-door services, and to provide convenient connections with road-oriented Western Europe. The fourth factor, operating in the direction of increased rail demand, is the environment: all economies are facing the problem of growing air and noise pollution in their major urban centers. Under certain circumstances, rail's environmental advantages will encourage more rapid development of rail services in urban commuter applications.

4. All of the above considerations will operate with particular force in the structural transformation which the former socialist economies in the CEE and the CIS countries are now undergoing. This is because the past practice of emphasizing production of basic industrial materials and on under-pricing of transportation relative to other activities has acted to create excessive use of transport. Figure 1, for example, compares the intensity of transport use (measured in ton-km of total transport per US$ of GDP - purchasing power corrected).<sup>2</sup> ' The uniform ranking of socialist economies toward the top furnishes strong evidence of their excess use of transport per unit of economic activity compared with use of transport in market economies. Figures 2-5 display typical examples of the reasons for transport intensity: all of the primary commodities and materials of construction, and most of the heavy, low value commodities generating tran.port activity, are produced in much greater quantities in the CEE countries and the former Soviet Union (FSU) than in the western economies.

Figure 1 <!-- Start of picture text --> Transport TonKrm per $ of GDP<br>(Road, Rail & Water)<br>Area, 000 Km2<br>F9U t22,2721 , .i<br>POLAND (305) 085<br>CSFR (125) 6,8<br>CHINA (9.597) 78<br>CANADA(2,005 Q7<br>BULGARIA V I I} I Qu72<br>USA 'O9.171 0.64<br>HUNJ(ARY (92-<br>INDLA (2.973) - Q<br>YUGOSIAVIA t255) S 4<br>SPAJN (4991 0.3 7<br>HOllAND 1341) - a<br>STEDEN (4129 3) 32<br>RQJ3QUM t301 - a 32 :<br>W.GERMAWYt244) 0.20 :<br>UK242 0.26:<br>ll-ALY (2794)1 -0{23'<br>FRANCE(548)- G2<br>AUSTRIA (83)- 0.21<br>0 1 2 a 4 5<br>Ton-Km per do GDP<br>GDP is Purchasing Power Adjusted<br><!-- End of picture text --> 2/ This is at best an approximate calculation. Detailed studies broadly confirm the conclusion that the former socialist economies are; highly transport intensive. See, for example, Esrm Bennatha-, Julie Fraser, and Louis S. Thompson, "Freight Transport Demand, Determinants and Intensity," World Bank, INUTD, September 1992.