rafmacalaba/datause-encoder-data huggingface dataset:
four predictor variables are highest for OECD countries and higher for all countries than for nonOECD countries; these are: TIMSS participation, national assessments, location in ECA and high-income economy. Values for three other predictor variables are lowest for OECD countries and lower for all countries than for non-OECD countries; these are: location in Sub-Saharan Africa, low-income economy and lower-middle-income economy.

**Table 5: Descriptive statistics**

||OECD co<br>(Obs = 1|untries<br>88)|All coun<br>(Obs = 1|tries<br>269)|Non‐OE<br>(Obs = 1|CD countries<br>081)|
|---|---|---|---|---|---|---|
||Mean|Std. Dev.|Mean|Std. Dev.|Mean|<br>Std. Dev.|
|OECD member|1.00|0|.15|.36|0|0|
|In PISA|.99|.10|.27|.45|.15|.36|
|In TIMSS within 5 years|.61|.49|.25|.43|.19|.39|
|In regional assessment|.03|.16|.08|.28|.09|.29|
|National assessment|.06|.25|.41|.49|.35|.48|
|East Asia and Pacific|.13|.33|.17|.39|.18|.39|
|Europe and Central Asia|.76|.43|.26|.44|.18|.38|
|Latin America and Caribbean|.04|.20|.19|.39|.21|.41|
|Middle East and North Africa|.01|.10|.10|.30|.11|.32|
|North America|.06|.25|.01|.12|.01|.07|
|South Asia|0|0|.04|.19|.04|.21|
|Sub‐Saharan Africa|0|0|.22|.42|.26|.44|
|Low‐income (lagged)|0|0|.25|.43|.29|.46|
|Lower‐middle‐income (lagged)|.01|.07|.26|.44|.30|.46|
|Upper middle‐income (lagged)|.15|.36|.20|.40|.21|.40|
|High Income(lagged)|.84|.43|.29|.46|.20|.40|

**Basic model.** First, variables comprising each advantage factor are examined separately, for: (a) all countries and (b) non-OECD countries (table 6). The first three columns of this table present results for all countries, and the second three columns present the results for non-OECD countries.

The odds ratios reported in this table show the increase in the odds of PISA participation for each of three groups of country characteristics: the country’s economic status at the start of the PISA cycle, its regional location, its capacity for assessment as indicated by prior experience; in addition, OECD membership is included for the “all countries” model.

The first column shows the strong effect of a _country’s economic level_ of development on PISA participation. High income countries are nearly 80 times more likely to participate as compared with low-income countries, and upper-middle income countries are 36 times more likely to participate. Among the non-OECD countries (column 4), these odds drop considerably, but remain highly significant.

11
of the BHPS. They were unable to reject the shift-share hypothesis<sup>12</sup> . Clark (2003) uses the first seven waves of the BHPS to confirm this early prediction. He finds that in regions with high unemployment, SWB falls less when losing a job. The same result is found in this paper, extending the sample to 16 waves of the BHPS.

This finding suggests that unemployment hurts - but it hurts less the more there is of it around. It suggest that labor market status is one important comparison affecting well-being. The question often posed is who do people compare to ? Who are the relevant others ? Is it the whole population or the people on the neighborhood? Is those of same sex, age, income and educational achievement? Data on relevant others from the survey can be used to test if employment in the reference group is also affecting well-being when losing a job. The OLS estimations to test the hypothesis are the following.

Where _Wit_ stands for well-being of individual _i_ at period t. _Xi_ stands here for all other determinants, year, region and children fixed fixed effects. _Uit_ = 1 when the respondent is unemployed. _RkU_ stands for the regional unemployment rate; and _U_<sup>_∗_</sup> = 1 when the relevant other is unemployed. The interaction terms are expected to be positive.

Following Moulton (1980) the estimation uses clustered standard errors for the regional unemployment rates. This is because the regional unemployment rate is the same for all individuals within the region. If the clustering is ignored, the repetition of the same value in one variable is biasing downwards the standard errors.

The results of the regressions on pooled data are very straightforward. Table 4, below, shows the ordered probit results. Table A.5, in the appendix shows the OLS results. In the simple specification, one can see that the regional unemployment rate has no effect on well-being. However, when one adds an interaction term between own unemployment and regional unemployment rate, the coefficient is positive and significant. This reflects the finding of the previous graph: unemployment hurts, but it hurts less the more there is of it around at the regional level.

The effect of spouse unemployment is also straightforward. Having an unemployed spouse significantly lowers one’s well-being. The effect is 35 times higher than the regional unemployment interaction. But the interaction term between own unemployment and the

> 12The shift-share hypothesis would hold if a rise in unemployment pushes the happier of the working force into unemployment.

15
||**Timeline for Leaps Surv**|**eys**|
|---|---|---|
|Activity|SurveyType|Date|
|**_Sampling Frame_**|Household Census<br>School Listing|August, 2003<br>August, 2003|
|**_Year 1 Surveys_**|Household-based Surveys<br>School-based Surveys|February – April, 2004<br>February – March, 2004|
|**_Intervention_**|Report Card Delivery|September, 2004|
|**_Year 2 Surveys_**|Household-based Surveys<br>School-based Surveys|November –December, 2004<br>January – February, 2005|

_<u>School-based surveys</u>_

# i. General School surveys

This survey is answered either by the owner or by the head teacher and collects information on fees, enrollment, infrastructure, funding sources, expenditures, school time-roster. Over 800 schools across 112 villages in our sample receive this survey.

# ii. Teacher surveys

There are three components to the teacher survey: (i) a teacher roster survey that collects basic demographic information on all teachers in the school; (ii) a head teacher survey that collects basic demographic information for the head teacher in the school, and information regarding their contracts and tenure at the school; and (iii) a teacher survey administered to Grade 3 teachers, which collects information about the teacher’s personal and educational background, their professional history, and other details about their work environment. Through these surveys, we collect information on over 6,000 teachers across our sample. The roster exercise gives us information on around 4,900 teachers and the other two more detailed surveys provide information on another 1,600 teachers.

# iii. Child Tests

We administered tests of English, Mathematics and Urdu (the vernacular) as part of the LEAPS survey. This exercise was bundled with the other school surveying activities and took two and a half hours to complete in each school. These norm-referenced tests were designed and validated by our team. Norm, rather than criterion-referenced testing was chosen since the former allows us to measure learning with higher levels of precision at
continues to have negative welfare effects on local and regional industries that compete with imports from overseas countries.

**Proposition 3** : _Countries that have production processes governed by a colonial model of natural resource extraction and primary agricultural commodities are more likely to face structural current account deficits than more diversified economies._

The proof draws on standard national income identity as follows: According to this standard formulation, total output at any time _t_ is the sum of total consumption expenditure, total investment expenditures, total government expenditure, and the current account, where CAt = EXt −IMt. In current account terms, identity (21) can be represented as: We are assessing the effects of the colonial production structure dominated by exports of primary agricultural commodities and natural resources on the country current account, which over time, is affected by the volatility of commodity prices. Hence identity (22) can be expanded to take the following representation: <!-- Start of picture text --> tn<br><!-- End of picture text --> <!-- Start of picture text --> tn<br><!-- End of picture text --> tn We would like to establish thatlim t=t0 CAtn →−∞. For any given country, ∃ t ∈(1, ∞) such t→∞<sup>∑</sup> that CAt < 0 ⇒ IMt > EXt. In practice, such a scenario is highly likely and reflects the current account deficit. The deficit may be the result of terms of trade deterioration, volatility of commodity prices, but also changing patterns of global trade increasingly dominated by manufactured goods. According to available estimates, manufactured goods accounted for more than 80% of total exports in 2010, up more than 60 percentage points from the 1970s [UNCTAD (2012)].<sup>30</sup> These patterns of global trade may actually exacerbate the risk of current account deficits for primary commodity export countries.

> 30 Export diversification and rising share of manufacturing in aggregate output and exports have been the main drivers of sustained economic growth rates and reserve accumulation achieved by East Asian emerging market economies. According to available estimates, manufactured goods accounted for over 80 percent of East Asian emerging markets’ total exports in the years preceding the global downturn [UNCTAD (2012)].

21
investment in natural resources. Indeed, the threat of violence as we discuss it above can also be interpreted as limited enforcement of ownership rights. In this sense, the theoretical content of the literature examining the impact of property rights on investment, from Demsetz (1967) to e.g. Besley (1995a) and McMillan et al. (2002), all of which find a positive link between secure property rights and investment at the microlevel, is applicable to our question and can offer predictions about the possible impact of violence. Finally, Echeverry et al. (2001) provide a comprehensive brainstorming of various ways in which violence may hamper capital accumulation, including skewing portfolios towards less productive assets.

The literature still leaves significant space to breach. Among the above there is a virtual absence of papers analyzing within-country variation. Most empirical studies are crosscountry and therefore susceptible to omitted variable bias and endogeneity. Further, very few papers have looked at portfolio composition. Thus this paper is novel in various ways. First, it uses a unique Colombian micro-level dataset to examine the impact of violence on savings. Second, it analyzes the reaction of portfolio composition rather than absolute assets to violence.

The rest of this paper is structured as follows: the next section (2) gives some background on the history and types of violence present in Colombia, section 3 explains the conceptual framework, section 4 discusses the dataset in the context of this framework, section 5 presents the empirical results and section 6 concludes.

# **VIOLENCE IN COLOMBIA**

Colombia has long battled with exceptionally high levels of various kinds of violence. Leftwing guerrilla movements, mainly the ELN (National Liberation Army) and FARC (Red Armed Forces of Colombia) have gathered strength since the mid 1980s and 3
<!-- Start of picture text --> +<br>South<br>°<br>A<br>nN a a Fh Na<br>z&<br>A-* A—A<br>Oo<br>2005 2007 2009 = 2011 2013 2015 2017<br><!-- End of picture text --> <!-- Start of picture text --> +<br>rn<br>° aI South<br>pe[> iy A EO EINW<br>4 »s<4 af, -ANE<br>A g aw<br>Ne , "<br>N Pp eA wv) PP<br>g a<br>x a<br>iu<br>Oo<br>2005 2007 2009 2011 2013. 2015 2017<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>a<br>© ANE<br>lo A<br>Qe<br>K & A<br>+ x A<br>aa o—B D<br>id 5 x 2 a? Ss a—A gouth<br>& . _ Z 3 .<br>N 3 A>m—a = a Z<br>y<br>Oo<br>2005 2007 2009 2011 2013. 2015 2017<br><!-- End of picture text -->
with �= �h where _h_ represents worker human capital and � reflects the returns to human capital conditional on effort and time, which can be thought of as a measure that includes time-invariant characteristics such as individual skill or genetic endowments. In the context of agricultural labor, worker human capital, _h_ , includes characteristics of the worker including their health status, which varies over time. When the effort intensity of work is less than one, �< 1, equal effort will be used per hour worked as there are diminishing returns to additional effort on earnings. Firms then choose � and �, as well as monitoring costs, to maximize income.<sup>3</sup> In our piece rate wage setting with fixed work hours, we can use the reduced form earnings function to disaggregate the effects of changes in health status on effort, measured using physical activity data, into labor supply and productivity effects.<sup>4</sup> Workers cannot trade off effort and hours worked in our study context, and this facilitates the identification of changes in health status on worker effort.<sup>5</sup> Dillon et al. (2014) defined the return to effort partly as a function of health perceptions that in turn were determined by a worker’s true health status, _H,_ as well as health information, _I_ , since both changes in _H_ and changes in _I_ appeared to affect worker behavior. Incorporating this insight into the Beckerian model, health status is clearly one element in the human capital vector _h_ , that is h= h(�). Information about health, on the other hand, does not necessarily affect health status directly, at least in this static model, but new information about health, especially if unexpected, may affect the perceived cost of effort and hence the effort choice supplied by the worker, that is �= �(�). Therefore, we can specify the return to effort (per unit time) as partly a function of health perceptions, �(�) = �h(�)�(�)<sup>�</sup> . The total earnings function

> 3 In Becker (1977), the trade-offs between production technology and monitoring are fully derived with respect to the firm’s choice of the effort intensity of work, �, and the distribution of worker endowments, �, employed. 4 In the theoretical model, monitoring costs and production technology are choice variables, but our single study site allows us to abstract from differences in production technology across workers and differences in monitoring, as the firm has a uniform monitoring system. Differences could arise in supervision of different groups of employees and we consider this potential source of unobserved heterogeneity in our fixed effects strategy discussed in more detail below.

> 5 Workers cannot choose hours on the plantation. They are picked up from their local villages by a plantation bus and returned to their village at closing. All workers have the same hours supplied at the plantation conditional on labor supply.

5
**TABLE 3. Test of Parallel Trends before Privatization in the Network Access Sample**

|Variables|**A**<br>|<sup>**a**</sup><br>|**B**<br>|<sup>**a**</sup><br>|**C**<br>|<sup>**b**</sup><br>||**D**<sup>**c**</sup><br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|
|Time trend|0.002|−0.003|<br>0.010**|<sup>0.008*</sup>|<sup>0.003</sup>|<sup>−0.002</sup>|<br>0.006**|<br>0.001|
||(0.002)|(0.003)|(0.004)|(0.005)|(0.002)|(0.003)|(0.002)|(0.004)|
|Time trend by treatment status|0.004|0.004|0.004|0.004|−0.000|−0.005|<br>−0.007**|<sup>−0.010**</sup>|
||(0.004)|(0.006)|(0.004)|(0.006)|(0.003)|(0.005)|(0.003)|(0.005)|
|Education||0.030|<br>|0.038|<br>|0.037||0.041***|
|||(0.027)|<br>|(0.033)||(0.027)||(0.015)|
|(log) Household per capita<br>||0.205*||0.234**||0.185*||0.098|
|income||(0.105)|<br>|(0.107)||(0.106)||(0.104)|
|(log) Accumulated precipitation||0.052**||0.050||0.046**||0.053***|
|||(0.021)||(0.043)||(0.021)||(0.020)|
|Year effects|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|416|416|261|261|400|400|270|270|
|Log likelihood|−199.18|−184.35|−126.12|−116.78|−191.76|−176.84|−124.75|−122.75|
|Sampleperiod|1986|–2005|1993|–2005|1986|–2005|1986|–2005|

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ Estimates are obtained using a Papke & Wooldridge Fractional Logit Model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city fixed effects.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.<sup>a</sup> Treatment Units (three cities): Maldonado, San Carlos, and Pan de Azúcar; Control Units (32 cities): Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del Yí, Sarandí Grande, Tacuarembó, Tranqueras, Treinta y Tres, Trinidad, and Young.

> <sup>b</sup> Treatment Unit (one city): Maldonado; same Control Units as in A.

> <sup>c</sup> Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

28
# 5.2 Employment Participation: By Type

We next examine the effect of displacement status on the likelihood that an individual will participate in different types of work, namely: (a) wage employment; (b) self-employment (non-farm business), (c) own or family farm activities, and (d) unpaid work in the family business.

Interestingly, there is no statistically significant difference in female participation in wage employment between refugee and host community households across regions. We find similar results for male participation in wage work in Somalia and Benishangul-Gumuz regions, while Eritrean male refugees are less likely to participate in wage employment compared to their hosts (significant at 10%).

However, there are significant differences between refugees and hosts in participation in selfemployment, household farm activities, and family business (results for family business are presented in Table A3).

_Table 4: Participation in different types of employment by gender: matched results_

||Wage em<br>|ployment<br>|Self-emp<br>|loyment<br>|Famil<br>|yFarm<br>|
|---|---|---|---|---|---|---|
||Female|Male|Female|Male|Female|Male|
|_Hosts - reference_|||||||
|Refugee - Somali|0.051|0.034|-0.002|-0.097|-0.048<sup>*</sup>|-0.176<sup>***</sup>|
||(0.040)|(0.069)|(0.033)|(0.062)|(0.029)|(0.053)|
|Refugee - Eritrea|-0.008|-0.118<sup>*</sup>|-0.059|-0.146<sup>**</sup>|-0.012|-0.049|
||(0.042)|(0.063)|(0.051)|(0.058)|(0.025)|(0.035)|
|Refugee - Sudanese|-0.030|-0.026|-0.140<sup>***</sup>|-0.234<sup>***</sup>|-0.232<sup>***</sup>|-0.231<sup>***</sup>|
||(0.027)|(0.033)|(0.033)|(0.048)|(0.034)|(0.034)|
|Number of Obs.|2002|2002|2002|2002|2002|2002|

Notes: The dependent variables are participation in paid employment, self-employment, and family farm activities. All regressions include camp fixed effects. Robust standard errors are reported in parentheses. Asterisks indicate statistical significance at 1%<sup>*******</sup> , 5%<sup>******</sup> , and 10%<sup>*****</sup> levels.

Sudanese and South-Sudanese refugees, both male and female, are significantly less likely to be self-employed and participate in family farm activities compared to their hosts. This is partly due to refugees are less likely to have access to land and own their own business (see Table 1).

While we find no significant difference in participation in family farm activities between Eritrean refugees and their hosts, male Eritrean refugees are less likely to be self-employed by 15 percentage points compared to their hosts.

Female participation in self-employment shows no significant difference between Somali refugees and their hosts, while the difference in participation in family farm activities is only 19
<!-- Start of picture text --> 3<br>@2002 2011<br>w 2.5<br>So<br>£<br>= 2<br>o<br>w<br>6 1.5<br>2<br>&<br>5 1<br>i=l]<br>205<br>fe)<br>incomplete general special tertiary<br>secondary secondary secondary<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>m2002 m2011<br>w 2.5<br>no<br>=<br>ce 2<br>o<br>o<br>o 1.5<br>Te}<br>0<br>‘ot<br>no<br>205<br>0<br>incomplete general special tertiary<br>secondary secondary secondary<br><!-- End of picture text -->
other oil rich countries such as Qatar, Saudi Arabia and Algeria where household expenditure as percentage of GDP can vary between 11 and 35 percent.<sup>10</sup> Household final consumption is essentially a small fraction of output as a whole because oil dominates the economy (producing more than two-thirds of GDP). Only a small share of oil proceeds accrues to households via wages and public transfers while a bigger share accrues through subsidies, which do not appear in actual expenditure.

**Table 5: Household statistics projected to 2013**

||Population<br>(persons)|Number of<br>households|Household<br>size<br>(persons)|Total<br>expenditures<br>(LYD)|Expenditures<br>per capita<br>(LYD)|Expenditures<br>per household<br>(LYD)|
|---|---|---|---|---|---|---|
|Quintile_1|1,936,699|203,399|9.5|1,842,216,192|951|9,057|
|Quintile_2|1,512,025|203,373|7.4|2,288,316,928|1,513|11,252|
|Quintile_3|1,264,391|203,346|6.2|2,580,271,872|2,041|12,689|
|Quintile_4|992,019|203,392|4.9|2,745,245,952|2,767|13,497|
|Quintile_5|666,346|203,331|3.3|3,077,710,080|4,619|15,136|
|Total|6,371,480|1,016,842|6.3|12,533,761,024|1,967|12,326|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and World Bank staff calculations.

In what follows, the incidence and impact analyses are presented separately for food products and energy products. The analysis is conducted separately because of the different subsidy systems (universal for energy but quota-based for food), which require a different set-up for the subsidies simulation model. Also, differences in the relative importance of indirect effects (as noted above) call for a different approach to interpreting the results. The analyses that follow are based on SUBSIM, a subsidies simulation package produced by the World Bank (www.subsim.org).

# **4. Food subsidies**

# **4.1.The distribution of food subsidies**

Food subsidies are relatively progressive but a third of these subsidies does not reach households. Below, we quantify the size of subsidies received by households at different income levels. The results suggest that food subsidies are relatively progressive in Libya, mostly thanks to the quota system by which they are administered. However, only about 65 percent of the budgetary costs of subsidies reach households. The difference is probably explained by “leaks” from the subsidy system, including waste from illegal resale of subsidized items outside of the quota system at close-to-market prices and perhaps administrative costs that cannot be clearly separated and accounted for.

Our estimates are an _upper bound_ of the subsidies received by households. This is because the analysis is based on the assumption that all households purchase the entire amount of quotas they are entitled to.<sup>11</sup> This may not always be the case as some households may choose not to go to cooperatives to purchase

> 10 <u>http://data.worldbank.org/indicator/NE.CON.PETC.ZS.</u>

> 11 We make that assumption when in the survey there is no separate expenditure data for subsidized versus nonsubsidized quantities for a given product.

8