rafmacalaba/datause-encoder-data huggingface dataset:
To illustrate the importance of allowing the effect of commodity prices on revenue to vary across countries, Figure 4 displays on the vertical axis the point estimates of an univariate regression of the change in commodity revenues (as a share of MAGDP) on the log change in the commodity price index. The predicted impact on resource revenue of a 100% increase in the commodity price index ranges from 0% to 45% of MAGDP. This range is also wide within SSA countries (0% to 40%). On the x-axis, we plot the 2008–2012 average commodity revenues as percent of 2010 GDP, ������� . The estimates are almost proportional, suggesting yy that 1 is close to 1 and is stable across countries (0.92, on average; 1.01 for SSA). ii CCRRC We get a very similar estimate of 1 in our first-stage estimates. δδ δδ

# _C. Data_

To estimate equations (4)–(6), we use annual panel data on government expenditure and commodity resource revenues over 1980–2016 for 56 commodity exporters (22 in SSA). The criteria to add a country in the panel is to have sizable commodity exports (more than 5% of GDP or one-third of total exports, on average, in 2008–2012) and to have at least two consecutive years of data for all of the variables included in the regression.<sup>46</sup> Appendix Table 5 displays the list of countries included in the panel as well as the data sources, and the sample period for each variable. Appendix Table 7 provides summary statistics of all variables in the regressions.

To construct our spending variable (LHS of equation (4)) we obtain data on “General Government Total Expenditure, Percent of GDP” (GGEY), “Gross Domestic Product, Current National Prices” (NGDP), and “Inflation, Average Consumer Prices” (CPI) from the International Monetary Fund (IMF) World

> 46 Commodity export data are taken from the UN Comtrade Database. See Appendix Table 1 for details on commodity exports for each of the 56 countries included in the sample.

34
# **3.5 Contemporary Predictors of Rural Non-Farm Employment**

## **3.5.1 Individual Predictors of Rural Non-Farm Employment**

We close our analysis of the predictors of rural non-farm employment by exploring the role of education, gender and community identity in predicting non-farm employment with contemporary data. As the last NSS employment survey was undertaken in 2011, we rely on household survey data from the Consumer Pyramids (CP), which began its household surveys in 2014. Unlike the NSS which is a repeated cross-section, undertaken once every 5 years, the CP tracks households over time in a panel framework, with each household surveyed thrice every year. We select the year 2015 for our analysis, as starting towards the end of 2016, a number of policy interventions affected the functioning of the economy.

Unlike the NSS, the CP data provides only a coarse mapping of individuals to their respective occupations. Thus, instead of precise sectoral employment outcomes, we explore whether the individual is a) employed as a wage laborer or industrial worker; b) operates a small business; c) engaged in formal business activity; d) a qualified self-employed entrepreneur; or e) a white-collar professional. The empirical specification is similar to equation (1), with flexible controls for district-level factors through district and town/village fixed effects.<sup>12</sup> We also use survey-month fixed effects to account for any seasonality based on the timing of the survey. All specifications include sample weights to make the data representative.<sup>13</sup> Standard errors continue to be clustered by district.

We start by studying the predictors of LFP, unemployment and participation in nonfarm activities (Table 11). The outcome of interest in the first three columns is a dummy equaling 1 if the individual participates in the labor force; in columns (4)-(6), a dummy equaling 1 if the rural worker is unemployed; and in columns (6)-(9), a dummy equaling 1 if the rural worker is engaged in some non-farm activity. For each outcome of interest, we first estimate a specification where we only include individual and household covariates, in addition to a survey month fixed effect. Subsequently, we saturate the specification with district (town/village) effect to flexibly account for local factors affecting labor market choices.

The results are remarkably consistent with those obtained from the historical NSS data. First, we note that the coefficients are stable to the inclusion of district or town fixed effects, indicating that after controling for individual and household predictors, district-level factors have limited predictive power. Nonetheless, we focus on the most restrictive specification

> 12 The CP provides identifiers for the level of sampling – the city or town for urban households, and villages for rural households – enabling us to use town/village fixed effects.

> 13 The sample weights become particularly important for the CP as it over-samples urban households.

27
mobility is negatively affected by strikes in Bangladesh, which reduces output growth relative to a typical workday.

# C. Health and Environmental Externalities

Transport involves various social and environmental externalities, including road accidents, air and noise pollution, and associated emotional stress and health issues. Car accidents and related injuries have been found to increase with traffic density (Bauernschuster et al., 2017), leading to higher automobile insurance premia (Edlin and KaracaMandic, 2006). The health effects of transport-induced pollution—mainly from nitrogen oxides (NOx), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter (PM), and unburned hydrocarbons—are well documented in the literature. These effects include cardiovascular and pulmonary problems (Bauernschuster et al., 2017), low birth weight, prematurity and increasing risks of infant mortality (Currie and Walker, 2011; Knittel et al., 2016). These health effects, in turn, may have economic implications. Empirical evidence from London (Duffy and McGoldrick, 1990) and Los Angeles (Evans and Carrère, 1991) suggests that exposure to highly congested traffic increases the level of psychological stress, which may negatively affect task performance (Stokols et al., 1978) and proofreading abilities (Schaeffer et al., 1988). In contrast to these findings, however, a recent study using a large sample of driving instances from the American Time Use Survey (ATUS) finds that congestion during rush hour commuting has little impact on happiness, sadness, stress, and fatigue.

With the rise of big data, the opportunity to better understand congestion-induced social and environmental externalities has emissions can drastically improved. Traffic now be tracked with more precision using urban sensors in combination with crowdgenerated points of interest (Thakuriah et al., 2017). For example, Nyhan et al. (2016) analyze GPS data from a taxi fleet of over 15,000 vehicles in Singapore to estimate emissions from different sources of pollutants at high spatial resolution and temporal scale. Gately 11
consolidated maximum of 35% without infringing GATT obligations<sup>5</sup> , although an investigation proving injury or threat thereof was necessary in order to adopt measures. The same requirement applied to the adoption of MCVs.

# **<u>Graph</u> 2: Applications Submitted and Investigations Initiated, 1981-2002**

<!-- Start of picture text --> 70 120<br>60<br>100<br>50<br>80<br>Tariff Reductions<br>40<br>60<br>30 24.5 26.25<br>20 20 20 40<br>20<br>15 15 15<br>10<br>11 11 11 11 11 11 11 11<br>10 10 10<br>9 8 20<br>10 7 6<br>0 0<br>19801981198219831984 1985198619871988198919901991199219931994199519961997199819992000200120022003<br>Years<br>Applications submitted Investigations conducted RER Average Tariff<br>Number<br><!-- End of picture text --> <u>Source: Central Bank of Chile and www.cndp.cl</u> , and Graph 1.

From the point of view of resource allocation, these measures -surcharges and MCV- were inefficient, as they affected imports independently of their origin or whether their prices were distorted or not. Consequently, it was presumed that there was a “distortion” in international markets in general and companies were forced to choose more expensive suppliers. Besides, when these measures were applied to intermediate goods, the effective protection of the final good was affected. However, apart from the domestic

> 5 In the relevant section, Article 10 pointed out that “3 per cent, 5 per cent, 8 per cent, 10 per cent, 12 per cent, 15 per cent, 18 per cent and 20 per cent surcharges should be established _ad valorem_ ”....

9
33 allowed to practice by law, 1<sup>22</sup> have been rare indeed (with around 600 in Slovenia). The number of private attorneys is expected to increase rapidly with the increasing role of private market forces. New drafts of laws prepared at the beginning of 1992123 are designed to regulate the profession and set the higher standards for entrance through a bar examination.

Furthermore, those lawyers that do work in legal professions tend to be inadequately prepared for the legal demands of a market economy. Until the mid-1980s the law schools' curricula and law practice provided little exposure to market-oriented commercial law principles. Social property and all relations and obligations stemming from it were the principle topics of study and work. This began to change, however, in the late 1980s, when the principles and institutions of industrial market economies began to creep into law curricula. Many law professors had been formally educated in the<sup>West,</sup> and they could draw on their earlier learning to introduce these new areas of study.

But while current law students are getting increasing exposure to market-oriented commercial law, the job of retooling existing judges and lawyers is a major challenge. Although judges in particular remain respected for their honesty and integrity, they understandably lack experience and expertise in many of the more complex areas of law applicable to market economies. Technical assistance, training, and time can all help to remedy this situation, as can increased publication of legal articles and court decisions.

# The Court Svstem

The Slovene court system is divided into three levels, with 8 basic courts, 4 appellate courts, and one supreme court, in addition to the constitutional court discussed earlier.124 Twelve specialized "courts of associated labor", which deal mainly with labor disputes in socially-owned enterprises, also still continue in operation. As described earlier, cases in basic courts are handled by panels of professional and lay judges, while those in higher courts are handled exclusively by professional judges. Slovenia's 14 courts are currently staffed by about 500 judges and almost 7000 lay judges. 125 122 In some socialist economies lawyers were not--and in Romania's case are still not--allowed to practice independently. See Gray, et. al., Romanian Legal Framework (1991).

**123** Osnutek zakona o odvetniitvu (Draft Law on Lawyers], Internal Material, Government of the Republic of Slovenia, March 1992.

124 Zakon o rednih sodiiaih (Ordinary Court Law], Official Gazette SRS, No 10 (1977), with most recent amendment Official Gazette SRS, No 8<sup>(1990).</sup> 125 Statistical Yearbook of Slovenia, 1990.
To more closely examine whether camps attract in-migration and the expansion of economic productivity, we examine population and nightlights as outcome variables in the regression: yit t t i (2) ii ii ii ii = ωω+ κκ(t dd ∗ttaat tt ) + μμ + δδ + εε Regression 2 is identical to Regression 1, but the outcome variable is continuous. We examine two dependent variables: the natural log of the population in the 100m tile the pixel falls in<sup>12</sup> (using annual data 2010-2020), and the nighttime lights score for the pixel (using annual data 2014-2020). Like with Regression 1, we adjust Regression 2 so that we can test for parallel trends. After dropping observations from 2017 or later, we test two treatment year placebos in separate regressions: one assigning 2015 as the placebo treatment year, and one treating 2016 as the placebo treatment year.

# **5.3 Robustness Check**

It is plausible that changes outside the camp’s official boundaries may be due to unofficial camp expansion. Prior to the construction of fencing, camp residents may have expanded their settlement areas beyond the demarcated border of the camp in order to alleviate population pressure. To ensure that any changes in the treatment area are not due to unofficial camp expansion, we conduct a robustness test in which we repeat Equation 1, omitting all pixels 0-1 km from the camp boundary.

# **5.4 Heterogeneity Analysis**

Some of the camps sit alongside the Teknaf Wildlife Preserve. Given the importance of protecting this area’s forest cover and biodiversity, we conduct a heterogeneity analysis to see if pixels in protected areas are deforesting more or less than pixels in non-protected areas. To do so, we repeat Equation 1 for the sub-sample of treatment and control pixels in protected areas and additionally repeat the regression using only treatment and control pixels outside protected areas.

> 12 None of the sample pixels has an estimated population of zero. Hence, a natural log transformation will not induce bias but will facilitate the interpretation of the coefficient estimate.

20
3 provide a fuller understanding of the main issi -as and the underlying dynamics, the findings from this period are compared with those of Pfeffermann (1968) for the 196(s and, whenever possible, with those of Svejnar (1984) for the mid 1970s.

As mentioned above, our main aim is to provide an understanding of the functioning of the industrial la' 'r market and evaluate the effects of the employment and wage s3tting practices on enterprise efficiency. In undertaking this task, we pay particular attention to government wage and employment regulations; the role of trade unions and the organizational characteristics of firms; the national and skill composition of the labor force and the corresponding labor productivity and wage differentials; the existence of training schemes and skill bottlenecks; the effect of education, experience, and training on worker earnings; and the relationship between a worker's income and the migration of family members from the countryside.

Our approach is to combine institutional analysis with econometric estimation. In Chapter 2 we provide a brief survey of Senegal's economic performance, focusing on the evolution of the industrial sector. In Chapter 3 we describe the main features of the Senegalese industrial relations system, and in Chapter 4 we discuss basic statistical evidence relating to the characteristics of the labor force, employment, and worker earnings. Chapter _5_ contains an institutional analysis of the impact of labor regulations and the industrial reliti6ns system on economic performance in industry; chapters 6 and 7 present the corresponding econometric analysis. Chapter 6 also contains estimates of the productivity effects of the national and skill composition of the labor force, different forms of enterprise ownership, and foreign trade variables. This analysis is based on data from a relatively sizable enterprise sample which we collected during the course of the research project. Chapter 7 presents econometric estimates of the determinants of worker earnings in a smaller (sub) sample of modern sector industrial enterprises. Chapter 8 is devoted to exploring the relationship between industrial wages and the size of the extended family. Finally, Chapter 9 presents the summary and policy conclusions.
48 with the Labor Service after they had hired them. Procedures for recruiting vary by skill level: The most common procedure for hiring unskilled workers (ouvriers-maneuvre) appears to be to recruit them at the gate as daily workers and offer them permanent contracts if they perform well. As seen in Table _S5_ over two-thirds of the managers we surveyed indicated they follow this practice. In observing the number of men waiting at the gates, we could see that the supply of daily workers is unlimited in the relevant range. Workers are sometimre hired in this manner because they are recommended by someone who works in the firm.

TABL 5S2 Smpl Methods of Reruiting Emloyees at Varius Sill _Lvls_

|Method of<br>Recruiting|Unskilled<br>Workers|Skilled<br>Workers|Technical<br>Personnel|Managers|
|---|---|---|---|---|
|Hire FustasDaily Workers|69 %|-|-||
|Ask- Workers for<br>Recommendadon|1S %|1S %|8 %||
|Promotion Within the Frm|-|23%|_1S_%||
|AdvertiseinNewspaper|15 %|24 %|8 %||
|Hire DirectlyfromSchool|-|8 %|8 %||
|Hire from Other Firms in<br>Senegal|-|-|8 %|1S%<br>|
|Europe|-|--||1s5%|

Note: These figures represent percentages of the total sample of managers (17) that gave these answes Answers were not provided by some managers for some ki groups. Source: Authors' suvey of managers, 1986.

Skilled workers and white coUlar e.nployees are generally hired through newspaper advertising or directly from schools or other firms. Internal promotion also plays an important role. Although most firms tend to hire their technical personnel from other firms,
for the attacks of the other aggressor group. Therefore, we control for both types of attacks in the same regression model (Specification C). The third concern is that the results may be driven by endogenous sorting of the local population. On the one hand, the population residing in areas closer to conflict may decide to relocate to safer areas. On the other hand, Boko Haram attacks led to the displacement of over 2 million people; thus, the results could be driven by the responses of displaced households included in the DHS sample, which may have different gender norms (e.g., regarding employment, fertility choices, or violence within the household). As the DHS data do not contain a variable that indicates whether a household is displaced, we use as a proxy variable the number of years of residence in the cluster, taking five years or more—the highest category—as a threshold. We conduct the analysis focusing on households that have resided in the cluster for at least five years (Specification D).

# **5 Results**

## **5.1 Boko Haram and Fulani Violent Events**

We start by providing the results on Boko Haram violent attacks in Table 2 for women (top panel) and men (bottom panel). This table displays the results of the estimated effects according to the baseline specification and the four alternative specifications.

Concerning the financial outcomes, we observe a positive effect of distant Boko Haram attacks on the employment of women and men for all regression models (Column I). The coefficients are statistically significant at the 5 percent level, except in various model specifications for the estimated effect on female employment, in which they appear statistically significant at the 10 percent level. Consistent with this overall increase in economic activity at the household level, the point estimates of the effects on the probability of independently owning a house or land are positive and statistically significant among women and men (Column II).<sup>15</sup> Yet, while we observe statistically significant improvements in auton-

> 15We further test the results focusing on co-ownership of assets and find them to remain robust. These results are available upon request.

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26 Piron, V. (1999). "Urbain-Interurbain: La Problematique est devenue globale". _Transport. No._ 393. Jan., Feb.

Quinet, E. _(I1998), Principes d'Economie des Transports,_ Economica, _Paris._ Skamris, M.K. and B. Flyberg (1997), "Inacurracy of traffic forecasts and costs estimates on large transport projects", _Transport Policy,_ Vol. 4, No.3, pp<sup>141</sup> -46 Small, K.A. and Winston, C. (1999), _"The Demand for Transportation: Models and A4pplications ",_ in Gomez-Ibanez, Tye and Winston Tirole, J. (1997), "Commentarios a la propuesta de Engel, Fisher and Galetovic para las concesiones de careteras", _Estudios Publicos,_ No. 65, pp<sup>2 0 1</sup> -<sup>1 4</sup> Trujillo, L. and Jacob, M. (1999), "Prediccion de demanda para infraestructura de transporte en Espana", documento de trabajo, Departamento de Analisis Economico Aplicado, Universidad de La Palmas de Gran Canaria Verbeke, A, E. Declercq and D. Teurelincx (1996), "The future growth of contained traffic in the Port of Antwerp", pp 51-70, in Winkelmans, W. and T. Notteboom (eds) Ville, J. (1970), "Le paradoxe de la congestion", mimeo, Ministere de l'Equipement, Paris, France Winkelmans, W. and T. Notteboom, eds. (1996), _The issue of prognoses in maritime transport and seaport production in Belgium and Poland, a conceptual and empirical study,_ Garant, Leuven, Belgium
# **Annex Table 7: Deviations from threshold pre- and post DSA shocks in SSA countries (Percentage points)**

# **<u>7A: Public DSA, Debt-to GDP ratio</u>**

|||<br><br>Deviati<br>**Situa**|<br> <br>on from Th<br>**tion post‐s**|reshold<br>**hock**|**situ**<br>Deviati|<br>**ation pre‐s**<br>on from Th|<br>**hock**<br>reshold|
|---|---|---|---|---|---|---|---|
|**GROUP**|**STRESS TESTS**|**Max**<br>**Breach**|**Length (yrs) **<br>**of deviation **|**Average**<br> **deviation**|**Max**<br>**Breach**|**Length (yrs**<br>**of deviation **|**Average**<br> **deviation**|
|Whole sample|A2|83.1|7.4|10.0|122.1|2.9|4.4|
||A3|165.8|4.2|8.7||||
||B1|178.6|5.6|9.4||||
||B4|80.8|4.3|5.1||||
|Fragile|A2|38.9|8.9|5.7|122.1|4.6|6.2|
||A3|165.8|5.1|13.4||||
||B1|178.6|7.7|18.8||||
||B4|80.8|5.7|6.0||||
|Oil Exporting|A2|35.4|4.5|3.6|26.8|3.5|2.3|
||A3|65.7|6.5|6.9||||
||B1|40.2|11.0|8.8||||
||B4|28.1|3.8|2.6||||
|Frontier LIC|A2|52.7|6.1|6.2|122.1|1.6|5.0|
||A3|165.8|1.6|6.7||||
||B1|178.6|2.2|8.4||||
||B4|80.8|3.8|3.8||||
|HIPC (post CP)|A2|55.0|7.2|6.8|122.1|2.6|2.6|
||A3|165.8|3.1|4.0||||
||B1|178.6|5.1|5.7||||
||B4|80.8|3.9|3.0||||

33