rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

The Syrian Civil War that began in 2011 has had an enormous human cost. The United Nations puts the number of people who have fled their homes at 9 million. While 6.5 million of these refugees are internally displaced in Syria, 2.5 million have become refugees in Syria’s neighbouring countries: Turkey, Lebanon, Jordan and Iraq. At first, the number of refugees arriving in Turkey was relatively small at about 7,600 in November 2011 according to United Nations High Commission for Refugees (UNHCR) statistics. The situation had reached crisis proportions by the end of 2012 when the total number of registered refugees in Turkey was approximately 135,000. The flood of refugees continued and reached above a million and a half by the end of 2014.

The refugee crisis has already led to research on the economic impact of hosting refugees in Turkey. Two recent papers analyze the impact of the Syrian refugee crisis in Turkey on native employment; Ceritoglu et al. (2017) and Del Carpio and Wagner (2015).<sup>1</sup> The main difference between the two is methodological. While the former uses a difference-in-differences (DD) methodology, the latter employs an instrumental variables (IV) strategy. Both papers find a negative effect on informal employment, but Del Carpio and Wagner (2015) also finds positive effects for native high-skill employment. In this paper, we complement the findings of these papers by analyzing the effects of refugee inflows on firms rather than employment. More specifically, we analyze the effects of refugee inflows on entry of new firms, new foreign firms, gross profits and total sales in hosting regions.

Up to 2014, the Syrian refugees’ destination in Turkey was limited geographically to the location of the refugee camps in the border region. The size of the inflow is large enough to allow for the estimation of the effects on the outcomes using a DD model, which is the strategy employed by Ceritoglu et al. (2017). Potentially worrying however is that the treated regions are close to the Syrian border and might be affected in other ways than the refugee crisis such as trade, which would lead to biased estimates. Furthermore, the DD approach might be less appropriate after 2013 since refugees began spreading to the rest of Turkey from 2014 onwards and started moving to Europe in late 2014 and 2015 1
- HALLEGATTE, S., A. VOGT-SCHILB, J. ROZENBERG, M. BANGALORE, AND C. BEAUDET (2020): “From poverty to disaster and back: A review of the literature,” _Economics of Disasters and Climate Change_ , 4, 223–247.

- IPCC (2014): “Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,” Tech. rep., IPCC, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

- KOCORNIK-MINA, A., T. K. MCDERMOTT, G. MICHAELS, AND F. RAUCH (2020): “Flooded cities,” _American Economic Journal: Applied Economics_ , 12, 35–66.

- NAKAMURA, S. (2017): “Tenure security premium in informal housing markets: A spatial hedonic analysis,” _World Development_ , 89, 184–198.

- PERALTA-QUIROS, T., T. KERZHNER, AND P. AVNER (2019): “Exploring Accessibility to Employment Opportunities in African Cities: A First Benchmark,” _The World Bank_ , Washington, DC, Policy Research working paper, no. WPS 8971.

- RABASSA, M. J. AND J. I. ZOLOA (2016): “Flooding risks and housing markets: a spatial hedonic analysis for La Plata City,” _Environment and development economics_ , 21, 464–489.

- ROSEN, S. (1974): “Hedonic prices and implicit markets: product differentiation in pure competition,” _Journal of political economy_ , 82, 34–55.

- UN (2019): “World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420),” _United Nations, Department of Economic and Social Affairs, Population Division_ .

- WB (2017): “Greater Antananarivo: Urban poverty and resiliance study,” _The World Bank_ , unpublished manuscript.

- ——— (2021): “Global Economic Prospects, January 2021,” _The World Bank_ .

22
**Figure 6.  Simulation results from the restructuring model with different configuration of parameters: two typical trajectories for inequality indices**

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An attractive feature of the model is the conceptualization of restructuring: not as one–time shift in the behavior of agents, but as a whole array of outcomes with different degree of rent appropriation by insiders in partly restructured enterprises. The empirical study of the first ten years of transition revealed the coexistence of new, partly restructured and unrestructured firms as a defining feature of the move from a command to a market economy (World Bank 2002e). This introduces an additional source of variability and hence inequality, which is captured by the model.

However, a comparison of predictions from the model with empirical evidence shows a surprising reversal of patterns between Central and Eastern Europe and the CIS. The inverse U-shaped trajectory of inequality seems to emerge not in the CEE, but in some CIS countries. The failure of the model to predict the actual evolution of inequality may be a result of its limitations of the model<sup>8</sup> , or alternatively the effect of mitigation of inequality through offsetting policy measures.

Despite these limitations, the ability of the model to portray a large variation in the levels and shape of the development of inequality is instructive. It suggests that there is probably no single “transition” story as far as the evolution of inequality is concerned. Furthermore, the model results are broadly consistent with the story emerging from the earlier description of different drivers as being one which can yield a wide variety of outcomes across countries

> 8 This is essentially a model of labor reallocation which omits capital (or mixed) income, thereby by-passing one of the most important features of transition.  Furthermore, parameters of the distribution within each sector are taken as exogenous  and constant.

17
|Constant|-4.087***|-6.878***|-1.612***|-0.888***|-1.386***|-0.441|3.019***|2.510***|
|---|---|---|---|---|---|---|---|---|
||(0.523)|(1.232)|(0.189)|(0.318)|(0.402)|(0.735)|(0.430)|(0.802)|
|Observations|4905|4874|4905|4879|1430|1313|1381|1138|

Source: Authors’ estimation using RLMS, ESS and LiTS for year 2016.

Note: This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their work conditions, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. Dependent variables in columns (1)(4) are from the RLMS, (5) and (6) from the LiTS and (7) and (8) from the ESS. All dependent variables are defined as binary. The LiTS does not include information on occupation and the ESS does not include a location variable for urban or rural. All specifications include region dummies. Establishment size of place of work is reported in three categories in the RLMS and the LiTS (under 10, 11-100 and 100+) and in five categories in the ESS (under 10, 10-24, 25-99, 100-499 and 500+). The smallest firm size category is excluded in all specifications. Other omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively.

45 Informal Employment and Worker’s Wellbeing in Russia / June 2019
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5 deteriorated among the poorest in four countries (Ghana, Mali, Senegal and Tanzania) and improved in four (Madagascar, Uganda, Zambia and Zimbabwe). But short-run malnutrition, or wasting (measured by weight for height), has increased among the poorest quintiles of six countries (Ghana, Madagascar, Mali, Senegal, Uganda and Zimbabwe). In general, the data indicate a major problem of increased wasting during the 1990s including among the poor. This is not fully understood, and clearly calls for further investigation.

**Table 3. Malnutrition by wealth quintile for eight African countries**

_Percent of children between 3 and 36 months of age with anthropometric z-score less than -2_

||Gha|na|Mada|gascar|M|ali|Sen|egal|Tanz|ania|Uga|nda|Zam|bia|Zimb|abwe|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|SurveyYear|1988|1993|1992|1997|1987|1995|1986|1992|1991|1996|1988|1995|1992|1997|1988|1994|
|_Heightforage:_|||||||||||||||||
|Poorestquinule|34|38|53|50|28|38|27|35|43|46|48|43|49|46|41|23|
|Secondquintile|33|30|45|40|29|39|23|30|44|44|45|40|45|49|37|24|
|Thirdquintile|30|29|51|51|25|34|24|30|43|42|44|40|39|43|27|_25_|
|Fourthquintile|27|23|50|49|26|32|25|20|40|39|42|33|30|33|25|22|
|Richestquintile|21|17|44|46|17|21|13|14|26|28|27|25|27|27|12|12|
|_QI/Q5_|_1.6_|_2.2_|_1.2_|_1.1_|_1.6_|_1.8_|_2.1_|_2.5_|_1.7_|_1.6_|_1.8_|_1.7_|_1.8_|_1.7_|_3.4_|_1.9_|
|_Weightfor height:_|||||||||||||||||
|Poorestquinble|7|16|6|10|12|28|7|15|9|8|2|6|7|_5_|1|_5_|
|Second quintile|9|10|8|7|11|22|4|14|7|10|4|7|7|7|2|4|
|Thirdquinb le|8|15|7|7|13|24|7|12|5|9|4|7|_5_|6|1|_5_|
|Fourtbquintile|8|10|4|5|10|23|8|12|6|9|0|4|6|5|1|6|
|Richestquintile|7|9|4|5|9|23|4|8|7|6|0|4|6|4|1|_5_|
|_QI/Q5_|_1.0_|_1.8_|_1.5_|_2.0_|_1.3_|_1.2_|_1.8_|_1.9_|_1.3_|_1.3_|-|_1.5_|_1.2_|_1.3_|_1.0_|_1.0_|

Source: Sahn et al (1999).

Most countries have experienced declines in mortality among the poor, the exceptions being Kenya, Zambia and Zimbabwe (Table 4). The trends are not always uniform across wealth groups, with a widening of the mortality gap between rich and poor. The ratio of mortality levels among the poorest to the richest quintiles has increased in most cases-where mortality has been falling, it has fallen faster among the richest group. The exceptions are Zambia and Zimbabwe.
**Table 3: GDP per capita growth volatility and household credit as a share of GDP**

|Dep. Var:|OLS|OLS|OLS|FE|GMM|OLS|
|---|---|---|---|---|---|---|
|stddev_gdppc|(1)|(2)|(3)|(4)|(5)|(6)|
|hhcredit|-0.0106***|-0.0125***|-0.0116**|-0.0131*|-0.0108|-0.0175|
||(0.001)|(0.000)|(0.017)|(0.095)|(0.105)|(0.144)|
|hhcredit<sup>2</sup>||||||0.0001<br>(0.284)|
|financ.open.|0.0008***|0.0009***|0.0004*|0.0006|0.0000|0.0006***|
||(0.000)|(0.000)|(0.078)|(0.328)|(0.955)|(0.004)|
|tradeopen.|0.0006|0.0001|0.0026|-0.0017|0.0043**|0.0018|
||(0.708)|(0.965)|(0.114)|(0.708)|(0.010)|(0.246)|
|initialgdppc|-0.1797|-0.0948|-0.0116|0.0583|-0.1315|-0.212|
||(0.118)|(0.436)|(0.958)|(0.865)|(0.574)|(0.174)|
|institutions|||-0.0975||||
||||(0.469)||||
|ECA||-0.333*|-0.434**||||
|||(0.051)|(0.042)||||
|financialcrisis|||1.19|1.11***|1.68***||
||||(0.178)|(0.000)|(0.000)||
|Constant|2.60***|2.66***|2.65***|2.15***|2.14***|2.74***|
||(0.000)|(0.000)|(0.000)|(0.003)|(0.000)|(0.000)|
|# of Obs.|220|220|178|216|216|220|
|R-squared|0.135|0.150|0.215|||0.139|
|Prob > F|0.000|0.000|0.000|0.000|0.000||
|# of countries||||39|39||

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

The regressions show a statistically significant and negative relationship between the volatility of GDP per capita growth and household credit. When household credit rises as a share of GDP, it is associated with reduced output volatility, even after accounting for initial GDP per capita. The quadratic term is not significant. However, consumption per capita volatility does not have a consistent association with the share of household credit in GDP. Instead, the share of household credit in total credit (the relative importance of credit to individuals versus firms) is negatively associated with consumption per capita growth volatility and this relationship is 14
**Table 1. Population changes for Taiping and non-Taiping prefectures**

||**Control**|**Ta**|**iping**|**Early**|**Taiping**|**Late**|**Taiping**|
|---|---|---|---|---|---|---|---|
||Mean<br>(1)|Mean<br>(2)|Diff.<br>(3)|Mean<br>(4)|Diff<br>(5)|Mean<br>(6)|Diff<br>(7)|
|Population Growth in 1851|0.128|0.114|-0.014|0.123|-0.004|0.094|-0.034|
||(0.092)|(0.079)|(0.014)|(0.042)|(0.015)|(0.125)|<br>(0.023)|
|Population Growth in 1880|-0.006|-0.475|-0.469***|-0.412|-0.406***|-0.605|-0.598***|
||(0.526)|(0.547)|(0.080)|(0.572)|(0.095)|(0.480)|<br>(0.128)|
|Population Growth in 1910|0.173|-0.294|-0.467***|-0.226|-0.399***|-0.433|-0.606***|
||(0.524)|(0.465)|(0.078)|(0.489)|(0.093)|(0.388)|<br>(0.126)|
|Population Growth in 1953|0.464|-0.074|-0.538***|-0.059|-0.523***|-0.104|-0.569***|
||(0.456)|(0.476)|(0.070)|(0.506)|(0.083)|(0.422)|<br>(0.111)|
|Population Growth in 1982|1.030|0.475|-0.555***|0.532|-0.498***|0.358|-0.672***|
||(0.452)|(0.467)|(0.069)|(0.477)|(0.081)|(0.436)|<br>(0.111)|
|Population Growth in 2000|1.222|0.649|-0.573***|0.704|-0.518***|0.537|-0.685***|
||(0.452)|(0.498)|(0.070)|(0.508)|(0.082)|(0.469)|<br>(0.111)|
|Population in 1820||||||||
|(Baseline)|4.311|5.231|0.920***|5.051|0.740***|5.602|1.291***|
||(1.057)|(0.690)|(0.150)|(0.692)|(0.180)|(0.532)|<br>(0.252)|
|Longitude|110.155|117.126|6.971***|115.635|<br>5.481***|120.189|<br>10.03***|
||(5.415)|(2.954)|(0.759)|(2.408)|(0.907)|(0.814)|<br>(1.280)|
|Observations|||55||37||18|

Note: Population Growth in year _t_ = ln (Population in year _t_ / Population in 1820). The control group covers 211 prefectures not in the Taiping jurisdiction.

Standard errors in brackets. The differences indicate the difference of the mean between the treatment and the control groups.

46
**Renegotiation of Concession Contracts in Latin America**

# **1 Introduction**

Traditionally the provision of infrastructure services in most developing countries -as well as developed ones- was provided by government owned enterprises. However, these enterprises, at least in developing countries- have proven to be fairly inefficient and used for political objectives. A pletora of studies of the relative performances of public and private enterprises done in the past thirty years find significantly superior performance by private (and/or privatized) enterprises, at least with respect to productive efficiency (see for example Megginson and Netter (2001) and Kikeri and Nellis (2002)). Explanations differ as to why this is so. Aside from the incentive-for-profit reason and arguably a more professional know-how in management and operating procedures and use of appropriate technology, the most important reason may be that privatization renders difficult and/or costly the day-to-day intervention in enterprise operation by governments and politicians, making manipulations less likely. But perhaps the leading reason behind the strategy to bring private sector participation in infrastructure has been the urgent need for sizable investment. To improve performance and coverage, most of the state owned enterprises urgently needed significant investments. Given the scarcity of public funds for investments and the competing investment needs in the social sectors, most countries have opted for the transfer of the provision of infrastructure services to the private sector. Private sector participation can and has been accomplished in a variety of forms, ranging from management contracts, to concessions and to full privatizations with significant success. Practically, at least in Latin America and the Caribbean region, seldom a call to the private sector to take over and operate on an infrastructure service has had no takers.

The reform process to improve and extend infrastructure services has been additionally fueled by the realization by developing countries that infrastructure levels and quality significantly matter for economic growth and poverty alleviation, and that their current levels and quality are not adequate to secure those desired levels of growth. The belief and the facts are that infrastructure services -electricity, water, telecommunications, roads, railroads, ports and airports- are critical to the operation and efficiency of a modern economy. They enter as critical inputs in the provision of goods and services and impact significantly in the productivity, cost and competitiveness of the economy. Policy decisions regarding their provision have ramifications throughout the economy, and poor infrastructure services often limit competitiveness in other markets. There are plenty of empirical studies illustrating the impact of infrastructure on economic growth, among the more recent are Canning (1998), Calderon, Easterly and Serven (2002), Calderon and Serven (2002). A 1 percent increase in the stock of infrastructure can increase GDP by 2
Lebanon R2, Belize). Given the wide range in timing of studies and vaccination rates, we take into account these differences in our moderator analysis examining study-level heterogeneity.

Our samples recruited from social media reported significantly higher vaccination levels than the national average at the time of the survey in all studies. This suggests that the unvaccinated population in countries with higher coverage already had access to the vaccine (compared to countries with low vaccine access) and, therefore, it is plausible that the unvaccinated in these cases were particularly resistant to vaccination. In addition, this finding highlights some of the potential uniqueness of our study samples—these samples recruited through social media are not representative of the general population, and results should be interpreted with this in mind.

_Table 2. Summary of study-level moderators of full sample and survey experiment sample included in the meta-analysis._

|**_Region_**|**_No. of_**<br>**_Studies_**|**_Start dates of_**<br>**_Data Collection_**|**_Mean_**<br>**_COVID-19_**<br>**_vaccination_**<br>**_rate in_**<br>**_sample_**|**_Mean_**<br>**_National_**<br>**_COVID-19_**<br>**_vaccination_**<br>**_rate at_**<br>**_time of the_**<br>**_experiment_**|**_Mean_**<br>**_GDP per capita,_**<br>**_PPP_**<br>**_(constant 2017_**<br>**_international $)_**|**_Mean_**<br>**_National_**<br>**_Trust in_**<br>**_doctors_**<br>**_and nurses_**<br>**_(1-4)_**|**_Mean_**<br>**_National_**<br>**_Perceived_**<br>**_vaccine_**<br>**_safety_**<br>**_(1-5)_**|**_Mean_**<br>**_National_**<br>**_Perceived_**<br>**_vaccine_**<br>**_effectivenes_**<br>**_s_**<br>**_(1-5)_**|
|---|---|---|---|---|---|---|---|---|
|Middle East & North<br>Africa|14|Jan-21 to Apr-22|37%<br>(0%, 86%)|22%<br>(0%, 54%)|16,655<br>(4,768; 47,315)|3.0<br>(2.7, 3.2)|4.5<br>(4.3, 4.6)|4.5<br>(4.4, 4.7)|
|Sub-Saharan Africa and<br>the Sahel|5|Aug-21 to May-22|39%<br>(0%, 72%)|9%<br>(0%, 19%)|2,367<br>(1,060; 3,757)|2.9<br>(2.5, 3.4)|4.3<br>(4.1, 4.7)|4.5<br>(4.4, 4.7)|
|Europe, Asia & Pacific|5|Jun-21 to Dec-21|45%<br>(16%, 64%)|21%<br>(1%, 39%)|12,630<br>(3,948; 18,307)|3.0<br>(2.9, 3.1)|3.9<br>(3.3, 4.2)|4.1<br>(3.8, 4.4)|
|Latin America &|4|Mr-21 t Ar-22|41%|22%|6,997|3.0|4.3|4.6|
|Caribbean||a o p|(0%, 89%)|(0%, 59%)|(3,102; 10,244)|(3.0, 3.1)|(4.1, 4.6)|(4.5, 4.6)|

_Note_ . Experiment start date is the month of Facebook ad launch for the first and last intervention in the region. COVID-19 vaccination rate and GDP per capita show the mean for the region with the minimum and maximum in parenthesis.

16
improves households’ ability to smooth the idiosyncratic effects of rainfall shocks on household income and consumption. In a follow-up study focused explicitly on coping with agricultural risks associated with adverse weather shocks, Giles and Yoo (2007) finds that the ability to migrate leads to reduced precautionary saving and increased consumption.<sup>21</sup> Precautionary saving as a share of total savings is cut in half (from 30.5% to 16.6%) as the share of a village working as migrants increased from 2 to 22 percent of registered working-age laborers.

Due to the existence of informal risk sharing arrangements against idiosyncratic shocks, the risk coping role of migration can be true even for households without a family member currently working as a migrant. Indeed, with a RCRE sample, Giles and Murtazashvili (2013) again using the timing of ID card distribution for identification, and show that a one percentage point increase in the share of a village workforce employed as a migrant is associated with a 3.2 percentage point decline in the probability of falling into poverty, and among the poor, the same increase in migration is associated with a 3.5 percentage point reduction in the probability of remaining poor.

A more recent paper using the RCRE panel dataset confirms the role of migration in reducing the possibility that households face consumption variability, and specifically, periods with a large drop in consumption (Kinnan et al. 2018). This reduced consumption risk allows farm households to invest in higher-risk productive activities. The paper uses a novel identification strategy to control for migrant selection: an interaction between intensity of contact with a destination province during the historical sent-down youth program and specifics of _Hukou_ reform of the destination provinces. Finally, the authors use a simple model to show that the quantitative changes in consumption and risky investments are consistent with accepted relative risk aversion parameters.

# **4.4 Recent Trends in Migrant Earnings**

Average household annual migrant earnings increased from less than 8,000 yuan in 2004 to more than 30,000 yuan in 2014 (Figure 9).<sup>22</sup> Both increases in migrant labor supply (Figure 5 above) and increased wages of low-skilled workers (Li et al. 2012) contribute to increased migrant earnings observed at the household level. Moreover, the share of migrant earnings in total household income has also increased, from about 41% in 2003 to 54% in 2014. It is important to note that “migrant earnings” ( _da gong shou ru_ ), as estimated in the RCRE surveys, are gross estimates of migrant earnings in the city and are considerably

> 21Unproductive forms of precautionary savings, in the form of grain stocks and other liquid assets, were identified as an important distortion in rural household consumption in the early 1990s  (Park, 2006).

> 22The earnings are in real terms, adjusted for inflation by a provincial consumer price index with year 2000 as the base year and Henan as the base province.

18