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

Although Egypt (pop. 83 million in 2011) experienced striking economic growth alongside a variety of developmental improvements from 2004 till 2010, spatial inequality and poverty persist. Egyptians in urban and Lower Egypt enjoy higher living standards than those in rural and Upper Egypt, yet internal migration rates are surprisingly low compared to other countries.

This paper offers three explanations for the low migration rates: 1) low educational level, 2) labor is tied up in agricultural activity either as paid workers or unpaid family workers, and 3) rural households’ ability to raise a portion of their food offsetting the impact of soaring food prices and reducing the incentive to migrate. The paper also finds two telling characteristics of internal migrants: 1) they are more likely to find employment than non-migrants; and 2) they earn higher wages, in particular the more educated individuals.

# **Literature Review**

All existing studies address the issue of internal migration in Egypt without, however, suggesting why the rates are comparatively low: Wahba, “An Overview of Internal and International Migration in Egypt” (2007) used the Egypt Labor Market Panel Survey (ELMPS 06) to demonstrate that while internal migration increased in 1998 -2006, the rate remained very low. The author notes that both rural-to- urban and urban-to- rural migration increased in that period as did commuting patterns.

Zohry, “The Development Impact of Internal Migration: Findings from Egypt” (2009) discussed the main motivations behind internal and international migration in Egypt drawing on field work in two governorates (Cairo and Beni Suief ). Zohry suggested that migrants were more often forced to move by dire economic necessity rather than the wish to seek a better living situation.

# **Stylized Facts**

# Data

This study used the Labor Force Survey conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS) for the first quarter of year 2010. The survey has over 60 questions, clustered in three sections: 1) demographic and professional status (28 questions); 2) employed characteristics (26 questions); and 3) unemployed characteristics (5 questions). The survey has 88,000 respondents.

2
# 5. The Allocation of Foreign Direct Investment across Countries

This section examines the international allocation of inward FDI, as tabulated the World Bank WDI database. Our dependent variable is per capita FDI in constant 2000 US dollars, winsorized at the tail 1 percent.<sup>3</sup> We add a constant to this quantity because some countries have negative FDI inflows (negative FDI inflow represents repatriation of previous investment).<sup>4</sup> Dropping the negative FDI inflow observations leads to qualitative similar results. However, we are not aware of significant economic reasons to exclude the observations.

Our intentions are twofold. First, we wish to further investigate our thesis that “good government” attracts FDI. Second, we wish to see if the FDI flowing into China behaves similarly to that flowing into other countries.

To these ends, we regress each country’s FDI inflows on a set of country characteristics associated with the quality of government. We exclude China from these regressions and then ask how well the estimated parameters explain FDI flowing into China.

FDI is large if foreign corporate investors regard a location’s investment opportunities highly. Obviously, investment opportunities are more abundant in locations with better institutions, where government officials are not corrupt, bureaucracies are efficient, and the rule of law is generally upheld. At the same time, positive shocks on investment opportunities often entice governments seeking to attract foreign capital to provide these institutions. Hence, a simple relationship between measures of government quality and foreign direct investment could be misleading. At the very least, to sort this out, our empirical investigation should incorporate a proxy for the presence of profitable investment opportunities.

> 3 The winsorization is done to prevent the influence of huge outliers and measurement errors.  This variable is highly skewed.  The one percentile value is -79.0, while the 99 percentile is 1064.0. The minimum is - 2615, and the maximum is 259837.

> 4 In total 6.3% of the observations are negative.  We add 80 to FDI per capita so that the minimum of the winsorized FDI per capita is 1.

13
# **5. Conclusion**

Monitoring and enforcement issues have been the objecl: of a very limited number of empirical analyses at plant level. In developing countries, the difficulties of accessing data may explain this state of fact. Given the very limited number of empirical analyses of the nature conducted in this paper, we hope to have provided, a better understanding of the impact of the regulator's monitoring and enforcement act:ivities on the behavior of industrial polluters. Our analysis of plant-level environmental performance for the city Zhenjiang suggests that inspections do have a statistically significant impact on firms' environmental performance, and are a better determinant of the firms' performance than is the variation in pollution levies. However, it cannot be concluded from this analysis that pollution levies do not have an impact on firms' environmental performance. Only a similar dataset covering numerous cities across China would allow us to delienate the effective impact of inspections and pollution levies on firms' environmental performance in China. This is the object of ongoing research. Our results also demonstrate that citizens' complaint are an important determinant of inspections, and therefore, via inspections, of environmental performance. Hence, this would suggest a greater role for the regulator to embark on information and education policies.

Whether or not these results support that additional resources be devoted to monitoring activities cannot strictly be addressed with the data currently available. Indeed, in order to do so, there would be a need to compare the costs of performing additional inspections with the net benefits of reduced pollution emissions. However, given the high level of air and water pollution currently observed in Zhenjiang, given the impact that these may have solely on health and productivity loss, it is reasonable to estimate that inspections do increase social net benefit in Zhenjiang.'<sup>7</sup> Further research in this field will prove to be crucial and provide key policy implications. In particular, it does appear of interest to examine more thoroughly the Dasgupta et al. (1997) provide estimates of abatement costs and health damages associated with air pollution in China.

21
# **2. Data: Measuring Mental Health in Multi-purpose Household Surveys**

The psychological health literature has used two approaches to measure mental distress through surveys. The first approach attempts to diagnose specific psychiatric illnesses with symptom data collected through survey interviews. The most widely used form of this approach is the Composite International Diagnostic Interview (CIDI) in its various formats, translations, and revisions. A second approach in low income countries is the use of instruments intended to measure general psychological distress, rather than diagnose specific manifestations of mental illness per se. Common examples include variants of the General Health Questionnaire (GHQ) of Goldberg (1972) and the Mental Health Inventory (MHI-5) of Veit and Ware (1983). These measures have been found to perform well in a number of settings in detecting major depression, general affective disorders and anxiety disorders (Berwick et al. 1991, McCabe et al. 1996). However, both approaches have found large differences in the overall case prevalence and proportion of respondents answering “yes” to specific questions across countries, which has been ascribed in part to differences in cultural norms related to disease attribution and the stigma of mental illness (Patel et al., 1998; Aidoo and Harpham, 2001; Cross-National Collaborative Group, 1992; WHO International Consortium in Psychiatric Epidemiology, 2000; Demyttenaere et al., 2004).

The multipurpose surveys used in this paper follow the second approach, each fielding a widely-used mental health screening instrument in the context of general household surveys. Table 1 summarizes the survey year, geographic coverage, mental health instrument, and sample size in each country. Nationally (or near-nationally) representative surveys of over 10,000 households were carried out in Mexico and Indonesia, each using a variant of the GHQ to measure mental health. A nationally representative survey of over 5,400 households in BiH used the Center for Epidemiological Studies Depression Scale (CESD) of Radloff (1977), a 20-question self-reported depression scale. We also use two smaller special purpose surveys. The first are data from a longitudinal study of 300 households in Delhi, India, which do not differ in observable characteristics from a representative sample of households in this city (Das and Sanchez, 2003). This survey uses the most comprehensive mental health instrument, the 90-question Symptom Checklist 90 Revised (SCL-90R). The second special survey is a survey of 230 Tongan households taken from villages in which some individuals had applied for an emigration lottery (Stillman et al. 2006), which used the MHI-5 to measure mental health.

The data assembled here offer several advantages over those used in many previous studies. First, the surveys sample households from a population representative frame, whereas several existing studies sample opportunistically, such as patients at health clinics (e.g. Patel et al. 1998), with possible biases arising from non-random utilization of health clinics. Second, surveying households rather than

- 4 -
Table 5: Impact of the Intervention on Student Progression

|||Repetition Rate|||Dropout Rate||
|---|---|---|---|---|---|---|
||(1)<br>Lower Primary|(2)<br>Upper Primary|(3)<br>Overall Primary|(4)<br>Lower Primary|(5)<br>Upper Primary|(6)<br>Overall Primary|
|Treatment|_−_2_._249_∗_|_−_0_._798|_−_1_._773|0_._308|0_._181|0_._087|
||(1_._339)|(1_._393)|(1_._170)|(0_._826)|(0_._823)|(0_._744)|
|Constant|25_._108_∗∗∗_|18_._627_∗∗∗_|21_._039_∗∗∗_|5_._210_∗∗∗_|6_._614_∗∗∗_|5_._922_∗∗∗_|
||(1_._494)|(1_._466)|(1_._370)|(0_._628)|(0_._739)|(0_._648)|
|Observations|385|380|385|385|380|385|
|R-sqr|0_._041|0_._051|0_._072|0_._018|0_._028|0_._024|

_Notes:_ This table looks at the impact of the school leadership on Repetition Rate (columns 1-3) and Dropout Rate (columns 4-6) at the school level. All regressions are adjusted by the value of the outcome variable observed at MLSS baseline. First row of the table shows point estimate coefficients and statistical significance. Robust standard errors are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

Table 6: Impact of the Intervention on Test Scores (Heterogeneity by PTR in lower primary at baseline)

||(1)<br>Math Score|(2)<br>English Score|(3)<br>Total Score|
|---|---|---|---|
|Treatment|7_._739|_−_0_._813|3_._585|
||(9_._012)|(9_._236)|(8_._757)|
|Below Median PTR in Lower Primary|11_._592|4_._262|7_._970|
||(8_._737)|(9_._288)|(8_._566)|
|Heterogeneous Treatment Impact|12_._759|23_._384_∗_|17_._700|
||(12_._955)|(13_._850)|(12_._788)|
|Constant|489_._581_∗∗∗_|493_._805_∗∗∗_|491_._809_∗∗∗_|
||(6_._152)|(6_._375)|(6_._014)|
|Observations|4346|4346|4346|
|R-sqr|0_._008|0_._008|0_._009|

_Notes:_ This table looks at the heterogeneous impact of the school leadership on student level Test scores (columns 1-3) with respect to school-level Lower Primary (Grades 1-4) Pupil-Teacher Ratio (PTR) at baseline. The heterogeneous treatment impact is the estimated coefficient for the value of the treatment effect interacted with the variable of interest. Analytical sample restricted to MLSS schools observed in baseline and endline. All regressions are adjusted by the value of the outcome variable observed at MLSS baseline. The outcome variables are: Math Knowledge Score (column 1); English Knowledge Score (column 2); Total Knowledge Score (column 3). All regressions include clustered (at school level) standard errors. Robust standard errors (clustered at the school level) are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

29
# **III.Europe and Central Asia**

Europe and Central Asia (ECA) accounted for 5 percent of global investment during 2010-15. Investment growth in the region decreased sharply, from a 10.2 percent in 2010 to 0.4 percent in 2015. Partial data for 2016 suggest that investment is bottoming out in 2016, led by easing investment contractions in Russia and Ukraine. However, regional investment growth remains well below its long-term (1995-2008) average of 6.5 percent a year.

The slowdown in investment growth in the ECA region was initially concentrated in the Central Europe in the aftermath of the Euro Area’s debt crisis of 2011-12 and associated recession. The post-crisis recovery in Central Europe was weak, reflecting impaired banking systems and corporate sectors in the aftermath of the Euro Area crisis. Lingering concerns about armed conflict and related geopolitical tensions (Russia, Ukraine), policy uncertainty in several major regional economies, and adjustment to the terms-of-trade shock in energy exporters (Russia, Azerbaijan, Kazakhstan) have weighed on regional investment growth.

Meanwhile, current and prospective investment needs are sizable. Investment and major reforms are needed to increase productivity and set the stage for a sustained growth recovery. However, efforts to address under-investment are likely to be constrained by the need for sustainable financing.

# **III.1. How has investment growth in the ECA region evolved?**

The recent investment growth slowdown was sharp and broad-based. In 2015, investment growth remained below its long-term averages in three-quarters of the countries in the region, and was negative in one-quarter of them, including Belarus, Russia, and Ukraine (Figure 9). Between 2010 and 2015, investment growth trends differed markedly between commodity importers, which are located in Central, Eastern, and Southeastern Europe, and commodity exporters, mainly Russia and the economies of Central Asia.

The overall slowdown was partly a correction from historically high investment growth prior to the global financial crisis. Pre-crisis, large capital inflows and credit booms fueled investment growth in the western part of the region as financial systems became more integrated with those in the Euro Area. Proximity to, and rapid convergence with, the Euro Area appeared to promise bright growth prospects as regional labor and product markets became increasingly intertwined (World Bank 2010). In the eastern part of the region, pre-crisis investment growth was buoyed by resource development encouraged by high global commodity prices.

19
Table 7: Imports of intermediates versus other goods

||BEC clas|sifcation|Schott (2004)|classifcation|
|---|---|---|---|---|
||OLS|IV|OLS|IV|
|Dependent variable:|_log_(1 +_imp_)|_log_(1 +_imp_)|_log_(1 +_imp_)|_log_(_imp_)|
||(1)|(2)|(3)|(4)|
|**A. Intermediate goods**|||||
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1094|0.6815**|0.2132***|0.8566**|
||(0.0788)|(0.3240)|(0.0573)|(0.3124)|
|First stage :|||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||0.0027***||0.0027***|
|||(0.0003)||(0.0003)|
|Observations<br>46|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.5137|0.5027|0.5278|0.5101|
|Kleibergen-Paap rk Wald-F-stat||111.795||111.795|
|**B. Other goods**|||||
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1507***|0.5627***|0.1136*|0.4929**|
||(0.0511)|(0.1680)|(0.0619)|(0.1906)|
|First stage :|||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||0.0027***||0.0027***|
|||(0.0003)||(0.0003)|
|Observations|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6259|0.6226|0.6246|0.6220|
|Kleibergen-Paap rk Wald-F-stat||111.795||111.800|
|Importer-exporter-year efects|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the sub-samples for imports of intermediate and other goods. Columns (1)-(3) report the OLS results, while columns (2)-(4) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
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TABLE 10 Coverage for univariate and multivariate filter-based estimates

|**Economy**|**Sample**<br>**period**|**Economy**|**Sample**<br>**period**|**Economy**|**Sample**<br>**period**|
|---|---|---|---|---|---|
|Australia|1981-2024<br>|**East Asia and Pacifi**|**c**|Paraguay|1994-2024|
|Austria|1995-2024<br>|China|1992-2024|Peru|1998-2024|
|Belgium|1995-2024<br>|Indonesia|2001-2024|Uruguay|1997-2024|
|Canada|1981-2024<br>|Malaysia|2005-2024|**Middle East and**|**North Africa**|
|Croatia|2000-2024<br>|Mongolia|2010-2024|Bahrain|2008-2024|
|Cyprus|1995-2024<br>|Philippines|1998-2024|Egypt, Arab Rep.|2007-2024|
|Czech Rep.|1996-2024<br>|Thailand|1993-2024|Iran, Islamic Rep.|2012-2024|
|Denmark|1991-2024<br>|Vietnam|2008-2024|Jordan|1992-2024|
|Estonia|1995-2024<br>|**Europe and Central**|**Asia**|Kuwait|2010-2024|
|Finland|1981-2024<br>|Albania|2008-2024|Morocco|1998-2024|
|France|1981-2024<br>|Azerbaijan|2001-2024|Saudi Arabia|2010-2024|
|Germany|1981-2024<br>|Bulgaria|2000-2024|Tunisia|2000-2024|
|Greece|1995-2024<br>|Georgia|2003-2024|**South Asia**||
|Hong Kong SA<br>China|R,<br>1990-2024<br>|Hungary|1998-2024|India|1997-2024|
|Iceland|1995-2024<br>|Kazakhstan|1996-2024|**Sub-Saharan Afr**|**ica**|
|Ireland|1995-2024<br>|North Macedonia|2000-2024|Botswana|1994-2024|
|Israel|1995-2024<br>|Poland|1996-2024|Cameroon|1999-2024|
|Italy|1981-2024<br>|Romania|1995-2024|Kenya|2009-2024|
|Japan|1981-2024<br>|Turkey|2001-2024|Lesotho|2007-2024|
|Korea|1981-2024<br>|**Latin America and C**|**aribbean**|Namibia|2000-2024|
|Latvia|1995-2024<br>|Argentina|2004-2024|Nigeria|2010-2024|
|Lithuania|1995-2024<br>|Belize|1994-2024|South Africa|1981-2024|
|Luxembourg|1995-2024<br>|Bolivia|1990-2024|Tanzania|2010-2024|
|Malta|2000-2024<br>|Brazil|1990-2024|||
|Netherlands|1981-2024<br>|Chile|1996-2024|||
|New Zealand|1988-2024<br>|Colombia|2000-2024|||
|Norway|1981-2024<br>|Costa Rica|1991-2024|||
|Portugal|1995-2024<br>|Dominican Republic|2007-2024|||
|Singapore|1981-2024<br>|Ecuador|2001-2024|||
|Slovak Republic|1995-2024<br>|El Salvador|1990-2024|||
|Slovenia|1995-2024<br>|Guatemala|2001-2024|||
|Spain|1995-2024<br>|Honduras|2000-2024|||
|Sweden|1981-2024<br>|Mexico|2000-2024|||
|Switzerland|1981-2024<br>|Nicaragua|2006-2024|||
|Taiwan|1982-2024<br>|Panama|2007-2024|||
|United Kingdom|1981-2024|||||
|United States|1981-2024|||||

Source: World Bank.

Note: Forecasts for 2022Q2-2024Q4 are based on the lag structure of the estimation.

57
**Table 6 (Continued)**

|||**Log Total Value**|**of Food**|**Consumed**|**Per Capita**||
|---|---|---|---|---|---|---|
|||IHPS|||FIAS||
||1|2||1|2||
|**Weather Shocks†**|||||||
|Weather Shock|-0.050|||-0.088|||
||(.032)|||(.070)|||
|Current Shock||-0.047|||-0.082||
|||(.032)|||(.073)||
|Sequential Shock||-0.152|*||-0.228|*|
|||(.078)|||(.117)||
|**Aid * Shock**|||||||
|Aid|-0.129|-0.155||-0.193|-0.235||
||(.098)|(.101)||(.148)|(.154)||
|Aid * Weather Shock|-0.023|||0.064|||
||(.134)|||(.186)|||
|Aid * Current Shock||-0.019|||0.010||
|||(.139)|||(.220)||
|Aid * Sequential Shock||0.196|||0.395||
|||(.223)|||(.248)||
|**Explicit HH Targeting Characteristics**|||||||
|Dummy, Female Head|-0.063|*<br>-0.064|*|-0.110|**<br>-0.112|**|
||(.034)|(.034)||(.053)|(.053)||
|Dummy, Children<3|-0.019|-0.018||-0.009|-0.006||
||(.021)|(.021)||(.035)|(.035)||
|Dummy, All Members>59|-0.117|*<br>-0.118|*|-0.110|-0.117||
||(.069)|(.069)||(.108)|(.110)||
|Dummy, HH Member Disabled|-0.024|-0.024||0.000|-0.003||
||(.024)|(.024)||(.042)|(.042)||
|**Wealth**|||||||
|Wealth Index|0.643|***<br>0.645|***|0.519|***<br>0.530|***|
||(.094)|(.095)||(.183)|(.188)||
|# Mobile Phones|0.054|***<br>0.055|***|0.015|0.018||
||(.012)|(.012)||(.022)|(.023)||
|**Constant**|11.514|***<br>11.467|***|10.889|***<br>10.852|***|
||(.297)|(.298)||(.535)|(.542)||
|# of Obs.|4412|4412||1999|1999||
|R2 (within)|0.323|0.321||0.240|<br>0.229||
|<br>R2 (between)|0.624|0.624||0.468|<br>0.467||
|<br>R2 (overall)<br>**Notes:*****/**/* denote statistical si<br>**Weather Shock**is a dichotomous varia|0.518<br>gnificance a<br>ble that is e|0.517<br>t 1/5/10 percent re<br>qual to 1 if the hous|<br>spective<br>ehold ha|0.373<br>ly. Standard<br>s been expo|<br>0.368<br>Errors in parenthe<br>sed to a weather sh|<br>ses.**†**<br>ock in|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **† Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

32