rafmacalaba/datause-encoder-data huggingface dataset:
Levine 2003; and Djankov, McLiesh, and Shleifer, 2006). To control for these factors we include an index of Creditor Rights (ranging from 0, weak, to 4, strong) developed by Djankov, McLiesh, and Shleifer (2006) and a dummy to control for countries with British legal origin (i.e., dummy equals 1 if legal system is based on Common Law). An alternative view of the determinants of financial development, stresses the importance of geography and initial endowments (Acemoglu, Johnson, and Robinson, 2001, 2002). To control for these factors, we include countries’ absolute latitude, a frequently used proxy of endowments (Beck, DemirgucKunt, and Levine, 2003).<sup>10</sup> Since our measures of legal institutions and endowments do not vary over time, these variables are not included in the fixed effect estimations and only appear in the random effect regressions.<sup>11</sup> We first examine the relationship between financial development and remittances by running fixed effects (FE) and random effects (RE) regressions, ignoring the potential for biases due to reverse causation, omitted factors, or measurement error. FE and RE estimations make different assumptions about the error term in equation (1). In the FE model, the error term is the sum of α i and ui,t where α i represents individual specific fixed parameters to be estimated and ui,t are independent and identically distributed errors with zero mean and constant variance In the RE regressions, both α i and ui,t are independently distributed and, furthermore, both are assumed to be independent from the regressors in the equation. In conjunction with these estimations, we report F-tests for the joint significance of the fixed effects and Hausman tests comparing the efficiency of random vis-à-vis fixed effect estimates.

> 10 The original paper by Acemoglu et al. (2001) uses settlers’ mortality data as a measure of endowments. However, this information is only available for a subset of former colonies. Using this data restricts our sample of countries, therefore, we prefer to use absolute latitude as a proxy for endowments.

> 11 Beck, Demirguc-Kunt, and Levine (2003) show that the impact of variables such as religion, ethnic diversity or political structure on financial development is neither significant nor very robust. Thus, we do not control for these factors when investigating the effect of remittances on financial development.

11
# **Table 5a - Random Effects (GLS) Welfare Regressions with Different Sets of Regressors**

||||**Balanc**|**ed panel**||||
|---|---|---|---|---|---|---|---|
|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|**y=B+a+b+c+d**|**y=B+a+b+c**|**y=B+a+b+d**|**y=B+a+c+d**|**y=B+b+c+d**|**y=B+a+b**|**y=B+a+c**|**y=B+a+d**|
|`0.135`|`0.229`|`0.138`|`0.102`|`0.147`|`0.233`|`0.185`|`0.104`|
|`-1.41`|`(2.33)*`|`-1.44`|`-1.05`|`-1.52`|`(2.36)*`|`-1.86`|`-1.07`|
|**9**|**10**|**11**|**12**|**13**|**14**|**15**|**16**|
|**y=B+b+c**|**y=B+b+d**|**y=B+c+d**|**y=B+a**|**y=B+b**|**y=B+c**|**y=B+d**|**y=B**|
|`0.24`|`0.148`|`0.115`|`0.187`|`0.241`|`0.194`|`0.114`|`0.194`|
|`(2.42)*`|`-1.52`|`-1.17`|`-1.89`|`(2.43)*`|`-1.94`|`-1.16`|`-1.94`|
||||**Unbalan**|**ced panel**||||
|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|**y=B+a+b+c+d**|**y=B+a+b+c**|**y=B+a+b+d**|**y=B+a+c+d**|**y=B+b+c+d**|**y=B+a+b**|**y=B+a+c**|**y=B+a+d**|
|`0.103`|`0.183`|`0.103`|`0.074`|`0.108`|`0.183`|`0.155`|`0.074`|
|`(2.24)*`|`(3.93)**`|`(2.24)*`|`-1.59`|`(2.32)*`|`(3.92)**`|`(3.28)**`|`-1.58`|
|**9**|**10**|**11**|**12**|**13**|**14**|**15**|**16**|
|**y=B+b+c**|**y=B+b+d**|**y=B+c+d**|**y=B+a**|**y=B+b**|**y=B+c**|**y=B+d**|**y=B**|
|`0.183`|`0.106`|`0.08`|`0.155`|`0.181`|`0.144`|`0.075`|`0.141`|
|`(3.85)**`|`(2.26)*`|`-1.69`|`(3.29)**`|`(3.81)**`|`(3.03)**`|`-1.59`|`(2.96)**`|

L egenda: z-stat under coefficients; (*) Significant at 5%; (**) Significant at 1%; y = Real cons./Pov. Line; B = Real Soc. Ben./Pov. line; a=hfem, hage30_45, hage46_60, hage60plus, hprim, hsec, htert; b=nchildm dep3_4, dep5plus; c=rural, summer; d=terremprate, growth.

24
population _N_ and a population living under the poverty line _q_ , the _FGT_ measures are computed as follows: where _α_ measures the power of the FGT indices. We present results for _α_ = 0 (poverty headcount), _α_ = 1 (poverty gap) and _α_ = 2 (squared poverty gap). This is done for the baseline as well as each of the scenarios.

# **3. Data, calibration and scenarios**

# **_3.1 Data and calibration of the model_**

In this section, we present our data sources, including the 2004 social accounting matrix on which the CGE model is based, the key parameters used for the static and dynamic calibration of the model, and the household survey data used for the poverty analysis. We focus our analysis on the impact of the increase in the price of rice, since this is the main imported food.

The CGE model is calibrated to a 2004 social accounting matrix (SAM), which includes 26 production sectors, 28 goods and services, 2 production factors, 11 household groups, 6 tax accounts, 2 capital accounts, and the rest of the world account (on the construction of the SAM, see Nouve et al., 2005). Rice production is captured in the SAM through two activities: food crop production whereby the paddy is produced, and cereal milling whereby the crop is transformed into the rice actually sold on markets.

According to the SAM, as shown in Table 1, the value of local rice production (milling) in Mali amounted to CFAF69 billions in 2004 (approximately US$ 140 million), representing 3.1 percent of the GDP of the year. Nearly 80 percent of the value of rice production comes from the 9
The results for women are shown in Table 10. In the full sample estimation results in Table 6, public school graduates have a greater chance of obtaining a formal job, while private vocational attendance is associated with a higher likelihood of unemployment. The positive association between public schools and formality holds for low scoring women as well. With respect to unemployment, low scoring women that attend public general are far less likely to be unemployed.

Table 10. Estimated Effect of School Type on Employment and Job Quality, Women, by test score

|||**Low sco**<br>|**res**<br>|||**High sco**<br>|**res**<br>||
|---|---|---|---|---|---|---|---|---|
||LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|LFP<br>LPM|Unemployment<br>LPM|Formal<br>LPM|Wage<br>OLS|
|Public Vocational|0.075|0.149**|0.017|0.213|-0.025|-0.026|-0.040|-0.155|
||(0.092)|(0.076)|(0.104)|(0.321)|(0.064)|(0.047)|(0.086)|(0.183)|
|Private general|0.054|0.118*|-0.132*|0.154|-0.109|0.029|-0.086|0.093|
||(0.072)|(0.069)|(0.069)|(0.259)|(0.103)|(0.066)|(0.084)|(0.232)|
|Private vocational|0.075|0.195***|-0.193**|0.167|-0.040|0.019|-0.023|-0.328*|
||(0.073)|(0.070)|(0.079)|(0.160)|(0.072)|(0.066)|(0.074)|(0.193)|
|Average among public<br>general graduates|0.551|0.069|0.574||0.706|0.157|0.554||
|R-squared overall|0.317|0.509|0.733|0.649|0.357|0.463|0.701|0.522|
|Observations|770|443|394|287|726|495|430|350|

Notes: *** 1% significance, ** 5% significance, * 10% significance; figures are marginal effects; standard errors in <u>parentheses, they are robust to heteroskedasticity and clustered at subdistrict level. Low scores are below median</u> Most striking are the different effects of school type on wages for low and high scoring women. The differences are not statistically significant, but they are large in magnitude. For women as a whole, Table 6 shows that public vocational is associated with a wage penalty of 12 percentage points (without test scores) and the private vocational penalty is about 15 percentage points. For low scoring women, however, Table 10 shows that women who attend vocational public and private vocational school earn approximately a 20 percent and 17 percent wage premium, respectively. Meanwhile, high scoring public and private vocational graduates earn a 16 and 32 percent wage penalty, respectively. Higher scoring women, like higher scoring men, appear to suffer the largest penalty for vocational education.

30
discussed in Section IV. We use 10 public goods measures (covering eight areas of public goods), which are described in Appendix A2. These variables reflect changes in physical _infrastructure_ during a president’s time in office as well as changes in _outcomes_ . For example, we use data on the number of schools to measure investment in schools and education, whereas we use data on the percentage of children given various immunizations as an outcome measure of investments in health. These measures are used in a basic DID estimation strategy (Equation 2), with results given in Table 7.

Of our 10 measures of public goods, five measures exhibit no statistically significant difference in provision between male and female presidents: children’s health, bus trips, public toilets, borewells, and common taps. Of these five, the point estimates are quite small in all cases with the exception of common taps. For the other five measures, we find that GPs with and without a woman in a reserved president seat had statistically significantly different changes in provision from 1991 to 2005: women-reserved GPs added fewer schools, fewer household drinking water connections, fewer household toilet connections, and fewer operational streetlights, and the distance to the nearest concrete road was farther away. In all cases, the change in the level of public goods indicates lower provision in women-reserved GPs versus unreserved GPs. Although we will not place too much emphasis on precise numbers, these differences are of non-trivial magnitude. The results suggest that women-reserved GPs had close to one fewer school, 13 fewer operational streetlights, 49 fewer household toilets, and 50 fewer household drinking water connections, and the nearest concrete road was 0.08 kilometers farther away compared with unreserved GPs.

Table 8 shows the results of a DID estimation with the following covariates added (Equation 3): the number of villages that compose the GP, GP female and SC population ratios 18
personality factors - openness to experience (also called intellect or culture), conscientiousness, extraversion, agreeableness, and neuroticism (or emotional stability) - summarizes a large number of distinct, more specific personality traits, behaviors, and beliefs. Measures of behaviors and beliefs focus on what individuals actually do rather than self-appraisal of skill competence. Given the absence of an agreement on a single set of behaviors and beliefs, there are a multitude of tests measuring a range of dimensions based on the researchers’ theoretical biases (Almlund et al. 2011).

# **III. Data and Methodology**

# **3.1 Data**

This paper is intended to be a review paper and thus does not present original research. Instead, it draws from the published literature across a range of fields. All the sources used as data for this paper generated statistics based on raw data from employer surveys, most of which were specialized surveys collected for the purpose of understanding employer skills needs. The sample frame for each cited study is available from the authors.

The sample was drawn from a search of five databases. First, we conducted a search on Google Scholar using a large set of key words (see Table 1). This search produced papers from literature across various fields, including economics, human resources psychology, industrial sociology, management and education. Second, we searched in IDEAS-RePEc<sup>13</sup> using the set of key words in Table 1. We conducted the search within the following JEL codes<sup>14</sup> : J23 (Labor Demand) and J24 (Human Capital; Skills; Occupational Choice; Labor Productivity) which are part of the J2 code corresponding to Demand and Supply of Labor. Third, we searched the EconLit database<sup>15</sup> using the key words listed in Table 1. Fourth, we searched the education literature through ERIC (Education Resources Information Center<sup>16</sup> ), one of the main databases dedicated to education literature. In addition we consulted the publications of institutions dedicated to research in the education field such as the NCEE (National Center on Education and the Economy) and the IAE (International Association for the Evaluation of Educational Achievement). Finally, we conducted a search of the psychology literature through the database of the

> 13 IDEAS-RePEc (Research Papers in Economics) is the largest bibliographic database dedicated to Economics in which most of the papers are fully downloadable. http://ideas.repec.org/

> 14 Classification by the Journal of Economic Literature (JEL) which is widely used in research papers in Economics.

> 15 EconLit is the <mark>American Economic Association’s electronic bibliography. w</mark> ww.aeaweb.org/econlit/

> 16 www.eric.ed.gov

7
19

# Key Feature of Retirement Systems

Retirement systems in most countries are extremely complex, particularly when plans interact and overlap to create an institutionally complex web of rules. In addition, multiple-pillar systems behave differently depending on each country's specific mix of public and private components. A brief description of the two most important aspects of retirement plans is offered to aid in understanding how the many different approaches to retirement systems work: benefits and financing. Instead of cataloging all possible retirement systems, the key characteristics common to most plans are identified, making reference to Individual country plans in selected Instances. Unless otherwise noted, specific country retirement system examples throughou **t** his section are taken from Tables 4 and S describing retirement system characteristics in OECD nations.

1. Retirement System Coveraae Rules and Practice: Whom does a country's retirement system cover, and which aroups are 3xcluded by law or de facto from participation?

In many countries, participation In a formal retirement system is restricted to citizens who are wage and salary workers, and in some cases only fractions of the entire workforce are covered." At times, one tier of the retirement system is limited in coverage but other tiers are unrestricted. Thus in Finland for instance, five years of residence suffice for coverage under the basic public pension scheme which does not require citzenship. Japan, by contrast, allows only resident citizens to be eligible for the basic pension plan, though residency is not required for a second tier of employees' pension. As a result, coverage rates vary widely across countries. For example in the United States, most private sector workers are covered by a public social security pension system, but only about 45 percent by the private retirement system.
# **1. Introduction**

The world continues to struggle with high levels of pollution caused by human activities such as fossil fuel consumption, industrial production, and depletion of forests. These produce excessive greenhouse gasses heating the planet and have devastating effects on the environment and humanity especially in developing countries. In fact, the developing countries are responsible for 63 percent of current carbon emissions globally (Center for Global Development 2015).<sup>6</sup> The urgency of addressing the threat of global warming has led countries to combine their efforts to combat climate change and adopt a legally binding international treaty, the Paris Agreement.<sup>7</sup> This became effective in 2020 and aims at limiting global warming to well below 2 (preferably 1.5) degrees Celsius compared to pre-industrial levels. In this regard, environmental regulations and their enforcements have become a critical mechanism for reducing emissions and their detrimental effects on nature and humans as widely discussed in the literature. For example, Danish et al. (2019) find that the current environmental regulations are successful in achieving pollution reduction targets in Brazil, the Russian Federation, India, China, and South Africa and are also responsible for an inverted U-shaped relationship between income and pollution. Similarly, using yearly data for 17 European Union countries over the last two decades, Neves et al. (2020) show that environmental regulations are effective in reducing CO2 emissions in the long-run, while policies supporting renewable energy sources tend to decrease CO2 emissions in both the short- and long-term.

Many developing countries have sizable informal sectors operating outside government control: Schneider et al. (2010) show that from 1999 to 2006 the informal sector GDP accounted for more than 50 percent of total in Bolivia, Georgia, Guatemala, Panama, Peru, Tanzania, and Ukraine. According to the World Bank (2021), in emerging markets and developing economies the informal sector contributes about a third of GDP and more than 70 percent of employment of which self-employment accounts for more than half (Ohnsorge et al., 2021). The presence of large informal sectors in developing countries might incur significant risks to the planet, as according to Baksi et al. (2010) informal sectors pose a serious challenge to the enforcement of environmental regulations in these countries. Biswas et al. (2012) argue that the environmental impact of informal sectors can be very significant as they include many pollution intensive activities, such as leather tanning, brick making, metal working, resource extraction, urban transportation with old and inefficient vehicles and production in small scale or family-based factories. Moreover, many developing countries lag behind advanced economies in terms of environmental policies:

> 6 https://www.cgdev.org/

> 7 <u>https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement</u>

2
Table 3: Total Factor Productivity Growth: Average in 1950-1995 and Change During Periods of Reform(%), OLS Regressions (Percent)

|Recession Dummy|None||| DREC1||| DREC2||
|---|---|---|---|---|---|---|
|Country / Period|1950-95|Reform<br>(change)|1950-95|Reform<br>(change)|1950-95|Reform<br>(change)|
|Argentina<br>Bolivia<br>Brazil<br>Chile|-1.4<br>(0.154)<br>-2.5<br>(0.032)<br>-1.3<br>(0.245)<br>-1.3<br>(0.229)|6.9<br>(0.008)<br>2.9<br>(0.026)<br>1.7<br>(0.400)<br>2.9<br>(0.047)|2.1<br>(0.001)<br>0.3<br>(0.688)<br>0.4<br>(0.651)<br>1.8<br>(0.059)|3.9<br>_(0.005)_<br>1.2<br>(0.186)<br>1.4<br>(0.350)<br>1.9<br>(0.079)|2.3<br>(0.001)<br>0.7<br>(0.441)<br>2.1<br>(0.010)<br>2.9<br>(0.009)|4.0<br>_(0.005)_<br>1.6<br>(0.084)<br>0.6<br>(0.614)<br>0.2<br>(0.847)|
|Colombia|0.4<br>(0.485)|0.4<br>(0.593)|0.4<br>(0.485)|0.4<br>(0.593)|2.3<br>(0.000)|_-0.5_<br>(0.271)|
|Costa Rica|-1.7<br>(0.201)|1.3<br>(0.263)|0.3<br>(0.786)|1.2<br>(0.184)|0.9<br>(0.316)|0.9<br>(0.227)|
|Ecuador|-0.9<br>(0.570)|3.1<br>(0.159)|1.3<br>(0.469)|0.7<br>(0.740)|1.5<br>(0.318)|2.8<br>(0.127)|
|El Salvador|-1.3<br>(0.139)|_1.5_<br>(0.136)|0.5<br>(0.465)|0.2<br>(0.761)|1.1<br>(0.162)|0.9<br>(0.260)|
|Guatemala|-0.3<br>(0.581)|0.1<br>(0.931)|0.7<br>(0.145)|-0.7<br>(0.238)|1.0<br>(0.011)|0.4<br>(0.328)|
|Guyana|-1.5<br>(0.069)|6.0<br>(0.007)|1.2<br>(0.091)|4.8<br>(0.003)|3.3<br>(0.001)|3.0<br>(0.054)|
|Honduras|-0.3<br>(0.739)|-0.2<br>(0.864)|0.9<br>(0.181)|0.0<br>(0.962)|2.5<br>(0.000)|-0.6<br>(0.397)|
|Jamaica|_-0.5_<br>(0.53)|0.1<br>(0.93)|2.0<br>(0.028)|-1.2<br>(0.337)|3.1<br>(0.000)|-0.5<br>(0.611)|
|Mexico|-3.5<br>(0.004)|1.2<br>(0.372)|-0.8<br>(0.415)|1.9<br>(0.956)|0.9<br>(0.753)|3.8<br>(0.899)|
|Nicaragua|-4.2<br>(0.001)|2.3<br>(0.248)|-0.9<br>(0.543)|1.9<br>(0.299)|0.9<br>(0.549)|3.8<br>(0.028)|
|Paraguay|-1.3<br>(0.099)|0.4<br>(0.784)|0.0<br>(0.990)|-0.7<br>(0.536)|_1.5_<br>(0.020)|1.1<br>(0.238)|
|Peru|_-1.5_<br>(0.132)|2.4<br>(0.16)|0.8<br>(0.327)|2.0<br>(0.112)|2.2<br>(0.024)|0.0<br>(0.947)|
|Uruguay|0.9<br>(0.223)|1.1<br>(0.587)|2.9<br>(0.000)|0.4<br>(0.785)|3.3<br>(0.000)|-0.2<br>(0.862)|
|Venezuela<br>|-1.2<br>(0.18)<br>|3.2<br>(0.049t<br>|_0.5_<br>(0.458)<br>|2.1<br>(0.075)<br>|_3.5_<br>(0.000)<br>|0.8<br>(0A440)<br>|

Notes: (*) The average for theperiod 1950-95 i's the coefficient on the constant in equation (3), while the change during the periods of Reform is the coefficient on the variable DREF in equation (3); P-values in parenthesis; DREC is activated when GDP growth is negative; DREC2 is activated when TFP growth (calculated with a capital share of 0.4) is negative. Source: Authors' calculations.
larger (-2.9) than when year fixed effects were controlled for. Temperature was not significant when the WASP variables were included. However, when temperature and precipitation were included as the only independent variables, this time precipitation was significant but temperature was not. The regression coefficient for precipitation was positive. This result implies that the significance of national average precipitation and temperature is sensitive to specification and does not provide much confidence in the meaning of the statistical significance.

**Table 3: GDP per Capita Growth with Country FE’s**

Dependent variable: GDP per Capita Growth

||(Precip)|(Temp)|(P,T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br>|(T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip|0.01<br>(0.01)*|**|0.02<br>(0.01)*|**||||||
|Temp||0.15|0.18||0.08||0.12||-0.12|
|||(0.17)|(0.17)||(0.21)||(0.22)||(0.26)|
|WASP_minus1||||||-2.19<br>(0.51)*|-2.22<br>**(0.51)***|-2.91<br>(0.97)**|-2.88<br>*(0.98)***|
|WASP_plus1||||0.62<br>(0.52)|0.63<br>(0.52)|||-1.89<br>(1.08)*|-1.89<br>(1.08)*|
|Observations|4898|4898|4898|2790|2790|2962|2962|1686|1686|
|Countries|181|181|181|178|178|180|180|133|133|
|R-squared|0.001|0.000|0.002|0.001|0.001|0.007|0.007|0.006|0.006|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1).

The regressions on the agricultural value added for the nations of the world provided our most difficult interpretation of results. Overall, in our most robust model specification none of the climate variables were statistically significant. In some specifications temperature and the WASP(-1) variable were significant. It is likely due to the selection of agricultural valued added as the dependent variable. Since this is a percent of a nation’s GDP, its value can increase even when actual agricultural GDP decreases if total GDP decreases at a greater rate. For these reason we present these results with caution.

15
# **F1IaE 2** BALANCE OF PAYMENTS

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