rafmacalaba/datause-encoder-data huggingface dataset:
sets expected. By contrast Algeria, Lebanon, the Syrian Arab Republic and the United Arab Emirates, only report 1 or 2 recent microdata sets.

# 3.5 Accessibility of Microdata Nationally

Having collected data does not necessarily imply that the (anonymized) microdata is publicly accessible. For all the data categories, we examine NSO websites<sup>15</sup> for accessibility of the microdata indicated to have been collected. This is reported in Table 3, where entries are only provided where in Table 2 it was indicated that a recent microdata set has been collected. Of the 81 microdata sets, only 16 are accessible to a user visiting NSO websites. Of these, only 5 can be downloaded immediately: the 2018-19 Lebanon Labor Force and Household Conditions Survey (LFHLCS), the <mark>2014 Morocco National survey on Household Consumption and Expenditure, the 2015 Tunisia National survey on budget, consumption and household living standard</mark><sup>16</sup> <mark>,</mark> the 2017 Tunisia National Population and Employment Survey and a subset of the 2014 population census microdata for Morocco. All others require prior registration.

We conclude that NSOs in the MENA region face two major challenges with respect to microdata. Except for price data which are up to date across the board, in all other data categories only about half the countries have up to date microdata sets on which they can draw. Note that this is a very lenient interpretation as microdata sets collected as far back as 2014 are counted towards being up to date. If a stricter definition of up to date were used, the number of countries with recent data would fall lower.

With respect to making the data that has been collected publicly available, NSOs in the region face even more challenges. Only 16 microdata sets, out of a potential 140 that ideally would have been collected, and 81 that have been collected, are downloadable from NSO websites. Consequently, and depending of the definition used, only 10%-20% of the expected microdata are available to the public on NSO websites. Within the price and health data categories none of the NSOs makes microdata publicly available.

> 15 Microdata available nationally may also be on the platforms of other national agencies besides the NSO. If this is the case, we examine the website of the national agency as well.

> 16 For the <mark>2015 Tunisia Budget survey,</mark> it is important to note that not all variables are included in the microdata set available for immediate download.

11
OECD (2009), _The Economics of Climate Change Mitigation. Policies and Options for Global Action Beyond 2012_ . Paris: Organization of Economic Cooperation and Development.

Rosendahl, Knut Einar, and Jon Strand (2009), Leakage From the Clean Development Mechanism. Discussion Paper 591, Research Department, Statistics Norway.

Schneider, Lambert (2007), _Is the CDM Fulfilling its Environmental and Sustainable Development Objectives? An Evaluation of the CDM and Options for Improvement_ . Oeko Institut: Berlin.

Stern, Nicholas (2007), The Stern Review: The Economics of Climate Change. Cambridge: Cambridge University Press.

Strand, Jon and Rosendahl, Knut Einar (2009), Endogenous Project baselines and Additionality in CDM. Unpublished, World Bank.

Wara, Michael W. (2008), Measuring the Clean Development Mechanism’s Performance and Potential. Climate. _UCLA Law Review_ , 55, 1759-1803.

21
time series showed, namely if the amount over expected loss is considered to be load then it varies considerably over time.

**Table 7.  Cross Section analysis – Model (1b)**

|_Coefficients_<br>**Second Quart**|_Standard Error_<br>**er 2006 (41 observ**|_t Stat_<br>**ations)**|_P-value_|
|---|---|---|---|
|Original||||
|Expected Loss<br>3.337374|0.191179|17.45681|2.6E-20|
|Multiple R = 0.9402||||
|**First Quarte**|**r 2008 (83 observa**|**tions)**||
|Original||||
|Expected Loss<br>1.681447|0.142756|11.77848|2.57E-19|
|Multiple R = 0.7928||||

# **Component Perils**

The expected loss can also be broken into component perils, as it was done in the previous section. Model (1’), where the intercept is set at zero, becomes Model (1b’) _Current Yield = Σp [bp* (Original Expected Loss)p]_ (6) However, note that in the case of Q2 2006 only 41 deals are available, so that the degrees of information for separating out the perils are even less than in the time series analysis. The results are shown in Table 8. The presence of so many “not significant” variables commends caution<sup>15</sup> .

**<u>Table 8.  Cross Section Analysis – Model (1b) with Component Perils</u>**

||_Coefficients_|_Standard Error_|_t Stat_|_P-value_|
|---|---|---|---|---|
||<br>**Second Quar**|**ter 2006 (41 observ**|**ations)**||
|US Wind|3.44806|0.323957|10.64359|1.62E-12|
|US Quake|4.465869|1.40003|3.189838|0.002999|
|Euro Wind|3.365641|0.760508|4.425514|8.97E-05|
|Euro Quake|0|0|N/A|N/A|
|Japan Wind|6.921184|6.968441|0.993218|0.327421|
|Japan Quake|1.984895|0.900147|2.20508|0.034112|
|Other Perils|1.777366|1.848708|0.96141|0.342942|
|Multiple R = 0|.9459||||
||**First Quarte**|**r 2008 (83 observa**|**tions)**||
|US Wind|2.396642|0.252253|9.500951|1.47E-14|
|US Quake|2.187185|0.39567|5.527794|4.38E-07|
|Euro Wind|-1.0669|0.618171|-1.72591|0.088428|
|Euro Quake|1.702749|1.169533|1.455922|0.149533|
|Japan Wind|1.726069|1.521742|1.134272|0.260246|
|Japan Quake|1.114602|0.785889|1.418269|0.1602|
|Other Perils|2.199775|1.031283|2.133046|0.036149|
|Multiple R = 0|.8355||||

15 Regressions have been re-run eliminating the “not significant” variables. The coefficients of the remaining variables change – but not markedly – so we have deliberately left the poorer fit to show the larger picture.

15
available is an analogous democracy index ranges from 0 to 10, but with a larger number means a more democratic government. Our _responsible government_ variable is the democracy index minus the autocracy index, a measure called _polity2_ in the database. It captures the extent to which a political regime is responsible to its people, the larger the number the stronger the democratic checks on the political system.

In addition to our executive power limitation variables, we consider a range of measures of the more general quality of government. Two of these are explicit appraisals of the country’s quality of government: _Rule of law_ is an ICRG survey result gauging the state of law and order in each country. It ranges from 1 to 6, with higher values connoting greater general respect for the rule of law.<sup>5</sup> It contains a law component, which captures the strength and impartiality of the legal and political establishment in judicial matters, and an order component, which captures the extent to which residents of a country accept established legal and political institutions as the solely legitimate way to make and implement laws and to adjudicate disputes.

_Control of corruption_ is also from the **ICRG** survey on the likelihood that government officials illicit side payments for their private pockets. This variable characterizes the extent to which corruption is controlled. It ranges from 0-6, and a higher value means a better control over corruption.

We use another set of variables to capture the track record of the government in terms of fostering investment opportunities. These are: _Expected growth_ is the country’s _predicted_ real per capita GDP growth rate based on linear regression of the past five year’s growth rates. We interpret a high past growth rate as both indicative of profitable investment opportunities and a track record of the country’s government fostering, or at least not impeding, their exploitation.

> 5 ICRG data has the advantage of covering the majority of countries from 1982 on. For details, see Knack and Rahman (2004) or <u>www.prsgroup.com.</u>

15
and 9 means Excellent. We then aggregate these individual (rescaled) grades to obtain an aggregate grade point ranging from 5 to 45, with grades 5 and 45 representing the lowest and highest grades respectively. We also compute the total grade points for STEM (mathematics and science) and non-STEM (social studies, Kinyarwanda, and English) subjects. It is important to emphasize that the grading system was unchanged during the study period, thus ensuring comparability of the grades across years. Nonetheless, for ease of interpretation and to ensure consistency with the test scores of the individual subjects, we standardized the grade points for the aggregate, STEM, and non-STEM subjects. Finally, using the geo-location of all primary schools in the country and data on the location and year of establishment of coffee mills, we spatially match each school to the nearest coffee mill.

## **3.2 DHS Data**

To explore potential mechanisms, we use data from the Demographic and Health Surveys (DHS) to understand the potential mechanisms. The DHS data is a nationally representative survey and covers a wide range of issues including, but not limited to, health, education, and household assets. For the purposes of this study, we focused on anthropometric measures (wasting and underweight) of children under five years, school attendance, and household wealth. The main advantage of the DHS over the other surveys such as the Integrated Household Living Conditions Survey (EICV) is that it is geocoded, allowing us to spatially match survey locations with the coffee mills datasets to determine treated and control households.

## **3.3 Complementary Data**

We complement our analysis with extensive data on coffee intensity at baseline from Guariso and Verpoorten (2018). Coffee intensity is measured as the number of coffee trees per sq km in 1999, compiled from the Rwanda coffee census. We also include data on road density at the municipality level, average annual temperature, total precipitation, and coffee suitability.

# **4 Empirical Strategy**

Our baseline specification is a difference-in-difference (DID) design, where we compare the differences in test scores of students who were exposed to coffee mills in early life to those born earlier in the community and hence not exposed to the mills in early life, with counterparts born in communities without a coffee mill. Essentially, our estimation strategy leverages plausibly exogenous variations in the establishments of the coffee mills across space and time (see Figure 7
**Figure 9: Average monthly food consumption shares by food group (percent)**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Bread and Meat Fish Milk, Oils and Fruit Vegetables Sugar, jam, Food Coffee, tea Water, soft<br>cereals cheese and fats honey & products and cocoa drinks,<br>eggs chocolate n.e.c. juices, etc.<br>HBS 2007 HBS 2011/12 NPS 2008/09 NPS 2010/11 NPS 2012/13<br><!-- End of picture text --> _Note:_ All values are food shares by food group relative to the monthly per adult equivalent food consumption, nominal. _Source_ : HBS 2007 and 2011/12 and NPS 2008/09, 2010/11 and 2012/13.

# **F. Non‐food consumption**

Table 9 shows non‐food consumption per adult per month as well as non‐food consumption separated by groups of goods and services. All values are presented in nominal terms, real terms, and adjusted for inter‐year price variation using the CPI. In general, non‐food expenditures are larger in the HBS compared to the NPS. Among the sources of these differences is the fact that the first two waves of NPS did not include “clothing and footwear”. NPS also does not include “restaurants and hotels”. Moreover, it is worth noting that the value of “housing, water, electricity, gas and other fuels” in the HBS 2011/12 is almost 3 times larger than the value in the NPS 2010/11 and that the “recreation and culture” expenses are over 25 times larger in the HBS; however, expenses on education and miscellaneous items are around 3 times larger in the NPS.

32
# **References**

Abu-Ghaida, Dina and Stephan Klasen (2004), “The Costs of Missing the Millennium Development Goal on Gender Equity” _World Development_ 32(7):1075-1107 Acemoglu, Daron, Simon Johnson, and James A. Robinson (2005) "Institutions as a Fundamental Cause of Long-Run Growth." In Handbook of Economic Growth, Volume 1A, ed. Philippe Aghion and Steven N. Durlauf, 385-472. Amsterdam and San Diego: Elsevier, NorthHolland.

Amin, Mohammad and Asif Islam (2016). "Can Gender-Based differences in the Law Disempower Women? Evidence from Firm-level Data on Female Managers in Developing Economies" Mimeograph (2016) http://works.bepress.com/mohammad_amin/65/ (Accessed 3/26/2016) Amin, Mohammad and Asif Islam (2015) “Does Mandating Nondiscrimination in Hiring Practices Influence Women's Employment? Evidence Using Firm-Level Data” _Feminist Economics_ 21(4): 28-60 Baliamoune-Lutz, Mina and Mark McGillivray (2015), “The Impact of Gender Inequality in Education on Income in Africa and the Middle East” _Economic Modelling_ 47(2015):1-11 Bandara, Amarakoon (2015), “The Economic Cost of Gender Gaps in Effective Labor: Africa's Missing Growth Reserve” _Feminist Economics_ 21(2): 162-186 Branisa, Boris, Stephan Klasen, and Maria Ziegler (2013) “Gender Inequality in Social Institutions and Gendered Development Outcomes.” _World Development_ 45:252–68.

Branisa, Boris, Stephan Klasen, Maria Ziegler, Denis Drechsler, and Johannes Jutting (2013) “The Institutional Basis of Gender Inequality: The Social Institutions and Gender Index (SIGI).” _Feminist Economics_ 20(2):29-64.

Dijkstra, A. Geske (2006) “Towards a Fresh Start in Measuring Gender Equality: A Contribution to the Debate.” _Journal of Human Development_ 7(2): 275–83.

Dulfo, Esther (2012), “Women Empowerment and Economic Development,” _Journal of Economic Literature_ 50(4): 1051-1079 Dollar, David and Roberta Gatti (1999), “Gender Inequality, Income, and Growth: Are Good Times Good for Women?” Working Paper 20771, World Bank.

Economic Commission for Africa. 2004. The African Gender and Development Index. Addis Ababa: Economic Commission for Africa 23
Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2007). The affect heuristic. _European Journal of Operational Research_ , _177_ (3), 1333‐1352.

Spader, J., Ratcliffe, J., Montoya, J., & Skillern, P. (2009). The bold and the bankable: How the Nuestro Barrio telenovela reaches Latino immigrants with financial education. _Journal of Consumer Affairs_ , _43_ (1), 56‐79.

Stango, V., & Zinman, J. (2009). Exponential growth bias and household finance. _The Journal of Finance_ , _64_ (6), 2807‐2849.

Sunden, A. E., & Surette, B. J. (1998). Gender differences in the allocation of assets in retirement savings plans. _The American Economic Review_ , _88_ (2), 207‐211.

Sunstein, C. R., & Thaler, R. H. (2003). Libertarian paternalism is not an oxymoron. The University of Chicago Law Review, 1159‐1202.

Thaler, R. (1981). Some empirical evidence on dynamic inconsistency. _Economics Letters_ , _8_ (3), 201‐207.

Thaler, R. H., & Benartzi, S. (2004). Save more tomorrowTM: Using behavioral economics to increase employee saving. _Journal of political Economy_ , _112_ (S1), S164‐S187.

Tufano, P., Flacke, T., & Maynard, N. W. (2010). Better Financial Decision Making among Low‐Income and Minority Groups.

Tversky, A., & Kahneman, D. (1974). Heuristics and biases: Judgement under uncertainty. _Science_ , _185_ , 1124‐1130.

Vohs, K. D., Baumeister, R. F., & Loewenstein, G. (2007). _Do emotions help or hurt decision making?: a hedgefoxian perspective_ . Russell Sage Foundation Publications.

Willis, L. E. (2011). The Financial Education Fallacy. _American Economic Review_ , _101_ (3), 429‐34.

Wilson, T. D., & Schooler, J. W. (1991). Thinking too much: Introspection can reduce the quality of preferences and decisions. _Journal of personality and social psychology_ , _60_ (2), 181‐192.

25
Statistics from baseline, actual implementation of the program, and the empirical strategy are summarized in Sections 4, 5, and 6. Section 7 presents the overall impacts of the program. Results are discussed in sections 8, 9, and 10. Finally, conclusions and policy implications from this empirical work are laid out in Section 11.

# **2. Context**

Over a decade ago, Sierra Leone emerged from a brutal, decade-long civil war (1991– 2002), but the impacts linger. The civil war severely impacted the country’s education system leading to large-scale devastation of school infrastructure, severe shortages of teaching materials, overcrowding in many classrooms in safer areas, displacement of teachers, frequent disruptions of schooling, psychological trauma among children, poor learning outcomes, weakened institutional capacity to manage the system, and a serious lack of information and data to plan service provision (World Bank 2007). The education sector is still in the process of being rehabilitated. The post-war “catch-up” phase and the accompanying construction boom supported by donors, together with the Government’s Education Act of 2004 which abolished primary education fees for all, have all contributed to this rehabilitation, driving a sharp increase in access and gross enrollment. Nonetheless, in 2011 more than half of the working age population in Sierra Leone had no formal education, with an average attainment of just 4 years of schooling (World Bank 2014).

The Education Act of 2004 stipulates universal basic education – 6 years of primary school and 3 years of junior secondary school. Enrollments in primary school more than doubled since 2000, with the gross enrollment rate (GER) reaching 115.7 percent in 2011. Two-thirds of children enrolled in primary education in Sierra Leone attended government schools, 31 percent attended religious schools, and only 6 percent (composed predominantly of children from the two richest quintiles) attended private schools. The primary completion rate was 69 percent in 2011, 11 percentage points below the average for sub-Saharan Africa (World Bank 2014).

Several challenges to the provision of basic education persist, many of them supplyrelated. As of 2008, sixth-grade pupils from 5,000 primary schools competed for places in fewer than 300 junior secondary schools, for example. Although the government abolished school fees, primary education is still not completely free because schools impose a variety of charges on their students, such as informal tuition fees and auxiliary costs such as school uniforms and books. Our baseline data (see Section 3.2 for more details) show that around 28 percent of the schools charged some degree of fees from parents. As many as 70 percent of the schools received subsidy payment for school fees 4
**Figure 2. Revenue from Taxes on International Aviation (a) Total Global Revenue**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>(b) Compensation for Developing Countries<br>25<br>20<br>15<br>10<br>5<br>0<br>(c) Revenue for Climate Finance and Other Uses<br>25<br>20<br>15<br>10<br>5<br>0<br>compensation) $25 CO2 price compensation) $15 CO2 price compensation) compensation)<br>$25 CO2 price (no developing country (developing country (developing country $40 CO2 price (no developing country<br>$billion<br>$billion<br>$billion<br><!-- End of picture text --> Source: See text.

40. **These numbers are broadly consistent with those presented in AGF (2010b)** . Workstream II reports revenue estimates for climate finance of approximately $1–6 billion. The lowest figure is for a $15 per tonne CO2 price, with 25 percent of the revenue raised in developed countries going toward climate finance, while the highest figure is for a 50 percent climate finance share and a carbon price of $40 per tonne CO2. Intra-EU flights are excluded from the revenue base in AGF (2010b), implying a somewhat lower revenue figure than above.

# **D.   Cost Considerations**

41. **Pricing aviation fuel would be a highly cost effective source of revenue compared with broader fiscal instruments** , in the absence of comprehensive, upstream pricing across all fossil fuel products and countries. The tax would reduce carbon emissions by suppressing fuel demand and (moderately) alleviate local air quality, noise, and congestion problems 27
|Urbanization (%)<br>WBL Index||||||1.682***<br>(0.422)<br>17.365**<br>(8.736)|
|---|---|---|---|---|---|---|
|Women can work the same<br>night hours as men Y:1 N:0||||||2.816|
|||||||(2.830)|
|Women can work in the same<br>industries as men Y:1 N:0||||||-1.293|
|||||||(2.066)|
|Ratio of Female to Male||||||2.502***|
|Population|||||||
|||||||(0.567)|
|Fertility Rate||||||-16.125***<br>(3.677)|
|Starting a Business (DTF)||||||0.079**<br>(0.039)|
|Inflation (%)||||||0.057<br>(0.065)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>effects|||||||
|Constant|28.879***|-93.348**|-94.276**|-113.860**|-110.443**|417.745**|
||(1.998)|(44.156)|(46.013)|(46.173)|(49.689)|(172.440)|
|Number of observations|<br>44,539|<br>44,539|<br>43,616|<br>41,444|<br>36,944|<br>36,944|
|R-squared|0.476|0.477|0.483|0.501|0.510|0.513|
|Standard errors in brackets. All<br>Sample size varies due to missi|standard error<br>ng data.|s are Huber-|White robust|and clustered o|n country-tim|es-industry.|

40
# **I. Introduction**

Why is it that in some countries and time periods, a given trade and exchange rate regime supports large scale production for foreign markets, while in other countries or time periods, the same policies appear to induce a minimal export response? Put differently, why are cstimates of export supply equations so sensitive to the time period or country under study?

In a recent series of papers Richard Baldwin, Paul Krugman, and Avinash Dixit have proposed an answer.' Thay begin from the assumption that non-exporters must incur a sunk entry cost in order to enter foreign markets. This mnakes the current-period export supply function dependent upon the number and type of producers that were exporting in previous periods. Further, it means that transitory policy changes or macro shocks can lead to permanent changes in market structure, and thus that trade flows may not be reversed when a stimulus is removed. That is, sunk entry or exit costs produce 'hysteresis" in trade flows. Finally, when future market conditions are uncertain, sunk costs make patterns of entry and exit depeodent upon the stochastic processes that govern variables such as the exchange rate. Under plausible assumptions, greater uncertainty makes trade flows less responsive to changes in these variables. None of these implications of sunk costs is captured in standard empirical export supply functions, and all could contribute to the "instability" of empirical relationships.<sup>2</sup> To date, attepts to empirically validate the sunk-cost hysteresis framework have focussed on 'See, in particular, Dixit, 1989a and 1989b; Baldwin, 1988 and 1989; Baldwin and Krugman, 1989; Krugman, 1989).

2 In their review of empirical studies of price and income elasticities for traded goods, Goldstein and Khan (1985, pp. 1087-1092) report a very wide range of estimates for the supply elasticity of total exports from developed countries. They conclude that 'excluding the United States, the supply-price elsticity for the total exports of a representative industrial countly appears to be in the range of one to four. The supply elasticity for U.S. exports is probably considerably higher than that, perhaps even reaching ten to twelve.' They also discuss some evidence indicating that the response of export supply to price changes is slower than demand-side adjustments. They speculate dh this may refict start-up costs associated with export production or greater unceUity assocated with sHling abroad.

I