rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 12: Drought scenario – Prices response<br>6.0 % % 6.0 12.0 % % 12.0<br>5.0 Import prices 5.0 10.0 Industry prices 10.0<br>Factor prices<br>Livestock prices<br>4.0 4.0<br>Consumption prices 8.0 8.0<br>Crop prices<br>3.0 3.0<br>6.0 6.0<br>2.0 2.0<br>4.0 4.0<br>1.0 1.0<br>2.0 2.0<br>0.0 0.0<br>0.0 0.0<br>-1.0 -1.0<br>-2.0 -2.0 -2.0 -2.0<br>2020 2025 2030 2035 2040 2020 2025 2030 2035 2040<br>Source: World Bank analysis.<br>Note: Percent deviation from baseline. Exchange rate defined as Uruguayan Pesos per USD.<br><!-- End of picture text -->

**As output and exports contract, fiscal and external accounts deteriorate.** Government revenues fall as a direct result of the deterioration in economic activity, partially offset by the price increase. Spending on social security payments increases by 5.6 percent (its share of GDP increases by 0.5 percentage points), which coupled with the assumption of fixed nominal spending in other expenditure categories results in a worsening of the fiscal balance by 0.7 percentage points of GDP. This leads to increased government borrowing, which raises the level of government debt (Figure 13). Combined with balance sheet effects due to the depreciation of the currency (Figure 14) and with a lower level of GDP in the year of the drought, this increases the debt-to-GDP ratio by 2.4 percentage points. As the economy recovers from the drought, a period of stronger economic growth and a return to normal levels of government revenue allows the debt-to-GDP ratio to stabilize at around 0.4 percentage points above its baseline levels. The debt-to-GDP ratio is permanently higher due to the increased value of USD denominated debt accrued during the period of the drought.

20
contrast the cohort and time effects, the latter referring to the comparison of the same age group at two different points of time.

In order to do this, one can look at the first age groups, 16–25 and 26–34, like synthetic cohorts. Namely, the 26–34 age group in 1996, 2002 can be compared directly to the 16– 25 age group in 1988, and, to a lesser extent, the 35–49 age group in 1996, 2002 can be compared to the 26–34 age group in 1988. From 1988 to 1996 and to 2002, the percentage of persons in the category of incomplete primary schooling decreased, and this decline was higher than that experienced by the 16–25 age group (who were in the 26–34 age group in 1996). The opposite took place for the highest level of instruction. In other words, improvements throughout the educational process in Mexico were significant, both for those entering the system (higher coverage) and for those already in it (higher efficiency).

|_Table 2.5 Educational Distribution by Age Group, 1988, 1997 and 2002 _|
|---|

|_Age group_|_Primary_<br>_incomplete_|_Primary_<br>_complete_|_Lower_<br>_secondary_<br>_complete_|_Uppeer_<br>_secondary_<br>_complete_|_University_<br>_complete_|
|---|---|---|---|---|---|
|_1988_||||||
|16-25|8.5|26.5|36.7|23.7|4.6|
|26-34|12.6|23.7|23.1|22.5|18.2|
|35-49|24.0|33.3|16.8|14.3|11.6|
|50-65|46.1|27.2|9.9|9.0|7.8|
|Total|18.5|27.7|24.1|18.9|10.7|
|_1996 _||||||
|16-25|5.9|23.8|38.6|24.9|6.8|
|26-34|6.9|19.6|27.0|26.1|20.4|
|35-49|15.7|27.4|18.2|18.6|20.1|
|50-65|36.1|29.2|12.2|11.5|11.0|
|Total|12.9|24.4|25.7|21.6|15.4|
|_2002_||||||
|16-25|4.8|20.5|42.8|24.5|7.5|
|26-34|5.6|17.4|32.0|25.6|19.4|
|35-49|10.8|23.9|23.7|20.7|20.9|
|50-65|26.3|29.0|15.5|12.9|16.3|
|Total|10.0|21.9|29.5|21.9|16.7|

_Source:_ Calculations based on the ENEU (third quarter).

Also concerning the interaction between age and education, one can argue that developments in the educational system have more impact on the new generations than on the elderly. To investigate this, it is necessary to contrast the behavior of inequality 16
East Asian Tigers and India; the developing world suffered a twenty-year growth collapse, from which it has only recently emerged (Figure 3).

Figure 3 <!-- Start of picture text --> S9rmoohd Medan Growth Raes, 1958-98<br>67- OEC<br>6- DC<br>2 \<br>-1 W<br>0<br><!-- End of picture text --> While the causes of the global recession in the 1974-1994 are fairly well-known, it is instructive to examine some of the differences between those countries that weathered the storm, and those that did not. In his study of a large sample of developing countries, Rodrik (1999) finds compelling evidence that weak public institutions and (ethnically) divided societies responded worse to the shock than did those with high quality institutions and united societies.

We find something similar here. We define as most cohesive those societies in the lower half of ethnolinguistic fractionalization and in the upper half of share of the middle class, and as least cohesive the reverse. We see that more cohesive societies have always grown faster than less cohesive societies, but the difference only became pronounced with the recession in the latter in the 1980s, with a tepid recovery that failed to close the gap in the 1990s (figure 4).

17
17

# _3.5. Enabling Environment and Other Variables_

The focus of the 4x2 matrix—and the Global Financial Development Database—is on financial system characteristics. It does not explicitly include variables capturing financial sector policy, such as features of financial sector regulation and supervision. The conceptual reason for focusing on financial system characteristics is that those indicators bridge the gap between policy measures and final objectives. They function as ―intermediate‖ indicators and targets. To some extent, this is an analogy with monetary policy, where intermediate targets have a relatively clear link to the policy variable (e.g., central bank‘s interest rate) and an impact on the policy target (e.g., future inflation rate).

One group of the ―other variables‖ relates to the microstructure of the financial sector. The 4x2 framework (Table 1) captures the overall broad structure of the financial sector, in particular the relative sizes of financial institutions and markets, and the relative sizes of the key segments (total assets of banks and nonbanks; outstanding volumes of stocks and bonds). Also relevant are indicators of structure within the individual financial segments, such as the concentration ratios (Herfindahl index, shares of various types of financial institutions in total assets and in GDP, shares of individual markets in total market capitalization). Some of these measures (e.g., the percent of assets of three or five largest financial institutions in GDP), are important for the stability dimension, because they provide a rough approximation for the potential for impact in the case of a major financial disruption (e.g., Čihák and Schaeck 2010). More generally, recent research suggests that structure has an impact on performance, so it is important to include these indicators as part of the ̳enabling environment‘.

Another relevant group of indicators relates to the degree of international integration. This includes indicators such as consolidated foreign claims of BIS-Reporting Banks to GDP or measures cross-border banking interconnectedness, such as the degree of centrality (e.g., Čihák, Muñoz. and Scuzzarella 2011). These variables have a close link to several of the 4x2 dimensions, particularly to financial stability, and can be thought of as part of the enabling environment.

Finally, another group of indicators relates to the features of the underlying financial infrastructure. This includes basic indicators on information disclosure, contract enforcement and other quantitative characteristics of financial infrastructure. Examples of variables include public registry coverage (percentage of adults), private bureau coverage (percentage of adults), procedures to enforce contract (number), time to enforce contract (days), and cost to enforce contract (% of claim).

# **4. Aggregation and Other Practical Issues**

The 4x2 matrix of financial system characteristics (Table 1) is a useful way of visualizing the multi-dimensional nature of development in the financial sector. Each of the proxy variables is
# Is Export Diversification the Best Way to Achieve Export Growth and Stability? A Look at Three African Countries

by Ridwan Ali Jeffrey Alwang and Paul B. Siegel

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Export Diversification-<br>An Overview|6|
|3.|Measures of Export Performance and Diversification|8|
||3.1 Export Concentration<br>3.2 Portfolio Variance<br>3.3 Export Diversification and Structural Change|10<br>11<br>13|
|4.|Methods|13|
|5.|Data|16|
|6.|Results|16|
||6.1 International Prices, Volumes, and Instability<br>6.2 Malawi|17<br>21|
||6.3 Tanzania|26|
||6.4 Zimbabwe|29|
|7.|Conclusions|32|
|Endn|otes|38|
|Refer|ences|43|

> * Authors are listed in alphabetical order, and share senior authorship. Their respective affiliations are Division Chief, Agriculture Division, Southern Africa Department, World Bank; Assistant Professor, Department of Agricultural Economics, Virginia Tech; and Assistant Professor, Department of Agricultural Economics and Rural Sociology, University of Tennessee.

The authors acknowledge the helpful comments provided by Robert E. Christiansen, Senior Economist, Agriculture Division, Southern Africa Department, World Bank.
# **V Why no improvement?**

Apart from possible spillover effects as will be carefully discussed in Section VI, three possibilities may explain the findings of the “no academic performance effects” of solar products. First, it is possible that the treated households did not use solar lanterns due to the products’ malfunctioning. Second, it may be the case that the treated households used the products for purposes other than education. Third, it is possible that learning under solar lighting is not systematically more productive compared with the alternatives. The treated households might have used the solar products for nighttime study, which ensured an essential input for the education production function; however, bright lighting alone may not be sufficient to improve children’s learning and school performance. In what follows, we examine each possibility in detail to identify why we did not observe significant impacts of modern lighting technology on educational achievement.

## **Usage of solar lanterns**

The estimation results depicted in Table 4 enable us to reject the first possibility. In columns (a)―(l), reported in the first two panels of Table 4, we analyzed the average hours of use (per day) of different lighting products by the treated households compared with the control households, separately for the rainy and off-rainy seasons and each lighting source.<sup>13</sup> As before, for each dependent variable, we reported the regression estimates with and without the baseline controls. As the results indicate, the provision of solar products significantly reduced the use of kerosene products. This was particularly so for households that received all three solar products. While statistical significance and magnitudes were somewhat weaker compared with the case of kerosene products, similar implications can also be obtained for flashlights, implying that treated households efficiently substituted kerosene products with solar lanterns.

Columns (m)―(r) of the final panel in Table 4 show the impact of solar lanterns on the logarithmic value of household expenditures. As can be clearly observed from the results in columns (o) and (p), households that received three (one) solar products reduced their kerosene expenditure by approximately 75% (50%) over the last 12 months of 2013―2014, as compared with the control households. As explained in subsection S.3.1 in the supplemental appendix, during the follow-up survey, many treated households that reported zero expenditure on kerosene (presumably due to the substitution of solar lamps for kerosene products) were dropped from the estimations. We assume that the treatment group reporting on kerosene expenditure during the follow-up included a greater proportion of households more likely to use kerosene products than the corresponding control group. If this was the case, then the true treatment impact on kerosene expenditure may be even more negative. Consequently, this significant reduction in

> 13Unlike students’ time-use that is analyzed in Table 5 based on a time-diary information, these average hours resulted from information collected in a follow-up household survey.

13
- Jacob, B. A. and L. Lefgren (2003). Are idle hands the devil’s workshop? Incapacitation, concentration, and juvenile crime. _The American Economic Review 93_ (5), 1560–1577.

- Jaitman, L., D. Caprirolo, R. Granguillhome Ochoa, P. Keefer, T. Leggett, J. A. Lewis, J. A. Mej ́ıa-Guerra, M. Mello, H. Sutton, and I. Torre (2017). The costs of crime and violence: New evidence and insights in latin america and the caribbean. _Inter-American Development Bank_ .

- John, O. and S. Srivastava (1999). The Big-Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives. In L. Pervin and O. John (Eds.), _Handbook of Personality: Theory and Research_ , pp. 102–138. New York: Guilford Press.

- Jones, D., D. Molitor, and J. Reif (2018). What do workplace wellness programs do? evidence from the illinois workplace wellness study. Technical report, National Bureau of Economic Research.

- Kahneman, D. (2011). _Thinking, fast and slow_ . Macmillan. New York and London.

- Kassam, K. S., A. R. Markey, V. L. Cherkassky, G. Loewenstein, and M. A. Just (2013, June). Identifying Emotions on the Basis of Neural Activation. _PLoS One 8_ (6), e66032.

- Korjus, K., A. Uusberg, H. Uusberg, N. Kuldkepp, K. Kreegipuu, J. Allik, R. Vicente, and J. Aru (2015, February). Personality Cannot Be Predicted from the Power of Resting State EEG. _Front Hum Neurosci 9_ (63), 1–7.

- Krueger, A. B. (2003). Economic considerations and class size. _The Economic Journal 113_ (485), F34–F63.

- Krug, E. G., J. A. Mercy, L. L. Dahlberg, and A. B. Zwi (2002). The World Report on Violence and Health. _The Lancet 360_ (9339), 1083–1088.

- Lazear, E. P. (2001). Educational Production. _The Quarterly Journal of Economics 116_ (3), 777– 803.

- Lee, D. S. (2009). Training, wages, and sample selection: Estimating sharp bounds on treatment effects. _The Review of Economic Studies 76_ (3), 1071–1102.

- Lewis, D. O., S. S. Shanok, J. H. Pincus, and G. H. Glaser (1979). Violent juvenile delinquents: Psychiatric, neurological, psychological, and abuse factors. _Journal of the American Academy of Child Psychiatry 18_ (2), 307–319.

- Loewenstein, G. (2000). Emotions in economic theory and economic behavior. _The American Economic Review 90_ (2).

- LPG (2016). Amenazas de pandillas es la quinta causa de desercin escolar.

- Mahoney, J. L., M. E. Parente, and E. F. Zigler (2010). After-School Program participation and children’s development. _Handbook of Research on Schools, Schooling, and Human Development_ , 379–397.

- Mahoney, J. L., H. Stattin, and D. Magnusson (2001). Youth recreation centre participation and criminal offending: A 20-year longitudinal study of swedish boys. _International journal of behavioral development 25_ (6), 509–520.

38
|Multi-firm Y:1 N:0|Dummy variable equal to 1 if the firm is part<br>of a larger parent firm and 0 otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Power Outages (day hours)|Total hours of power outages experienced by<br>the firm in a typical month over the last fiscal<br>year. The figure is divided by 24 to convert to<br>day-hours.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Finance Obstacle (0-4 scale)|Severity level of access to finance as an<br>obstacle to firms’ day to day operations. The<br>severity level is defined on a 0-4 scale as: no<br>obstacle (0), minor obstacle (1), moderate<br>obstacle (2), major obstacle (3), and a very<br>severe obstacle (4).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Overdraft Y:1 N:0|Dummy variable equal to 1 if the firm has<br>overdraft facility and 0 otherwise.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Courts Obstacle (Country-level cell average)|Firms were asked how severe is the (lack of<br>proper) functioning of courts as an obstacle for<br>their current operations. Responses were<br>recorded on 0-4 scale as: no obstacle (0), minor<br>obstacle (1), moderate obstacle (2), major<br>obstacle (3), or a very severe obstacle (4). We<br>take the average value of the reported scores<br>where the average is taken over all other firms<br>(that is, excluding the firm in question) in the<br>same country where the firm is located.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Courts Obstacle (Country-industry-firm size<br>level cell average)|Firms were asked how severe is the (lack of<br>proper) functioning of courts as an obstacle for<br>their current operations. Responses were<br>recorded on 0-4 scale as: no obstacle (0), minor<br>obstacle (1), moderate obstacle (2), major<br>obstacle (3), or a very severe obstacle (4). We<br>take the average value of the reported scores<br>where the average is taken over all other firms<br>(that is, excluding the firm in question) in the<br>same country-industry-firm size group or cell.<br>Country is where the firm is located. Industry<br>is defined at the 2-digit ISIC Rev. 3.1 level (7|

56
13 Table 8: Weighted Lem Squae (with l/DPr2 as tho weight) for Barro Type Regresion with Inequality masures.

|Dop. Var|(1)<br>LOPC7088|(2)<br>LaPC7088|(3)<br>LaPC7088|(4)<br>LoPC70s8|_(5)_<br>LGPC7O88|
|---|---|---|---|---|---|
|IofOba|e1|74|74|74|81|
|Consnt|0.0197 O<br>(I .99)|0.0466<br>(3.84)|0.0362<br>(3.59)|0.0456<br>(4.05)|0.0343 *<br>(3.33)|
|SaDPPC70|40.0014<br>(-2.37)|-0.0008<br>(-1.07)|-0.0008<br>(-1.16)|-0.0009<br>(-1.25)|-0.0002'<br>(-3.52)|
|RBVC7085<br>_______________|-0.0134<br>___<br>(-1.33)|-0.0130<br>(-1.45)|-0.0130<br>(-1.47)|-0.0118<br>(-1.35)|-0.0165*<br>(-1.74)|
|ASSP7085|-0.0189<br>(-0.86)|-0.0239<br>(-1.07)|-0.025S<br>(-1.15)|-0.0261<br>(-1.19)|-0.0261<br>(-1.27)|
|PP170DVN|-0.0098 O<br>(-1.76)|-0.0100 *<br>(-1.98)|-0.0101<br>(-2.00)|-0.0097'<br>(-1.95)|-0.0087<br>(-1.66)|
|CPRIM60|0.0059<br>(0.65)|0.0055<br>(0.67)|0.0052<br>(0.64)|0.0053<br>(0.65)|0.0073<br>(0.85)|
|CSBC60|0.0019<br>(2.68)|0.0064<br>(0.73)|0.0046<br>(0.53)|0.0045<br>(0.52)|0.0167"<br>(2.50)|
|S0oV7088<br>l________________|-0.0369 O<br> <br>(-1.71)|-0.0447O<br>(-2.17)|-0.0455<br>(-2.24)|-0.0458<br>(-2.28)|-0.0405 *<br>(-2.01)|
|GIN<br>l_________________||-0.0589"<br>(-3.43)||||
|THEL|||-0.040<br>(-3.68)|||
|COBFVAR|||____________|-0.0250"<br>~(-3.85)_|_<br>_<br>_<br>_<br>_|
|RTP40<br>_________________|_<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _|_ _ _<br>__________________|_<br>__________________|_<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _|-0.2741*<br>_ _ _<br>(-3 .34)|
|R-Squued|0.64|0.72|0.72|0.73|0.68|

Even under ideal conditions inequality data is perceived as poor quality. In this case the problem is exacerbated by using income distribution data from different sources, for different income receiving unit, and for different years. Some observations are from the early seventies (and a few from the eighties for RTP40), hence endogeneity may be a concern. To correct these problems, two stage least squares is performed. In addition to the Barro regressors the following instruments are used: a dummy for socialist economies (SOC from the Barro Wolf data set), a dummy for African countries, a dummy<sup>for</sup> Latin American countries, log of initial per capita GDP and squared log of initial per capita GDP (from Summers and Heston (1991)). Using these instruments, the coefficients are slightly more negative than
_HOTTER PLANET, HOTTER FACTORIES_ _23_ consistent. For manufacturing firms located in climatic zones _Zone_<sup>_Q_</sup> (24<sup>3</sup> _._ 9<sup>0</sup> _C,_ 25 _._ 7<sup>0</sup> _C_ ] and _Zone_<sup>_Q_4a10Cincreaseindeviationsfromthelong-termaverageis</sup> (25 _._ 7<sup>0</sup> _C,max_ )<sup>,</sup> associated with productivity losses of 36 and 26 percent respectively, compared to the reference group. The impacts on the productivity of firms in the service sector are negative and significant in _Zone_<sup>_Q_</sup> (23<sup>2</sup> _._ 7<sup>0</sup> _C,_ 24 _._ 9<sup>0</sup> _C_ ]<sup>and</sup><sup>_ZoneQ_</sup> (25<sup>4</sup> _._ 7<sup>0</sup> _C,max_ )<sup>categories</sup> but not in the middle group, suggesting additional non-linearity impact as the temperature increases across the various zones. These effects in the manufacturing sector are larger than the effects we observe in the baseline model, suggesting that manufacturing firms explained a larger share of the productivity losses in our baseline result. These results suggest that the effects are heterogeneous and non-linear, varying by industry and temperature zones.

Table 5—: Estimation of Log Sales per Worker: By Sector

||Manufacturing|Services|
|---|---|---|
|WBT Deviation_×_Zone<sup>_Q_2</sup><br>(23_._7<sup>0</sup>_C,_24_._9<sup>0</sup>_C_]<br>|-0.014<br>(0.031)|-0.185<sup>_∗∗∗_</sup><br>(0.046)|
|WBT Deviation_×_Zone<sup>_Q_3</sup><br>(24_._9<sup>0</sup>_C,_25_._7<sup>0</sup>_C_]<br>|-0.446<sup>_∗∗∗_</sup><br>(0.059)|0.007<br>(0.079)|
|WBT Deviation_×_Zone<sup>_Q_4</sup><br>(25_._7<sup>0</sup>_C,max_)|-0.301<sup>_∗∗∗_</sup><br>(0.093)|-0.405<sup>_∗∗∗_</sup><br>(0.114)|
|Narrow sector dummy|Yes|Yes|
|Country FE|Yes|Yes|
|Year FE|Yes|Yes|
|Country x year FE|Yes|Yes|
|Population (25km radius)|Yes|Yes|
|Road infrastructure (25km radius)|Yes|Yes|
|Pollution (25km radius)|Yes|Yes|
|_Observations_<br>R<sup>2</sup>|48,521<br>0.392|38,135<br>0.375|

Standard errors are in parentheses. All models control for firm age, ownership, export status, firm size (employment), proportion of permanent workers, proportion of skilled workers, 55 dummy variables indicating a narrow industry classification, and dummies indicating World Bank regions. All WBT categories are included separately in the model, in addition to the interaction terms. The reference category for climate zone is the bottom quartile in the distribution of WBT which is less than or equal to 23.7<sup>0</sup> _C_ . The minimum WBT in our sample is 14.5<sup>0</sup> C. FE = fixed effect; km = kilometers; OLS = ordinary least squares; WBT = wet-bulb temperature. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1

# Income Grouping

A key departure of this study from earlier studies is its use of a globally comparable database of nonagricultural firms across climate zones, regions, and income
<!-- Start of picture text --> Model Data 95% CI Model Data 95% CI<br>5 6<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1<br>1<br>0 0<br>2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012<br>SD(Size) SD(Conditional Size)<br>(a) Size of attacked ships (Std Dev) (b) Size of hijacked ships (Std Dev)<br>Model Data 95% CI Model Data 95% CI<br>3 0.8<br>0.7<br>2.5<br>0.6<br>2<br>0.5<br>1.5 0.4<br>0.3<br>1<br>0.2<br>0.5<br>0.1<br>0 0<br>2006 2007 2008 2009 2010 2006 2007 2008 2009 2010<br>SD(Ransom) SD(Delay)<br>(c) Ransoms (Std Dev) (d) Length of captivity (Std Dev)<br><!-- End of picture text --> Figure 7: Model fit of second moments Note: The figures plot the second moments by year of the size of attacked and hijacked ships, ransoms, and length of captivity in the data v.s. the model simulated at the benchmark parameters listed in Table 1. The confidence intervals are 1-standard-deviation bands. Data sources: IMB (2012), UNODC-WB, and authors’ calculations.

by 2009 and 2010. In 2011, the simulated success rate was 10.2 percent. However by 2012, simulated pirates that were still in the business hijacked smaller ships as a response of private maritime security, increasing success rates to 28.4 percent.

Panels (c) and (d) in Figure 6 plot the average size of ships targeted by pirates and effectively hijacked over time, both in the actual and simulated data. The data shows that the average size of the ships attacked by pirates increased over the years: in 2007, ships attacked were 30 percent larger than the previous year and by 2012 they were 3.6 times larger. With the presence of navy patrols, pirates targeted ships slightly smaller but over 28