rafmacalaba/datause-encoder-data huggingface dataset:
Table 3 presents results for the income share accruing to the 4<sup>th</sup> quintile. Column (1) reports effects for an early stage of economic development as measured by GDP per capita in 1970; column (2) reports effects for a recent stage of economic development as measured by GDP per capita in 2010. Specifically, for column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) (see their column (5) of Table 1), with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) (see their column (5) of Table 1), with the values of countries' GDP per capita in 2010. Brueckner and Lederman's estimated relationship for country _c_ and period _t_ is: (4) ln(GDP per capita)ct = -263*Q4ct + 40*Q4ct*ln(initial GDP per capita)c where Q4 is the income share of the 4<sup>th</sup> quintile.

From column (1) of Table 3, one sees that a rise of the upper middle class would have had a significant negative effect on GDP per capita for the majority of ASEAN countries for levels of GDP per capita in 1970. Specifically, for the average (median) ASEAN country, a one percentage point increase in the income share held by the fourth quintile would have decreased GDP per capita by around 0.2 (0.2) log. In contrast, for current levels of GDP per capita, a rise of the upper middle class would have a significant positive effect on aggregate output in all the ASEAN countries. This is shown in column (2) of Table 3. For 2010 levels of GDP per capita, a one percentage point increase in the income share held by the fourth quintile would increase GDP per capita by around 0.9 (0.7) log in the average (median) ASEAN country.

Tables 4 reports results for the net (i.e. after tax and transfers) Gini. The predicted effects reported there are generated based on the following estimates of Brueckner and Lederman (2015): (5) ln(GDP per capita)ct = 90*NetGinict – 13*NetGinict*ln(initial GDP per capita)c From column (1) of Table 4, one sees that a decrease in income inequality after tax and transfers would have had a significant negative effect on GDP per capita for the majority of ASEAN 7
31 Table 7: Contributions to total agricultural NRA<sup>a</sup> from different policy instruments, developing and high-income countries, 1972-76 and 1984-88 (percent)

|(a) Developing countries|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||**1972**|**1973**|**1974**|**1975**|**1976**|**1984**|**1985**|**1986**|**1987**|**1988**|
|**Border measures**|||||||||||
|Import tax equivalent|22|2|2|8|6|7|7|8|9|8|
|Export subsidies|4|0|0|1|1|1|1|1|1|1|
|Export tax equivalent|-26|-18|-24|-22|-9|-20|-10|-14|-19|-22|
|Import subsidy equivalent|-6|-5|-5|-2|-1|-1|-1|-1|-1|-2|
|**_ALL BORDER MEASURES_**|**-22**|**-21**|**-28**|**-16**|**-4**|**-14**|**-3**|**-6**|**-11**|**-15**|
|**TOTAL NRA(incl. domestic measures)**|**3**|**-14**|**-29**|**-17**|**-2**|**-15**|**-2**|**-5**|**-9**|**-13**|
|(b) High-income countries|||||||||||
||**1972**|**1973**|**1974**|**1975**|**1976**|**1984**|**1985**|**1986**|**1987**|**1988**|
|**Border measures**|||||||||||
|Import tax equivalent|25|18|15|21|30|33|34|50|49|42|
|Export subsidies|4|2|1|2|2|2|4|7|7|5|
|Export tax equivalent|0|-1|0|0|0|0|-1|0|0|0|
|Import subsidy equivalent|-1|-3|-3|-1|-1|0|0|0|0|0|
|**_ALL BORDER MEASURES_**|**27**|**17**|**13**|**22**|**31**|**35**|**37**|**57**|**56**|**46**|
|**TOTAL NRA(incl. domestic measures)**|**29**|**18**|**13**|**24**|**32**|**46**|**52**|**70**|**69**|**59**|

a All entries have been generated by dividing the producer subsidy equivalent of all (including domestic price, non-product-specific and ‘decoupled’) measures by the total agricultural sector’s gross production valued at undistorted prices.

Source: Authors’ calculations based on NRA estimates from Anderson and Valenzuela (2008).
For consumption models in Burkina Faso, Niger and Senegal, where geocodes are not available in the surveys, we define the treated as those experiencing a level of rainfall ( ) during the previous growing season (June to November of the previous year) below the long run mean . The control iitt−1 RR group is the one experiencing the long run mean or higher level of rainfall. For the Northern Nigeria ii RR<sup>�</sup> consumption model (North East, North West and North Central zones) and for the weight for age Sahel model (including Northern Nigeria, Northern Ghana, Mali, Burkina Faso and Senegal), defines the greenness index (NDVI), as explained in the data section below. iitt−1 RR

# **5 Data and Descriptive Statistics**

The Sahel region is facing continuing food and nutrition crises with many vulnerable households struggling to recover. This situation is exacerbated by different environmental, economic, social, political, and religious events that may hit governments, communities and households, undermining all the efforts to fight hunger and chronic poverty. In order to identify the level of households’ resilience this study relies on four types of data, namely, i) consumption data from household surveys conducted by local National Statistical Offices; ii) the Demographic and Health Surveys (DHS) designed, implemented and collected by the United States Agency for International Development (USAID); iii) the monthly level of precipitation from 1998 to 2012, produced by NASA through the Tropical Rainfall Measuring Mission (TRMM) project; and finally iv) the Normalized Difference Vegetation Index (NDVI) also collected by a satellite operated by NASA.

## **5.1 Consumption Data**

For Burkina Faso, information on households’ living conditions is drawn from the Surveybased Harmonized Indicators Program (SHIP) including a set of harmonized variables on household consumption, access to infrastructure i.e. water, electricity, education and health care, employment status, education and health. These variables have been extracted from the Enquête Burkinabé sur les Conditions de Vie des Ménages (EBCVM) conducted by the National Statistical Office in 2003 and 2009/2010, and covering the 13 regions of the country. The survey undertaken in 2003 includes a sample of 8,510 households, whereas the one carried out in 2009/2010 is made up of 8,470 households. Data are deflated using a 2005 deflator. As welfare threshold we use the national poverty line constructed in 2010.

In Nigeria we selected only three Northern regions since their agro-climatic conditions tend to be similar to those of other Sahel countries. We used the General Household Survey (GHS) panel consisting of 5,000 households. The panel covers the periods 2010/2011 (Wave 1) and 2012/2013 (Wave 2). It is representative at the national and zonal (geo-political) levels. Besides the questions asked in a normal GHS survey, it contains data on agricultural activities and other household income activities. Consumption data are collected using a 7-day recall period. In every panel wave, households 12
<!-- Start of picture text --> , 692.2<br>588.2 645.2aY ZGYY<br>5366 Y ZY,Y YZY<br>%ZY|G|GG<br>486.4 ” ZY GY G<br>441.4 y, Y Y Y Y<br>me "4 ZO |G Gab<br>3628 Ay 2G GuwTM<br>8 YZ Y Y Zz 3883<br>ag Ga A<br>295,0 309.8 3275 ,<br><!-- End of picture text -->

<!-- Start of picture text --> 233.5<br>198.6<br>ng 8 es 1017<br>587 G2 81M 70 155 874 74 785<br>632028! 7 2g Oe537 5 : 4a 65.0 351830 373mm536 44159.0 56.049 |co].<br>2008 2009 2010 2011 20l2 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>@ Total Global Sukuk Matured @ Total Global Sukuk Issued @ Total Outstanding Global Sukuk @ Total Sukuk Demand 4% Sukuk Supply& Demand Gap<br><!-- End of picture text -->
For both mathematics and science, the overall picture is not adequately described by the reported changes at the mean. The improvement in performance is highly heterogeneous across the distribution of scores and more so for science; however, the pattern is very different between mathematics and science. For mathematics, the grade improvement increases at higher points in the distribution, so that the increase for already well performing students is higher compared to students who performed poorly. The p90/p10 ratio of score increase in mathematics is 1.35.

The opposite pattern is observed for science. The largest improvement applies to students at the bottom decile of the score distribution - 62 points; this is an impressive increase at it amounts to an improvement of about 50% within 4 years. The pattern of performance improvement declines monotonically as one goes to higher points in the distribution and at the 90<sup>th</sup> percentile is approximately 38 points (or by about 10%).

For mathematics as well as for science, the contribution of improved characteristics to the overall increase in scores is rather small. At the median they contribute 17% and 21% to the overall increase in scores in mathematics and science respectively. The contribution of improved characteristics varies from 13% (19%) at the bottom decile to about 25% for both mathematics and science at the top. Across the distribution, therefore, characteristics contribute more to improved performance in the case of science compared to mathematics.

Table 3a: Decompositions of over-time change in Mathematics score at percentiles: Ghana, 2003-07

|Percentile|Total change|Characteristics|Median<br>Coefficients|Residuals|
|---|---|---|---|---|
|P10|30.85|4.36|32.96|-6.47|
||(1.56)|(1.74)|(2.18)|(1.85)|
|P20|33.28|4.83|31.69|-3.24|
||(1.63)|(1.79)|(1.82)|(1.53)|
|P30|34.49|5.28|30.44|-1.24|
||(1.61)|(1.74)|(1.77)|(1.32)|
|P40|35.47|5.83|29.67|-0.03|
||(1.61)|(1.74)|(1.74)|(1.15)|
|P50|36.14|6.29|29.35|0.49|
||(1.48)|(1.66)|(1.67)|(1.19)|
|P60|36.82|6.70|29.03|1.09|
||(1.36)|(1.73)|(1.70)|(1.38)|
|P70|38.00|7.27|28.97|1.76|
||(1.37)|(1.79)|(1.76)|(1.56)|
|P80|39.99|8.46|28.89|2.64|
||(1.58)|(1.84)|(2.25)|(1.74)|
|P90|41.59|10.22|28.30|3.07|
||(2.07)|(2.09)|(2.98)|(2.09)|

Standard errors in parentheses.

10
Yi =a+P-gi ***+(e,-P-U,)** (4) We identify the model with the assumption is that the observable determinants of governance included in x; are uncorrelated with the composite error term in Equation (4). In particular, these determinants of govemance must be uncorrelated with both the other determinants of income excluded from our simple specification (captured by ej), and with the measurement error in governance (captured by uj), i.e. E[x1 * e, ] = 0 and E[x, u;] = O. Under this assumption, the elements of x1 are valid instruments for measured governance gj* in Equation (4). Given such instruments, we can address the problems of omitted variables bias, simultaneity bias, and attenuation bias using twostage least squares (2SLS).

We follow Hall and Jones (1999) in using the fraction of the population speaking English, and the fraction of the population speaking a major European language (English, French, German, Portuguese or Spanish) as instruments. As Hall and Jones (1999) argue, a major feature of world history in the past several centuries has been the spread of Western European influence across countries. Together with this influence came an institutional and cultural background that in certain countries was conducive to the development of strong institutions and good govemance. Since the extent of this influence varies widely across countries and may safely be thought of as exogenous to economic outcomes centuries later, proxies for this influence such as the linguistic variables are good candidates for instruments.13

# Data

Our data are drawn from readily-available sources. The six governance indicators have already been discussed in the previous sections, and refer to the period 1997-98. We measure per capita income as the average over all available years 13 Hall and Jones (1999) also use distance from the equator as a proxy for the strength of past Westem European influences. We found this not to be a good instrument inour regressions as we could not reject the null hypothesis that it has a direct influence on per capita incomes in addition to its indirect effec **t** hrough governance. Only if we combine our direct measures of govemance with other measures of policy (such as the Sachs-Wamer index used by Hall and Jones (1999)) does an instrument set including distance from the equator pass tests of overidentifying restrictions. Similarly, in the Hall and Jones (1999) specificaton, tests of overidentifying restrictions fail if the policy variable is excluded from their measure of social infrastructure.

14
Table 9: Entry salary

|||Log (entr|y salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.298<sup>_∗∗∗_</sup>|-0.070<sup>_∗_</sup>|-0.065<sup>_∗_</sup>|-0.089<sup>_∗∗_</sup>|
||(0.048)|(0.042)|(0.038)|(0.043)|
|Schooing in years||0.057<sup>_∗∗∗_</sup>|0.061<sup>_∗∗∗_</sup>|0.034<sup>_∗∗∗_</sup>|
|||(0.006)|(0.009)|(0.008)|
|Age at hire||0.022<sup>_∗∗∗_</sup>|0.020<sup>_∗∗∗_</sup>|0.013<sup>_∗∗∗_</sup>|
|||(0.004)|(0.003)|(0.003)|
|Father’s pri.school||0.052|0.058<sup>_∗∗_</sup>|0.020|
|||(0.033)|(0.029)|(0.027)|
|Mother’s pri.school||0.043|0.056<sup>_∗_</sup>|0.016|
|||(0.035)|(0.033)|(0.037)|
|Math z-score||0.069<sup>_∗_</sup>|0.087<sup>_∗_</sup>|0.089<sup>_∗_</sup>|
|||(0.039)|(0.046)|(0.049)|
|Language z-score||-0.101<sup>_∗∗∗_</sup>|-0.098<sup>_∗∗_</sup>|-0.081<sup>_∗_</sup>|
|||(0.036)|(0.046)|(0.049)|
|Big 5 index z-score||0.021|-0.012|-0.082<sup>_∗∗_</sup>|
|||(0.045)|(0.032)|(0.036)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.174|0.391|0.387|0.220|
|N|3396|3390|3390|3390|

_Note_ . The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

31
women. But any given DC accumulation yields<sup>lower annualbenefitstowomen,</sup> especially if gender-specific tables are used,<sup>asinLatinAmerica.</sup><sup>_Widowhood._The</sup> greater longevity of women also means that they<sup>are more likely to become widows than</sup> men are to become widowers; hence survivors'<sup>pensionsareof keyimportanceto</sup> women. The social custom for husbands to<sup>beolderthanwivesexacerbatesthis</sup> importance of survivors' benefits. In Chile women<sup>in urban areas were almost as likelyas</sup> men to receive a pension. However, for women<sup>thepensionisawidow'sorsocial</sup> assistance pension in almost half the cases, while<sup>formenitisalmostalwaysanown-</sup> earned pension. In Mexico the disparity is even<sup>greater (Table2).Withoutsurvivors'</sup> benefits, non-working widows are likely to find<sup>themselveswithout monetarymeans and</sup> even widows who have a pension of their own<sup>find their household incomecutby far</sup> more than their cost of living when their husband<sup>dies,duetoscaleeconomies.Asa</sup> result of women's greater longevity, during which<sup>they outlivetheir husbandsand use up</sup> their family savings, poverty among the old tends<sup>tobeconcentrated amongwomen,</sup> when they are very old-unless provisions are<sup>includedmaking that outcomelesslikely.</sup> Survivors' benefits are often included in social<sup>security systems,buttheprecise</sup> arrangements vary. Joint annuities play a major<sup>role in the new LatinAmericansystems.</sup> IlmRlCadtns fo r mnunlftiPRlIr wrefoirm Given this as background, we conjecture that recent<sup>reforms that were designedto</sup> link benefits more closely with contributions<sup>willproduce lowerown-pensionsfor</sup> women, especially women who specialize in<sup>home-work.Of course,inpure defined</sup> contribution systems, a lower pension is directly<sup>attributabletolowercontributions,if</sup> men and women have both been subject to<sup>thesame rateof returnoninvestments.</sup> In this sense, lower pensions for women might be<sup>interpretedas"equaltreatment."</sup> However, it also may signal a very low standard of<sup>livingfor olderwomen,whichsocial</sup> security was designed to avoid.

In part to mitigate this eventuality, all multi-pillar<sup>systemscontain"public"</sup> defined benefit elements, usually financed by general<sup>revenues,which deviatefrompure</sup> defined contribution. We hypothesize that these<sup>generatetransferpayments thatfavor</sup> women, but detailed arrangements such as degree<sup>of targetingtolow earners,eligibility</sup> 7
Policy Research Working Paper 5343

# **Abstract**

Using the dating algorithm by Harding and Pagan (2002) on a quarterly database for 23 emerging market economies (EMEs) and 12 developed countries over the period 1980.Q1–2006.Q2, the authors proceed to characterize and compare the business cycle features of these two groups. They first find that recessions are deeper and more frequent among EMEs (especially, among LAC countries) and that expansions are more sizable and longer (especially, among East Asian countries). After this characterization, this paper explores the linkages between the cost of recessions (as measured by the average annual rate of output loss in the peak-totrough phase of the cycle) and several country-specific factors. The main findings are: (a) adverse terms of trade shocks raises the cost of recessions in countries with a more open trade regime, deeper financial markets and, surprisingly, a more diversified output structure. (b) U.S. interest rate shocks seem to have a significant impact on the cost of recessions in East Asian countries. (c) Recessions tend to be deeper if they coincide with a sudden stop, but the effect tends to be mitigated in countries with deeper domestic credit markets. (d) Countries with stronger institutions tend to have less costly recessions.

This paper—a product of the Office of the Chief Economist, Latin America and the Caribbean Region—is part of a larger effort in the department to characterize economic fluctuations in the region and across the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ccalderon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 9: Skill Formation at Different Points of the Life-cycle**

|**Period**|**Early years**<br>**(0-5)**|**Childhood**<br>**(5-12)**|**Adolescence**<br>**(13-16)**|**Early and Middle**<br>**Adulthood (18-**<br>**29) and(30+)**|
|---|---|---|---|---|
|**Type of Skills**|Basic cognitive<br>Foundational<br>socio-emotional|Basic Cognitive<br>Socio-emotional|Basic Cognitive<br>Socio-emotional<br>Higher-order<br>cognitive|Socio-emotional<br>Higher-order<br>cognitive<br>Technical|
|**Key Actor**|Family, ECD<br>programs|Family, schools,<br>peers|Schools, peers,<br>mentors, family|Higher education<br>institutions,<br>training<br>institutes, work<br>place|
|**Sample programs**<br>**to Guide Actors to**<br>**Build the Skills**(for<br>a list of evidence-<br>based programs,<br>see Guerra and<br>Modecki,<br>forthcoming)|Quality parenting<br>(Nuevo Postnatal,<br>Program on<br>Cognitive<br>Development,<br>Early Enrichment<br>Program)<br>Child-focused ECD<br>(Perry Program,<br>Head Start)|Holistic curriculum,<br>methodology, and<br>evaluation system (<br>Chile, RCCP)<br>After-school/extra-s<br>programs/activities<br>Success Teams)|teaching<br>monitoring and<br>KIPP, EPSIS, Enseña<br>chool/out-of-school<br>(BBBS, Student|Apprenticeships<br>(Jóvenes<br>programs)<br>Skills certification<br>system, support<br>systems for<br>worker transition<br>to firms where<br>new learning can<br>occur|

_Source: own elaboration based on World Bank (2010) and Guerra and Modecki (forthcoming)_ 41
# **3. Growth of unorganized tradable industries and 1-person establishments**

This section depicts the growth patterns for Indian manufacturing. We start at the aggregate level, showing the important growth of informal tradables and 1-person establishments. We then consider industry- and state-level trends.

## _Aggregate data trends_

Table 1a documents the core trends. Panel A presents employment levels, and Panel B displays establishment counts. Per Figure 1, we disaggregate the totals into four groups by sector (informal vs. formal) and the tradable nature of the industry. Below each data row we provide the growth rate relative to the prior period. The last four columns provide the share compositions in 1989 and 2010 and the medium-run growth rates to 2010 from 1989 and 2000.

The rise of employment in informal tradables is quite remarkable. In 1989, this group accounted for 6.2 million workers, 19% of Indian manufacturing employment, and 24% of manufacturing establishments. By 2010, this group has expanded to 16.4 million workers, 39% of manufacturing employment, and 55% of manufacturing establishments.<sup>3</sup> The most pronounced period of growth was during the 1994 to 2000 period, but even after 2000, informal tradables added more jobs and establishments than any of the other groups. In total, informal tradables posted 166% and 193% growth in employment and establishments, respectively, across the two decades. Figure 1 provides a visual display of these growth patterns.

Among the other trends, formal tradables shows the most growth from 1989 to 2010, with employment in particular posting a 131% gain. Even with this growth, however, the share of jobs in the overall manufacturing sector accounted for by formal tradables stands at 10% in 2010. By

> 3 Thomas (2013), while noting similar trends to those identified in this paper, documents different levels of informal manufacturing employment than those presented in this paper. For instance, while we find roughly 47 million informal sector manufacturing employees in 2005/06, Thomas finds roughly 55 million in 2004/05. The source of this discrepancy is that Thomas pulls his employment figures from NSSO Employment and Unemployment Survey, which is conducted at the household level, while we pull our figures from the NSSO Unorganized Manufacturing Survey, which is conducted at the enterprise level (similar to the ASI). The enterpriselevel survey gives a lower estimate when calculating employment levels across all years, but the trends are very similar.

7