rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: The distribution of life satisfaction (LiTS III) <!-- Start of picture text --> Economic Subjective well−being Life satisfaction<br>1 2 3 4 5 1 2 3 4 5<br>Aggregated Responses Responses<br>Job satisfaction Financial satisfaction<br>1 2 3 4 5 1 2 3 4 5<br>Responses Responses<br>40<br>40<br>30 30<br>20 20<br>Percent Percent<br>10 10<br>0 0<br>30<br>40<br>30<br>20<br>20<br>Percent Percent<br>10<br>10<br>0 0<br><!-- End of picture text --> Source: LiTS III. The first graph shows the distribution for the subjective ranking. The others focus on other subjective questions. The distribution of responses to the questions ""Are you satisfied with your life" (upper right), "Are you satisfied with your job?" (lower left), and "Are you satisfied with your financial situation?" (lower right) with responses (1) Strongly disagree, (2) Disagree, (3) Neither agree or disagree, (4) Agree, and (5) Strongly agree.

**The individual determinants of the subjective ranking: the importance of nonincome factors** A more compelling evidence of the well-being nature of the subjective ranking variable is the fact that factors other than current income affect its variability. Ravallion and Lokshin (2002) have shown that individuals’ economic welfare as selfassessed by their subjective ranking is influenced by non-economic factors such as their health, their education and their perspectives about future welfare. Similarly, Cojocaru and Diagne (2015) show that, apart from the factors mentioned before, subjective ranking is also correlated with beliefs about society – namely, whether connections are vital to get ahead in life and whether the cause of need in society is either (lack of) luck, laziness or injustice.

In this section we extend the analysis carried out in the literature by quantifying the effect that factors other than current income have on individuals’ self-rated welfare in a common scale. We propose to estimate the following specification for individual _i_ in country _c_ 16
# Contents

|1|Introduction|3|
|---|---|---|
|2|The Model|6|
||2.1<br>Individuals . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>7|
||2.2<br>Production of the Final Good . . . . . . . . . . . . . . . .|. . . . . . .<br>8|
||2.3<br>Production of Intermediate Goods . . . . . . . . . . . . . .|. . . . . . .<br>10|
||2.4<br>Design Sector . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>11|
||2.5<br>Government . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>13|
||2.6<br>Market-Clearing Condition and Labor Supply<br>. . . . . . .|. . . . . . .<br>13|
|3|Equilibrium|14|
|4|Properties of Equilibrium|17|
|5|Middle-Income Trap and Public Policy|19|
|6|Extensions|22|
||6.1<br>Enforcement of Property Rights . . . . . . . . . . . . . . .|. . . . . . .<br>22|
||6.2<br>Infrastructure and Time Allocation . . . . . . . . . . . . .|. . . . . . .<br>23|
||6.3<br>Labor Market Rigidities<br>. . . . . . . . . . . . . . . . . . .|. . . . . . .<br>23|
|7|Policy Implications|24|
|8|Concluding Remarks|26|
|A|ppendix|28|
|Re|ferences|32|

2
<!-- Start of picture text --> s 0.40 0.36<br>=z<br>= 0.30 0.29 0.28<br>328 0.25 | i 0.25 0.24<br>Bgeo020 | |60.16<br>£3 = Before<br>~~ 0.15<br>5soo=z 0.100.05 ae oe<br>0.00: — i im<br>2 Treatment Control Treatment Control<br>North & Southeast Jalapao & Northeast<br><!-- End of picture text -->
**Table A1.  Explanatory variables used in the analysis – definitions and data sources** _(continued)_

|**_Name_**|**_Abbreviation_**|**_Source_**<sup>c</sup>|**_Description_**|**_Scale_**|
|---|---|---|---|---|
|Minimum wage level|_MIN_WAGE0,_<br>_MIN_WAGE_|Eurostat and own<br>calculations with data<br>of national statistical<br>offices of Russia and<br>Ukraine|_MIN_WAGE0:_<br>Minimum<br>wage<br>level<br>expressed as percentage of average wage in<br>industry and services._MIN_WAGE_assigns<br>countries without a minimum wage sample<br>mean of_MIN_WAGE0_(in addition, a<br>dummy_NO_MINWAGE_for these countries<br>is included in the models).|0 to 100|
|No minimum wage|_NO_MINWAGE_|Eurostat|= 1 if a country does not have a minimum<br>wage,0 otherwise|0 or 1|
|Union density|_UNION_|Calculation with ESS<br>data|Trade union membership, share of all<br>employees(%).|0 to 100|
|GDP per capita<sup>a</sup>|_GDP_PC_|World Bank|Logarithm of GDP per capita, purchasing<br>powerparities|6.8 to 10.7|
|GDP growth <sup>d</sup>|_GROWTH_|Eurostat, OECD,<br>statistical offices of<br>Russia and Ukraine, and<br>own calculations|Real year-on-year GDP growth (quarterly<br>data), %|-50 to 20|
|Population with<br>immigrant<br>background|_IMMIGR_|Calculation with ESS<br>data|First generation immigrants and second<br>generation immigrants|0 to 100|
|Minority population|_MINORITY_|Calculation with ESS<br>data|Local born minority population without<br>immigrant background,%|0 to 100|
|Gini index<sup>a</sup>|_GINI_|Eurostat and World<br>Bank|Gini index of income inequality|0 to 100|
|Active labor market<br>policy expenditure<sup>a</sup>|_LMP_ACTIVE_|Calculation with OECD<br>and Eurostat data|Active labor market measures spending<br>(categories 20-70), % GDP per percentage<br>point of(LFS-based)unemployment|0 to 1|
|Passive labor market<br>policy expenditure<sup>a</sup>|_LMP_PASSIVE_|Calculation with OECD<br>and Eurostat data|Passive labor market measures spending<br>(categories 80-90), % GDP per percentage<br>point of(LFS-based)unemployment|0 to 1|
|Benefit net<br>replacement rate<br>for long-term<br>unemployed<sup>a</sup>|_UBSA_|OECD|Average unemployment benefit and social<br>assistance net replacement rate over 60<br>months of unemployment (%)|0 to 100|

_Notes_ :<sup>a</sup> Lagged 1 year in all specifications where used.<sup>b</sup> Lagged 1 year in some specifications. c In few cases, data on the closest available year or interpolation have been used to fill missing data. Relevant related data have been analyzed in these cases to assure validity of the procedure.<sup>d</sup> Lagged 1 quarter.

54
<!-- Start of picture text --> Pupil Classroom Ratio (PCR)<br>© Ratio MESIP over Non-MESIP districts<br>1.25 |<br>1.20 +] I<br>I °<br>1.15 >| ° ° I °<br>| °<br>° I<br>1.10 -| |<br>1.05 +<br>I<br>1.00 4<br>0.95 —<br>2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2010-20<br>Notes: Figures are WB calculations using EM|S databases for the school years 2010-11 to 2019-20<br><!-- End of picture text -->

<!-- Start of picture text --> Pupil Teacher Ratio (PTR)<br>© Ratio MESIP over Non-MESIP districts<br>1.25 5 |<br>1.20 + I<br>1.15 ~ I<br>e 8 |<br>ee |<br>° I<br>1.10 +} |<br>I<br>1.05 ~<br>I<br>1.00 +<br>0.95 —<br>2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2010-20<br>Notes: Figures are WB calculations using EMIS databases for the school years 2010-11 to 2019-20<br><!-- End of picture text -->

<!-- Start of picture text --> Pupil Classroom Ratio (PCR) in Lower Primary<br>© Ratio MESIP over Non-MESIP districts<br>1.25 |<br>I<br>1.20 +] °<br>I<br>°<br>I<br>I<br>115-0 I<br>I Ps<br>|<br>I<br>1.10 -| |<br>I<br>I<br>1.05 4<br>I<br>I<br>1.00 4<br>I<br>I<br>0.95 —<br>2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2010-20<br>Notes: Figures are WB calculations using EM|S databases for the school years 2010-11 to 2019-20<br><!-- End of picture text -->

<!-- Start of picture text --> Pupil Teacher Ratio (PTR) in Lower Primary<br>© Ratio MESIP over Non-MESIP districts<br>1.25 5 |<br>I<br>I<br>1.20 + I<br>°<br>°<br>I<br>1.15 ~ é<br>°°<br>I<br>I<br>1.10 +} |<br>| °<br>I<br>I<br>1.05 ~<br>I<br>I<br>1.00 +<br>|<br>I<br>0.95 —<br>2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2010-20<br>Notes: Figures are WB calculations using EMIS databases for the school years 2010-11 to 2019-20<br><!-- End of picture text -->
individuals. Manley et al. (2013) undertake a systematic review and meta-analysis on conditional and unconditional cash transfers and child nutrition and come to similar conclusions.

A relevant concern is the existence of intra-household inequality. The unitary model of the household (characterized by a single utility function) has found little support empirically, and various alternatives have been proposed (as reviewed by Chiappori and Mazzocco, 2015, and Baland and Ziparo, 2017). These alternative models permit new sources of inequality within households, such as in reservation utility levels. An extensive literature details intra-household inequalities in resource allocations and outcomes (as reviewed in World Bank 2012). There are two direct policy implications: targeting poor households may well miss some significantly disadvantaged individuals and targeted households may not allocate the benefits to the neediest within the household. This paper addresses the first issue.

It is well recognized in principle that household-level consumption or income-based measures do not allow for inequality within the household. There is also (largely qualitative) evidence that certain individuals are poor and/or vulnerable, but do not live in households that would normally be considered poor and so are hidden from view in standard data sources on poverty. Differentiation between men and women has been widely documented in human capital, legal protection, constraints stemming from social norms, roles and responsibilities, and control over resources (Ezememari et al. 2002). For Africa, there is evidence that household shocks affect men and women differentially, with women bearing the brunt of negative shocks (Rose 1999; Dercon and Krishnan 2000). Such differentiation can be expected to have consequences for measures of poverty and inequality. In an important early example, Haddad and Kanbur (1990) find that such measures for the Philippines are appreciably underestimated using standard household-level data, although the “profiles”—the comparisons of these measures across subgroups such as urban and rural areas—were found to be quite robust. Using a survey for Senegal that (unusually) collected a relatively individualized measure of consumption, Lambert et al. (2014) find significant inequalities within the household and a sizeable gender gap in consumption. Using the same data, De Vreyer and Lambert (2016) estimate that about one in eight poor individuals live in non-poor households. Using anthropometric data, Sahn and Younger (2009) find that about half of country-level inequality in the Body-Mass Index is within households rather than between them.

Other work has emphasized the poverty of specific types of individuals. Recent research on Mali confirms that widows—most of whom are absorbed into male headed households and can be quite young—experience significantly lower levels of individual (non-income) welfare indicators than women 7
observable characteristics and operating in the same country, 3-digit manufacturing sector and in the same size category.

Finally, in our robustness we will also test the robustness of our main findings to the comparison of firms located in the same geographical region (captured either by size dummies or by country-sector-city dummies). This is important since one of the reasons why some firms might use more skilled labor is its relative price. Assuming that labor markets are competitive at the local level, and that firms take prices as given in their localities, the relative price of skilled labor would be well captured by the country-sectorcity dummy.<sup>10</sup> Needless to say, there will still be several problems associated with interpreting  or  in a causal way. In particular, endogeneity, reverse causality and omitted variable problems are all likely to be sources of concern. We do not have a valid exogenous instrument for firm openness or technological innovation and thus will be severely limited in the extent to which we will be able to disentangle simple correlation from causality in our exercise. Reassuringly, however, our main findings will be robust to several specifications and subsamples. First, following Pavcnik (2003), we control for unobserved characteristics common to a geographic location or industry, which could affect both the demand for skills and firm openness. We test this by adding to our reduced form city and country-industry-city fixed effects. Second, we try to minimize omitted variable problems by exploring the detailed information available in the _Enterprise Surveys_ . In particular, it is possible that the more educated managers are simultaneously more likely to engage in international activities and also better in identifying more able/skilled workers. Controlling for the manager’s human capital (captured by its educational level) could mitigate this problem. Third, there is robust evidence that firms self-select into international activities, with the more productive and those with a more educated workforce being more likely to be more open (see e.g., Tybout 2000). In particular there is robust evidence of self-selection into exports and into foreign direct investment (e.g., Tybout 2000; Fernandes and Isgut 2005; Almeida 2007). One way to overcome this would be to assume that this positive correlation can be

> 10 Pavcnik (2003) and Fajnzylber and Fernandes (2004), and most of the literature, also omit the relative wages in the reduced form equation they estimate.

11
27 (matches and tobacco, etc.). The sample covers the period from 1980 to 1985 but some data were not available for 1985. As a result, we present data for both 1984 and 1985 as two different endpoints that can be used in assessing the evolution of employment in the sample. We should also mention that our sample data tend, in fact, to be more accurate than the official data because firms occasionally do not distinguish clearly between permanent and temporary workers in their reports to the government.

As the data in Table 4.4 indicate, permanent employment in the sampled enterprises as a whole grew very slightly in the 1980s. Moreover, our sample data indicate that the discrepancies in the employment and output trends are not as striking as the official TABLU 4.4

|_S-Ngal_<br>_Emp_|_loynant_of P<br>1the Ent|enn<br>t Woi<br>etpdw Simpl|ran by_Imlstry_||
|---|---|---|---|---|
||198G|1982|1984<br>|1985|
|Food|9,980|10.181|JO<br>9,886|10,072|
|Tobacco and Matches|.U7|466|512|476|
|Textfles, Clothing and'<br>Leather|4,540|4,641|4,568|4,655|
|Wood|236|205|230|211|
|Paper|297|401|371|374|
|Printing|93|191|115|113|
|Chemicals|1,279|1,690|1,673|1,455|
|Construction|597|634|474|449|
|Mechanical Industries|1,491|1,950|Z188|1,988|
|TOTAL|8,988|20,359|20,017|19,793|

Source: Enterprise sanple
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# Scenario database

Five policy choices were modeled, representing different ways of achieving universal access by 2030 across Sub‐Saharan Africa. Four depict different paths to universal access (table 1): in the “High quality” consumption policy the government chooses to increase access mostly via the grid with a quality and level of service enabling productive use, while in the “Basic” and “Middle range” the goal is to provide a more basic electricity service to unserved households. BAU depicts a world where newly connected households benefit from the same tier mix as the already connected households in the country when the progressive choice is a scenario where all newly connected households end up at Tier 3 in 2030 but passing through tier 1 and tier 2 in the meantime (universal access is reached earlier but with low tier level). In the fifth policy choice case, “constant access,” investments are made to maintain access and consumption at the current level for households—although they allow for growing industrial demand.

# **TABLE 1 POLICY CHOICE: NEW CUSTOMERS CONSUMPTION LEVEL**

|**Consumption**<br>**Strategies**|**Basic**|**Middle**<br>**range **|**High quality**|**BAU**|**Constant**<br>**access**|
|---|---|---|---|---|---|
|**Objective:**<br>**universal**<br>**access in 2030**|50 percent of<br>newly<br>connected<br>households<br>are assigned<br>to Tier 1, the<br>rest to Tier 2|Newly<br>connected<br>households<br>are assigned<br>to Tier 3|50 percent of<br>newly<br>connected<br>households are<br>assigned to Tier<br>4, the rest to<br>Tier 5|Newly<br>connected<br>households<br>are assigned<br>the current<br>Tier<br>breakdown|Newly<br>connected<br>households to<br>keep up with<br>demographic<br>growth, Tier<br>breakdown is<br>BAU.|

Source: Authors. Note: industrial demand varies independently of the access scenarios.

These five policy choices were assessed under dozens of scenarios for each of the 48 countries, by varying five parameters: population growth, urbanization rate, industrial demand growth, technology cost evolution, and fuel price.

The fossil fuel price was defined for two levels (low and high) based on the World Bank Commodities Price forecast (World Bank, 2016, July) for the short term and World Energy Outlook 2015 (IEA (2015)) low oil price scenario and current policies scenario the long term. The renewable technology cost and performance outlooks were defined in three levers (low, reference and high) which are based on the projected learning rates from (NREL, 2016).

# **TABLE 2 INPUT PARAMETERS**

||Values|
|---|---|
|Populationgrowth|5 values: 5 SSPs|
|Urbanization|5 values: 5 SSPs|
|Techno costs|3 values (NREL low,<br>medium and high ref)|
|Fuelprices|2 values: low & high|
|Industrial demand<br>growth|5 values: same as GDP<br>growth of the 5 SSPs|

6
# **1 Introduction**

The expansion of schooling in Sub-Saharan Africa over the last 50 years has made basic education more accessible to many of the world’s poorest: between 1970 and 2005, average schooling attained by young Africans rose from 2.6 years to 6.1 years, and continues to grow.<sup>1</sup> Increases in educational participation and attainment have coincided with rising literacy and formal sector employment across the continent, though the direction of causality is not clear. Wage returns to education have been shown in other developing country contexts (Duflo 2001), but similar patterns have not been demonstrated as convincingly in Africa. One complicating factor is that rates of employment are quite low. In 2008, for example, only 38 percent of Kenyan men were employed by someone outside their family.<sup>2</sup> In this context, the effects of education on human capital accumulation, occupational choice, and fertility decisions may in fact be more socially relevant measures of the returns to schooling than wage effects.

Most empirical studies find little evidence that African schools have positive effects on outcomes. Two recent papers find no positive academic effects at all: Lucas and Mbiti (2014) show that admission to higher quality secondary schools in Kenya neither raises the probability of completing secondary school, nor increases 12th grade test scores; de Hoop (2010) estimates that admission to a higher quality secondary school in Malawi increases the probability of remaining enrolled in an assigned school, but has no effect on test scores. These studies, however, only measure the change in academic per-

> 1Source: Barro and Lee (2010), tabulation based on 33 countries in sub-Saharan Africa.

> 2Source: DHS (2009). This includes all age groups and covers both rural areas and urban centers.

2
∑������hours��� �hours��<sup>�</sup> ). The results, given in table A10, suggest that week-to-week hours vary considerably, even among workers whose primary jobs may not be characterized as flexible by standard definitions. While fluctuations in hours worked may also reflect changes in labor demand, these changes suggest that it is at least possible to change hours from week to week.

While the similar level of variation in hours between wage and self-employed workers may seem surprising at first, recall that the majority of wage work is plausibly informal. This is true for both those paid regularly and irregularly: specifically, 61 percent of wage workers with irregular pay at baseline and 66 percent of wage workers with regular pay are in the service sector, where there is presumably flexibility to extend hours to meet demand, or devote extra time to work to increase demand via advertising. To provide further reassurance that the similar within-worker deviation in hours is not an artifact of noise in the data, we examine betweenworker variation in hours in the 2013 GHUPS round and the baseline of the high-frequency data we collect. Figure A2 shows that the distribution of hours worked look similar across wage and self-employment in both surveys, and there is no evidence of stronger bunching around benchmarks like 40 hours a week among wage workers.<sup>21</sup> Lastly, table A11 examines potential differences in responsiveness to illness shocks by household composition. While it is possible that households with more potential alternative caregivers free up employed adults from caregiving – thereby allowing them to increase their labor even more than adults in other households – there is no evidence of differentially increased labor supply by either men or women in households with elderly members or working age adults not in the labor force. By contrast, men in households with more children are more likely to

> 21 Moreover, in the high frequency baseline, the mean absolute deviation across workers is not statistically different between wage and self-employed for either men or women, and while it does differ in the 2013 GHUPS, the differences are statistically significant but relatively small in magnitude (3.3 hours for men and 1.4 hours for women).

28