rafmacalaba/data-use-mentions-extended huggingface dataset:
(0.558)|1132|49.374<br>(0.494)|-9.011***|
|Permanent|386|0.902<br>(0.015)|784|0.955<br>(0.007)|1170|0.938<br>(0.007)|-0.054***|
|Insufcient— recognition|385|0.249<br>(0.022)|784|0.281<br>(0.016)|1169|0.270<br>(0.013)|-0.031|
|Insufcient— salary|385|0.675<br>(0.024)|784|0.759<br>(0.015)|1169|0.731<br>(0.013)|-0.084***|
|Monthly salary (USD)|340|125.268<br>(1.254)|743|115.562<br>(0.598)|1083|118.610<br>(0.585)|9.706***|
|Choose teaching|381|0.635<br>(0.025)|775|0.732<br>(0.016)|1156|0.700<br>(0.013)|-0.096***|

_Notes_ : The value displayed for t-tests are the differences in the means between male and female teachers, using baseline data. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

7
<!-- Start of picture text --> Figure 5a: Concentration of Direct Transfers Figure 5b: Concentration of Direct Transfers<br>by Income Groups, Men by Income Groups, Women<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Non PSNP transfer Market income Non PSNP transfer<br>PSNP transfer Direct transfer PSNP transfer Direct transfer<br>Population share Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of direct transfers Cumulative proportion of direct transfers<br><!-- End of picture text -->

# _Indirect Taxes and Subsidies_

Most of the assignable goods that are identified in this study are subject to VAT and excise taxes. There is a substantial difference in the total value of annual consumption of these items by gender. The total value of annual consumption by men is birr 1,595 and by women birr 917 (Table 1). However, the effective tax rate, 19.4 percent, is the same for both genders (Table A1.2). In addition, the richest individuals (top 20 percent) spent more on assignable goods than the poorest (bottom 20 percent); the value of assignable goods, on average, is 35 percent of the non-assignable goods for the richest but only 7 percent for the poorest (Table 1).

**Table 1: Distribution of Assignable and Non-assignable Goods by Gender and Income**

||**Assignable
Firm registry data can provide estimates of jobs created in the formal sector but lack general information on job characteristics, and even more on workers’ profiles. An attempt at measuring skill needs is made in some modules of the Skills Towards Employment and Productivity Surveys (“STEP surveys”) directed to employers and implemented in several countries, including in Kosovo in 2015-2016. It identifies the existence of skills constraints across sectors and types of occupations. However, the data on demand for skills are not granular enough for specific feedback to policy makers on skills needs and curricula alignments. Detailed information on both technical and soft skills demanded by employers for specific types of occupations is not available, thereby limiting its usefulness for students and parents seeking to take career decisions. We show in Appendix D the differences in granularity level between job portal data and STEP data, to further highlight how job portal analysis can substantially contribute to the policy dialogue on skills development.

This paper is thus an example of how the analysis of job descriptions from online vacancies can generate useful and detailed complementary data on current demand for skills in a labor market. Our paper contributes to existing work on skills by (i) using data that directly reflect employers’ skills demand rather than the skills stock, and (ii) presenting demand for skills at the broad and granular levels.

# **IV. Methodology**

First, we define a _job posting_ as a unique job advertisement on a given job portal during a specific time interval in the year 2018.<sup>17</sup> This means that in the presence of duplicated postings at the same time across job portals, only a single job posting is taken into account.<sup>18</sup> Furthermore, a _vacancy_ is defined as a unique work position that is associated with a job posting. By definition, a job posting can therefore be posted as advertisement for several vacancies. For instance, when a firm is searching for 10 workers for a construction site by advertising only
International at the University of Minnesota. In both cases, the census questionnaires include a module on income in addition to the standard questions on household and individual characteristics. Furthermore, the harmonization protocol ensures the comparability of geographical units across census years. In all other country cases, we use the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), which includes country harmonized household surveys jointly constructed by the Center for Distributive, Labor and Social Studies (CEDLAS) at the Universidad National de La Plata and the World Bank’s Poverty Group for the Latin America and the Caribbean region.<sup>11</sup> Most countries have conducted the surveys on an annual basis since 2000, but the frequency varies by country.<sup>12</sup> Only in Colombia, the analysis at the 2<sup>nd</sup> administrative level relies on per capita value-added data for the last ten years from the National Administrative Department of Statistics (DANE). Since neither the value-added data source, nor SEDLAC provides household information, it is impossible to estimate place productivity premia after sorting in Colombia.

The criteria for selecting survey years included the availability of information to harmonize geo-codes across survey years at the lowest possible administrative level and maximize the number of surveyed locations across time. Two to three consecutive surveys were selected to have adequate coverage at three specific time periods in the past twenty years: the early 2000s, the late 2000s, and the late 2010s. Thus, the sample includes only countries with enough information to identify sub-national administrative units, ensure comparability across space and time, and minimize standard errors of point estimates at lower administrative levels.

In some instances a particular survey was not used for reasons not mentioned above. A decision was made to exclude the 2000 Panama survey to minimize location re-coding. From 2001 onwards, Panama’s administrative units remained stable up to 2017. El Salvador was also excluded from the analysis because of potential issues related to the switch in currencies in the early
non-labor income or other household members’ wages, on the other hand, increases leisure and thus reduces labor supply. According to Psacharpoulos and Tzannatos (1989), Blau et al. (2010), and Ferber and Winkler (2010), empirical evidence suggests that female labor supply is usually more responsive to wages than to changes in non-labor income. However, these dynamics depend on each country’s context.

For example, Klasen and Pieters (2015) estimate a probit model of FLFP to decompose the effect of supply and demand factors explaining the low and stagnated FLFP rate in urban India. Using cross-sectional surveys for five years, they found a strong negative income effect ―due to increasing male income and education― that contributed to a withdrawal of women from the labor force. On the other hand, they argue that the substitution effect found has become weaker in the last years: women’s education effect on FLFP, although positive, has been diluted by the erosion of positive selection into higher education and rising marriage market returns.

In any case, more educated women usually access to higher wages, which explains why educated women have higher involvement in the labor market than less or uneducated women. This reinforces the importance of education on FLFP (Lam and Duryea, 1999; Matas, Raymond, and Roig, 2010; Contreras, De Mello, and Puentes, 2011). Nevertheless, Mexico has experienced precarious wage growth over the last decades, yet FLFP has still grown. One explanation is that an incremental share of highly educated women is entering the labor market. In this paper, we contribute to the literature by testing the extent of the substitution effect after a period of economic downturn.

7
. The measure of inequality is the Gini for all studies except for part of the Hagerty (2000) study. Some papers estimate the Gini from the data set used while others extract the gini from other data sets. The Gini can also be estimated for countries, regions, Primary Sample Units (PSU) or particular reference groups. All studies use, in conjunction with the inequality measure, one or more measures of income such as income (in continuous or categorical form), lagged income, relative income or measures of countries’ wealth. Most studies use country or regional fixed effects but two studies do not while years fixed effects are used by all studies with longitudinal data except one. Finally, some papers report the use of robust standard errors and/or cluster estimations while other papers do not report how the standard errors have been estimated. In the next section, we will put forward some hypotheses on how these diversities in choices may contribute to explain diversity in results.

# **3 Some hypotheses**

There are several factors that may lead to controversial empirical results on the correlation between happiness and income inequality. Some of these factors relate to the specific data available or to the choice of the inequality measure made by the researcher. Other factors relate to econometric choices that may or may not relate to the data at hand. We discuss these two groups of hypotheses in turn.

The _choice of the inequality measure_ is a first critical choice. Some studies use Gini 4
trend since the early 1990s and it has averaged roughly 0.6-0.7% in the 2010-2014 period (Figure 1.6). Human capital is a commonly measured using the average years of schooling, though in our forward-looking simulations we use a broader measure based on the World Bank Human Capital Index that includes schooling quality and population health. In the 1980s and early 1990s, human capital grew at around 2% but now has slowed to 0.6%. As it is harder to increase the average years of education when people are already well educated, countries often experience a slowing growth rate of human capital over time. We expect this trend will continue for Malaysia as it moves to high income status.

Total population growth was close to 1.3% over the past 5 years, after experiencing a downward trend since 1990 (Figure 1.7). Before 1990, population growth averaged 2.5-3%. Slower population growth is typical of developing economies that transition into high-income economies and is expected to continue. Slowing population growth also affects the share of population of working age (15-64), which in turn affects the size of the labor force (Figure 1.8). The share of the population between the ages 0 to 14 has 3 World Bank (2020) projects a growth rate of -0.1% under the baseline and -4.6% under a low-case scenario for 2020. 5
area among signatories covered by forest in 2000, the number of signatories that previously signed agreements with environmental provisions and an indicator for a positive numbers thereof, numbers and indicators of signatories in Africa, North America, Latin America, Europe, East or Southeast Asia, Central or South Asia, and Australasia and the Pacific, and finally, country indicators for agreement membership.<sup>7</sup> Because of the high dimensionality of this set of covariates (e.g. there are more than 100 country indicators alone), straightforward logistic estimation is not feasible. However, any _ad hoc_ variable selection performed _ex ante_ opens up our experimental design to concerns about selection bias in our matched sample arising from our specification choices.

We attempt to remove ourselves from the model selection via the use of a least absolute shrinkage and selection operator (LASSO) for our logit model selection and estimation. Logit LASSO estimation is a form of penalized maximum likelihood estimation where the penalty function is the _l_ 1-norm of **_β_** coefficients in Equation (3). This penalty functions as a model selection criterion as extraneous covariates will be dropped from the model to avoid incurring the penalty. Formally, LASSO solves the following optimization problem: where _l_ ( _·_ ) is the logit log-likelihood function, **_β_** = _{β_ 1 _, β_ 2 _, . . . , βk}_ and _∥_ **_β_** _∥_ 1 denotes the _l_ 1-norm of **_β_** (i.e.<sup>�</sup><sup>_k_</sup> _j_ =1<sup>_|βj|_).</sup><sup>_λ_istheso-calledregularizationparameterthatcontrolsthepenaltyonthe</sup><sup>_l_1-</sup> norm of **_β_** and the corresponding shrinkage. In principle, _λ_ is chosen by the researcher, however we adopt an automated approach to _λ_ selection. Our preferred _λ_
-0.00858|0.001654|0.005738|
|**Firm’s size**|-0.03722|-0.14764|-0.0644|-0.01389|
|**Goal**|0.006756|-0.05809|0.015847|0.028457|
|**Rural area**|0.002582|-2.8E-05|-0.00083|0.006074|

_Note: decomposition of the Q effects in Table 3. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 29
**Table 3.** Average share of decision-making activities in which women and men are not directly involved in the decision but have effective power

||Women|Men|Diff|
|---|---|---|---|
|Share of all decisions not directly involved but|0.13|0.22|0.08***|
|have effective power|(0.26)|(0.32)|(0.02)|
|Observations|746|716||

_Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All estimations are computed using survey weights to address sampling. Shown difference is 0.08 rather than 0.09 due to rounding. Those who are directly involved in all seven decisions are excluded from these estimates._

# **6. Who has effective power across household types?**

In this section, we compare direct engagement and effective power in decisions across activities of husbands and wives within different types of households. We then explore the decision-making power of young unmarried men and women who live with their parents.

According to the qualitative data, marriage represents the watershed moment after which a man takes on the role of husband along with decision-making power. The decision-making dynamics vary, however, by the household composition. There are different processes through which intra-household power dynamics unfold in a couple household as compared to extended family households, for example. Contrary to the previous tables, where we showed results for all men and women in our sample households, in Table 4 we focus only on husbands and wives. In a couple household, where the husband and wife are the only adults, when an individual is not directly involved in the decision, the other spouse is typically making the decision. In these households, husbands and wives are involved directly in the same share of decisions
than among male workers.

> 15 Trends in the IHDS panel are similar to the NSS with one prominent exception. In the IHDS panel, female workforce participation rates increase from 66% to 69% between 2005 and 2012.  This is in marked contrast to the NSS which suggests a 12-13 percentage point decline in labor force participation (LFP) rates over the same period. We can only speculate that the difference arises because of the ways in which work, particularly subsidiary/part-time work that is particularly common amongst women is captured in the two surveys. Chatterjee et al. (2015) also note differences between the NSS and the Census which shows no decline in female LFP between 2001 and 2011.

> 16 Interestingly, there is almost no decline in the share of farming and animal care in total workers, for men and women alike, when the definition of workers is unrestricted; i.e., also includes those who worked for less than 240 hours during the past year (not shown, but available upon request to the authors). This suggests that self-employed agricultural activities tend to occupy a large number of workers for short durations, possibly to supplement other incomes or generate self-consumption, rather than be a main source of income. Also, the importance of agriculture as an important source of income has fallen between 2005 and 2012.

12
MENA Region Focus Economics January Forecasts, from 2015 to 2020, covering 56 countries, and are not forced to be a common sample with WB-GEP and IMF-WEO. Log of SCI (lagged) is the one-year lagged log of Statistical Capacity Indicator. Growth Volatility (lagged) is the one-year lagged rolling 10-year standard deviation of realized growth rates. Log of GDP Per Capita (lagged) is the one-year lagged log of GDP per capita in constant 2010 US dollars. Export Commodity Price Shocks (T) is the same-year log change in export commodity prices, it captures the direction of the commodity price shocks. Internal Conflicts Shocks Dummy (T) is the same-year dummy variable for internal conflict country-year pairs – as defined using the UCDP-Prio Dataset. Log of Total Population (lagged) is the one-year lagged log of population and is considered a proxy for the country size. Boom Dummy if Above Median Growth (lagged) is a one-year lagged dummy variable for economic booms if growth is greater than or equal to the previous 10year median growth. Note that the unforeseen shocks taken into account in this regression occurred during the same year of the forecast, after the GDP growth forecasts were published in January.

16
# Are Labor Supply Decisions Consistent with Neoclassical Preferences?

Evidence from Indian Boat Owners<sup>_∗†_</sup> Xavier Gin ́e Monica Martinez-Bravo Marian Vidal-Fern ́andez World Bank CEMFI University of Sydney

> _∗_ This paper is a substantial revision of Gin ́e et al. (2010). The first version of paper was circulated in 2009 under the title “Intertemporal Substitution or Reference-Dependent Preferences? Evidence from Daily Labor Supply of South Indian Boat Owners”.

> _†_ We thank Kevin Lang, Daniele Paserman, Dilip Mookherjee, Manuel Arellano, Pedro Mira, Iv ́an Fern ́andez-Val, Jordi Jaumandreu, and Marc Rysman for suggestions and discussions. Thanks to participants at the 2009 IZA Summer School in Labor Economics and the Empirical Microeconomics Lunch at Boston University. We are grateful to Felipe Fern ́andez, Rexon Fernando, and Daniel Mart ́ınez for their assistance in obtaining international price data. Jiayi Wen and Lisa Oberlander provided excellent research assistance. Monica Martinez-Bravo gratefully acknowledges financial support from Ram ́on y Cajal Program [RYC-2013-14307]. Marian Vidal-Fern ́andez is grateful to the Fundaci ́on Ram ́on Areces for financial support. The views expressed in this paper are those of the authors and should not be attributed to the World Bank, its executive directors, or the countries they represent.
raises costs of importing equipment by (10 = 10% or less; 0 = more than 50%). This component is based on survey responses to this question obtained from the Global Competitiveness Report 2000.

Based on: World Economic Forum (2001), _Global Competitiveness Report 2001-2002_ (Oxford: Oxford Univ. Press). <u>http://www.freetheworld.com/release.html</u>

|**FINANCIAL**<br>**MARKETS**|||||
|---|---|---|---|---|
|**Variable name in**<br>**corresponding**<br>**database**|**Scale**|**Description**|**Years**|**Source**|
|**(name in our**<br>**database)**|||||
|iv. Avoidance of<br>interest rate controls<br>and regulations that<br>lead to negative real<br>interest rates<br>(efw_aiv)|0 to 10<br>(0= heavy<br>regulation)|Data on credit-market controls and regulations were used to<br>construct rating intervals. Countries with interest rates<br>determined by the market, stable monetary policy, and<br>positive real deposit and lending rates received higher<br>ratings. When interest rates were determined primarily by<br>market forces and the real rates were positive, countries<br>were given a rating of 10. When interest rates were<br>primarily market-determined but the real rates were<br>sometimes slightly negative (less than 5%) or the differential<br>between the deposit and lending rates was large (8% or<br>more), countries received a rating of 8. When
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.15  Airport charges overall by aircraft type for 18 sample airports**

Source: Derived from Bofinger 2009.

Note: Fraport‘s charges for the Frankfurt am Main airport can be found at the right end. On average, the airports levied charges that were 30 to 40 percent higher than those sampled at Fraport for the same type aircraft.

43
|_Indicator_|_Scale_|_Notation_|
|---|---|---|
|Government repudiation of contracts|0-10|_PRSI_|
|Risk of expropriation|0-10|_PRS2_|
|Corruption|0-6|_PRS3_|
|Rule of Law|0-6|_PRS4_|
|Bureaucratic quality|0-6|_PRS5_|

_Government repudiation of contracts_ is an indicator of institutional quality which addresses the possibility that foreign businesses, contractors, and consultants face the risk of a modification in a contract taking the form of repudiation, postponement, or scaling down. A country may initiate contract modification with a foreign business because of an income drop, budget cuts, political pressure, or a change in government economic and social priorities. _Risk of expropriation_ of private foreign investments assesses the likelihood of confiscation or nationalization. Both indicators proxy the government's ability to enforce contracts and not to renege on its promises. Even if private agents engage in mutual beneficial exchange or investment, it is worth noting that at least in theory they still face the risk of the government expropriating the results of their exchange. The channel through which we expect repudiation and expropriation risks to affect investment and growth is straightforward: should the likelihood of repudiations and expropriations be high, rational private agents will discount the future streams of income from investment at a higher rate which _ceteris paribus_ could lead to lower aggregate investment and slower economic growth.

The PRS indicators of _Corruption_ and _Bureaucratic Quality_ are intended to measure the corruption and strength of bureaucratic apparatus of the government respectively. Corruption and bureaucratic quality are viewed as impediments to investment and growth primarily because they have a potential of distorting the economic and financial environment by increasing the transaction costs faced by economic agents.

7