rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 1: Descriptive** **<u>Statistics – choice of sample</u>** <u>.</u>

||_National_|_Urban_|_Rural_|_Women_|_Men_|_Formal_|_Informal_|
|---|---|---|---|---|---|---|---|
|_Public sector (% of total employment)_|8.9|16.9|1.5|8.4|9.1|47.8|0.9|
|_Agriculture (% of total employment)_|41.6|5.5|75.4|59.7|36.2|4.8|49.2|
|_Sample as % of total Labor Force_|50.5|75.6|22.6|36.3|55.8|46.7|48.1|

Source: Author’s own estimates using the 2015 MLFS.

# _<u>Estimating the worker’s type</u>_

As mentioned above, the model assumes that self-employed individuals earn the value of their type, _y_ i. In turn, a worker type is, thus, assumed to be a function of a set of observable characteristics, so that _yi = Zi Υ_ . We can then write the earnings equation for self-employed workers using a standard Mincerian equation where _v ~ N (0, σv)_ is an individual’s idiosyncratic shock that affects income<sup>9</sup> : Since the model assumes that self-employed individuals are a selected sample (they prefer to take self-employment opportunities rather than available offers for formal/informal job opportunities), we correct for this possible selection bias using a standard Heckman correction.<sup>10</sup>

# _<u>Estimating the Productivity Functions</u>_

The distributions
unintended consequences on school leavers’ decisions to pursue or complete further education. Indeed, recent evidence from the U.S. suggests that active labor market policies targeted at students may delay or even reduce graduation rates among beneficiaries (Heller and Kessler, 2021).

Second, the incentive has large employment effects for graduates of vocational schools (and those planning to enter the labor market). Over the first year, average formal employment rates increase by 4.2 p.p. (16.8%). This effect is exclusively driven by a 5.1 p.p. (30%) increase in jobs with permanent contracts. Treatment effects persist after two years with average formal employment gains of 4.2 p.p. (14.5%), driven by a 5 p.p. (25%) increase in jobs with permanent contracts. These are intent-to-treat estimates of making youths _eligible_ to receive the bonus. In fact, more than half of youths who find formal employment fail to claim the incentive.

Third, we show that these results are due to employment gains on both the extensive and intensive margins. The share of vocational school graduates ever formally employed increases by 5.4 p.p. (10%) over the first two years. This average increase masks a shift between types of employment. The share ever working with a permanent contract increases by 8.6 p.p. (20.5%) while the share with temporary contracts decreases by 1.1 p.p. (4.8%). In addition, endline survey data suggests that about half of formal employment gains come from a reduction in informal employment. These shifts in employment types matter as formal jobs, especially those with permanent contracts, offer more training and social security benefits. We also find that workers in the treatment group are less likely to report adverse income shocks resulting from the COVID-19 pandemic.

The effects on retention depend on the contract type. While
$2.00|267|205|168|162|211|230|187|163|158|191|
|$2.01-$5.00|266|212|151|148|188|236|198|152|148|179|
|$5.01+|373|135|150|135|163|308|147|157|147|166|
|Total|278|246|192|181|254|239|213|178|173|221|

Table 7 is generally consistent with our econometric results, while suggesting that some nonlinear effects are not captured by the linear regression model in Table 5. For example, education seems to have minimal effects on pollution for the lowest income group, but pronounced effects when income is higher. Similarly, increased income doesn’t reduce pollution consistently for the lowest education group, but it has a strong effect at higher levels of education. The combined effects of income and women’s education are sufficient to approximately halve PM10 pollution, from near 300 ug/m<sup>3</sup> in the poorest, least educated groups to around 150 in the highest-income, best-educated groups. As our sample composition statistics clearly show in Table 8, most of the individuals (and households) in our survey are in the four table cells associated with the lowest income and education levels.

**Table 8:  Sample Composition**

|Income Per Capita<br>($US /Day)|A<br>0-1.00|dult Female E<br>1.01-2.00|ducation Lev<br>2.01-3.00|el<br>3.01+|Total|
|---|---|---|---|---|---|
|0-$.50|1,703|455|81|52|2,291|
of Besedes and Prusa (2006), several other papers have confirmed the finding that the median duration of export spells in developing countries is very short, typically two to three years. We investigate this aspect in the Lao data and present a simple view of the spell duration in Table 4 for each of the elements of the export space (firms, products and destinations as well as combinations). To do this, we disregard left and right censoring as well as gaps (absences during one or two years) and instead focus on the number of years that a unit is present in the data during the period of full coverage (2006-2009).

_Exporting from a small landlocked economy_ _12_
# **Discussion**

The age profile of COVID-19 mortality risk in 2020 was flatter (with relatively higher mortality at younger ages) in countries with lower incomes. This pattern is found using official COVID-19 death data from a large set of countries as well as excess mortality from a smaller set of countries which include high-income countries plus Colombia, Mexico, and Peru. Our analysis concerns the year 2020 and is therefore almost entirely unaffected by the arrival of COVID-19 vaccines and their unequal distribution across countries.

The findings in this paper can provide the basis for a more comprehensive accounting of the impact of COVID-19. For example, the younger mortality profile in developing countries means that in those countries the pandemic has killed large numbers of people who of working age, many of whom were their families’ breadwinners. Earlier global estimates suggest that more than 1.5 million children have experienced the death of at least one caregiver as a consequence of the pandemic.<sup>18</sup> This raises the concern that long-term impacts via lower investments in human capital of orphaned children will be particularly deep in developing countries. This is highly worrisome given the already massive declines in school enrollment and learning losses generated by the pandemic in developing countries.<sup>15–17</sup> A full understanding of age-specific patterns of excess mortality will require country-by-country analysis using cause-of-death data, where available, to pull apart estimates of direct COVID-19 deaths, indirect COVID-19 deaths, and averted deaths. A brief comparison of Colombia and the United States highlights how components of excess mortality can vary widely by country. In Colombia, both vehicular and homicide deaths fell during the country’s lockdown, reducing its excess mortality count. For 2020 vehicular deaths dropped 18.2 percent, and the number of homicides fell 5.4 percent, causing Colombia’s murder rate to reach its lowest level since 1979.<sup>19,20</sup> In contrast, the United States excess mortality estimate for 2020 reflects in part
1991)|3299|0.4101|0.1179|-0.2664|1.5442|
|ln Population in 1991|3300|9.2337|0.8054|6.6214|11.9770|
|Dependency Ratio in 1991|3300|0.4807|0.0447|0.3738|0.6205|
|ln Transport Cost to São Paulo in 1968|3299|7.6219|0.6676|4.3694|8.7367|
|ln Transport Cost to State Capital in<br>1968|3299|6.2990|0.6601|3.1135|7.9077|
|ln Proportion of Elderly in 1991|3300|-2.1936|0.2204|-3.6893|-1.5332|
|Illiteracy Rate in 1991|3300|37.7150|17.8150|2.4800|81.4600|
|Urbanization Rate in 1991|3300|49.2604|20.7435|2.7639|100.0000|
|Population Density in 1991|3300|32.7412|33.3495|0.2065|522.2028|
|ln 5 to 15 over 15 to 55 years old Ratio|3300|-0.7464|0.2243|-1.6218|-0.1870|
|Repetition Rate in 1991|3300|4.2817|0.1976|2.6575|4.5756|
|ln Employment Rate in 1991|3300|-0.3625|0.1406|-1.2429|0.1601|
|Delta ln Education Market Potential<br>(2000/1991)|3297|0.9707|0.1575|0.1650|1.6027|
|Percentage of Good Soils|3299|0.7881|0.3192|0.0000|1.0002|
|Rainfall - First Principal Component|3299|-0.
good teachers. Table 5 presents several definitions of salaries and personal income sources, since much of the argument over teacher compensation refers to what is meant by the term “underpaid.” An issue that may hamper the comparison of net earnings across occupations and locations is non-regular and additional benefits, and the way certain allowances are made available to teachers. The labor earnings and salary figures tell us something about fairness of compensation, while total income refers to the teachers’ standard of living. It is clear from the table below that labor earnings is the largest share of all the teachers' total income. This suggests that the teachers' standard of living basically depends on what they obtain as labor income. On the other hand, total incomes for _the mixed-skilled_ group _, the agricultural_ group _,_ and _the low-skilled_ group are more uniformly distributed among other income sources.

12
and Werner, A. 1996. "Relative Wages and Trade Liberalization<sup>in</sup> Mexico". Mimeo, IMF.

- Cassoni, Adriana, Steven G. Allen, Gaston J. Labadie (1999) "Union, Labor Market Regulation, and Employment in Uruguay." Mimeo, IDB.

- Chinoy, Saji, Pravin Krishna and Devashish Mitra (1999), "Trade Liberalization and Labor Demand Elasticities: Evidence from Turkey" mimeo, Brown University

- Davidson, C. (1984), "Cartel Stability and Trade Policy," _Journal of International Economics_ 17:219-237.

- Dunnes, T. and Roberts, M. J. 1993. "The Long-Run Demand for Labor: Estimates from Census Establishment data." U.S. Bureau of the Census, Working Paper CES 93-13.

- Easterly, W., Loayza, N., and Montiel, P. 1997. "Has Latin America's Post-reform Growth been Disappointing?" Journal of International Economics 43: 287-311.

24
.010|0.035|0.020|-0.138***|
||(0.044)|(0.050)|(0.050)|(0.072)|(0.046)|(0.043)|
|Uganda|-0.027|-0.142|-0.085|0.260|0.017|-0.138|
||(0.095)|(0.101)|(0.100)|(0.180)|(0.111)|(0.144)|
|Zambia|0.697***|0.524***|0.659***|0.797***|0.785***|0.349***|
||(0.050)|(0.063)|(0.056)|(0.084)|(0.053)|(0.045)|
|**Average**|**0.260**|**0.251**|**0.262**|**0.274**|**0.337**|**0.153**|

Notes: Each column reports the coefficient in the regressions of ln (monthly earnings) on (a dummy for) a type of teachers with controls. The comparison group are other wage workers in columns 1-5 and other public sector employees in column 6. Controls include gender, age, age-squared, urbanicity and (a dummy for) post-secondary education. Sample is restricted to workers with secondary education or more. The survey in Malawi did not differentiate the levels of teachers and the survey in The Gambia did not report weekly earnings. Standard errors in parentheses. *** p<0.01, ** p<0.
the fall is due to the decrease in the number of urban poor in India, due to the inclusion of imputed housing rent for homeowners in the standardized welfare aggregate.

**Figure 1. Sensitivity of the international extreme poverty rate when national and standardized consumption aggregates are used to measure poverty**

<!-- Start of picture text --> National consumption aggregate Standardized consumption aggregate<br>.25 .5 1 1.9 5 10 .25 .5 1 1.9 5 10<br>Daily Per Capita Consumption (2011 PPP USD) Daily Per Capita Consumption (2011 PPP USD)<br>BGD BTN IND LKA BGD BTN IND LKA<br>MDV NPL PAK MDV NPL PAK<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). The vertical line shows where the international poverty line lies.

**Table 12. Number of extreme poor using standardized and national consumption aggregates**

|||**Number of ext**<br>**(in milli**|**reme poor**<br>**ons)**|**Difference**|
|---|---|---|---|---|
|**Country**|**Year**|**Standardized**<br>**consumption**<br>**aggregates**|**National**<br>**consumption**<br>**aggregates**|<br>**(1)-(2)**|
|||**(1)**|**
the Human Development Report of 1990 of the United Nations Development Program (Hopkins, 1991), and the following UN focus on the Millennium Development Goals and later the Sustainable Development-Goals (MDGs and SDGs). More recent examples are the Stiglitz-Sen-Fitoussi (2009) commission report on the measurement of economic performance and social progress; the OECD’s “better life index”, or its attention to “inclusive growth”; the World Bank’s publications on rethinking the social contract (Bussolo et al., 2018; Devarajan and Mottaghi, 2015); and the ILO’s agenda for promoting “decent jobs”, and the three volumes of the International Panel on Social Progress . All these initiatives go in the direction of assessing aggregate social progress beyond the standard indicator of GDP growth.

Aside from their growing appeal with policy makers, these approaches based on subjective assessments have also been criticized. Two related strands of criticism are most frequent. The first points out that unobserved factors, such as personality traits, may be important determinants of subjective well-being (Ferrer-i-Carbonell, 2005; Lucas and Diener, 2008). Some go even further and claim that subjective assessments are unreliable because they are influenced by trivial factors such as weather, other short-term events, or the sequencing of the questions in the survey (Kahneman and Krueger, 2006). Deaton (2012) argues that “the SWB [subjective well-being] measures do a better job of monitoring short-run levels of anxiety than the medium-term evolution of the economy” (p. 1). The second frequent criticism underlines the difficulty in establishing causality, especially in cross-sections. Happiness, or subjective well-being, is such an extensive concept that it is difficult to identify whether a variable is causing it or whether it is the other way around (MacKerron, 2012, and references cited therein). Natural experiments which provide exogenous shocks (such as lottery wins in Gardner and Oswald, 2007 or political shifts in Frijters et al., 2004) as well as the availability of panel
- Jensen, Robert. 2012. Do labor market opportunities affect young women's work and family decisions? Experimental evidence from India. _Q J Econ_ 127 (2): 753-792.

- LaFave, Daniel and Duncan Thomas. 2016. Height and Cognition at Work: Labor Market Productivity in a Low Income Setting. _NBER Working Paper 22290._ National Bureau of Economic Research.

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

- Levine, Ruth, Cynthia Lloyd, Margaret Greene, and Caren Grown. 2008. Girls Count: A global investment and action agenda. Reprint, 2009. Washington, D.C.: Center for Global Development.

- Lloyd, Cynthia B and Juliet Young. 2009. New lessons: the power of educating adolescent girls: a Girls Count report on adolescent girls. The Population Council, Inc. http://www.popcouncil.org/uploads/pdfs/2009PGY_NewLessons.pdf.

- Manley, James, Seth Gitter, and Vanya Slavchevska. 2013. How effective are cash transfers at improving nutritional status? _World Development_ 48:133-155.

- Milligan, Kevin and Mark Stabile. 2009. Do child tax benefits affect the wellbeing of children?

40
consumption data in the two surveys and concerns around data quality in the 2009/10 survey, it is not possible to simply compare them to construct a trend and understand the country’s poverty dynamics.<sup>3</sup> This paper aims to address this issue by estimating a poverty trend in Nigeria for the period 2009-2019 through two separate approaches, which leverage different data sources. First, the paper presents the results of a “backcasting” exercise that uses the latest survey estimates of household consumption from the 2018/19 NLSS and sectoral GDP growth rates for the previous decade. Second, the paper uses surveyto-survey imputation methods: a simple model linking monetary and non-monetary variables is estimated with the 2018/19 NLSS and is used to impute into another household survey – the General Household Survey (GHS) – available in 2010/11, 2012/13, 2015/16, and 2018/19. One key contribution of the paper is that the results from these two alternative and very distinct approaches are robust and similar in magnitude. While both approaches carry certain caveats (see section 7.2 for an extensive discussion), the survey-to-survey imputation methods can offset some of the limitations of the backcasting exercise and vice versa.

The backcasting exercise maps macroeconomic data on sectoral real GDP growth rates to micro-data from the 2018/19 NLSS – via the household head’s sector of employment – then constructs estimates of the full consumption distribution for each year in the decade prior to 2018/19. This household consumption vector can then be used to calculate the share of the population living in poverty, defined using different poverty lines, for all years between 2009 and 2019. Using this backcasting approach, we estimate a poverty rate of between 42.2 and 46.3 percent in 2009, depending on the assumptions made about the pass-through rate from national accounts growth data to household consumption. The backcasted series suggests a small decrease in the poverty rate at the beginning of the decade and then stagnation, or
*|
|---|---|
|**Full sample**|**0.75**|
|**Counties**||
|Gjirokaster|0.88|
|Korce|0.87|
|Kukes|0.84|
|Vlore|0.79|
|Diber|0.76|
|Elbasan|0.76|
|Tirane|0.74|
|Shkoder|0.71|
|Berat|0.69|
|Durres|0.69|
|Lezhe|0.68|
|Fier|0.61|
|Data source:|School Stakeholder Survey|

> 26 In choosing the parameters for our game, we were guided by the particular characteristics and historical background of Albania. In the fear of finding a too low level of contributions to the group account, we designed the game so that coordination among participants would not be as difficult as in previously employed versions of the dichotomous public goods experiment.

> 27 Cardenas and Carpenter (2008) review studies employing public goods experiments in the field, and report that average contribution range from 33% of endowment in Chile  (Heinrich and Smith , 2004) to 81% in Peru (Karlan, 2005).

> 28 The public good game was played also by 5 teachers. The behavior of teachers is remarkably similar to that of parents, with 76% of teachers investing in the group account, and variations among counties and districts being highly statistically significant. Given the focus of the paper on participatory accountability, here we focus only on the behavior of parents.

15
using PRODES data as well as the monthly version which uses DETER data.11 Here, _c_ is the intercept, _φp_ is the autocorrelation coefficient of lag _p_ , _xt_<sup>_k_</sup> − _q_<sup>arethepredictors</sup> described in the previous section, lagged at least one and up to three years. _dt_<sup>_s_</sup> [ _t_ ≥ _s_ ]<sup>arelevel-</sup> shifting policy dummies that account for known past policy interventions and which remain active for all years after the intervention. The years for which policy dummies are permissible are selected based on knowledge of past policy actions – in this application we choose 2004, 2009 and 2012 (see Figure 6). These dummies are necessary to model the generalizable relationship between exogenous deforestation drivers and benchmark predictors, untainted by known policy effects. This leaves us with a large set of possible predictors. Using a variable selection method (LASSO) we select the most relevant predictors for deforestation (see Figure 12). Policy dummies are not subject to penalization. The model is estimated for annual and monthly deforestation rates. We impose a _L_<sup>1</sup> penalty on the coefficients _φp_ and _βq_<sup>_k_for variable selection (Tibshirani, 1996), where</sup> the penalty strength is determined through cross-validation.

The selection results show that four variables matter the most: previous deforestation, lagged REER percentage change, and lagged commodity prices (see Figure 12). The second and fourth principal components mostly capture the prices of beef, iron ore, sugar12 and hardwood logs (see Table 3). After selecting the variables, we refit a linear regression model with only the most important predictors (see Table 4) as an illustration. Jointly, they explain about 90% of the variation in the annual model and around 80% for the monthly model. Note that we also include level-shifting policy dummies for the years 2004, 2009 and 2012 for
. Even though the 7-day long list comes closest to the mean benchmark food consumption value in this experiment, it is difficult to extrapolate definitively that the 7-day long list will be the most accurate of the recall designs if it is applied in different settings. Because the net deviation of each module from the benchmark is the product of the contrasting influence of various types of reporting error, different settings may present differing magnitudes of underlying error types. The error decomposition analysis below is a first attempt to disentangle the relative influence of these types of reporting errors.

Beegle et al. (2012) also investigate the possible effect of salient and easily observed household characteristics—those assumed to determine actual consumption levels—on the accuracy of consumption reporting. The characteristics investigated include the following: (1) household size: it was determined that recall modules underreport consumption even more as the size of the household increases; (2) urban

> 7 While the experiment focused on food consumption measurement, each survey also recorded nonfood consumption. For less frequently purchased items, such as durable goods, clothes, and health care, all surveys and diaries employed a one-month or 12-month recall design (whereby households assigned to diaries were administered a nonfood consumption survey at the end of a two-week study period). For more frequently purchased nonfood items such as soap or transport, the consumption was either asked in recall form in the recall modules 1–5 (in which the period of recall corresponded to that for food) or recorded as diary entries for households assigned a diary.

> 8 Because the questionnaire wording and structure for the nonfrequent nonfood consumption section were identical across the eight modules, it is perhaps surprising to see significantly negative coefficients for modules 1, 4, and 7 relative to the benchmark. Such differences can result from three sources: respondent fatigue as the recalled items in these modules come after the lengthy food recall sections in modules 1–5 or after a two-week diary; cognitive framing; and variations in the ability to capture personal nonfrequent nonfood