rafmacalaba/data-use-mentions-extended huggingface dataset:
They argued that a too generous and non-selective guarantee provision worsened the firm market environment in two ways: the first was the effect of crowding out the private financial sector. The second and long-term negative effect was to make firms become more dependent on public support<sup>11</sup> .

In the case of Korea, Lee (2006) criticized that firms with low creditability will gradually receive the overdraft facility by excessive supply. It will eventually increase the credit default ratio and hence financial difficulties for government in the end.

Using a data from a cross-section of Bulgarian firms Gatti and Love (2008) estimate the impact of access to credit, as proxied by indicators of whether firms have access to a credit line or overdraft facility, on productivity. To overcome potential omitted variable bias of Ordinary Least Squares (OLS) estimates. These authors find credit to be positively and strongly associated with TFP.

Augier, Dovis and Gasiorek (2010) focuse on the role of the business environment in understanding differences in the performance of Moroccan firms. The evidence on the relationship between credit and productivity is strongly indicative of credit resources misallocation in Morocco.

Kasseeah (2011) studies the link between firm performance and firm characteristics of small and medium-sized enterprises (SMEs) in Mauritius. This author observes that the growth of SMEs in Mauritius depends mainly on access to finance and firm size. Access to finance is captured through access to overdraft facilities, line of credit and self reported measures of access to finance.

Butler and Cornaggia (2011) study the relation between access to finance and productivity. These authors exploit an exogenous shift in demand for a product to expose how producers adapt their productivity in the presence of varying levels of access to finance. These authors find that production increases the most over the sample period in areas with relatively strong access to finance.

Using a panel of Chinese manufacturing firms over the period 2001-2007, Chen and Guariglia (2011) find that, especially for illiquid firms, productivity is strongly constrained by the availability of
significant misreporting by transfer recipients in national surveys; and Nicholas and Wiseman (2010) used administrative records to evaluate and estimate better measures of poverty for social security recipients.

# <u>Data requirements and pitfalls</u>

The first pitfall related to use of “big data” is the quality and discontinuity of the data. Administrative records can be severely fragmented. Censuses and surveys rarely change, because the data should allow comparisons over time; if they do change, modifications respond to specific needs. In governmental programs, changes may occur due to legislative, regulatory, or political matters affecting the scope of the data and their use. Furthermore, in some administrative systems, individual records remain at the local source and only aggregate data are assembled centrally. In other systems, data might not even be centralized. Having several institutions or agencies responsible for data can interfere with quality control that a central agency or researcher can impart. In other words, these are obstacles to collection of quality, complete, and continuous data.

Coverage and content represent further drawbacks. With administrative data, target populations, topics, and all specific variables of interest are defined by administrative requirements or purposes. Contrary to surveys were the researcher can design the questions of interest, administrative records offer a limited source of information and variables. For example, when using tax records to analyze businesses or industries, databases need to be complemented with another source to better estimate production statistics (Brackston, 1987).

Administrative records might not be representative of the national population. Most governmental programs cover only a portion of the population with very specific beneficiary characteristics. Records can also suffer from misreporting; for example, tax reports can lack information on the many informal economic actors. Weaknesses in administrative data coverage and content are the main reasons why many empirical studies use data imputation based on surveys, or other alternative sources of data and information on sample populations.

Lastly, another pitfall is access and confidentiality. Depending on the regulatory premises of each country, access
s tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> operability over time (Helliot et al. 2009). The most common definitions are the intersection-let UU denote the longitudinal population of NN . tt←tt<sup>∗</sup> tt<sup>∗</sup> tt tt←tt<sup>∗</sup> , and includes the individuals tt<sup>∗</sup> tt population, UU = UU ∩UU , and the union-population UU = UU ∪UU who are still living and resident in the country at the time t. The longitudinal survey tt<sup>∗</sup> tt observes the same individuals over time, excluding both births and deaths during the period of The intersection-population is a proper subset of both UU and UU tt<sup>∗</sup> of UUstudy. However, the longitudinal population definition based on the intersection-population has some drawbacks related to the representativeness at time t and to the limitations in the study of the dynamics in the households’ composition. While the union-population best describes the population dynamics, it requires a dynamic sample design which introduces births and entrants to avoid bias. For this study, we propose a definition that takes the advantages from both approaches. , include: - tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> ; Based on this, the NN individuals of UU<sup>∗</sup>

- , include:

- - tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> ; - tt<sup>∗</sup> tt

- all the people of the intersection-population of households UU ∩UU

- all new members of the household of the individuals in the intersection-population, even<sup>∗</sup> .

- With this definition, the newborns and those immigrants
Figure 1: Household Total Energy Expenditure varies with education, expenditure and household size in the cross-section <!-- Start of picture text --> 0 5 10 15 20 4 5 6 7 8 0 2 4 6 8 10<br>A. Mean adult schooling B. Log per capita exp. C. Household size<br>TEE REE TEE REE TEE REE<br>AL (right axis) AL (right axis) AL (right axis)<br>2500 1.9 2500 1.9 2500 1.9<br>1.8 1.8 1.8<br>2000 2000 2000<br>1.7 1.7 1.7<br>1500 1500 1500<br>1.6 1.6 1.6<br>1000 1.5 1000 1.5 1000 1.5<br><!-- End of picture text --> _Source:_ Authors’ analysis based on data from 1998-1999 India Time-Use Survey and National Family Health Survey rounds 2, 3 and 4. _Note:_ Unconditional relationship between household Total Energy Expenditure (TEE) (daily per person) and mean years of education for members over 18, log per capita expenditure (dropping observations in the 1% left and right tails), and household size (up to size 10) in the 1998-1999 Time Use Survey (TUS) cross-section. Activity Levels (AL) is measured directly and Resting Energy Expenditure (REE) is imputed using the National Family Health Survey (see text for description). Each observation represents a household average. Note that 92% of households are between 2 and 8 members.

27
Bank’s human capital index (HCI) and country statistical capacity index (SPI). (We provide more technical details on these methods in Appendix 1, Part A.) There are, unsurprisingly, pros and cons with each method. The simplest is the dashboard method employed by the United Nations (2018c), which analyzes the SDG indicators as is; that is, it tracks progress on each and every indicator. This method offers straightforward interpretation, but its disadvantage is that, with so many indicators, it is difficult to evaluate progress for them as a whole. For example, it is hard to compare a country with good poverty reduction but weak environment protection with another which has the opposite performance.

Sachs et al. (2017) combine the dashboard approach with an index that is an average (i.e., the arithmetic mean) of all the standardized indexes obtained from Equation (1). While this procedure is somewhat more complicated than the dashboard approach and requires making certain assumptions, it offers a way to evaluate overall progress for all the indicators. But note that this index is driven by data availability.<sup>25</sup> In fact, Sachs et al. (2017) also offer a dashboard-based approach to keep track of progress on each of the 17 goals.

An alternative to Sachs et al.’s (2018) method of constructing the SDG index by the arithmetic mean is to construct it using either the geometric mean or the product of all components. (The former is the latter raised to the power of _1/N_ , where _N_ is the number of components in the product.) Put differently, we can multiply them together in a similar way to the World Bank’s recent HCI (Kraay, 2018). Alternatively, after obtaining this product, we can then take the geometric mean, as does the UNDP’s human development index (HDI) (UNDP, 2010). The advantage of the latter is the
last conclusion, in fact, is consistent with in-depth case studies reported by Hannum et al. (2009).

# **(6) Robustness Checks**

The empirical results on intergenerational mobility discussed so far are based on the sample of 18-35 years age cohorts of children in the survey year. To check robustness of the findings, we also estimated the mobility equation (14) in section (2) for alternative age ranges. The estimates for the age cohorts 18-30 years are reported in the online appendix; please see the Tables A2.IU.B, A2.IR.B, A2.CU.B, and A2.CR.B. The main conclusions regarding gender differences in intergenerational educational mobility remain intact.

We also check whether the conclusions are partly due to the effects of gender bias working through endogeneous fertility choices. To this end, we estimate the mobility equation using only the sub-sample of the first-born child, as the gender of the first born is usually not determined by parental preference. The CFPS survey on China is suitable for such an analysis. But we are unable to implement this for IHDS data in India, as the IHDS survey does not contain the information on the birth-order of the household head and spouse. The estimates based on the first-born sample for urban and rural China are reported in online appendix Tables A2.CU.C and A2.CR.C respectively for the 18-35 age cohorts; the main conclusions discussed in the text above are again robust. The estimates from the first=born sub-sample of the 18-30 age cohorts also support the main conclusions.

# **(7) Concluding Comments**

This paper provides a theoretical and empirical analysis of the implications of gender bias against girls for intergenerational educational mobility in developing countries. We develop a Becker-Tomes model where the parents self-finance children’s schooling because of credit market imperfections, and the girls may face bias in the family, the school, and the labor market. The model yields a linear conditional expectation
year followed by a rapid rise in consumption that drove poverty down by 3.2 percentage points in the following year. Third, the fastest poverty reduction occurred in the years 2017 and 2018. Thereafter, the rate of poverty reduction stalled considerably.

Headcount poverty rates at the international $3.2 and $5.5 poverty lines are shown in Figure 13 of Appendix 4.2. A similar reduction in poverty is observed for both lines. The average rate of poverty reduction at $3.2 and $5.5 was 2.1 and 0.8 percentage points per year between 2004 and 2011. By comparison, all years since 2015 clock an average rate of 1.2 and 0.6 percentage points poverty reduction per year relative 2011. The $5.5 line also shows poverty rising between 2018 and 2019. This dynamic is detected in the consumption data but not by changes in demographic and asset levels. The rise is mainly on account of urban households where headcount rates rose by 2.5 percentage 24Povcalnet projections can allow for some changes to the distribution. For instance, the 2014.5 estimate employs a pass-through rate of 0.559 for urban and 0.733 for rural areas; see box 1.3 in World Bank (2018) and box 1.2 in World Bank (2020) for details. However, the distribution within rural and urban areas is assumed to be unchanged.

> 25Note that rural poverty reduction in the decade(s) prior to 2004 was more modest and heterogeneous, see e.g. Lanjouw and Murgai (2009) and Himanshu et al. (2013).

39
# **Gender Bias in Agricultural Child Labor:  Evidence from Survey Design Experiments**

**Jose Galdo**

# **Ana C. Dammert***

**Degnet Abebaw**

JEL-codes: C8, J22, 012, Q12 Keywords: Survey design, farm labor, gender, labor statistics, child labor

* Jose Galdo is an Associate Professor at Carleton University, Ottawa; (e-mail: jose_galdo@carleton.ca). Ana C.  Dammert (corresponding author) is an Associate Professor at Carleton University, Ottawa (e-mail: ana_dammert@carleton.ca), Degnet Abebaw is an Independent Researcher (formerly at the Ethiopian Economic Policy Research Institute of the Ethiopian Economics Association), Addis Ababa, Ethiopia (e-mail: degnet06@yahoo.com). The authors gratefully acknowledge the financial support from IZA/DFID Growth and Labour Markets in Low Income Countries (GLM-LIC) program, grant agreement GA-C3-RA5-323. The authors would like to thank the editor and two referees, as well as seminar participants at the University of Maryland- College Park, 3ie-IFPRI Seminar, Ohio University, Dalhousie University, 2<sup>nd</sup> IZA/DFID GLM-LIC Research Network Conference, 2016 LACEA, 2017 Canadian Economics Association Meeting, 2018 ICABR Conference, and the 4<sup>th</sup> IZA/DFID GLM-LIC Research Network Conference for helpful comments and suggestions. Any errors and omissions are our own.
<!-- Start of picture text --> 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile of the reference distribution<br>Decile-specific relative proportion<br>Relative density<br>95% confidence interval<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>Relative density/proprtion<br>0.5<br>0.0<br><!-- End of picture text --> _Source:_ Authors’ calculations based on Morocco’s household surveys.

The overall results show that, guided by an increase in consumption, the households shift from the lower deciles of the distribution to the middle and the upper parts. Indeed, the values of the deciles in the upper tail of the distribution are higher than 1, which means that there are more households in that decile of the distribution in the last year of analysis than there were in the first year.

Panel (b) presents the effect due only to the median shift, i.e. the pattern that the relative density would have displayed if there had been no change in distributional shape, but only a location shift of the density. The effect of the median shift was quite large. This alone would have moved a substantial proportion of Moroccan households in 2000 out of the two lowest deciles of the reference distribution and placed them in the other upper deciles of the distribution.

Looking at panel (c), which shows the shape effect, it is clear that there is a concentration in the tails of the distribution. Values above 1 indicate that, in relative terms, there are more households in that decile of the distribution at the end of the examined period than there were at the beginning. Therefore, relative to the initial period
policy reform is expected.

# **4. The Mexico case study**

Mexico is well known for its history of monopolies, including TELMEX on fixed lines (privatized in 1990) and PEMEX in fuel (a state-owned enterprise opened to competition only after 2013), as well as an oligopoly in corn products, an important staple for households in the country.<sup>19</sup> These monopolies and oligopolies emerged because of government intervention and weak regulations and did not deliver the intended results they were originally established to meet (Del Villar 2009). Plagued by low productivity and limited innovation, resulting in high prices for consumers, these monopolies have become an integral part of Mexico’s paradoxical growth performance, especially because recent estimates have concluded that there exists an average 98.2 percent markup in goods across households (Aradillas 2018). This paper shows some stylized facts on the degree of concentration in the two industries selected (mobile telecommunication and corn products) to illustrate the simulation approach, as well as some descriptive statistics, including household expenditures on categories linked to these two industries.

The key data source necessary to use WELCOM is a representative household expenditure survey. In the case of the two sectors studied, the per capita household share of expenditures on mobile

> 18 See Abdelkrimm et al. (2018).

> 19 For TELMEX, see See Acemoglu and Robinson (2013). The category of corn products includes grain corn, maize flour, mass of corn, corn tortilla, toast, and other maize products.

10
skew an understanding of demand for technology. Direct questions on reasons for/against adoption that have been documented in more focused country-level studies are lacking in broader socioeconomic surveys, including skills/knowledge (Udry, 2018); information networks (Munshi, 2004; Moser and Barrett, 2006); other economic activities farmers may be involved in (Ibrahim, 2013); credit and other liquidity constraints (de Janvry et al, 2016); and other factors such as individuals’ desire to minimize volatility in expected profits (Zhu, 2019).

# Constraints to economic opportunity

Along with data on migration, access to skills and technology, socioeconomic surveys need to better capture constraints that rural individuals often face in seeking these economic opportunities. Some surveys have introduced questions on whether women need permission to leave the house for work (as in the DHS), as well as safety and commuting time to work; these are important for understanding mobility constraints for wage workers, but are less relevant for home-based workers or farmers.

Asking about a wider range of constraints in surveys is also in line with Kabeer (2012), who presents a research agenda on specific barriers and alleviating factors to women’s economic mobility. Survey questions in this area would essentially be qualitative/subjective in nature, but if asked carefully (and honed through cognitive testing to account for language and local context) could be helpful in understanding where policy might be more effectively targeted. Respondents could choose from multiple options affecting their economic mobility, including (a) time/distance to work, as well as markets and other institutions (financial, administrative, etc.); (b) social and work-related norms affecting the ability to work, work longer hours, and/or advance in one’s position — including discrimination in hiring and advancement, and the risk of harassment; (c) unpaid work burdens at home affecting available time, including childcare; and (d) lack of skills in that field (specifying which skills are needed)
growth without development‖ and Sierra Leone’s diamond conflict

> 5 The computation of Liberia’s Adjusted Net Savings is rendered delicate due to (i) the size of UNMIL transfers to Liberia (exceeding GNI by 37 percent in 2010), and (ii) uncertainty regarding actual private consumption. The estimate of a 16.7 percent Gross national Savings rate retains the assumptions that (i) 50 percent of UNMIL transfers are invested (military building and equipments). A new household survey to be conducted in 2012 is anticipated to significantly revise upwards private consumption, thus lowering Gross National Savings as a percentage of GNI.

- 5 -
# **I. Introduction**

This paper ignores national boundaries and studies the Sub-Saharan-Africa-wide distribution of consumption expenditure among individuals. By combining household survey data from as many African countries as possible (accounting for around three-quarters of the regional population), we can put all Africans on the same scale of consumption expenditure, regardless of their country of residence.<sup>2</sup> Inequality and other distributional characteristics are typically measured at the countrylevel (e.g. for a recent review of the evidence on Sub-Saharan Africa, see Beegle et al., 2016). This paper offers a complementary view and puts the disparities that exist within African countries into context with the disparities that exist within the region as a whole.

Our analysis is similar to studies of the global income distribution (Milanovic, 2005, Anand and Segal, 2015; Ravallion, 2014a) and we draw heavily on the analysis by Lakner and Milanovic (2015). Our analysis is perhaps best seen as referring to a sub-region of the global distribution. According to Milanovic (2005), there are three concepts of global inequality: (1) the inequality in per capita incomes between countries in the world, which is relevant to studies of income convergence across countries; (2) population-weighted international inequality, which assigns everyone the per capita income of their country of residence; and (3) interpersonal inequality, where everyone is assigned their own income. In this paper, we study the third concept as applied to Sub-Saharan Africa, i.e. the inequality in consumption expenditure among all people living in Sub-Saharan Africa. Implicit in our analysis is an African-wide social welfare function which treats persons irrespective of their country of residence. In the global context, this is referred to as a cosmopolitan social welfare function (Atkinson and Brandolini, 2010).<sup>3</sup> One might also have an instrumental concern for regional interpersonal inequality if extreme regional inequality contributes to conflict or migration.

The analysis in this paper is based
Regional dummy variables were still significant and the results on the remaining variables were essentially the same in both sets of regressions and similar to the reported 52 regression results

# **8. Conclusions**

In this paper we have looked again at a set of issues that have long been at the heart of economic analysis; nanely the factors explaining the size of govenmment and the consequences of government for growth, whether measured as income growth or other attributes of well-being. These questions remain particularly pertinent given the sharp growth in government size in the last half-century and the sharp disparity in growth rates that has characterised the same period.

We assembled a large cross-country data set and pooled observations across three decades. Our results point to a range of factors driving size. A robust association between government size and the level of income is not confirmed. Indeed, as technological change shifts the demands on government, this would further tend to dissipate the mechanical relationship between size and income. We do find that relative prices, the age dependency ratio, how long a country has been independent and political freedom to be important explanatory variables for growth of government. Our findings are also partial vindication of the argument that governments use their consumption to buffer external risk. We find that in low income countries govermnents use consumption more to

> **_52_** The re-estimated regression results are available upon request.

_55_
# **Table 1: Impacts of mental health on labor outcomes**

|Dependent variable:|Employ<br>|ment status<br>|Weekly l<br>($1,0<br>|abor income<br>00 AUD)<br>|
|---|---|---|---|---|
||Logit-FE|IV-Logit-FE|FE|IV-FE|
||(1)|(2)|(3)|(4)|
|Standardized mental health<br>scores|-0.046***|-0.064***|-0.005***|-0.192***|
||(0.009)|(0.009)|(0.001)|(0.055)|
|_First stage of 2SLS (dependen_|_t variable is stan_|_dardized mental healt_|_h scores)_||
|Trauma experience*Year<br>after arrival||-0.039***<br>(0.009)||-0.058***<br>(0.016)|
|Kleibergen-Paap F stat.||17.692||19.635|
|AR 95-CIs||[-0.111, -0.031]||[-0.400, -0.124]|
|Adjusted_p_-value|0.000|0.000|0.001|0.011|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|3,687|3,687|3,687|
|