rafmacalaba/data-use-mentions-extended huggingface dataset:
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# EDUCATION

Until the mid-1970's it was generally believed that fertility fell uniformly with education. This belief was the result of the fact that the model of demographic transition hypothesized fairly uniform falls in fertility with development and the data on the relationship between education and fertility came from developed countries or from the more developed underdeveloped countries of Latin America. The effect of education on fertility acts through three channels : the family's desired number of children, the natural fertility the family would have if no contraception were used and the use of contraception. The development of both tl.: economic model of fertility incorporating these channels (Easterlin, 197t the demographic model of the proximate determinants of fertility covet,rq natural fertility and the impact of contraceptive use (Bongaarts, 1976) have provided a rich structure for studying the channels through which education affects fertility. In addition, the data from the World Fertility Survey has complemented the many often small scale, not always comparable, data sets of earlier periods in establishing these channels empirically. We have thus learned an enormous amount about this relationship in the past 10 years. In 1979, I published a book on the relationship between educatior. and fertility based on the Easterlin model and the precursor to the proximate fertility model perfected by Bongaarts and reviewing a wide range of findings from the studies preceding, for the most part, the WFS surveys. That study found that the relationship between education and fertility was not always monotonically inverse in the poorest, most illiterate societies and in rural areas. In those circumstances, a small amount of education was
First, we estimate the key parameters of our NEG model using detailed data for 2007 and market access based on the road network that includes the NEN. The equilibrium solution of this model provides an accurate empirical description of the observed spatial distribution of income and wage levels. We then use this model to simulate the (real) income and wage levels which would have existed in 2007 had the NEN not been constructed – that is, the spatial distribution of our outcome measures if market access had not been significantly improved. By comparing these income levels with those under our original equilibrium solution we arrive at estimates of the short-run impacts of the NEN up to 2007.<sup>7</sup> Given an explicit NEG general equilibrium framework, we are able to fully capture the impacts of the NEN road network expansion, the totality of which would be missed by other impact evaluation approaches. Under the model, improving a link in a road network not only benefits the immediate vicinity, but also areas further away whose trade and travel passes through that link. Better transport infrastructure thus changes the relative strength of agglomeration and dispersion forces in the entire country. This shapes the geography of market access and consequently the spatial distribution of wage and income levels. Moreover, our NEG model incorporates the full set of price changes resulting from transport cost reductions, so that we can measure both nominal and real quantities, for instance real urban wages. However, we enhance the theoretical NEG model, in which wages are determined solely by market access via the wage equation, by also allowing for (empirically estimated) variations in the efficiency of labor both across prefectures and between the urban and rural sectors.

Our results suggest that Chinese real income in 2007 was approximately 6 percent higher than it would have been, had the NEN not been built. As already indicated, this is a short-run estimate that does not take into account possible additional effects associated with the longrun mobility of labor. It is also a one-off level effect, as opposed to
# **1. Introduction**

<mark>The COVID-19 pandemic and the associated containment measures led to dramatic declines in economic activity levels and disrupted labor markets worldwide, with potentially large distributional impacts. Empirical evidence from developed and developing countries is showing that traditionally disadvantaged workers --women, young, and low-educated workers—were hit hardest by the pandemic and, as a consequence, the patterns of existing inequalities are being</mark> exacerbated (Adams-Prassl et al., 2020; Fairlie et al., 2020; Lee et al., 2021; Kugler et al., 2021).

<mark>Workers in developing countries are often self-employed or working in informal arrangements with a relatively low earnings level. The containment measures particularly affect these groups of workers, leading to decreasing incomes and higher chances of falling into poverty. To design effective policies to protect the most vulnerable segments of the population, the distributional impacts of the pandemic need to be known. In the longer term, the calculations of prompt estimates on how individuals and households are affected during any crisis are crucial inputs for timely</mark> policy responses.

<mark>During the COVID-19 pandemic, different sources of data and methodological approaches have been used to estimate the distributional impacts of the crisis. In particular, phone surveys gained momentum during the pandemic as an instrument to gather information on the ground while respecting social distancing and lockdown policies. Phone surveys successfully collected information on respondents’ employment status and their perception of whether their household income declined, remained the same, or increased (Khamis et al., 2021; Kugler et al., 2021). A quite different approach to estimate the distributional impacts is through Computable General Equilibrium models using different scenarios of GDP reduction and transmission mechanisms, such as in the ILO (2020) and Vos et al. (2020) studies. In addition, some studies adjust prepandemic poverty lines using different scenarios of per capita income or consumption contraction or, alternatively, actual information on per capita
by increasing the government's expenditure on health care. Surveys conducted in OECD countries show that public expenditure on health care is significantly higher for older persons than for the remainder of the population. This is predominantly due to health problems and costly medical technologies being concentrated on the old. As such, an increase in the share of the aged generally leads to an increase in government expenditure on public health care services, which consequently, with a given level of tax revenue, tends to increase the budget deficit.

2Of course, there is also the obvious choice of using any combination of the three options.
**Figure 2** . Guinea: Correlation between sectoral share of total productivity and sectoral share of total employment

<!-- Start of picture text --> min<br>pu<br>ps<br>con<br>fire<br>tscman<br>csps wrt<br>ag<br>0 20 40 60 80<br>Sectoral share of total employment<br>Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> 80<br><!-- End of picture text --> <!-- Start of picture text --> Fitted values<br><!-- End of picture text --> Source: Authors’ calculations based on data from the INS.

_Note:_ Size of circle represents employment share in 2015; agr= agriculture; min=mining and quarrying; man=manufacturing; pu=public utilities; con= construction; wrt=wholesale and retail trade, hotels and restaurants; tsc=transport and telecommunications; fire=finance, insurance and real estate; csps=community, social and personal services; ps= government services.

# **4. Structural Change and Productivity Growth in Guinea**

# 4.1 Defining the contribution of structural change to productivity growth

Labor productivity growth in an economy can be achieved in one of the two ways. First, productivity can grow _within_ economic sectors by capital accumulation, technological change or reduction of misallocation _across_ firms. Second, productivity in the economy can grow by flow of labor form low-productivity to high-productivity sectors. This can be expressed using the following decomposition, borrowed from McMillan _et al_ . (2014): 12
# **References**

- <mark>Ager, P., & Brückner, M. (2013). Cultural diversity and economic growth: Evidence from the US during the age of mass migration.</mark> _<mark>European Economic Review</mark>_ <mark>,</mark> _<mark>64</mark>_ <mark>, 76-97.</mark>

- <mark>Aghion, P., Blundell, R., Griffith, R., Howitt, P., & Prantl, S. (2004). Entry and productivity growth: Evidence from microlevel panel data.</mark> _<mark>Journal of the European Economic Association</mark>_ <mark>,</mark> _<mark>2</mark>_ <mark>(2-3), 265-276.</mark>

- Ahlerup, P. (2009). The causal effects of ethnic diversity: An instrumental variables approach Working Paper No. 386. School of Business, Economics and Law, University of Gothenburg.

- <mark>Ahlerup, P., Olsson, O. (2012). The roots of ethnic diversity.</mark> _<mark>J Econ Growth</mark>_ <mark>, 17, 71–102.</mark> <u><mark>https://doi.org/10.1007/s10887-011-9075-0</mark></u>

- <mark>Alesina, A., & Ferrara, E. L. (2005). Ethnic diversity and economic performance.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>43</mark>_ <mark>(3), 762-800.</mark>

- <mark>Alesina, A., & Ferrara, E
performed in Section 2. I expect to find that only informal business owners from the formal business owner species are more likely to register their business after the implementation of SARE. In contrast, informal business owners from the wage worker species should be more likely to become wage workers after the reform since the reform also created more jobs.

Testing these hypotheses sheds light on the effect of SARE on firm formalization and transitioning to wage work for different types of informal business owners. At the same time, it provides a test of the classification of informal business owners into wage worker and formal business owner species. That is, if I find that informal business owners from the wage worker (formal business owner) species are more likely to become wage workers (formal business owners) when a reform makes it easier to do so, this confirms the validity of the species classification.

# **4. Identification Strategy and Transitions into Different Occupations**

This paper follows the identification strategy in Bruhn (2008 and 2011) to measure the effects of SARE on business registration and transitions to wage employment of informal business owners. In particular, I exploit the fact that the reform was implemented in different municipalities at different points in time to estimate the following equation where the outcome variable yict is a dummy variable indicating the occupation (e.g. formal business owner, wage worker) of individual i living in municipality c in quarter t. The regression includes municipality fixed effects, βc, and quarter fixed effects, γt. The variable SAREct is the reform dummy and, for each municipality, it is equal to one for the quarter in which the reform was implemented and for all following quarters. The vector EC1999 consists of control variables from the 1999 Economic Census interacted with a linear time trend, t. These variables are log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita. The standard errors of the regressions are clustered at the municipality level.

The
# **Does Respondent Reticence Affect the Results of Corruption Surveys? Evidence from the World Bank Enterprise Survey for Nigeria**

Bianca Clausen and Aart Kraay Development Research Group, The World Bank Peter Murrell Department of Economics, University of Maryland

> *Corresponding Author.  1818 H Street NW, Washington DC 20433, <u>akraay@worldbank.org.  The views</u> expressed here do not reflect those of the World Bank, its Executive Directors, or the countries they represent.  Financial support from the Knowledge for Change Program of the World Bank is gratefully acknowledged.  We would like to thank Daniel Berger for helpful comments.  We are particularly grateful to Giuseppe Iarossi and Giovanni Tanzillo of the Africa Finance and Private Sector Development team of the World Bank for enabling the placement of the random response questions in the Nigeria Enterprise Survey, and to Fares Khoury and his team at Etude Economique Conseil for the implementation of these questions.

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across type of goods and services purchased and country of origin. We thus follow this route and employ data from the TiVA initiative of the OECD. Similarly to other ICIO database (e.g., Timmer, 2012), the TiVA database harmonizes national IO tables and combines them with information from national accounts and bilateral trade statistics in goods and services to obtain an international input-output table (see OECD, 2013b for details). The estimation procedure allocates output from each country and sector to intermediate usage (by all sectors) or final demand across countries. While far from perfect and inevitably rife with measurement errors (especially compared with official trade statistics), this type of data is the only one that enables international comparison of public expenditures across countries and sectors.<sup>20</sup> Data on procurement contracts that has been used in related work (Herz and Varela-Irimia (2020); Fronk (2014); see also footonote 5) are limited to one or to a group of countries (U.S. or the EU) and

> 20An alternative approach that relies only on official trade statistics is used by Rickard and Kono (2014) (and adopted also by Gourdon and Messent (2019)). It indirectly identifies the effect of trade barriers on cross-border government procurement by allowing the effect of bilateral factors on trade as recorded by official statistics to vary with the size of the government procurement sector by country. An important limitation of this approach in our setting is that it departs from a structural gravity model: total government purchases, that enter overall expenditure in the gravity equation, are allowed to influence the direct effect of trade costs.

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washes hands with soap after defecation, and whether human or animal fecal matter is observed near the home. _Hindu_ is an indicator variable equal to one if the head of household is Hindu.

Household characteristics are denoted by _Xi_<sup>_h_,forwhichsummarystatisticsarereported</sup> in Table 1 (IHDS), Appendix Table 1 (DLHS-3), and Appendix Table 2 (NFHS-3). The list includes sociodemographic characteristics that may influence latrine ownership and other sanitation practices in India, which are: caste, occupation, and education of household head; household size, number of female members in the household, and age of survey respondent; whether the household has piped water; and proxies for household wealth (indicator variables for whether the household owns their house; indicator variables for the presence of electricity, a cell phone, television, bicycle, car, motorcycle; and distance to fuel source).<sup>13</sup> Characteristics of the household’s location are denoted by _Xjd_<sup>_l_,whicharedrawnfrom</sup> the 2011 Census.<sup>14</sup> These variables include measures of urbanization and local demographic composition: an indicator for urban area; log population density; share of population working in agriculture; share of population that are minority religious groups (Jain, Sikh, Christian, Buddhist); share of population that is Muslim; share of population belonging to a Scheduled Caste (SC) or Scheduled Tribe (ST); fraction of women who report being literate; and the sex ratio. These control variables are included because urbanization affects the availability of sanitation infrastructure and the returns to improved sanitation practices.<sup>15</sup> Further, the average demographic composition of areas is associated with different economic environments.<sup>16</sup>

> 13The data sets vary slightly in which of these variables are available. The IHDS data do not include information on homeownership. Only the IHDS data include the occupation of the household head and
Nabil Abbyad, Meryem Demirkaya, Austin Drenski, Jeffrey Horowitz, and Grace Kenneally. The dynamic gravity dataset: Technical documentation, 2018.

- Tilo F. Halaszovich and Aseem Kinra. The impact of distance, national transportation systems and logistics performance on fdi and international trade patterns: Results from asian global value chains. _Transport Policy_ , 98:35–47, 2020. ISSN 0967-070X. doi: https://doi.org/10.1016/j.tranpol.2018.09.003. URL https: //www.sciencedirect.com/science/article/pii/S0967070X17303670.

- Zheng Han. Price Discrimination in Transport Industry and the Gains from Trade. Technical report, SSRN: https://ssrn.com/abstract=3862530, 2021.

- Hercules Haralambides. Globalization, public sector reform, and the role of ports in international supply chains. _Maritime Economics & Logistics_ , 19(1):1–51, March 2017.

- C Knick Harley. Ocean freight rates and productivity, 1740-1913: the primacy of mechanical invention reaffirmed. _Journal of Economic History_ , pages 851–876, 1988.

- James Harrigan. Airplanes and comparative advantage. _Journal of International Economics_ , 82(2):181–194, November 2010.

- Keith Head and Thierry Mayer. Gravity equations: Workhorse, toolkit, and cookbook. In _Handbook of international economics_ , volume 4, pages 131–195. Elsevier, 2014.

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Policy Research Working Paper 10861 Do Psychosocial Stimulation, Parental Distress and Early Childhood Education Enrollment Show Different Associations with Early Childhood Development Outcomes for Boys and Girls?

Findings from a Phone Survey in Pakistan _Emily Franchett Amer Hasan Elizabeth Hentschel Mahreen Tahir-Chowdhry Heather Tomlinson Aisha Yousafzai Mina Zamand_ Education Global Practice July 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Authorities, and districts in the case of Malawi. We categorize moves into those within the community/village, moves out of the community but within the district, and moves out of the district.

We focus on respondents’ physical moves, as defined by the distance of the move from respondents’ baseline location given any move, regardless of changes that may have occurred in household composition. Additionally, within villages, a young person may have moved to a new dwelling. By that criterion, we observe an even larger share of young adults ‘moving.’ For those young people who have not, but stayed in the same dwelling, we observe a lot of change in the composition of their household. That is, many non-movers are exposed to others who move in or out of the dwelling. For example, about one-third of non-movers have a change in household headship between the first and last round of the MTM survey.

We find a considerable amount of movement over the course of 24 months. Just under a third of all respondents relocated to a village more than one kilometer away, and a fifth moved further than 10 kilometers from their baseline locations. Among those who moved 10 kilometers or more, about half of them moved across districts.

Respondents report several reasons for relocating. Our tracking experience indicates that many of these moves of adolescents and young adults may not fit well with traditional economic theories of migration. For example, moves can be temporary (e.g., a visit to a relative, lasting a few months) or, for women for whom there are few labor market options, not related to employment. Still, even if moves are not made with the sole purpose of formal employment, the moves can affect labor-related outcomes, as well as other important life events that affect economic well-being, including marriage and schooling. Men were far more likely than women to move for work-related reasons (22% versus 3%, respectively). The top three reasons reported 10
both the main and a randomly selected respondent, estimated uptake is significantly lower among randomly selected respondents compared to purposively selected main respondents. Effect sizes range between five percentage points in Malawi to 13 percentage points in Burkina Faso (Table 4, Panel A). Collecting proxy-reported information for all household members reduces estimated uptake even further, ranging from 13 percentage points in Malawi to 18 percentage points in Uganda (Panel B).

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# VI. Summary and Conclusions

For various purposes such as the implementation of structural adjustment programs (or giving related World Bank assistance), efforts to liberalize international trade barriers, or the development of an "early warning" system for disruptive imports, information on changing comparative advantage and the future composition of international trade is often a key input. In recognition of this point, this study tested the use of factor proportions indices as a predictor of developing countries' exports. Four main conclusions followed from this exercise. First, tests conducted for the 1965-1986 period showed that factor proportions data was a useful predictor of developing countries future exports. Developing countries significantly increased their total exports and share of labor intensive products, while their shares for other nonfuel products registered persistent declines. Second, the eviderce developed in this study suggests that factor proportions information is useful for longer-term trade projections. The 1965 indices labor intensities identified specific products that developing countries would do well in one and two decades later. In part, this may be due to the fact that most products manufactured by labor intensive methods in the 1960s continued to be produced by such methods in the 1980s (see Table 5 and Appendix Table 1). Third, this study noted that a relation existed between the level of labor intensity of a product and the export performance of developing countries. That is, developing countries generally exhibited a superior export performance in the most labor intensive products.

In view of the past success of factor proportions information for predicting developing countries trade, this study assessed the implications of recent data on labor intensities for the 1990s and beyond. Specifically, a
_x_<sup>1</sup> ( _x_ ) is a continuous and strictly increasing function of _x_ , so that, observing some level of expenditures _x_ ˆ<sup>1</sup> one can invert _x_<sup>1</sup> ( _x_ ) to infer what the ˆ overall level of expenditures is, obtaining, say, _x_ = _g_ 1(ˆ _x_<sup>1</sup> ).

We extend the ideas of Lanjouw and Lanjouw (2001) in two important ways. First, they give conditions under which head-count poverty statistics for a population will not depend on whether one uses total expenditures or a expenditures on a smaller aggregate as a proxy for total expenditures. We provide conditions under which all FGT poverty measures will be similarly invariant to the use of an aggregate. Second, by assuming that expenditures on different aggregates depend only on total expenditures, the Lanjouws implicitly assume that relative prices are unchanging. In a simple application of demand theory we show how to relax this assumption as well, allowing one to apply these methods to data on expenditures over time, where we may expect changes in economic conditions to lead to changes in relative prices.

We are interested in identifying a particular aggregate that can be used to measure poverty and changes in poverty over time. Our two

> 4. In what follows it’s useful to follow some common notational conventions in functional analysis, that blur the lines between variables and functions. For example, _x_<sup>1</sup> will always take a value, but may implicitly or explicitly be regarded as a function of other quantities, as in _x_<sup>1</sup> ( _x_ ).