rafmacalaba/data-use-mentions-extended huggingface dataset:
the top tail account for and how this may vary across countries or time is not yet clear. The research discussed in this paper provides evidence from a number of countries that better measurement of the top tail increases the estimated shares of the top of the distribution and thus can potentially explain much of the difference between survey and national accounts estimates of total income or consumption.

# Seminal papers

In the absence of representative survey data on income, Kuznets and Jenks’s (1953) pathbreaking study used aggregated data from tax statistics in the US to measure incomes at the top of the distribution, as well as population and total national income from national accounts as controls. Kuznets (1963) compiled similar information from a variety of sources for 18 countries, including 11 developing countries.<sup>3</sup> His conclusion was that the share of the top 5 percent was much larger in developing countries than in developed countries, although the share of the bottom 60% was similar in developed and developing countries. Since some of the data came from tax statistics, shares for the middle and bottom of the distribution were sometimes imputed using parametric methods- for example by estimating the Pareto coefficient for the top of the income distribution using tax data and using this to impute the shares for different parts of the rest of the distribution (Kuznets, 1963:15).

# Recent Literature

Piketty et al. (2022) note that after Kuznets’ work in the 1950s and 1960s, household surveys and the microdata produced from them became much more common. This meant that the entire income distribution could be examined, rather than the top (or top and middle) only, encouraging research on poverty as well as inequality in many more countries that undertook representative household surveys. This meant that the focus on the top end of the income distribution was mostly neglected for several decades, in favor of index measures of inequality that did not bring into focus the very top end of the distribution, unlike Kuznets’ work (Milanovic,
and Columbia. The numbers for Indonesia appear to be more like the Columbia numbers where distortionary impacts of minimum wages were found. Note that the Columbia numbers were from manufacturing firm data and may underestimate the number of workers at the lower end of the income distribution.

36/lnterviews with employers inn Indonesia suggested that smaller labor intensive firms such as small garment firms appeared to find the minimum wage somewhat problematic whereas the minimum wage was not an issue for the larger less labor intensive firms.

37/In the U.S. the minimum wage was typically fixed in nominal terms at about 55% of current average wage. Rising wages in the rest of the economy gradually erode the minimum wage and eventually the process is repeated.
# 2. Trends and spatial patterns of input use

The analysis in this paper uses data from about 3,000 households across Uganda, interviewed in the nationally representative Uganda National Panel Survey (UNPS) from 2005/6 to 2013/14. The Living Standards Measurement Study (LSMS) and Integrated Surveys on Agriculture (ISA) is a household survey program that supports the design and implementation of surveys such as the Uganda National Panel Survey (UNPS), and ensures comparability with other surveys being carried out under the LSMS‐ISA project in Sub‐Saharan Africa.

The UNPS has data on household characteristics, household consumption and income from a variety of income sources. It also contains a rich agricultural module. Five rounds of the UNPS are used in this analysis comprising data collected in 2005/6, 2009/10, 2010/11, 2011/12 and 2013/14. Households can be matched across rounds using a unique household identifier. The attrition in the UNPS was quite substantial between 2005/6 and 2009/10, but was moderate in the following two rounds (see Table 2.1). For the 2013/14 wave, the panel was rotated, and the survey lost a substantial portion of its original 2005/06 sample. For summary statistics, we use the sample of households engaged in agriculture weighted with cross sectional household weights. For the analysis of input adoption, we consider all households engaged in agriculture that were present in all five rounds of the UNPS: 1,108 households in total.

**Table 2.1. Attrition in the UNPS by wave**

||Sample|Original sample<br>retention|Split‐off<br>HHs|Total|
|---|---|---|---|---|
|2005/06|3,123|100|0|3,123|
|2009/10|2,607|83.5|367|2,
mark>Retrieved from https://www.bls.gov/cps/cpsaat18.htm</mark> <mark>World Bank. (2018).</mark> _<mark>Learning: To Realize Education's Promise</mark>_ <mark>. World Bank Group.</mark> <mark>World Bank. (2019a). Learning Poverty.</mark> <u><mark>h</mark> ttps://www.worldbank.org/en/topic/education/brief/learningpoverty</u> <mark>World</mark> Bank. (2019b). World Bank Country and Lending Groups.

<u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-</u> - <u>lending groups</u> <mark>World</mark> Bank. (2019c). World Development Indicators. <u>https://datacatalog.worldbank.org/dataset/worlddevelopment-indicators</u> <mark>World Bank. (2020). The Human Capital Project (HCP).</mark>

- <u>https://www.worldbank.org/en/publication/human capital</u>

<mark>World Bank.</mark> (2021). World Development Indicators. <u>https://datacatalog.worldbank.org/dataset/worlddevelopment-indicators.</u> 28
- 76 -

- Schwartz, Jordan, Luis Andres, and Georgeta Dragoiu. 2009. ―Crisis in Latin America: Infrastructure Investment, Employment and the Expectations of Stimulus.‖ Policy Research Working Paper No. 5009, World Bank, Washington, DC.

- United Nations. 2011. Final Report: United Nations Development Assistance Framework for Nepal 2008-2010. Available at <u>http://www.un.org.np/sites/default/files/report/united_nations_development_assistanc</u> - -

- <u>e_framework_2008 2010_ _evaluation_report.pdf</u>

- Ware, Glenn, Shaun Moss, Edgardo Campos, and Gregory Noone. 2007. ―Corruption in Public Procurement: A Perennial Challenge.‖ In _The Many Faces of Corruption: Tracking Vulnerabilities at the Sector Level_ , ed. Edgardo Campos and Sanjay Pradhan, 295–334. Washington, DC: World Bank.

- Wilson, Robert. 1977. ―A Bidding Model of Perfect Competition.‖ _The Review of Economic Studies_ 44: 511–18.

- Wolfstetter, Elmar. 1996. ―Auctions: An Introduction.‖ _Journal of Economic Surveys_ 10: 367–421.

- World Bank. 2005a. Project Appraisal Document: Rural Access Improvement and Decentralization Project. Report No. 31624-NP. The World Bank.

- World Bank. 2005b. _Infrastructure and the World Bank: A Progress Report_ . World Bank: Washington, DC.

- World Bank. 2007. Nepal Public Finance Management Review: Managing Public Finances for a New Nepal (No. 43384-NP).

- World Bank. 2008a. _Procurement under World Bank-Financed Projects: FY07 Annual Report_ . Operations Policy and Country Services. Washington, DC: World Bank.

- World Bank. 2009. Project Paper on Nepal Rural Access Improvement and Decentralization Project, 2009 (No. 50766-NP).

- World Bank. 2009. Enterprise Survey: 2009 Nepal. Available at

- <u>http://www.enterprisesurveys.org/ExploreEconomies/?economyid=
Survey (ACS) one-year samples for 2010 and 2019 available from IPUMS USA (Ruggles et al. 2022). Data on educational outcomes of origin countries are from the updated Barro-Lee Educational Attainment dataset (Barro and Lee, 2013), and GDP data are from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure traces the change in skill composition among migrants (panel a) and emigration probabilities to the United States by educational level (panel b) as low- and middle-income origin countries develop. The horizontal axis shows the increase, in percent, in per capita GDP in constant 2017 US dollars in purchasing power parity terms (2017 PPP$) of origin countries relative to the origin country’s baseline year. The vertical axis shows changes in educational composition (panel a) and emigration probabilities (panel b) relative to the origin country’s baseline year. The baseline year is defined for each origin country as the year with the lowest per capita GDP (2017 PPP$). Low-and middleincome countries were among the bottom two-thirds circa 1960 in terms of per capita GDP (2017 PPP$). CI = confidence interval.

# 4. Conclusion

This paper shows that the relationship between emigration and development is complex. The emigration response to development varies along a few dimensions. First, the income level of origin countries matters. Low-income countries have a more complex response than middle-income countries, which exhibit an “inverted-U” pattern whereby migration first rises, then plateaus, and then falls with development. Second, migration response differs by the type of destination. Migration from low-income origins to low-return destinations—neighboring countries or other low-income destination countries—falls in the initial stages of development. Emigration to high-return destinations increases steadily with income for middle-income countries but increases at a much slower pace for low-income countries. Third, the population size of the origin country does not matter in terms of the qualitative nature of the response. But the emigration response
The steady state is consistent with the growth of wages implied by the long-run equation (2). One implication for wages is that the equilibriurn value of nominal wages is smaller _ceteris paribus_ the larger the steady state growth rate of the unemployment rate when it is positive. Hence, the equilibrium value of the nominal wage _ceteris paribus_ is associated negatively with increases in the unemployment growth rate. Another implication of the steady state equation is that the equilibrium value of nominal wages is larger _ceteris paribus_ the larger is the steady state inflation rate when the latter is positive. The impact on wages from increased inflation is not one to one. Ten percentage points increase in inflation is associated with 4 percentage points increase in wages. Hence, inflation erodes wages in the long run.

Is there a wage determination model that fits the Zimbabwean data? First, we test whether the Zimbabwean data fit a real wage specification. The results indicate that real wages do not respond to the strength of demand in the labor market. Furthermore, the real wage hypothesis only holds if: (1) the restriction (w-p)=0 cannot be rejected; and (2) there exists a co-integrating vector involving all three variables w, u and p which has the sum of the coefficients equal zero. The first restriction cannot be rejected at a 1 percent significance level but is rejected at _5_ percent (F(1,20) = 4.84 [0.043*). Moreover, there does not exist a co-integrating vector containing all three variables. Hence, the real wage hypothesis can be rejected.

Second, we test other models of wage determination nested within the one presented in Table 4: (1) a first order error-correction model with wages adjusting towards a static equilibrium that depends on prices only (,B = X = p = 0) and this model cannot be rejected at a 1 percent level of significance but is rejected at a 5 percent level (F(1,20) = 4.85 [0
48 HMICs covering mainly formal employment and a sample of 37 LMICs capturing both formal and informal employment (for details, see Appendix 2). In addition, the dataset of HMICs enables us to estimate both trade-employment and tradeincome elasticities and therefore examine which of these channels is stronger.<sup>10</sup> We hypothesize that sectoral differences as well as countries’ type of engagements in trade and GVCs could explain differences in labor markets outcomes. For instance, recent work found the export-toemployment elasticities for business services to be larger than for manufacturing in a sample of 43 mostly high-income countries using the WIOD database (Goretti et al. 2019). We run the model by broad sector (agriculture, manufacturing, business services, other services) and also at the disaggregated industry level to identify possible differences across sectors. We also assess the role of a country’s type of GVC specialization and categorize countries into four basic GVC groups (World Bank 2020), differentiating between countries specialized in (1) commodities, (2) limited manufacturing GVCs, (3) advanced manufacturing and services, and (4) innovative tasks (for details, see Appendix 3).

Sections 3 and 4 use direct measures of employment and job activities in exports. We rely on the OECD Trade in Employment database for estimates of employment in exports for 45 goods and services sectors (based on ISIC Rev. 4 sectors) for over 50 countries for the period 1995-2018. (Appendix 4 describes the underlying computations in more detail). The novel database Job Activities in Exports adds the dimension of occupations and is thus able to provide estimates of jobs in exports for sector-occupation combinations which define the so-called activities in exports (Appendix 5 describes the underlying computations for job activities in exports).

Our paper is structured as follows. Section 2 estimates correlations between various types of trade and employment or labor income within sectors. Section 3 studies the role of job creation in supplying sectors versus export sectors, while section 4 examines job activities in exports
|_Agriculture_||||
|ag<br>~~h~~ybrid|Agricultural Hybrid|1km2|Fritz et al.<br>(2015)|
|lgp50<br>~~v~~6990|Length of Growing Period|10km2|FAO-GAEZ ver-<br>sion 3 (Fischer<br>et al. 2012)|
|rain<br>~~c~~rop<br>~~s~~h|Share of area with rainfed|1km2|GFSAD1000|
||crop||V1.0: (Telu-|
||||guntla et al.<br>2014)|

17
and 3/6 to Mexico for graft, while on a much less representative survey such as BERI which covers primarily developed countries, Mexico receives a rather poor rating of 1/7.

What can be done about these non-linearities? A very general solution to this problem might be to combine only the ordinal information in each indicator, i.e. the relative rankings of countries within each indicator. In particular, one can think of a given indicator as providing a ranking of pairs of countries according to their level of governance. The information in the relative rankings of countries from various indicators can then be combined by noting that if several indicators consistently rank country A as having better govemance than country B, this provides evidence that governance is in fact better in country A than in country B. Clearly, this approach has several advantages. First, it is computationally very simple. Second, we do not have to know the choice of units in which governance is measured , or whether indicators are representative or not. Third, it does not require any assumption of linearity in the relationship between governance and observed scores. However, this method will result.in larger standard errors for pairwise comparisons since it discards information in differences in the level of scores across countries and indicators.15 To illustrate the relative imprecision of such an ordinal aggregate, for every pair of countries j and j' on every indicator k, we construct an indicator variable x0j,j',k) which takes the value 1 if country j is ranked higher than country j' with respect to the aspect of governance covered by indicator k, and zero otherwise.16 A natural null hypothesis to test is that governance in countries jand j' is the same, i.e. that the probability x(,j',k) is equal to one is 0.5. Under the assumption that the errors are independent across 15 A further drawback of this method is that it is difficult to construct an aggregate ranking of countries according to
**Table 2. Medium-Term Income Growth Rate, Russia 1994-2015 (percentage)**

|**Panel**|**A: 1994-2004**||**2004**<br>|||
|---|---|---|---|---|---|
|||Poorest tercile|Middle tercile|Richest tercile|Overall|
||Poorest tercile|38.6|161.6|511.5|129.3|
|**1994**|Middle tercile|-30.5|29.8|143.7|32.0|
||Richest tercile|-81.4|-30.7|26.3|-13.1|
|**Pl**|**B 20042015**||**2015**|||
|**ane**|**: -**|Poorest tercile|Middle tercile|Richest tercile|Overall|
||Poorest tercile|176.2|330.2|625.4|300.2|
|**2004**|Middle tercile|30.1|110.5|212.5|108.8|
||Richest tercile|-37.7|10.7|76.0|29.6|

**Note** : Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 4941 panel individuals from the 5th and 13th rounds of the RLMS and 3719 panel individuals from the 13th and 24th rounds of the RLMS _._

35
types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.10:** Economic Outcomes

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Economic outcomes index**(SUR)||0.053|0.036|0.070|5053|
|||(0.032)|(0.037)|(0.040)<sup>_∗_</sup>||
|||[0.138]|[0.262]|[0.119]||
|(1) Physical asset index|0.000|0.020|_−_0.023|0.063|5052|
||(1.000)|(0.048)|(0.053)|(0.059)||
|||[1.000]|[1.000]|[0.404]||
|(2) Agricultural asset index|0.000|0.120|0.174|0.065|5051|
||(1.000)|(0.044)<sup>_∗∗∗_</sup>|(0.057)<sup>_∗∗∗_</sup>|(0.
</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05, _∗∗∗ p <_ 0 _._ 01.

# **5 Elasticity between Nighttime Lights and Economic Activity**

The elasticity between nighttime lights and GDP is key to quantify the link between nighttime lights and economic activity. However, both nighttime lights and GDP are measured with errors, complicating the estimation of the elasticity. In this section, we provide a novel framework to identify this elasticity, employing varying measurement errors in nighttime lights across countries.

## **5.1 Identification of the Elasticity: A Novel Approach**

Let _yi,t_ and _zi,t_ be the measured GDP growth and night light growth of country _i_ in quarter _t_ , respectively. Both _yi,t_ and _zi,t_ are observable. In particular, _zi,t_ is the average of monthly nighttime lights that are in turn predicated on daily observations. While daily observations are not accessible in our data, the number of effective days used to construct monthly observations is known. Let _Ni,t_ be the number of effective daily observations of nighttime lights for country _i_ in quarter _t_ , and _zi,t,j_ be the nighttime light growth in day _j_ . Then the relationship between quarterly and daily observations follows: 12
The dataset was constructed for the whole calendar year 2019 and aggregated to the country level by looking at parcels’ time difference between events using statistics such as mean, median and interquartile range for tracking the lead time of parcel movements. The sample comprised countries with more than a hundred inbound unique parcel shipments. The sample included 132 countries from all World Bank geographical regions and income groups (see Figure 1). Forty percent of low-income countries were represented in the postal dataset, while other income categories were represented at between 61 and 68 percent. The geographical coverage was more evenly distributed with all but one region having over half of the countries represented in postal statistics. Only Sub-Saharan Africa had less than 50 percent of its members represented as part of UPU’s information. A detailed overview of this dataset is available in Appendix 2.

_Figure 1: UPU coverage by region and income group_ <!-- Start of picture text --> 100.0%<br>79.3%<br>80.0% 71.4%<br>60.0% 52.6% 53.7%<br>40.0%<br>20.0%<br>0.0%<br>East Asia & Europe & Latin America & Middle East &<br>Pacific Central Asia Caribbean North Africa<br>Percentage of WB countries/region<br><!-- End of picture text --> <!-- Start of picture text --> 71.4%<br>66.7%<br>62.5%<br><!-- End of picture text --> 6
the explanation might lie in the quality of how the recipient countries were governed. Over the last decade numerous studies have looked at governance indicators – ranging from perceptions of corruption to the strength of property protections – to see if they influence economic and social outcomes.

A notable aspect of this literature has been the effort to systematically measure governance at the country level. In the early 1990s, few measures were available, but today, a number of organizations – including the World Bank, the World Economic Forum, Transparency International and Freedom House – publish and update such indicators regularly. In this work, the World Bank defines governance as: ...the traditions and institutions by which authority in a country is exercised, This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.<sup>2</sup> This definition is utilized in the Governance Matters series (which now includes annual data on countries from 1996 through 2006, see Kaufmann, Kraay and Mastruzzi, 2007) and it draws attention to the difficulty of measuring governance. How can we measure “processes,” “capacities” and “respect?” Kaufmann and Kraay (2008) describe the effort

> 2 Kaufmann, Kraay and Zoido-Lobatón (1999) appears to be the first statement of this definition.

______________________________________________________________________________________

-4-
important impacts on the decision to work in the formal sector and that removing them can increase welfare in a context where a non-contributory means-tested system for those uncovered is also available.

When it comes to the effects of specific policy tools, the evidence shows considerable differences across them. Tkhir (2021) shows in a set of counterfactual policy exercises performed on a general equilibrium model in Brazil that a number of separate policies satisfy the long-term fiscal goal of compensating for the effect of demographic change: increasing the payroll taxes, reducing the replacement rate, raising the required years of contribution, and increasing the retirement age. The increase in the payroll tax (from 11% to 34%) increases the share of informal workers by 39 percentage points (compared to the case of no reform), leading to the largest welfare loss of 17.5% compared to the alternatives. The decrease in the replacement rate from 70% to 60% instead increases long-run welfare by rising formal wages and the share of formal workers by 4 percentage points. In the long run, the reform increases lifetime welfare by 3.6%. The increase in the required years of contribution from 15 to 40, increases formal wages and reduces the share of informal workers by 7.6 percentage points, leading to a long-term welfare gain of 6.7%. The rise in the retirement age by 5 years slightly reduces the share of informal workers (0.4 percentage points) while having no major effect on wages; leading to a 7% welfare gain. Furthermore, there is evidence that raising the retirement age not only increases the employment level of workers close to retirement age but also of younger workers. Carta et al. (2021) quantify the effects of a policy-induced sharp increase in retirement ages on workers of different ages using Italian matched employer-employee data.

68