rafmacalaba/data-use-mentions-extended huggingface dataset:
employment. Descriptive analysis of the KMS data during this period revealed that among adults participating in the labor force, international migrants from Kerala were drawn from the middle of the education distribution, while domestic migrants were mainly selected from the upper range of the educational spectrum.<sup>7</sup> This fact, coupled with the empirical findings, suggests that for the less educated Keralite migrants moving overseas, there is a wage premium over non-migrants that generates asset gains to the origin household comparable to the gains that the more highly educated individuals moving for employment within India are able to produce. In order to maximize the gains from migration, households in Kerala appear to be behaving strategically when deciding who, and where to send their family members for employment.

In the next section, I present background on migration from Kerala. Section 3 lays out several theoretical considerations. In Section 4, I describe the data and estimation strategy. Section 5 presents the results. I discuss those results in Section 6, with the aim of explaining the key finding that asset gains to households from the two migration pathways are actually similar. Section 7 provides a brief conclusion.

# **2. Background: An Overview of Labor Migration in Kerala**

India’s experience with migration is noteworthy, making it an excellent setting for a study of the welfare gains generated by labor migration. With the remittances sent back to the country from abroad reaching nearly US$78.6 billion in 2018, India receives more money through this channel than any other nation in the world.<sup>8</sup> According to the Reserve Bank of India, the state of Kerala received an estimated 19 percent of India’s total overseas remittances in 2017.

> 7 One sees similar selection patterns with Mexican migrants. Kaestner and Malamud (2014), and the literature they review in that paper, find that Mexican migrants to the United States are drawn from the middle of the Mexican education distribution. Aguayo-Téllez and Martínez-Navarro (2013
” _Journal of Econometrics_ 108: 113-131.

Bond, Stephen R., Anke Hoeffler, and Jonathan Temple: 2001. “GMM Estimation of Empirical Growth Models.” CEPR Discussion Paper Series No. 3048.

Bottazzi, Laura, and Giovanni Peri (2003) - "Innovation and spillovers in regions: Evidence from European patent data." _European Economic Review_ (forthcoming).

21
.9|12.5|8.2|5.8|5.2|
|SampleSize|14653|904|117924|712|36542|504|3302|

_Source:_ Albanian data from Quarterly Labor Force Survey 2019; Jordan is from New Work Opportunities for Women Pilot Impact Evaluation 2010–2013; Mexico is from Trimester 1, 2014 National Survey of Occupation and Employment (ENOE); Morocco is from Household and Youth Survey 2009– 2010; Romania is from Household Labor Force Survey 2021; Sierra Leone is from 2014 Labor Force Survey; and Turkey is from Vocational Training for the Unemployed Impact Evaluation 2010–2012.

_Note:_ Morocco survey allowed multiple methods to be used, so responses add up to more than 100 percent. Romania survey combines newspaper advertisements and internet advertisements into one response category. Carranza and McKenzie (2023) provide full details.

Job matches between workers and firms can be highly location-, sector-, time-, and firmspecific, so that it seems unlikely in most cases that a central planner or government will do better than the private market in filling most job openings. In line with Table 1, throughout history and the growth of most countries, large increases in labor supply have been absorbed by the market without the need for the government to help millions of people find jobs. So why should the government get involved in providing job training and search assistance rather than leaving it to the private market?

Perhaps the most common reason that governments provide these services is based on political pressure for a government to show it is doing something about employment—and providing job training or search assistance is often politically easier than addressing other barriers to creating jobs. In political terms, the success of these programs can be based on visible inputs— like the number of people who finished a training program, or the number of towns with jobassistance centers—rather than the more difficult-to-observe effects like the number of workers 5
# **2. Literature review**

There is an extensive body of literature investigating the impact of the minimum wage on the economy. The opinions about its effects are usually seen either as a necessity to prevent exploitation of the low skilled labor and address income inequality, or as an artificially imposed price that creates excess labor supply and hence unemployment.

Most of these studies were conducted especially for the labor markets of advanced economies, such as the United States, the United Kingdom, Germany or Canada. New Zealand and Australia are the pioneers of the minimum wage policy. The literature on the impact of the minimum wage policy has significantly improved in the past years even for developing countries, where labor markets are more heterogeneous and conclusions about the positive and/or negative effects of such measures are even more difficult to be drawn.

Some empirical evidence based on US data suggests that an increase of the minimum wage leads to a decrease in employment. Brown, Gilroy and Kohen (1982) identified large negative effects for teenage workers, and still negative, but much smaller effects for young adult workers. Deere, Murphy, and Welch (1995) found that the 1990-91 increase of minimum wage by 27% reduced employment for all teenagers by 7.3% and for black teenagers by 10%. Burkhauser, Couch, and Wittenberg (2000) documented a decline in employment of 2% to 6% for each 10% increase in the minimum wage. Even the United States Joint Economic Committee surveyed all empirical research on the employment effects of minimum wage available in 1995 and concluded that a 10% increase in the minimum wage reduced teenage employment by 1% to 3%. Later on, these conclusions were shared by Neumark and Wascher (2013), but with a smaller effect for the young workers.

Some other studies did not find any negative effects on employment or even documented slightly positive effects. There is the well-known study by Card and Krueger (1994), based on survey data for New Jersey and Pennsylvania
these factors can result in agricultural production data of poor quality with large measurement errors. Surprisingly little attention has been devoted to measuring the extent of such errors and ways in which it could be minimized, although there are some initial attempts at taking this more seriously, for example by examining recall error more systematically (Beegle _et. al._ 2011).

While a number of countries, including China, routinely use diaries to obtain what are considered high quality data on household expenditure, diaries have seldom been used for estimating crop production, despite evidence in other fields of the potential benefits they can bring. Diaries are used in both developed and developing countries for recording food consumption and/or expenditures in household surveys such as Household Budget Surveys (HBS) and the Living Standards Measurement Study, or LSMS (Grosh and Glewwe 2000). In developing countries, their use and ultimate appropriateness has been questioned on several grounds, including the low level of literacy of respondents (Bowling 2002) and the presumption

> 1 Most recently, in a departure from previous methodological guidelines, the Global Strategy for Agricultural Statistics, a multi-agency effort by the UN, World Bank, FAO and USDA, among others, advocated for integrating the collection of agricultural statistics with a multi-topic framework, as to allow for a better understanding of agricultural processes and outcomes within the broader household economy.

4
of the new relationships are maintained even after the sale is over”.<sup>4</sup> Is that channel at work in Pakistan?

This paper combines macro and product-level analysis to document an asymmetric response to RER changes in Pakistan’s exports and then to test three different and complementary hypotheses to explain the relatively lower export response to depreciations. First, whether the response depends on the type of product that is being exported – homogeneous, traded in organized markets, versus differentiated. Second, whether it depends on supply constraints that different sectors may face, related to access to finance, structural financial dependence, and factor intensities. Third, whether export dollar-prices tend to fall when the rupee depreciates, due to some sort of pricingto-(source)market behavior of global buyers. To the best of our knowledge, this is the first study that empirically tests asymmetric export responses to exchange rate changes in Pakistan, and that offers empirical evidence backing three plausible determinants of weak export responses to RER depreciations. The case of Pakistan is relevant. It is an example of many low-middle income country contexts in which exporters operate with imperfect capital markets, inadequate complementary services to exporting, and connect to global buyers with some degree of market power.

To measure the asymmetric export response to RER changes, this paper uses macro-level data on exports, RERs, and global income, relying on a threshold autoregressive model to test long and short-term export elasticities with respect to RERs, as well as the speed of adjustment to departures from the long-term relationship. It then relies on detailed product-destination-year-level data on exports, combined with information on bilateral real exchange rates (BRER), measures of sectoral

> 4Also, Baldwin and Krugman (1989) show persistent trade (and export) effects associated to large changes in exchange rates). More recently, Das et al (2007) show that export responses to exchange rate changes depend on entry costs, prior export experience and expectations about the real exchange rate process.

3
**Figure 13: Survival Rate of BRICS Exports, 2002–12**

<!-- Start of picture text --> 0 5 10<br>Analysis Time<br>BRA CHN RUS IND ZAF<br>.6<br>.4<br>probability<br>.2<br>0<br><!-- End of picture text --> _Source_ : COMTRADE data.

Notes: Analysis time is in years, with year 0 = 2002 and year 11 = 2012. BRA = Brazil; CHN = China; RUS = Russia; IND = India; ZAF = South Africa.

0
below 150 on non-forested areas, suitable for rainfed crops. They occupy 29% of LAC’s land area. Almost the same number of people live at densities under 150 and occupy land that is rated marginal or unsuitable for rainfed crops; these lands constitute 45% of LAC, including 16% that is both poor for crops and under forest cover. And about 6% of the total population lives at densities <150, occupying land that is suitable for crops but currently largely forested. These lands, constituting about 25% of LAC, may present development-environment trade-offs.

# **Conclusions**

The quantitative results presented here must be taken cautiously. They are very much affected by the size and heterogeneity of the reporting unit. They are not necessarily comparable between countries (though they may be more consistent than existing estimates). Because these population densities are computed, for the most part, over municipios, lowdensity areas are likely to contain market towns or even small cities. Thus the mean population densities tallied here do not closely correspond to the ‘small-radius’ ideal discussed above. The GRUMP v 1, when available, should provide an improved estimate of density distributions.

Our intention was to illustrate some general propositions:

> 6 World Bank, _The Little Green Data Book 2003_ .
1980s. In contrast, the paper reported statistical results which suggest no meaningful, stable broad money demand relationship in Australia over the sample period. These results implied that while financial deregulation has made the broader monetary aggregates more volatile, given the increasing availability of financial assets with competitive returns, there have been only steady changes in transaction technology in Australia since the early 1980s; therefore, the demand for narrow money has remained stable. An implication is that money remains important for Australia in its use of monetary policy to target price stability. Thus, narrow monetary aggregate can be used to measure excess liquidity for forecasting or predicting its impact on inflation, asset prices, and the output gap.

Capasso and Napolitano (2012) investigated the consistency of the stability of money demand in Italy, before and after the European Monetary Union (EMU) and tried to determine the effect of a change in the currency regime on the monetary aggregates. They estimated the money demand function in Italy using quarterly data from 1977 to 2007, using the bounds testing co- integration procedure proposed by Pesaran et al. (2001). In order to compute the short- and long-run elasticities of the demand for money, they implemented Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUMSQ) stability tests proposed by Brown et al. (1975). The analysis is performed on broader money M2 and M3 also. Their findings revealed significant stability of money demand coefficients<sup>10</sup> in the short- and long-terms. This result applied to both M2 and M3. In order to further investigate long-term dynamics in the coefficients of money demand, Pesaran, et al. also employed Kalman filtering (Kalman 1960). They found that the introduction of the Euro has significantly increased the stability of money demand parameters particularly for M3, and the effects occurred before the introduction of the new currency in 1996 when the Lira was strongly linked to the Euro in preparation to the EMU.

# **_ii. Literature on Money Demand
</sup> _δS_<sup>1</sup><sup>_<C_</sup> _i_<sup>1</sup><sup>_<C_ ̄</sup><sup>_i−δC_</sup> _δS_<sup>1,coupledwithasufficientlylarge</sup><sup>_<u>δ</u>_</sup> _δS_<sup>orasufficientlylargenumberofindividualswith</sup> initial consumption _C_<sup> ̄</sup> _i −_<sup>_<u>δ</u>_</sup> _δSC_<sup> ̄</sup> _<u>i</u>_<sup>_< C_</sup> _i_<sup>1</sup><sup>_<C_ ̄</sup><sup>_i_+</sup><sup>_<u>δ</u>_</sup> _δSC_<sup> ̄</sup> _<u>i</u>_<sup>,therewillbeadeclineintheshareofhusbandsthatmightpotentially</sup> engage in acts of IPV.

> 15These patterns are corroborated by the summary statistics reported in Section 3.

7
(the world, or the OECD, for example). ( ) is the new arbitrary mean (standard deviation) for the standardized distribution. In the PISA procedure, it has a value of 500 (100). It is the distributions of _yij_ that are used in computing means and inequality indicators for each country _j_ in the PISA data set. As we will see in the next section, the operation described by equation (2), even if the IRT procedure that precedes it is taken as given, poses serious issues for inequality measurement.

In addition to standardized test scores, the PISA data set contains information on a number of individual, family and school characteristics for each test-taker. The presence of these covariates accounts for a large part of the interest of the research community on the PISA data. For the analysis of inequality of opportunity in education, we focus on a subset of these covariates that are informative of the family background and other inherited circumstances of the child. Ten such variables are used: gender, father’s and mother’s education, father’s occupation, language spoken at home, migration status, access to books at home, durables owned by the households, cultural items owned, and the location of the school attended (used as an indicator or a rural or urban upbringing).<sup>7</sup> Parental education is measured by the highest level completed and is coded using ISCED codes into four categories: a) no education or unknown level; b) primary education (ISCED level 1); c) lower secondary education (ISCED level 2), upper secondary (ISCED level 3), or post-secondary non-tertiary education (ISCED level 4); and d) college education (ISCED level 5)). Father’s occupation is classified using ISCO codes. We aggregate occupations into three broad categories: a) legislators, senior officials and professionals, technicians and clerks; b) service workers, craft and related trades workers, plant or
liabilities of the financial system as a percent<br>of GDP.|WDI database|
|Property rights|An index measuring the ability of individuals to<br>accumulate private property, secured by clear laws<br>that are fully enforced by the state.|Heritage Foundation<br>database (2007)|
|Inflation|Annual change in consumer price index (CPI)|WDI database|
|Trade openness|Sum of imports plus exports as share of GDP (in<br>percentage)|WDI database|
|Legal origin|A dummy variable taking 1 when a country’s law is<br>based on English common law; and 0 otherwise.|La Porta and others<br>(1998)|
|Rainfall|Average annual rainfall.|Ciccone (2011) and<br>WDI database|
|Creditor rights|The index measures the legal rights of creditors<br>against defaulting debtors in different jurisdictions,<br>and ranges from 1 to 4, with a higher value indicating<br>stronger creditor rights.|Djankov, McLeish,<br>and Shleifer (2005)|
|Rule of law|<br>A score measuring the extent to which agents have<br>confidence in and abide by the rules of society, and in<br>particular the quality of contract enforcement, the<br>police, and the courts, as well as the likelihood of<br>crime and violence|Governance Matters<br>(World Bank)|

21
# **_A profile on labor informality in Senegal_**

Labor informality is a widespread phenomenon in Senegal. The percentage of workers that can be classified as informal with the definition and data used in this study reaches the 93.7%, representing almost 98% of the employed population in rural areas and 89.5% among urban regions. A similar pattern can be seen across the eight WAEMU member countries using their respective 2018-19 EHCVM surveys, with average informality rates of 98.6% and 88.9% in rural and urban areas, respectively (Figure 3).<sup>9</sup> Informality is not only spread across rural and urban areas in Senegal, but also among different working conditions. Table 1 shows that the informality rate at the national level is almost 100% among unpaid workers, 97.5% within the group of employers and self-employed workers and lower but still very high for wage earners, with informality rates of about 84% nationwide and 80% in urban areas. In rural areas, the proportion of unpaid workers is much higher than in urban areas (32% and 12%, respectively), but even among salaried workers, informality is much higher.

In the Senegalese labor market, most of the workers are men, without any formal education and strongly concentrated in the Midwest and Capital regions, with important differences in the composition of the formal and informal populations. Table 2 shows that around 57% of all workers are male, 60% has no formal education (42.5% in urban areas) and most of them live either in the Midwest region (31%) or in the Capital (27%). Formal workers are even more predominantly male than their informal counterparts, as nearly 72% of formal workers are men. Moreover, formal workers are older, much more educated and typically married. While their mean age is almost 42 and more than 80 percent of them have a formal education and are married, the average
|**Tool name:**|**PovStat**|
|---|---|
|**What is it?**|An Excel-based software program that simulates the changes in poverty and inequality resulting over<br>time from changes in growth in output and employment.|
|**What can it be used for?**|PovStat may be used to simulate the poverty and inequality impact of policies affecting sector-level<br>output and employment growth rates.|
|**What does it tell you?**|PovStat simulates poverty and inequality measures under alternative growth scenarios. Forecasts of<br>varying levels of complexity may be computed, depending on the availability of reliable data and the<br>extent to which factors influencing poverty levels are incorporated. The simulations vary according to<br>optional projection parameters.|
|**Complementary tools:**|Other software programs that provide poverty and inequality forecasts include SimSIP Poverty (see the<br>table on SimSIP) and DAD (a software for distributive analysis).|
|**Key elements:**|On the basis of household-level data, the software translates differential output and employment<br>growth across sectors into differential growth in the per capita income or consumption of households<br>across those sectors. The tool simulates the impact on poverty of policies affecting output. It does this<br>by using the fact that poverty changes can be decomposed into two parts: a component related to the<br>uniform growth of income and a component due to changes in relative income. The simulations are<br>made under the assumption that the policy analyzed will be distribution neutral, or, conversely, that<br>there is a specific, quantifiable form for the distributional change. Changes in employment distribution<br>are accommodated by reweighing the sample households.|
|**Data/information:**<br>**
(2003), hereafter referred to as ELL, pioneered the small area estimation of poverty by combining household survey with population census data. The proposed approach imputes household income or consumption expenditure (multiple times) for each household into the population census, and then aggregates the multiple imputed values to obtain estimates of poverty at the municipality or district level. The population census arguably provides the richest possible data source for this purpose. It contains a comprehensive set of variables that are highly correlated with household income or consumption (although not, usually, income itself). Specifically, a census typically includes information on demographics, education, employment, housing characteristics, asset ownership, and community services (i.e. local amenities) for all households in the population. In the ELL procedure, a household survey is used to infer the relationship between these variables and household income, taking advantage of the fact that many of these variables are available in both the census and household survey.

2