rafmacalaba/data-use-mentions-extended huggingface dataset:
# The Wage E¤ects of Immigration and Emigration<sup>�</sup>

Frédéric Docquier<sup>a</sup> , Ça1⁄4glar Özden<sup>b</sup> , Giovanni Peri<sup>c</sup>

> a FNRS-IRES, Université Catholique de Louvain (frederic.docquier@uclouvain.be)

b The World Bank, Development Research Group (cozden@worldbank.org)

> c University of California, Davis and NBER (gperi@ucdavis.edu)

December 17th, 2010 Abstract Immigrants in Rome or Paris are more visible to the public eye than the Italian or French engineers in Silicon Valley, especially when it comes to the debate on the e¤ects of immigration on the employment and wages of natives in high-income countries. This paper argues that such public fears, especially in European countries are misplaced; instead, more concern should be directed towards emigration. Using a new dataset on migration ‡ows by education levels for the period 1990-2000, the results show the following: First, immigration had zero to small positive long-run e¤ect on the average wages of natives, ranging from zero in Italy to +1.7% in Australia. Second, emigration had a mild to signi...cant negative long-run e¤ect ranging from zero for the US to -0.8% in the UK. Third, over the period 1990-2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives. These patterns hold true using a range of parameters for the simulations, accounting for the estimates of undocumented immigrants, and correcting for the quality of schooling and/or labor-market downgrading of skills. All results go counter to the popular beliefs about migration, but they are due to the higher skill intensity of both emigration and immigration relative to non-migrants.

JEL Codes: F22, J61, J31.

Keywords: Immigration, Emigration, Complementarity, Schooling Externalities, Average
stays in the less risky, less demanding Mexican-side jobs that permit easier balancing of family responsibilities (Zabin and Hughes 1995). The literature does stress the importance of accumulated networking that appears to have caused some Mexican states to generate a disproportionate number of US bound migrants. To the degree possible, we controlled for such effects in the domestic regressions and, as detailed below, make additional adjustments in the simulations.

A second concern is whether the US variables are too “out of sample” to really treat the US as a “33<sup>rd</sup> Mexican State.” In fact, as table 4 shows, the ratio of the per capita income of Mexico’s richest state, the D.F. relative to its poorest is about 6.4 in nominal or 5.6 in real terms while the ratio of the average for the US relative to the D.F. is only about 1.9 or 1.5 PPP adjusted. That is, in terms of development the US is closer to Mexico City than Mexico City is to Chiapas. Nor are wage differentials radically different. The ratio of the Hispanic real wage in the US to the mean wage of the DF adjusted for PPP is roughly the same as the ratio of the real DF wage to that in Chiapas.

There may be some concern about the importance of the border representing fundamentally different “transport costs.” In fact, the evidence is strong that this is more a difference in magnitude than kind. Donato, Durand and Massey (1992) argue that “our data from Mexico reveal a fairly high probability of apprehension by INS combined with a near-certain probability of ultimately entering the United States.”(p 152) and that “ _every_ migrant who attempted a border crossing, whether apprehended or not, eventually gained entry” p 155 italics theirs. This suggests that border control serves more as a tariff than a quota.

The costs of movement are substantially different, but perhaps not so much as
We collected social norms’ data following an adaptation of the framework proposed by Bicchieri et al. (2014) and developed by Bussolo et al. (2024, Forthcoming). The survey tool specifically seeks to collect information on the main components of social norms, including: (1) _individual behaviors_ (IB) – what people do; (2) _personal beliefs or personal normative beliefs_ (PNB) – what people think they ought to do; (3) _social empirical expectations_ (SEE) – what people think others in their social group typically do; and (4) _social normative expectations (SNE)_ – what people think others in their social group think are appropriate behaviors within the group. Additionally, we collected information on (5) _reference groups_ – people who are reported to have influence on one’s perception of social norms or to enforce the norms, (6) _sanctions_ – the positive or negative consequences of conforming with or digressing from the perceived social norm, and (7) _conditionality of norms_ – specific circumstances or characteristics that might make a behavior more or less acceptable or that might trigger a stronger normative response.<sup>13</sup>

**Figure 1** summarizes the measurement framework used in this study to explore whether and to what extent social norms influence behavior around women working across the two broad themes identified, namely gender roles within the home and women in the public space. The specific focus on these domains and on married women is informed by the formative qualitative data collection conducted prior to the survey.<sup>14</sup> The model specifies that for a behavior to be driven by social norms, it must be conditional on both the social empirical and social normative expectations, with some level of social sanctions or rewards expected for

> 11 Nepal Demographic and Health Survey, 2016 and 2022.

> 12 Ministry of Labor, Employment, and Social Security, Nepal, 2020.

> 13 For a deeper discussion of social norms theory and
that is statistically valid for rural areas in India. After data cleaning, a total sample of 22,538 rural households and 12,325 urban households were included in this paper’s analysis.<sup>5</sup> The energy questions that were asked in the IHDS survey are more extensive than those of comparable surveys such as the Demographic and Health Surveys and the Living Standards Measurement Studies. The energy questions in the IHDS survey cover use of fuels, cash expenditures on fuels, the collection time for obtaining biomass fuels, and the types of stoves and electric appliances in the household. For electricity, there are also questions on the reliability of the power supply and the source of electricity for the household such as whether they are serviced by the local state electricity board or from a neighbor. These detailed energy questions allow for a much more detailed analysis of the welfare impact of the use of different types of energy. In fact, we next turn to the demand for energy in India.

# **Patterns of Household Energy Use**

Of the many factors that have constrained India’s development, energy usage figures prominently. Energy use per capita in India lags far behind that of other countries around the world (World Bank 2006). Moreover, there are wide disparities in energy use between urban and rural areas (where three-fourths of the country’s people live). This section highlights India’s existing patterns of household energy use, and estimates energy demand functions.

The patterns of urban and rural energy demand in India differ markedly (Table 1). In rural areas, patterns of energy use typically involve high reliance on traditional fuels, including wood, dung, and straw burned in inefficient stoves. In urban areas, only one-third of households use fuelwood, compared to nine-tenths of rural households. In terms of quantity of energy use, rural households consume about 132 kg of fuelwood per month, more than four times the amount

> 5 Because samples were drawn as a fixed number
literature. We, however, prefer refugee inflow to refugee camp as our main explanatory variable is constructed as the number of refugees weighted by inverted distance. Drawing experiences from Ethiopia and other refugee host communities, we believe that international aid and other benefits coming to our study communities are because of refugee flow (establishment of a refugee camp) and hence should be looked at as a mechanism through which refugee camps (refugee inflow) affects local communities’ livelihood. Unfortunately, we could not get data on refugee related international aid in the communities to undertake a causal mediation analysis. If there is any cause of concern on its omission, the Kebele fixed effects absorb the bias that could arise as refugee induced international aid is less likely to vary within Kebeles. If there are any other international aid programs that are not related with refugee inflow/the establishment of refugee camps, the aids are more likely to be placed at a Kebele level and hence less likely to vary within kebeles and the Kebele-fixed effects absorb most of the variation to mitigate the resulting bias.

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2020, covering all core subjects for grades 1 -11, and curricular areas meant for the last term of the academic year in three languages – Uzbek, Russian and Karakalpak. These lessons were broadcast through four government-owned television channels. Every day, four lessons per grade, each of 15-20 minutes duration were telecast (thus, a total of one to one and a half hour lessons for each grade), and the broadcasting timetable was announced well in advance for the week. In addition, the video lessons were also made available for students to access any time through the education portal _online maktab_ through the Telegram (instant messenger service) channel, which has 84,000 subscribers and an estimated 2.6 million views daily, as well as the Telegram channel of the MOPE, with 80,000 subscribers and 250,000 views daily.

Initial surveys also showed engagement with distance learning platforms. Four surveys (UNICEF Rapid Assessment – telephone survey, SISQE-UNICEF online poll – through SISQE web portal and U-Report poll – through SMS, Facebook, telegram, smartphone apps and Listening to the Citizens of Uzbekistan -L2C2) were conducted in the initial months after school closures in March 2020<sup>1</sup> and allow us to assess the reach and use of distance learning programs in the initial months of COVID-induced school closures. The four surveys show that most students (96 percent across four surveys) engaged in some form of study during the initial months of school closures (April – May 2020). Of these, a majority (82 percent) continued education through distance learning while some students (15 percent) were engaged in self-study using textbooks, teacher support, or by watching YouTube and other websites.<sup>2</sup> Of the students who did not continue education in the initial months (3 percent), the major reasons cited were irregular power supply, unavailability of TV or internet at home, and unavailability of the TV channels that broadcast lessons.

Furthermore, the Government of
for home use) should not be considered employment as no income is earned.<sup>20</sup> The problem with this argument is that it goes against decades of revisions of the SNA, all designed to include more economic activities, not less. As SSA countries such as Ghana, Nigeria, and Uganda make progress on implementing the 1993 SNA recommendations on including non-market activities such as household production, their employment data needs to catch up too. Calculating national accounts under the 1993 SNA revision, including the Social Accounting Matrix (SAM) used in checking consistency and balance, requires more extensive data on non-wage activities from household surveys. Mismeasurement at the micro level will pose problem for the macro statistics. The argument here is that it is possible to measure employment in low-income countries in a manner consistent with the SNA and internationally accepted definitions if the questionnaire design takes into account the reality of how economic activities are performed in the country context. The problems occur when this does not happen. Statistical offices need to recognize this, and raise their game.

The flagship household survey of most SSA countries is their 12 month multi-purpose one (including household consumption), which is used to monitor household welfare outcomes, including poverty. Over time, as these surveys were used for policy analysis, it was recognized that employment outcome variables (including earnings) need to be included in order to discern the growth-poverty linkages and the reasons why growth was or was not shared. As a result, more surveys are adding questions on the economic activities of household members. To be useful, these surveys need to use formats that are adapted to a low-income country

> 20 This argument was recently made in ILO (2012), which was a draft discussion document.

24
of Incentives in Procurement and Regulation,_ the MIT Press: Cambridge, Massachusetts.

**14For** surveys on principal-agent models, see David Sappington, "Incentives in Principal-Agent Relationships," _Journal of Economic Perspectives, 5(2),_ 1991, p. 45-66; Bengt Holmstrom and Jean Tirole, "The Theory of the Firm," in Schmalensee R., and R. Willig eds., _Handbook of Industrial Organization,_ Amsterdam: North-Holland, 1989.

8
Table 30. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Using U.S. O*Net database. All workers. 2005-2010

|Dependent variable:||Anal|ytical|||Interp|ersonal|||Ma|nual||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000489||0.000465|0.000159|0.000577||0.000544|0.000396|-0.000512||-0.000468|-0.000297|
||(0.000217)**||(0.000221)**|(0.000316)|(0.000217)***||(0.000221)**|(0.000310)|(0.000248)**||(0.000250)*|(0.000343)|
|Share of hhlds with computer||0.0671|0.0865|0.0988||0.0877|0.115|0.137||-0.121|-0.158|-0.188|
|||(0.0525)|(0.0452)*|(0.0487)**||(0.0667)|(0.0628)*|(0.0719)*||(0.0935)|(0.0879)*|(
proportionate rate of poverty reduction due to other factors is the same between the two groups. Under PILE, we would then expect that poverty will fall less rapidly among the landless, given that rising landlessness will put a break on their rate of poverty reduction. That implication is not borne out by the data for Vietnam as a whole; indeed, the trend rate of poverty reduction between 1993 and 2004 is slightly higher for the landless (Table 1). However, while this version of PILE does not offer a convincing explanation of these data, the above test cannot reject PILE for the MD, where the rate of decline in poverty is lower for the landless.

30
party or what is called _generalized reciprocity_ . There is strong evidence for the role of reciprocity in labor market choices and performance, both from lab experiments as well as from combining survey and experimental data . The game was selected because the extent to which the students respond positively to being entrusted with resources in the game may be a predictor of their response to being entrusted with public resources in real life. Thus, it might act as a proxy for their preference for or behavior in public service. We therefore expect that individuals with higher intrinsic motivation behave more reciprocal in the generalized trust game.

The second game is the Third Party Punishment Game as set out by Fehr and Fischbacher . In this game Player 1 can give any share of a received amount to Player 2; while Player 3 can impose a fine on Player 1 when she judges his behavior to be unfair. How much Player 3 is willing to pay to punish Player 1 is a direct measure of the strength of their

> 32 The items were taken from the California Psychological Inventory (CPI) which has been used in research activities during the last 40 years and is very appropriate to use with students (see http://www.cps.nova.edu/rcpphelp/CPI.html) 33 The games are anonymous and follow the standard procedures of experimental economics (they are played in a classroom, for real money, and follow a precise script).

14
Table S2.5: EFFECTS ON RECIPROCITY OF TRANSFERS, BY WEALTH CLASS

||Reciprocity<br>of transfer<br>links|Re<br>ultra<br>poor|ciprocity o<br>other<br>poor|f transfers<br>middle<br>class|with<br>upper<br>class|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Treatment|0.032|0.005|0.069***|0.040|0.049|
||(0.021)|(0.033)|(0.024)|(0.025)|(0.031)|
|Treatment_×_Endline|0.089**|0.079*|0.051|0.094**|0.142***|
||(0.035)|(0.041)|(0.044)|(0.039)|(0.048)|
|Mean in control|0.510|0.627|0.560|0.449|0.267|
|Adjusted R-squared|0.219|0.190|0.208|0.237|0.198|
|Observations|12774|2792|4823|9474|3135|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (2) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level
finding for two reasons. First, differences in returns between women- and men-owned firms may in part reflect underlying discrimination against women. This is much harder to address using policy tools than simply providing more resources to women (see for example, Nix et al et. 2016 for more discussion on this point). Thus, a substantial proportion of the productivity gap that we find in the informal sector can be closed by simply providing better resources to women such as education, manager experience, and physical capital, without having to deal with the more complex and difficult problem of gender discrimination. This is under the assumption that discrimination plays a relatively lower role in accessing resources relative to the returns to these resources. Second, roughly similar returns to factors for women- and men-owned firms implies that there is little or no efficiency cost of shifting resources from men-owned to women-owned firms. Thus, greater gender equality in resources does not necessarily lead to lower labor productivity in the informal sector overall.

# **6. Discussion and Conclusion**

There are both efficiency and human rights arguments for advancing equality between men and women. While the literature is replete with studies on the gender productivity gap for the formal private sector, very little is known about the informal private sector. The key questions are whether such gender productivity gaps persist in the informal sector, and if so, what are the key contributors that exacerbate or alleviate these gaps. These are the questions this study attempts to answer, and to our knowledge, this is the first study to do so using a unique firm-level cross-country database. Informal businesses are hard to capture as by definition they are not included in registration lists, and thus, it is difficult to gather data on how they function. While the data we employ is not as comprehensive as firm-level data sets of the formal sector, our study provides a baseline for future 24
EU are identified separately. There are five different macroeconomic policy instruments that are embedded in the data, including taxes, subsidies, tariffs, and payments for water.

Morocco‘s irrigated agriculture is organized in 9 water districts (ORMVAs), 2 of which are isolated and barely involved in the economy. Thus, the modeling framework refers to the remaining 7 ORMVAs. Among the 82 agricultural and agriculture-related production activities, 66 are in crop production, five in livestock, and 11 in processing agriculture, both up and downstream from the farm firm. To capture the spatial nature of irrigated agriculture, 66 crop production activities are further distinguished according to whether they are within or outside the seven ORMVAs. Among the 33 activities within the water authority perimeters, 21 are irrigated crop production and 11 are rainfed. Because water is either costly or presently impossible to transport between perimeters, the seven ORMVAs are further subdivided into 20 perimeters.

The data are organized into a social accounting matrix (SAM). The data include perimeter level information on water charge fees, cropping mix, water and land allocation by crop and area, employment of labor and capital and intermediate input use by crop. National level data on employment, trade, nonfarm production and resource flows are also entered into the SAM. These data are used to calculate the parameters of the model.

Unlike a standard SAM that often includes only national level data, the Morocco SAM in this study is multi-dimensional, taking into account crop production activities. A schematic presentation of the major features of the macro framework is presented in Figure 3.

Data for the farm model ( ̳micro‘ model) are from the same data source as those for perimeters in the ̳macro‘ model, i.e., each perimeter is aggregated from farm level data. For this reason, the production activities in the farm model are compatible with the ̳macro‘ CGE model. For the analysis preformed here, the representative farm is chosen from the irrigated area, and hence, only irrigated
standard formulation of those estimations. These coefficients are used to calculate the intermediate use part of the regional SAM by multiplying the total supply in the national SAM. The regional tax accounts and value added are introduced afterward by assuming that the residual is the capital value. Output by region and sector is then calculated by summing each activity’s intermediate use, activity taxes, and value added accounts. Investment, government (central and local), household, and export demand for commodities are introduced next. Margins are calculated by disaggregating national margins based on total consumption in the regions. Lastly, when available, commodity taxes are raised using regional tax data (for VAT and tariffs) and total consumption in regions when not (sales and excises).

Next, components of household accounts are regionalized. We start by introducing labor income by compensating employees at the regional level as labor income for the households in that region. We assume all compensation paid to labor in a region goes to the households in that region without transfers to the other areas. We use regional data from household surveys (EU-SILC data<sup>5</sup> ) for debt payment receipts and lending to government, enterprise transfers, and government transfers.

The national accounts are recorded under the region "Italy". National government, debt, capital, investment, savings, and taxes are kept at the national level. We do not have one of those accounts in every region. However, payments or receipts from those accounts can be regional. For instance, direct tax payments by households are regional as households are regional.

Once all blocks are calculated, we estimate the flow of commodities for final and intermediate consumption using a non-linear programming model that minimizes the deviation from supply and demand from the gravity estimations given the supply and demand from each region. The remaining imbalances, which are generally very small, are moved to stock changes if the commodity has stock changes in national SAM. If not, they are moved to household consumption by adjusting household