rafmacalaba/datause-encoder-data huggingface dataset:
leading to higher wages, and Furtado and Theodoropoulos (2010) find that intermarriage in the U.S. increases the probability of immigrant employment which they attribute to better access to job networks. Marriage can also provide a faster pathway to naturalization, which can benefit migrants in terms of access to public sector jobs, signal commitment to stay in the host country, and reduce discrimination (Govind, 2021).

The result is that many immigrants marry natives from the country they move to, possibly after dissolving a marital union that predated migration. Meng and Gregory (2005) find that 32 percent of married immigrants in Australia in 1996 are intermarried, with this rising to 47 percent of those who arrived in Australia aged 19 or lower. Haandrikman (2014) notes that the partner market for Swedes has expanded considerably with EU expansion and increased diversity of the migrant population, so that the share of native Swedes in binational marriages doubled from 8 percent in 1991 to 16 percent in 2008. Govind (2021) states that marriage between a French person and a foreigner accounts for 13 percent of all marriages in France. Lichter et al. (2015) use the 2008-2012 American Community Survey to document that 37 percent of immigrant men who married in the U.S. in the past year got married to a U.S. native. This rate of intermarriage differs substantially across immigrant nationalities, with Bohra-Mishra and Massey (2015) finding it highest for immigrants from Europe and Central Asia.

# **3.5 Migration can also change other members of the family, such as children and other household members**

In addition to changing the decision of whether, when, and who to marry, migration can also change decisions about having children. This can occur through multiple channels: through an income effect, a spousal absence effect, and through changes in fertility norms and contraceptive knowledge (Hildebrandt and McKenzie, 2005). This can change the number and timing of childbirths. Moreover, to the extent that migration changes who individuals form relationships with, it not only changes whether they have children, but who these children are–for example, rather than having a child with someone from their home community, they instead have a different child with someone from the destination community.

Changes in marriage will also change the formation of the extended family, in terms of the composition of in-laws. Moreover, migration of one individual can also change the partition of the non-migrant family members into distinct households. Drawing on data from a rotating 15
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# **1. Introduction**

The relationship between school inputs and education outcomes is of fundamental importance for education policy and has been the subject of hundreds of empirical studies around the world (see Hanushek 2002, and Hanushek and Luque 2003 for reviews of US and international evidence respectively). However, while the empirical public finance literature has traditionally paid careful attention to the behavioral responses of agents to public programs<sup>1</sup> , the empirical literature estimating education production functions has rarely accounted for household re-optimization in response to public spending. This is a critical gap because (a) household responses to education policies will mediate the extent to which different types of education spending translate into learning outcomes, and (b) parameters of education production functions are typically not identified if household inputs respond to changes in school-level inputs (see Urquiola and Verhoogen 2009 for one such example in the context of class-size).

We develop a dynamic model of household optimization that clarifies how increases in school-provided inputs translate into learning outcomes. We then test the main predictions of the model in two very different countries – Zambia and India – using unique matched data sets of school and household spending, and panel data on student achievement. A key contribution of this paper is our ability to measure household spending changes and student test-score gains in response to both _unanticipated_ as well as _anticipated_ changes in school funding. The former measures the production function effect of increased school funding (a partial derivative holding other inputs constant), while the latter measures the policy effect (a total derivative that accounts for re-optimization by agents).

The theoretical framework of a dynamic forward-looking model provides a useful guide to the key issues. In this framework, households' optimal spending decisions will take into account all information available at the time of decision making. The impact of school inputs on test scores depends then on (a) whether such inputs are anticipated or not and (b) the extent of substitutability between household and school inputs in the education production function. The model predicts that if household and school inputs are technical substitutes, an anticipated increase in school inputs in the next period will decrease household contributions that period. Unanticipated increases in school inputs limit the scope for household responses, leaving

> 1Illustrative examples include Meyer (1990) on unemployment insurance, Cutler and Gruber (1996) on health insurance, Eissa and Leibman (1996) on the EITC, Autor and Duggan (2003) on disability insurance.  See Moffitt (2002) for an overview on labor supply responses to welfare programs.

1
in a petition or named in related petitions. That is, rather than having to parse out the injury that might arise from each separate firm or country named in a petition(s), its focus is on the aggregate level of imports from the subject countries and the associated injury to the domestic industry.<sup>7</sup> The literature has identified a number of circumstances where the domestic industry might feel justified in filing an AD petition. In general these conditions are divided into shocks that occur either in the foreign market or in the domestic market. Staiger and Wolak (1992) focus on shocks to the foreign market and construct a model of stochastic foreign demand that potentially generates an excess supply that is dumped on the domestic market when foreign demand realizations are sufficiently low. A case can also be made that a positive foreign demand shock leads to dumping as in Clarida (1993) (due to excessive entry) and Hartigan (1996) (due to an interaction with financial frictions that constrain the home firm). Despite these mechanisms Knetter and Prusa (2003) fail to find a statistically significant relationship between changes in source country GDP and filing rates in the domestic country. However, the level of aggregation seems to be important for this result. Using more detailed 3 digit ISIC data Crowley (2009) finds that a negative foreign demand shock is a significant determinant of the probability of a domestic industry filing an AD petition.

While foreign shocks are viewed as a likely motivation for foreign firms to alter their pricing in the domestic market, the weight placed on the material injury criterion suggests that other shocks may also be relevant. In particular, there may be scope for a negative domestic shock to be used as a basis for filing a petition since it is likely to be associated with both a reduced domestic price (which raises the likelihood of a domestic/foreign price differential) and injury to the domestic industry. Both Knetter and Prusa (2003) and Crowley (2009) find that domestic shocks play an important role in filing behavior, along with a number of other studies (see also Bown and Crowley (2013a), Staiger and Wolak (1994), Finger (1981), Herander and Schwartz (1984), Feinberg and Hirsch (1989), Hansen (1990), Krupp (1994), Lichtenberg and Tan (1994), Furusawa and Prusa (1996), Blonigen (2000), Sabry (2000)).

While the current literature provides insight into the source of shocks that are likely to precede an AD petition, their reliance on aggregate data means that they can only estimate propensities to file among broadly-defined domestic industries. Yet AD petitions relate to very specific products. For example the 3-digit ISIC classification defines 28 manufacturing sectors whereas the HS defines over 15,000 products. Given that the typical case involves approximately eight separate HS-10 codes, the

> 7This process is formally known as cumulation.

5
in Malaysia (across natives and immigrants) by 0.35 percent, which is simply a consequence of downward-sloping demand for output produced in Malaysia. If the scale effect were actually smaller than the substitution effect then immigration would result in both declining immigrant and native wages.

The differences between the OLS and IV estimates provide evidence on the factors that influence the location decisions of immigrants. The IV estimates of the impact on immigration on native employment in an industry-region are considerably larger than the OLS estimates. This suggests that immigrants are not systematically hired by industries experiencing positive demand shocks, but rather in particular by industries in diffi culties, likely in an attempt to cut costs and survive. The OLS estimates of the relationship between immigration and immigrant wages are smaller than the IV estimates, and frequently insignificant. The IV estimates tell us that immigration does decrease immigrant wages, but the OLS estimates suggest that immigrants tend to go to industry-regions where their wages are increasing; and those two effects partially cancel each other out.

An advantage of the instrumental variable approach is that it addresses certain measurement problems. For example, there are a substantial number of immigrants who are undocumented and illegally employed in Malaysia. These are likely undercounted by the LFS, resulting in attenuation bias (if illegal and legal immigration flows are uncorrelated) and other more complicated biases (if they are correlated) in the OLS estimates. But for the instrumental variable estimates to be consistent, it is only necessary that the flows of illegally employed immigrants are uncorrelated with the instrument (conditional on the included fixed effects). This is likely to explain why the IV estimates are larger than the OLS.

33
on the basis of surveys. More recently, data have been collected on the spread of microfinance following CGAP efforts and Microcredit Summit. These cover the number of people with access to a savings account. For some countries, there are data from household surveys, such as the Living Standard Measurement Survey (LSMS)-type. Of these LSMS-surveys, some 27 have covered some dimensions of households’ use of financial services (see Honohan, 2004c). Still, and with the exceptions of some developed countries such as Sweden, much of the data collected in these general households surveys is very basic and limited in terms of the various dimensions of use and access (quantity, costs, quality). Access by households to credit, although typically only one-quarter in terms of number of access to savings and arguably less important in terms of growth and development, has been equally difficult to document at the level of households. Many countries, for example, do not even have data on the aggregate level of consumer credit, in part, as not just banks are providing that, but also non-bank financial institutions.

Data on firms’ use and access to financial services are equally limited. While there is much information on listed firms’ financial structure and their access to (some forms of) external financing, there is much less information on the unlisted firms and especially limited information on small firm finance access. Mostly data come from surveys, such  as those conducted by the World Bank (World Bank Economic Survey WBES, Investment Climate Assessments  ICAs), or by national agencies such as the US Federal Reserve Boards, UK Bank of England, EU, etc. Some data come from central bank statistics and advocacy groups (e.g., US Small Business Administration, chambers of commerce, and equivalents). Again, the data are basic and limited in terms of various dimensions of access (quantity, costs, quality). Access to credit dominates the data collection efforts, with access to savings services less of an issue, although payment services are important as well for firms. Mostly data are collected on use of and access to banking services, and much less data are available on other financial services, such as insurance, leasing, factoring and the like.

8
technical green skills. This paper leaves out of consideration those indirect or neutral jobs, as it is assumed that advanced labor market information systems would provide the signals to the workforce of which jobs are in demand and hence worth pursuing. This paper also excludes the worker's protection layer from the definition since it is not the main objective of the skills policy, and it is addressed by another set of policy instruments.

This note contributes to the understanding of skills policy in the context of the green transition as follows. Section 2 presents a conceptual framework to identify green jobs, departing from the state-ofthe-art definition of the production function and resulting in four measurement approaches. Then, it discusses their suitability for skills policy along with their data requirements and limitations when applied in advanced and developing countries, becoming a reference point in the literature. Section 3 discusses the methodologies that can be used to identify green occupations under the task approach and introduces one based on text analysis. Specifically, the proposed methodology constructs a dictionary of green terms and applies text analysis to an occupational database containing task statements with the objective of measuring the green task intensity of occupations. This section also discusses the strengths and weaknesses of the methodology vis-à-vis relying on the O*NET Green Economy Program, a well-recognized list of green occupations in the U.S. Finally, section 4 presents analytic applications of the green occupation taxonomy obtained to the case of Indonesia to illustrate the potential of the proposed methodology. More specifically, it describes selected green occupations' characteristics: gender, education, in-demand skills, and wage premium. Figure 1 visually summarizes the organization of this note.

The intended audience of this paper is analysts and policy makers working on skills policy around the world. The proposed definition and measurement methodology can be deployed in any country that relies on ISCO-08 or any other country that uses an occupational classification system containing task statements. We are making available to the community of analysts a toolkit that includes: (i) a readme file; (ii) the dictionary of green terms, which can be easily modified to reflect the country's green economic development; (iii) the STATA do files that classify green tasks and create the green task intensity index for each occupation – with minor modifications, this do-file can be applied to any occupational database with task statements; and (iv) an excel file with the classification of all task statements in ISCO-08 into green, green potential, and non-green, as well as all 4-digit occupations with their respective green task intensity index. The recommendation to analysts is to directly use the excel with the classification if working with a database of occupations that uses ISCO-08 at the 4-digit level. However, if using an occupational database with task statements at a more disaggregated level, running the toolkit to get a more accurate description of the occupation is preferred.

3
Table 8c: Industry-level competition analysis in manufacturing sector - ERP tariffs

||Baseline estimation|Excluding indu<br>weights|stry<br>Excluding initial level of<br>dependent variable<br>Includ<br>empl.|ing initial female<br>share for industry|Including initial female<br>empl. share for industry<br>and its change|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||||A. Change in log total employment|||
|Decline in industry tariff|-0.063|0.041|-0.085|-0.043|0.092|
||(0.068)|(0.189)|(0.056)|(0.078)|(0.054)|
||||B. Change in log female employment|||
|Decline in industry tariff|-0.257|-0.021|-0.366|-0.253|0.139|
||(0.226)|(0.185)|(0.216)|(0.229)|(0.173)|
||||C. Change in female employment share|||
|Decline in industry tariff|0.254|-0.107|0.213|n.a.|n.a.|
||(0.290)|(0.111)|(0.299)|||
||||D. Change in male segmentation|||
|Decline in industry tariff|-0.050|0.182*|-0.193|-0.053|-0.016|
||(0.130)|(0.106)|(0.145)|(0.127)|(0.075)|
||||E. Change in female segmentation|||
|Decline in industry tariff|0.116|0.207*|0.069|0.094|0.006|
||(0.088)|(0.110)|(0.088)|(0.084)|(0.058)|
||||F. Change in combined segmentation|||
|Decline in industry tariff|0.016|0.181*|-0.109|-0.013|-0.016|
||(0.077)|(0.103)|(0.090)|(0.081)|(0.092)|

Notes: See Table 8a.
differences represent the parameters to be estimated in our empirical work at any given point in time. Indeed, there are very few papers that take into account the _duration in program implementation_ as an important parameter of interest, with the exception being Behrman, Cheng and Todd (2004), Armecin et al (2005), and Gertler (2004). Based on household survey data, these papers suggest that the marginal impact of increased program exposure is positive _at a given point in time_ . Obviously, a simple comparison among communities with different lengths of exposure is likely to produce biased estimates to the extent that the phase-in is not made at random. In order to account for this potential selection into different durations, we adopt an empirical strategy that was first proposed by Behrman, Cheng and Todd (2004) that only require the use of program participants. The attractiveness of applying these methods to the monitoring data is that the differential effect of exposure can be estimated _at different points in time_ . As time progresses, the pattern of the differential effect can provide suggestive evidence on how the returns evolve over time.

Why should duration matter in the context of this type of program, or more generally in the context of social programs? Duration might simply reflect _composition effects_ : as in any other program that is gradually phased-in, it takes time for the program to fully cover the target population. At the program inception in the community only a subset of the target population will have benefited from it. Over time, a larger fraction of life for these children is naturally going to be exposed to the program. Assuming a positive impact of the program, communities that are exposed for longer duration should therefore have on average better outcomes relative to areas where children have exposed for less. Under this assumption, the returns to exposure should be positive and decreasing over time. At the limit, when both sets of communities have their children fully exposed to the program, the differential effect should be converging to zero.

A complementary, though not exclusive, explanation depends on the nature of the intervention. Communitybased nutrition programs target at children early in life (generally from zero to three years of age), and promote behavioral change in the context of existing resources, acting through improvement in the knowledge of child care and feeding practices<sup>4</sup> . In this context, duration might matter per se because of _learning effects_ . These effects may

> 4 Mothers are taught the importance of exclusive breastfeeding (at least for infants), the timing and composition of complementary feeding, and hygiene and nutrition practices, especially when the child is ill. The basic package of growth monitoring and promotion is usually accompanied by nutrition IEC and nutrition education, micro-nutrient supplementation to

4
Thirdly, Figure 9 shows that both treatments had similar effects on improving respondents’ willingness to get the COVID-19 vaccines regardless of their degree of involvement in household decision making. For example, among respondents who are involved in decision making, there was an almost 10 percentage point impact on people’s willingness to get vaccinated, while among respondents not involved in household decision making, there was a slightly larger than 10 percentage point impact on people’s willingness to get vaccinated. As such, the results from the online survey show that not only is involvement in decision making uncorrelated with willingness to get a vaccine, but there is also an absence of a notable difference in elasticity of preferences based on this dimension. In fact, there are only minor differences between the overall share of respondents willing to get vaccinated across this dimension.

# **4.2.4 Second round impact from treatments**

We illustrate that there were potentially “second round” effects from accurate information about the benefits of COVID-19 vaccines as treated respondents were more likely to encourage other household members to get vaccinated. As this is a “downstream” measure of the impact of information we combine treatments to maximize statistical power. We show that the treatments had a non-trivial impact on the likelihood respondents would encourage a household member to get vaccinated.

The overall effect was driven entirely by respondents who were in households where other members were unwilling to get vaccinated (see Figure 10a). Specifically, the share of respondents in the treatment groups that would encourage others to get vaccinated in their households was around 63 percent, while it was 55 percent in the control group (representing a treatment effect of 8 percentage points). The descriptive results of the online survey illustrate that respondents encouraging others in their household to get vaccinated would likely have additional benefits by creating a pro-vaccination social norm in the household. Among the unvaccinated in the control group, around one-third reported that a family member was the most likely person who could change their mind about getting the COVID-19 vaccine. As 27