rafmacalaba/datause-encoder-data huggingface dataset:
25 wage and employment patterns in Malaysia. It is important to note that earnings analysis typically uses labor force survey data, which consists of data on the sources and levels of income for a particular worker as well as his/her individual characteristics. Unfortunately, only household-level data is available for this analysis on Malaysia; this distinction does not diminish the validity of the results, however.

In order to analyze the determinants of income, it is necessary to establish at the outset the appropriate measures of income. The analysis considers two dependent variables, the logs of (i) total labor income and (ii) net household income. The reasons are two-fold. In order to determine the effect of immigration and labor market policy on employment and wages, I use total labor income, which is equal to earnings from wages and other employment. This allows me to detect pay practices that discriminate against non-Malaysians, for example, as well as wage discrimination (positive or negative) against ethnic groups among Malaysian citizens. On the other hand, I do not wish to exclude other types of income that may also be affected by labor management policies. I therefore use household income including all sources, thus creating a more complete picture of various economic activities and the associated remuneration across population groups. After-tax income, i.e. net household income, represents a more appropriate measure of disposable income and at the same time avoids biasing the results with respect to non-citizens, who generally have a smaller tax burden. Two separate regressions are run, one for each dependent variable.

What factors affect household income? Earnings vary by occupation, experience, skill level and employment status (i.e. employee, employer or self-employed). The independent variables used in this regression analysis are consistent with the vast literature on earnings functions. They include: age and age<sup>2</sup> , years of schooling, region of employment, status of employment, household size and category of principle occupation. Because age and work experience are correlated, I do not include work experience as an independent variable; moreover, the age<sup>2</sup> variable is used to capture the shape of the age-earnings profile. The survey data does not directly quantify skill level; as a result, I rely on the measure of years of schooling as a proxy for skill level.<sup>13</sup> j use additional individual characteristics on gender and citizenship to capture discrimination effects. The results from the two regressions are shown in Table 4 below. All of the variables are significant, and the adjusted R<sup>2</sup> terms are fairly high at 0.36 and 0.38 for dependent variables labor and net household income, respectively, suggesting that these 9 independent

> ]3 Despite common practice, Gupta (1998) points out that years of schooling is unlikely to reflect perfectly individuals' skill level or productivity.
were used as part of the stratification of the survey.

Besides, for each data set and indicator, we set 100 observations as the minimum required to produce estimates for subgroups following common practice (e.g. Duerto Valero 2019). Hence, for a given data set, disaggregation may be possible for some indicators and not others, especially when some indicators require focusing of subsamples: for instance, for employment, for men and women separately, we were able to disaggregate the employment population ratio across both disability and sex, while this was not feasible for the unemployment rate, i.e. the share of the unemployed out of the labor force, the idle rate for youths, and the share of workers in manufacturing or managerial positions as the sample sizes for disaggregated samples were often fewer than 100 observations. Results of further disaggregation based on disability and other characteristics in Appendices A to C are available from the authors.

11
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fixed effects models. Based on our preferred estimator in column (3), firm material costs slightly decline due to participation in the carbon trading. However, this effect is not significant at any reasonable level of confidence. Column (4) shows that a one percent increase in the usage of allowances in phase 1 is associated with a 0.7 percent increase in the material costs. As explained earlier, because allowances and other material inputs are both likely to be correlated with unobserved exogenous shocks, we do not interpret the result as a causal effect.

Table 8 Results on Unit Material Cost for Cement

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Phase 1 Participation|-0.0997***|-0.00232|-0.0174|-0.0648|
||(-5.52)|(-0.21)|(-1.18)|(-1.14)|
|Phase 2 Participation|-0.0853***|0.00533|-0.015|-0.123|
||(-4.65)|-0.44|(-0.79)|(-1.61)|
|Phase 1×Log(Surrendered)||||0.00665*<br>(1.76)|
|Phase 2×Log(Surrendered)||||0.0272<br>(1.03)|
|Phase 1×Log(Allocated)||||-0.00285<br>(-0.77)|
|Phase 2×Log(Allocated)||||-0.0183<br>(-0.65)|
|Plant Fixed Effects|No|Yes|Yes|Yes|
|Country-by-Year Fixed Effects|No|No|Yes|Yes|
|Observations|1080|1080|1080|1080|
|Adjusted_R_<sup>_2_</sup>|0.068|-0.001|0.085|0.091|

Note: Dependent variable is the unit material cost, defined as the ratio of total material cost to operating revenue. Standard errors are clustered at the firm level and t statistics are shown in the respective parentheses. *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

Table 9 summarizes regression results on the determinants of firm turnover. The estimated parameters of the OLS and firm fixed effects models in columns (1) and (2) suggest that participating in EU ETS is associated with a large and statistically significant (in the case of fixed-effects model) increase in turnover. We suspect this seemingly counterintuitive result could be explained by country-specific shocks contemporaneous with the implementation of EU ETS. For example, some countries may have facilitated state aid to balance the impact of the carbon regulation. Indeed, once we control for country-year fixed effects as in our preferred specification in column (3), the correlation between program participation and firm turnover 18
Notes: bootstrapped standard errors in parentheses (200 replications); all estimates are statistically significant at 1% significance level.

It was this switch from the widening to the contracting contribution of sectoral (public/private) segregation that contributed to the shrinking of the gender wage gap in Georgia and in some years played the primary role in that process. For example, between 2004 and 2007 the gap within the public sector widened and its contribution to the gap increased from 29 to 43 log points (Figure 5) (intra component). The overall gap nevertheless contracted during this period because, on the one hand, the sectoral segregation turned in women’s favor as public sector wages increased (inter-component dropped from 13 log points to 0) and, importantly, the gap within the private sector also shrank (intra private in Figure 5 decreased from 22 to 20 log points) The latter finding highlights the growing role of the private sector in contributing to the reduction in the gender wage gap in Georgia.

Hence, the gender wage gap in Georgia exists primarily because the gaps are considerable within both public and private sectors rather than due to the proportionately greater presence of women in the public sector (sectoral segregation). To what extent can these findings be explained by the differences in the composition of the female and male workforce? The BMZ decomposition reveals that 42 percent of the gender inequality within industries (or the intrasector component) is due to explanatory factors, such as marital status, educational attainment, and industry of employment (Figure 5). This finding implies that women’s joint characteristics within each sector place them at a disadvantage relative to men. However, the unexplained portion is even greater: 58 percent of the within sector inequality is due to unobserved factors, such as discriminatory practices in wage setting and promotion. In terms of sectoral segregation (or the inter-sector component), the composition effect is small but negative, suggesting that, given gender differences in the characteristics of workers, more women than men should be employed in these sectors. However, due to unobserved factors (potentially including discriminatory employment practices), this is not the case. Hence, even though the sectoral segregation dynamics appear to increasingly favor women, women nevertheless face unobserved barriers, both in terms of wages and the selection into sectors.

Figure 5. Decomposition of inter- and intra-industry components into the composition and structure effects.

16
4 economic perspective their return after a short stay may represent a costlier option than continuing to invest in their successful integration. Countries with a flexible labor market, strong investment climate, and a welcoming attitude to immigrants tend to see the economic benefits of refugee inflows materialize faster.

The paper also develops the following messages:

- The economic and demographic situation across advanced countries<sup>6</sup> varies, as do the number and attributes of asylum seekers who reach their shores. While some generalizations are possible, there is no “one size fits all” return policy, just as there is no single optimal immigration policy. However, all nations are defined by who lives within them, their laws and control of borders. So, there is a strong legal case for rejecting asylum applications that are clearly specious. This is necessary not only for retaining control of immigration policy but also to preserve the integrity and political viability of the asylum system.

- In many advanced countries today the purely economic case for returning migrants, as distinct from the legal case – whether they are asylum seekers, failed asylum seekers, refugees, or irregular economic migrants – is weak. This conclusion is drawn from a careful examination of the macroeconomic, fiscal and labor market impacts on the advanced host country of the inflow of immigrants. The bottom line is that the aggregate economic effects of migration, including those of refugees, in most instances are beneficial.

- More specifically, the overall fiscal impact of refugee flows is likely to be small. It is negative at first due to the high cost of relocating refugees and hosting them until they find jobs and learn the language. Since most refugees are young, studies suggest that their fiscal impact becomes positive on a cash‐flow basis a few years after arrival and positive on a present value basis a few years after arrival. Because asylum seekers are often left in limbo for protracted periods, and the permission to work is delayed or denied, the fiscal cost of hosting them is higher and their contribution to the host economy is smaller than it could be.

- Most refugees are low‐skilled. The benefit of refugee arrival on their employers, on firms generally, on the price of many non‐tradable products and services, and on the vast majority of native workers who do not compete directly with them and who are complementary to them in production, are likely to be felt at about the pace that refugees are allowed to and find work. Immigration generally, including of refugees, tends to be supportive of long‐term economic growth. The dampening effect on the wages of low‐skilled native workers of refugee arrival is likely to be small. Some crowding out of public services may occur in refugees concentrated localities. However, this is less likely in contexts where the population is declining or has slowed down markedly.

6 These are the “high‐income countries” as identified by the World Bank using an income criterion.
However, one can also observe systematic differences between females and males based on the unpaid domestic responsibilities undertaken in the week prior to the survey interviews. Consistent with the gender norms on household caretaking that are prevalent in Indonesia (Setyonaluri et al., 2021), we find that the burden of domestic work responsibilities falls disproportionately on women; nearly _all_ of the females in the sample (96 percent) are engaged in household caretaking during the past week prior to the survey, while only six out of 10 male respondents engage in household caretaking.

One can also observe significant gender differences in terms of employment outcomes and/or employment characteristics. Females are far less likely to be employed in Indonesia; only 54 percent of female respondents are employed compared with 81 percent of male respondents. Similarly, throughout the analysis period, females are also 26 percent less likely than their male counterparts to participate in the labor force. Among females who do participate in the labor force, they face the same probability of getting a job as their male peers, which is observed by the relatively similar unemployment and/or temporary unemployment<sup>18</sup> rates. More than half (54 percent) of the female workers in the sample work in the services sector. However, nearly 30 percent of female workers work as unpaid family workers, compared with only 7 percent among male workers. They are also somewhat less likely to be formally employed<sup>19</sup> (by 8 percentage points relative to men), and they tend to be paid less (both on a monthly and an hourly basis) than their male peers. This evidence points to a significantly lower quality of employment among females in the Indonesian labor market.

> 18 We define temporary unemployment as respondents who are usually working but have not been working in the past week.

> 19 We follow Statistics Indonesia’s simplified formality definition in defining formal employment; a worker is designated as formal worker if he/she works as an employee or if he/she is an employer that is assisted by permanent workers.

18
duplicative workers are less likely to be retained at the consolidated firm.

# **4.2.1 Over-placed Workers**

As Panel (b) of Figure 5 shows, hours reductions appear to be increasing in the wage rate. A potential channel through which labor restructuring might materialize is disrupting implicit contractual relationships with employees who earn substantial firm-specific rents. To investigate this channel, we leverage estimates of firm and worker effects (Abowd, Kramarz and Margolis, 1999; Card, Heining and Kline, 2013) using a similar method to Schmieder, von Wachter and Heining (2022) and Lagaras (2023).

We identify over-placed workers, workers at a target firm with a higher employer wage premium than is expected for workers’ skills that are valued equally across employers. We estimate the median firm wage premium within each quintile of the worker fixed effects distribution. We then characterize over-placed workers as those at firms with a higher-than-median firm wage premium. These over-placed workers are receiving more generous pay from the target firm than the average worker with the same level of human capital receives. Thus, for a consolidated firm, these workers are likely more expensive than the human capital that they provide to the firm.

Figure 6 shows retention and hours results for over-placed and not over-placed workers. A starkly larger retention effect is found for over-placed workers: over-placed workers experience 13.05% and 11.9% reductions in retention and hours, respectively, at the consolidated firm compared to statistically insignificant 3.9% and 2.8% reductions for not over-placed workers. Differences between over-placed and not over-placed workers are statistically significant at the 95% level. Further, when we examine other outcomes in Appendix Figure E4, we see that these workers experience much larger overall reductions in work at any firm (including both the extensive margin of employment and the intensive margin of hours worked) as well as larger reductions in labor income and total income. Together, these results suggest that over-placed workers experience sustained losses after losing their high firm-specific rents post-takeover. These findings are consistent with consolidating firms restructuring their workforce in order to increase efficiency post-takeover.

19
Shocks contribute to poverty persistence and limit upward mobility (Carter and Barrett, 2006; World Bank, 2013). Realized shocks can have lasting consequences on a broad range of outcomes, including health or education (Alderman et al., 2006; Maccini and Yang, 2009). To cope with income shocks, households at times cut consumption and deplete physical or human capital (Burke et al., 2014; Carter et al., 2007; Janzen and Carter, 2018). In addition, the mere existence of risk (ex-ante) pushes households to adopt costly, low-risk strategies that reduce income variability at the expense of forgoing higher-risk higher-return activities (Elbers et al., 2007; Stoeffler et al., 2020; Zimmerman and Carter, 2003). The large welfare costs of risk and realized shocks reflect imperfections in insurance markets and households’ risk-management mechanisms (Jalan and Ravallion, 1999; World Bank, 2009). Climate change will likely amplify these costs.

The concept of resilience broadly characterizes the capacity to resist and recover from shocks. Barrett and Constas (2014) define “development resilience” as “the capacity over time of a person, household or other aggregate units to avoid poverty in the face of various stressors and in the wake of myriad shocks”. While its merits and novelty with respect to earlier work on “vulnerability” have been debated, the concept of resilience is now at the core of efforts to better articulate the nexus of humanitarian and development programs (FAO, 2016; Béné et al., 2017; Serfilippi and Ramnath, 2018). It has also renewed attention on poverty dynamics and the role of shocks (Béné et al., 2012). At the same time, resilience measurement is challenging and ideally relies on long-term or high-frequency panel data sets (Béné et al., 2017; Cissé and Barrett, 2018; Upton et al., 2016). Recent empirical studies have attempted to construct resilience indicators, assess factors associated with household resilience or understand whether suggested resilience indicators correlate with greater well-being or capacity to cope with shocks (Alfani et al., 2015; d’Errico et al., 2018; Knippenberg et al., 2019; Smith and Frankenberger, 2018). In this paper, we study the impact of cash transfers on households’ ability to mitigate the welfare effects of shocks. Specifically, we measure whether cash transfer beneficiaries hit by a climatic shock are better able to smooth consumption compared to cash transfer non-beneficiaries hit by the same shock. We also consider mechanisms through which households achieve consumption smoothing and become more resilient, including savings, asset accumulation and income smoothing in agriculture and off-farm household enterprises. While we are not capturing resilience in a fully dynamic perspective (which would require longer panels), our approach is consistent with the 3