rafmacalaba/datause-encoder-data huggingface dataset:
# **2.1 Impacts of Climate Change on Labor**

## **2.1.1 Labor Availability**

Climate change is already leading to increased levels of rural to urban migration through exposure to heat shocks.<sup>2</sup> Baez et al. (2017) document that heat shocks increase levels of migration to urban centers among unskilled youth in Central America and the Caribbean. Thiede, Clark Gray, and Mueller (2016) find similar results in eight South American countries: higher exposure to temperature shocks leads to higher migration, mostly driven by migration to urban areas. Their results are in line with evidence from Mueller, C. Gray, and Kosec (2014) in Pakistan. All three studies find that temperature shocks are more likely to drive rural to urban migration than exposure to natural disasters or rainfall shocks.

Migration as a result of temperature shocks points to potential sectoral reallocation. R. Cai et al. (2016) document that the temperature-migration link only holds for out-migration from countries that are the most dependent on agriculture. While not specific to migration, Colmer (2021) shows that the capacity of the manufacturing sector to absorb agricultural workers critically determines the severity of economic losses from temperature shocks in India. Albert, Bustos, and Ponticelli (2021) document a similar phenomenon in Brazil. Such links point to potential complementarities between policies for climate migration and policies for structural transformation. However, Cattaneo and Peri (2016) find that the relationship between climate and migration only holds in middle-income countries: in poor countries, they find that temperature shocks reduce the likelihood of migration. Their work highlights the critical role market frictions may play in limiting the adaptive potential of migration.

In theory, firms can adapt to climate-driven changes in labor availability by adjusting firm locations. For instance, Jin et al. (2021) document higher establishment closures in U.S. counties exposed to abnormally high temperatures due to lower local demand. It is not clear

> 2Boustan et al. (2020) detect increases in out-migration from U.S. counties affected by severe natural disasters, but we did not find empirical evidence on effects on migration from natural disasters in LMICs.

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the role of soft skills, and section 7 studies the role of competition. Finally, section 8 concludes.

# **2 Malaysia’s Labor Market**

Malaysia is a multi-ethnic country where Malays represent 57% of the population, Chinese represent 23%, non-Malay Bumiputera (which means ”sons of the soil”) represent 12%, Indians 7%, and other ethnic groups the remaining 1%.<sup>2</sup> This paper focuses on three of these ethnicities: Malays, Chinese, and Indians. Based on the 2020 census, 63.5% of Malaysians are Muslim, 18.7% are Buddhist, 9.1% are Christian, and 6.1% are Hindu, with the majority of Malays being Muslim.

Malaysia’s federal constitution expresses that there shall not be employment discrimination on the basis of race, gender, religion, descent, among others. Despite this constitutional decree, research has previously documented evidence of ethnic-based discrimination (Lee and Khalid (2016); Guan (2005)). Malays are also more likely to be employed in the public sector, as well as in governmentlinked corporations, while individuals of Chinese and Indian descent are more likely to be employed in the private sector (Lee, 2012). In 2022, Malays made up 77.5% of the public sector, while nonMalay Bumiputera, Chinese, and Indians made up 12.1%, 5.7%, and 3.8% respectively(Lee, 2023). In this way, the labor market in Malaysia seems to be segmented in two: a Malay-dominated public sector and a private sector that tends to be dominated by individuals of Chinese-ethnicity(Lee and Khalid, 2016).

Labor statistics also reveal a wide gender gap in employment . According to Malaysia’s 2022 Labour Force Survey (LFS, 2022), 54 percent of women are employed, compared to 79 percent for men. This gap persists across all ethnicities. The main reason given for women not participating in the labor force is housework or family responsibilities, cited by 62.9 percent of women who are out of the labor force (LFS, 2022). Qualitative evidence by Schmillen et al. (2019) suggests that this is due to prevalent social norms that consider most housework, including providing care at home, the responsibility of women. Hence, the lower participation rate in itself does not necessarily reflect discrimination or the relative inability of women to find a job. However, the same study provides evidence of wage discrimination against women, with women earning less compared to men. The

> 2The term ”Bumiputera” typically includes Malays. For the purpose of clarity, this paper differentiates between Malays and non-Malay Bumiputera, with the latter being excluded from the correspondence study.

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The implementation of the plan has entailed developing a systematic institutional structure as well as the design of a research-friendly environment. One element of this structure is ICR (Intelligent Cosmos Research Co. Ltd.), an organization specifically designed to support strategic R&D efforts and to facilitate industrial applications of R&D. Fourteen R&D corporations based on academic seeds have been established through joint investments by the national government<sup>5</sup> and private companies. ICR has worked to initiate 12 research projects under the Regional Consortium R&D System of MITI. Another element is the Academic Society, which seeks to establish creative academic activities in a comprehensive manner, and to serve as a guide for the future development of the region (the number of universities and members affiliated were 70 and 890 in 1995). In parallel with implementation efforts inside the region, the TIC group worked to persuade the central government to focus its policy on the region, eventually leading to support and encouragement for regional initiatives, since carrying their plan into effect would require both radical policy changes at the national level and regional institutional innovation. They won government approval to make a comprehensive survey of measures to promote the TIC plan, and survey committees made up of members of the academic, industrial and governmental communities were formed by eight ministries and agencies (NLA, ME, STA, MAFF, MITI, MC, MPT, and MT<sup>6</sup> ) respectively. It was unusual for

> 5 Three investors of quasi-governmental special corporation were Japan Key Technology Center, Bio-oriented Technology Research Advancement Institution and Drug Fund Advance Reaction-Relif and Research Promotion.

> 6 Abbreviations: name of ministries and agencies.

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productivity equation, the coefficient of the training variable increased from 0.10 to 0.94. The coefficient of the external training variable increased from 0.14 to 0.23."1 One possible explanation for the negative correlation is that firms are more likely to train when demand (and the opportunity cost of labor's time) is low.'<sup>2</sup> We are not convinced by this explanation but are unable to offer an alternative. We note, however, that several other studies have reported similar findings with endogenized training for a variety of countries at different stages of development, e.g. Dearden, Reed, and Van Reneen (2000) on British industry, Tan and Batra (1995) on manufacturing in five middle-income countries in East Asia and Latin America, and Nielsen and Rosholm (2002) on three African countries.

**Table 10: Coefficients for Training**

|Wag|e equationcoe|fficients|Productivity equation|coefficients|
|---|---|---|---|---|
||Exogenous<br>Training|Endogenous<br>Tramning|Exogenous<br>Training|Endogenous<br>Training|
|Traming|0.13|0.78|Training<br>0.10|0.94|
|External|||External||
|trainng|0.26|0.79|training<br>0.15|0.45|
|_Source_Aut|hor'scalculation|s basedonEN|TRAM-ENESTYC.||

# **iii. Potential Experience**

The model also includes workers' potential experience, which is defined as _age-S-_ 6, where S represents the number of years workers have accumulated in that firm. Both level and squared forms of potential experience were included in the model, which allowed for non-linearities. The results of the analysis were inline with the expected positive effects of potential experience on wages in 1993 and 1999 (see Table 11). The positive effect of experience on wages increases at a younger age, and it continues at an older age but at a decreasing rate. In 1999, potential experience has a positive effect on wages and productivity in all firm sizes. However, the hypothesis test indicates that workers benefit more than firms from potential experience.

> " These results are available upon request

> 12 Dearden, Reed and Van Reneen (2000) make this argument to explain the triplmg of productivity and

> wage imnpacts of training when trainng is endogemzed.

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for the EU-15 from being a net exporter to being a net importer of rapeseed oil and a substantial expansion of EU-15 rapeseed production. All world vegetable oil prices increase considerably. The expansion of bio-ethanol induces a dramatic reduction of EU soft wheat exports. The analysis also accounts for set-a-side land coming back into agricultural production and for the impact on the livestock sector. Gohin’s land supply is similar to that of Keeney and Hertel (2009), with land being allocated in two steps, first between forest, pasture, and crops, and then among crops. Gohin does not explicitly mention the values of his CET parameters.

<!-- Start of picture text --> Lwheat Lcoarse grains Loilseeds<br>σCET 3<br>L L L<br>pasture other field crops grains & oilseeds & protein<br>σCET 2<br>Lmisc Lrice Lfield crops &pasture<br>σ3 ≥ σ2 ≥ σ1<br>σCET 1<br>L _<br><!-- End of picture text --> Figure 5. Land allocation in Huang et al. (2004) Bouët et al. use the MIRAGE model of the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) to analyze the same policy and use a land decomposition also based on AEZ data. They show that EU mandates would increase EU ethanol production by 373 percent and EU sugar beet production by more than 15 percent by 2025. EU biodiesel production would increase under a mandate by 34 percent in 2025. To reach EU consumption mandated targets, the full removal of tariffs on EU imports of ethanol and biodiesel would have to be complemented by a domestic consumption subsidy. Only the trade liberalization scenario stimulates foreign production of sugar cane with Brazilian production increasing by 7.6 percent in 2025. These EU biofuel policies imply a significant reallocation of land in favor of sugar cane/sugar beet and oilseeds for biodiesel sectors, in Europe and/or in South America. EU land allocated to sugar beet would be increased by 11 percent under an EU mandate.

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that have been launched, the early lessons from this experience, and the impact evaluation study we propose to undertake in the future. This study is being undertaken by a collaboration of researchers from three institutions: Pratham Mumbai Education Initiative (Pratham), the Poverty Action Lab at the Massachusetts Institute of Technology, and the World Bank.

Section 2 of the paper briefly describes the survey that was undertaken. Section 3 reports the survey findings on what people know, what they expect, and how they participate in local service delivery. Section 4 describes interventions that are currently underway to provide information about local agencies in education services, and advocate public action to improve learning achievement of children. Section 5 outlines future plans for evaluating how these interventions impact local participation and children’s learning achievement. Section 6 concludes.

# 2. **The survey**

The state of UP, where this study is being undertaken, is the most populous state in India, with 166 million persons, and according to the latest Census (2001), is one of the five worst performing states in terms of basic literacy. A survey of parents, teachers, village education committees, and learning levels of children was undertaken in the rural district of Jaunpur during March-April 2005.<sup>4</sup> Jaunpur served as an appropriate location for the intervention and accompanying research because: 1) its reading level and other learning indicators were close to the state average and 2) it was relatively untouched by other Pratham programs. The first criterion is important for external validity: if the intervention is shown to be successful here, it will likely be successful in other average districts in UP. The second criterion is important for internal validity: launching the program in a district with no Pratham presence allows us to randomly assign a pure control group of villages from which we can compare and obtain a true measure of the impact of our intervention.

The sample was selected as follows:

> 4 The survey was designed by research collaborators from the three institutions mentioned here, funded by the World Bank, implemented by Modus Analysis and Information Pvt Ltd (Mode), and monitored on the ground by Pratham.

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# **Acronyms**

BPO Bangladesh Post Office CCT Conditional Cash Transfer CTR Cost-to-transfer Ratio DiD Difference-in-Differences EGPP Employment Generation Project for the Poorest FSSP Female School Stipend Program GoB Government of Bangladesh HIES Household Income and Expenditure Survey LGSP Local Government Support Project MDG Millennium Development Goals MIS Management Information System NCC Narayanganj City Corporation NNP National Nutrition Program PMT Proxy-Means Test POS Point-of-Sale RD Regression Discontinuity UP Union Parishad 2
INThCOLCT1ON LXXvI

|Infant mortatity,<br>198-90|**Ago-sox**<br>structure,<br>1985|Mostrecent<br>contraceptive<br>prevalence|Country,<br>aconmy, or<br>territory|
|---|---|---|---|
|UNPVSR<br>10/89 (offIeial<br>est.)|U.N. 1988 revision|U. . 1988|Norway|
|U.N. 1990 revision<br>|U.N. 1988 revision<br>||_Owmn_|
|U.N. 1990 revision|Bankest.|CPS 1984485|Pakistan|
|U.M. 1990 revision|U.N.1988 revision|RossSte.l1988|Pan<br>am|
|ESCUAStat.<br>Yearbookfor Asia<br>|U.N. 1988 revision|Pop,Ref.Bureau 1990|Papua New Guinea|
|nd the Paeffic 1968||||
|U.N. 1990 revisIon|U.N. 1988 revision|Ross at at. 1988|Paraguay|
|U.N. 1990 ravlalon|U.N. 1988 revision|OHS 1986|Peru|
|U.N. 1990 revision|U.N. 1988 revision|CPS1986|PhilIppines|
||||Pltcalrn<br>Island|
|UNPVSi 10/88 (official<br>est.)|U.N. 1988 revision|U. . 1988|Poland|
|Eurostat<br>1989|Eurostat<br>1989|U.N. 1988|Portugal|
|UWPVSI 10/89|U.N. 1988 revision|Aess St at. 198|Puerto<br>Rico|
|U.N. 1990 revision|U.N. 1988 revision|-|Octcr|
|U.N. 1990 revisIon|U.N. 1988 revision||Reunion|
|Based on officiat<br>est.|U.N. 1988 revision|U.N. 1988|Romwnia|
|U.N. 1990 revision|U.N. 1988 revision|Ross et aL. 1988|Rwanda|
||||San Marino|
|8Snkest.|Bankest.||Sa TomTand|
||||Principe|
|U.N. 1990 revision|U.N.1988revision||SaudiArabia|
|Bnk est.|Official<br>est.|DHS1986|SenegcL|
|UNPVSR<br>7/1988|Based on UNDY 1984||Seychelles|
|U.N. 1990 ravislon|Bank<br>.st|Ross etcL.1988|SIerra Leon|
|UWPVSR 1/89 (officlat<br>est.)|U.N. 1988revision|Ross at at. 1988|Singapore|
|Bank**est.**|Bank est.|Pop. Ref. Bureau 1988|SoLomon<br>Islands|
|U.N. 1990 revision|Bank est.|RossStaL.1988|Somatia|
|U.N. 1990 revision|U.N. 19U8 revision|U.N. 1988|South<br>Africa|
|Based on Eurostat 1989|Eurostat1989|U.N. 1988|Spain|
|Based on DHS 1987|UN. 1988 revision|OHS1987|Sri**Lanka**|
||||St. Helena|
|**Based**on official<br>MR, StatistIc|s<br>Based or 1980 census|U.N. 1988|St. KItts and|
|Office,<br>Plaaning<br>Unit|||Nevis|
|<br> <br>Based on officiel<br>eat.|Based on 1980 census|U.N.198|St. Lucia|
||||St. Pierre<br>and<br>|
||||Niquelon|
|UNPVSR 10/89|Based on UNDY 1983|U.N. 1988|St. Vincent<br>and<br>|
|U.N. 1990 revision|U.N. 1988 revision|WFS 1979|the Granadines<br>Sudan|
|**_UR_**|0.008**<br>(8.10)|0.002**<br>(5.42)|0.003**<br>(7.83)|0.005**<br>(5.14)|0.007*<br>(1.99)|0.008**<br>(4.58)|0.002**<br>(5.45)|0.009*<br>(4.14)|
|---|---|---|---|---|---|---|---|---|
|**_CRE_**|0.003*<br>(2.45)|0.007**<br>(3.04)|0.008**<br>(3.49)|0.008**<br>(3.14)|0.008**<br>(2.14)|0.002*<br>(2.21)|0.006**<br>(3.35)|0.003*<br>(2.79)|
|**_SINFRA_**||||||0.20**<br>(3.13)|-|-|
|**_PDEN_**|-0.0003*<br>(-2.11)||-0.0003<br>(-1.71)||-0.0003<br>(-0.70)|-|-|-|
|**_DUM_**||||||0.08*<br>(2.04)|0.09*<br>(2.26)|0.11*<br>(2.19)|
|AR (1) (p-<br>value)<br>AR (2) (P-<br>value)<br>Sargan test<br>(P-value)<br>No.<br>Instruments<br>Hansen test<br>of<br>Exogeniety|(0.01)<br>(0.12)<br>(0.23)<br>26<br>0.26|(0.03)<br>(0.18)<br>(0.26)<br>27<br>0.19|(0.04)<br>(0.46)<br>(0.42)<br>34<br>0.12|(0.05)<br>(0.11)<br>(0.09)<br>37<br>0.45|(0.00)<br>(0.09)<br>(0.33)<br>33<br>0.22|(0.00)<br>(0.20)<br>(0.52)<br>29<br>0.08|(0.00)<br>(0.12)<br>(0.09)<br>41<br>0.13|(0.00)<br>(0.11)<br>(0.22)<br>32<br>0.23|

_Notes: ** and * denote significance at 1 and 5 % levels. Figures in the brackets are t-statistics._ The results also confirm that investment, represented by gross-fixed capital formation, has a positive impact – positive and significant coefficients - on infrastructure levels (see Table 5). All else equal, a 1% increase in investment rate (as a ratio of GDP) causes an increase in the physical infrastructure index score by 0.002 to 0.007%. Higher investment rates increase the supply of infrastructure facilities, directly and indirectly. The latter occurs through its multiplier effect on the economy, which leads to increased demand for infrastructure. The results align with previous studies such as Cerra et al _._ 2017. Existing studies, such as Nayyar (2008) and Mallick ( 2013), also suggest that the differences in investment levels<sup>14</sup> across states are responsible for divergence in their income per capita.

We also investigate the role of fiscal variables (capital expenditure and internal debt) in building the stock of physical infrastructure. The coefficient of capital expenditure<sup>15</sup> is positive and significant, indicating that an increase in capital expenditure by State governments leads to physical infrastructure development. The role of public capital expenditure in providing infrastructure, especially in developing countries, has also been highlighted in previous studies such as Castells and Solé-Ollé (2005), Zheng et al. (2013), and Cerra et al. (2017). As expected, internal debt significantly negatively impacts physical infrastructure development. A rise in internal debt increases the debt, leaving fewer resources for infrastructure development. Further, higher debt by states pushes the cost of borrowing by

> 14 Factors such as human capital, fiscal and economic factors, state-specific incentives and strategies and labor productivity are found to be the major determinants of investment across states (Mallick, 2013b). 15 We consider capital expenditure alternatively to investment as the former is part of investment.

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in all countries in which the multinational might operate. This is so in terms of both the intensive margin (the level of investment in host country _h_ , conditional on some investment taking place there) and, given the assumed absence—for now—of locational fixed costs, for the extensive margin (whether to invest in _h_ at all).<sup>17</sup> The critical difference between the _I_ - _METR_ and the familiar _KF_ - _METR_ is that the former allows for interactions in production and revenues across the affiliates within a multinational group:<sup>18</sup> if there are none, the _I_ - _METR_ reduces to the standard _KF_ - _METR_ .<sup>19</sup> More precisely, the structure of _I_ - _METRh_ shows that taxation affects the multinational’s investment in _h_ through two channels. The first is through the term ∆ _h_ ( _δ,_ **_T_** ), which we refer to as the ‘(statutory) rate differential’. This, in turn, serves as a sufficient statistic for the impact of statutory tax rates, in all jurisdictions, on cross-border investment decisions. And, beneath the somewhat heavy notation, it has a straightforward structure.

In the numerator of ∆ _h_ is the excess of a weighted average<sup>20</sup> of the statutory tax rates in countries _j_ other than the host _h_ over that in the host itself, with the weight _δhj_ attached to the statutory tax rate in _j_ being the proportion of the additional revenue to the rest of the multinational group that is generated by increased investment in _h_ which accrues to the affiliate in _j_ . It is this rate differential term that captures the implicit profit shifting inherent in the cross-country allocation of investment.

Through ∆ _h_ , an increase in the tax rate abroad, in some _j_ = _h_ , tends<sup>21</sup> to increase investment in the host country _h_ if, and only if, _∂Rj/∂Fh <_ 0, so that the cross effect is negative: intuitively, an increase in _Tj_ then makes it more attractive to invest in _h_ because doing so reduces the amount of profit in _j_ that has now become more heavily taxed. Comple-

> 17How the latter part of this conclusion is affected by the presence of locational fixed costs is taken up in Section 5.

> 18A further difference, of course, is that while the _KF_ - _METR_ depends only on tax parameters the _I_ - _METR_ also depends on the structure of the multinational’s operations.

> 19Because then _αh_ = 0.

> 20 We use this term for brevity, though it should be noted, however, that since it may that _∂Rj/∂Fh >_ 0 for some _j_ = _h_ (even though, by assumption, this cannot be so in the aggregate), the weights may not all lie between zero and one.

> 21In the informal comparative statics that follow we simplify by treating the various derivatives of the affiliates’ revenues as constants.

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