rafmacalaba/datause-encoder-data huggingface dataset:
# **Conclusions**

This section addresses two key concluding questions. Based on evidence from the literature, what can we conclude about the employment effects of climate policy in developing countries? And what are the most promising directions for future knowledge work to fill the important gaps in what we know?

## **A. What Do We Know about Employment Effects of Climate Policy?**

Tables 3-5 provide an overview of surveyed methods that have been used for the analysis and understanding of the general economic effects, developing country effects, and policy questions that relate to the interplay of labor markets and climate policies.

Unfortunately, none of the various strands of research gives an ideal answer to the question of how climate policy affects employment. Micro-econometric studies provide useful information about effects on regulated industries, but cannot address broader effects (even worse, for studies that use unregulated industries as a “control group,” those broader effects may bias estimates for the regulated industries). Macro-econometric studies attempt to measure aggregate effects, but it is hard to have any confidence in those estimates, because the correlations they study may well change in the presence of new policies.

General equilibrium simulation models address these problems. They incorporate direct effects and broader spillovers, and because they are based on more fundamental models of the structure of the economy, they should be much more robust to policy changes. But most of these models assume that the labor market clears perfectly, implying that there are no unfilled jobs or involuntarily unemployed workers. Models that relax this assumption show great promise, especially those that explicitly incorporate search frictions, but these search-friction models are very new to the literature on employment effects of regulation and have not yet been widely deployed.

37
35 73. To concluds, the labour-substituting trends In agriulture, coupled with the frequency of leasing and sharecropping, argue strongly that the land reform has not served to *tbe the rural population to the land, but **has** bon associated with an accelerating process of rural outmigratlon. There Is no Indication that the intensity of this process varies signiflcantly between ejidos and small private farms. The Incidence of outmigratlon Is probably the same for both tenure categorles sico the mean holding size Is the same, the prevalence of minfundla restricting the scope for on-farm employment expansion. On the other hand, while average land quality Is now roughly the same for ejido and small private farm sectors, the proportion of land left out of agricultural use Is somewhat higher In the case of small private farms (according to the 1981 census data).

# VII. ACCESS TO CREDIT

74. Compared to small private farmers, ojldatarios depend more on statelending Institutlons for credit because the agrarian reform law prevents tham from mortgaging their land: since they cannot use their parcels as collateral It Is difficult for them to secure loans from commercial banks. The agricultural development bank, Banrural (in common with previous state Institutions that lent money to the ojidos) waives the collateral requirement and exercises weak sanctions against loan default. This Is one aspect of the paternalistic relatonship between state banks and ejidatarlos, a paternalism that also colours Banrural loans to private farmers.

75. The agricultural development bank, Banrural, accounts for about 45 percent of total lending to agriculture and Is the main source of credit for small farmers In both the ejido and the private sectors: about three-quarters of Its
_4_ _Mexico: The 1997 Pension Reform_ additional retirement income from contributions to a specialized housing fund based on an employer-paid contribution of 5 % of wages for private sector employees administered by the National Workers' Housing Fund Institute _(Instituto del Fondo Nacional de la Vivienda de los Trabajadores -_ INFONAVIT).

Originally, the IMSS's pension program was designed to be a collective fund financed from contributions from workers, employers and the state. The original IMSS-IVCM can be characterized as a partially-funded defined benefit scheme. However, since the very beginning, it operated as a PAYG scheme as the fund's actuarial reserves were used to finance other social insurance activities, particularly health. Until the 1970s there were no actuarial reserves to face pension liabilities. This was possible because a stable macroeconomic situation and a young population generated enough resources to finance current pension liabilities and at the same time finance health requirements.

|Formnal<br>Informnal|19.8<br>13.7|59.1<br>40.9|
|---|---|---|
|TOTAL|33.5|100|
|~NINUNi||111>KYw:i111i<br>1 ,|
|_Public Sector Schemes_|_1.4_|_11.4_|
|ISSSTE, States, and Armed Forces (a)|1.3|10.6|
|PEMEX (b)|0.1|0.8|
|_Total Covered_|_12.3_|_100_|
|_Not Covered_|_21.2_||
|Self-employed or underemployed<br>Unemployed|19.5<br>1.7||

Sources: IMSS, PEMEX, and _Encuesta Nacional de Empleo, 1995._ (a) Institute for Security and Social Services for Government Workers _(Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado)_ (b) The State-owned petroleum company _(Petr6leos Mexicanos)_ To receive _disability_ benefits, a worker had to contribute to the IVCM system for at least 150 weeks. Despite the existence of a formal process for reviewing disability requests, a striking fact is that 30% of current pensioners are receiving disability pensions. To be entitled to receive an _old age_ pension, the insured was required to be 65 years old, be currently working and have contributed a minimum of 500 weeks. The same conditions applied for severance but the minimum age was 60 years. In case of _death,_ the survivor's pension is paid to the widow and/or dependents, or, in case they do not exist, to the contributor's parents. The insured had to contribute at least 150 weeks.

The system contained a guaranteed minimum pension equal to the minimum wage. To address the erosion of the real value of pensions, in 1989 pensions were indexed to changes in the minimum wage. At least 500 weeks of contributions were necessary to qualify for the minimum pension. Pensions are paid according to a formula contained in the old Social Security Law which favors workers at the bottom of the wage scale. The pension base is calculated as a percentage of the average wage in nominal terms for the last 5 years, plus a fraction for each year of contribution in excess of 10.
more skilled laborers<sup>10</sup> . In both countries the share of the informal sector increased (almost 9% in Indonesia and a little less than 0.5% in Mexico).

Below we discuss the results of the calibration and how these results explain the differences in labor market adjustment displayed by the two economies. Tables 3 and 4 present the results.

**Table 3:  Calibration Results for Indonesia Pre and Post‐Crisis**

|||Pre‐<br>Crisis<br>1997<br> <br> <br>|Post<br>Crisis<br>2000|%<br>change|
|---|---|---|---|---|
|**Agriculture**|||||
|Agricultural TFP|A0|0.14|0.18|32.47|
|**Formal sector**|||||
|Formal Sector TFP|A1|1.74|1.55|‐10.81|
|Rate of Arrival of Shocks to the Formal Sector Jobs|λ|1.41|1.15|‐18.30|
|Rate of job destruction of formal jobs|‐|1.02|0.87|‐14.77|
|Rate of Arrival of Formal Sector Job Opportunities|m(θ)|7.83|6.26|‐20.04|
|Duration of a Formal Job|d1|0.98|1.15|17.34|
|Vacancy rate|v|2.8821|2.4702|‐14.29|
|**Informal Sector**|||||
|Rate of Job Destruction in Informal Economy|δ|0.00|0.16|5607.14|
|Rate of Arrival of Informal Sector Job Opportunities|α|0.15|1.15|646.60|
|Duration of an Informal Job|do|354.50|6.26|‐98.23|

**Table 4: Calibration results for Mexico Pre and Post‐Crisis**

|**2000‐II**<br>**2001‐IV**<br>**%**|
|---|

10 In addition, surveys in Indonesia have changes in the questionnaires that make classification of workers in the informal sector not strictly comparable. We are exploring this further to understand how much of this can explain the 19% increase in informal earnings.
2010 floods, individuals who had experienced higher levels of rainfall had lower aspirations than those who had experienced average levels. The negative effect was especially strong among the poor, and among those most vulnerable to weather shocks (Kosec and Mo 2017).

Shocks leading to higher assets can positively influence aspirations, by affecting how much people are able to think ahead. In Mozambique, an agro-input subsidy and a matched savings intervention had a large and significant impact on the planning horizon of households that were not self-sufficient in maize production (Laajaj 2017). Since poor individuals with a longer time horizon tend to accumulate more assets, the betterment of their economic prospects may put them in a virtuous circle in which higher aspirations encourages the individual to take initiatives in the present in order to reach his goals.

# **Achievements**

Data from a four-country study show that average education aspirations evolve nonlinearly during adolescence, differently across countries, suggesting that they may change according the situations faced by adolescents (Favara, Chang, and Sánchez 2018). In India and Vietnam, adolescents downgrade their aspiration to complete university between ages 12 and 15 but eventually raise again their aspirations at age 19. In Peru, which had the highest initial level of aspirations of the four countries, adolescents also reduce their aspirations between ages 12 and 15 but experience an even higher drop between ages 15 and 19. In Ethiopia, adolescents raise their aspirations slightly between ages 12 and 15 but slightly reduce them at age 19. The important changes in average aspiration levels at age 15 may be linked with the completion of secondary education—for example, Indian and Vietnamese adolescents might perceive more difficulties or start to feel higher norms for their future at that age and report aspiring to less education—to readjust their aspirations at age 19, when the possibility of attending university is more tangible.

# **Beliefs about oneself**

A fundamental channel through which circumstances or the social environment influence individuals’ aspirations is through their beliefs about themselves and their abilities. In India, the importance of young women’s self-efficacy for their education and employment (i.e. their confidence about their ability to achieve goals in those fields) is largely mediated through increased aspirations: an increase in young women’s self-efficacy correlates with an increase in the desire to study and an increase in the likelihood that they aspire to be engaged in paid employment outside home as an adult (Roy, Morton, and Bhattacharya 2018). In this case, knowing other successful businesswomen and feeling connected to the broader social network play important and independent roles in facilitating education and 24
first month before birth is significant. Accordingly, we include this variable in the final estimates that are reported in Table 2. All of our estimates include monthly dummy variables (excluded from the tables for brevity) to ensure that our results for salinity (which varies greatly between wet and dry seasons) are not simply reflections of other seasonal factors that affect the incidence of infant mortality.

We obtain strong results for salinity despite our monthly controls because, as Table 1 shows, salinity exhibits substantial independent variation, both over time within locations and across locations at each point in time. In any case, the risk of seasonal correlation is minimal, since our salinity variable is separately lagged for each child relative to birth month, not calendar month.

To test robustness and parameter stability, we present results for cluster salinity generated from distance bounds of 40, 30 and 20 km.<sup>16</sup> As Table 2 shows, reducing the bound has a very large impact on degrees of freedom: Available observations fall from 8,183 at 40 km to 4,847 at 30 km and 2,072 at 20 km. These sharp reductions have consequences for the statistical significance of all estimated parameters, as would be expected. In the 40 km estimate, salinity in the month before birth is one of the five variables that have statistical significance of 95% or higher (the others are male household head, mother’s age, wealth<sup>17</sup> and spring water source). In the 30 km estimate, salinity is one of three variables with significance of 95% or higher. In the 20 km estimate, none of the variables has 95% significance. In the transition from 40 km to 20 km, the parameter estimates for salinity remain stable at .02 or slightly higher.

> 16 These three distance bounds are sufficient to establish the clear relationship linking geographic proximity, sample size and estimation robustness.  Our impact estimates for salinity are effectively identical for bounds of 40, 30 and 20 km.  However, as Table 2 shows, estimated standard errors rise (and t-statistics fall) as degrees of freedom fall sharply with progressive reduction of the distance bound.

> 17 The DHS surveys include self-reported wealth in five twenty-percentile classes.  Prior experimentation revealed significant effects for the top two classes separately, but no significant difference in magnitudes.  We have therefore combined the two classes into “Top 40%” for the final estimates reported in this paper.

11
The economic assumptions include a decline in inflation rates to Western European levels, a constant labor share in GDP, and a moderate decline in the unemployment rate. The demographic assumptions are the same as the baseline scenario developed by Hablicsek (1995), and imply a declining population and a significant increase in the old age dependency ratio. The system dependency ratio also increases as a result, although this increase is somewhat moderated by the assumption that labor force participation rates converge gradually to the levels prevailing in Western Europe. This implies a moderate increase in labor force participation rates from the current levels, especially for women.6 Under these demographic and economic assumptions, and in the absence of reforms, the Hungarian PAYG system would generate growing deficits, as shown in Figure 3. The deficits would grow to around 2 percent of GDP at the end of the first decade of the century, and would converge to around 6.5 percent of GDP in 2070, the end of the projection period. The result is essentially due to the assumption of a declining rate of inflation and to adverse demographic trends. The decline in the rate of inflation implies increasing real average pensions and replacement ratios, because of the full backward wage indexation rule (prevailing before the reforms), and also because of smaller inflationrelated losses in entry level pensions (the former benefit formula contained several indexation parameters that made entry level pensions very sensitive to inflation).

Whereas the decline in inflation is the major cause of the early deficits, demographic factors dominate the results after the first decade. The projected fluctuations in the deficit closely mirror the old age and system dependency ratios, as shown in Figure 4. Both ratios increase between 1995 and 2017, followed by a ten year period of stability, followed by another increase. The old age dependency ratio nearly doubles to 65 percent at the end of the projection period, while the system <!-- Start of picture text --> lgume 3: Balmans of he ic Pension Sdeme inthe I Figure 4: Old Age and System Dependency<br>Atsence of Iefoim, 1997-2070 Cm % of GW Ratios in the Absence of Reforms, 1997-2070<br>(in %)<br>0.0<br>-1.0 140-<br>120-<br>-2.0 0<br>-3.0 ~~~~~~~~~~~~~~~~~80-<br>-4.0 ~~~~~~~~~~~~~~~~~~60-<br>40-<br>-5.0 ~~~~~~~~~~~~~~~~~~~~~~~~20<br>1997 2003 2009 2015 2021 2027 2033 2039 2045 2051 2057 2063 2069<br>199720032009 20152021 20272033203920452051205720632069 - Dependency ratio -Old age dependency ratio<br><!-- End of picture text -->

**6** Labor force participation for women in their 30s and 40s are assumed to increase from around 70 percent to around 75-83 percent. This assumption was adopted because labor force participation rates declined significantly during the early stages of the transition in the 1990s, from Hungary's historical levels and from the levels prevailing in most European countries.

_5_
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# Table 8: Average Indices of Trade Compatibility and Explanatory Variables

|Co|untry|Average<br>Index|Coefficient<br>of Export<br>Concentration|Per Capita<br>Income($)|Aggregate<br>Income ($b.)|Percentage of<br>Manufacturing in<br>Production of Goods|
|---|---|---|---|---|---|---|
|1.|Mexico|.41|.36|3,470|295|70.8|
|2.|Brazil|.38|.16|2,719|425|72.0|
|3.|Argentina|.32|.20|6,050|200|71.3|
|4.|Colombia|.25|.34|1,290|45|48.0|
|_5._|Costa Rica|.22|.33|2,000|6|54.2|
|6.|Guatemala|.22|.32|980|10|37.1|
|7.|El Salvador|.22|.45|1,170|6|_55.5_|
|8.|Uruguay|.20|.27|3,340|10|67.6|
|9.|Panama|.17|.34|2,440|6|37.9|
|10.|Venezuela|.17|.80|2,900|59|50.2|
|11.|Peru|.16|.32|950|21|73.9|
|12.|Chile|.14|.44|2,730|37|43.0|
|13.|Ecuador|.14|.52|630|12|46.9|
|14.|Jamaica|.11|.66|1,340|3|54.9|
|15.|Nicaragua|.10|.37|410|1|37.6|
|16.|Honduras|.09|.46|580|3|40.0|
|17.|Bolivia|.07|.36|680|_5_|26.5|
|18.|Paraguay|.07|.47|1,340|6|35.8|
||U.S.A.|.52|.14|23,120|5,904||

Source: Author's estimates and World Bank data
32 the success of any substantive reform. Some important procedural issues are discussed Llow.

# Withholding Mechanisms.

Withholding of tax at the source is an extremely important tool of tax administration, particularly in developing countries where enforcement after-the-fact is hampered by a shortage of administrative resources. As with other aspects of tax design, withholding mechanisms should be as simple as possible.

# Employment income.

Withholding of income tax on employees is an indispensable tool for collecting income tax revenues. Both Jamaica and Indonesia collect over 90 percent of personal income taxes through withholding. Employers should be required to withhold income tax on all payments to employees of wages, salaries, and honoraria, in whatever form. Withholding should also be required on payouts of pension benefits by pension funds, provided that the contributions were tax-deductible.

Care should be taken to insure that the withholding tax is not simply a gross payroll tax. This requires that the tax be personalized-that the amount to be withheld be calculated separately for each employee by applying the general tax rate schedule taking into account the personal exemptions for which that employee is eligible. Employers should be required to supply the tax department with a record of taxes withheld for each individual employee and to inform such employee of the amount withheld. Making an employee aware that taxes are being paid on his or her behalf can be an important first step in introducing that employee to a system of personal income taxation. As discussed above,
16 period 1997-2000, the three months of 2001 up to the launch of UC in April, and then 57 months during and after UC implementation.<sup>9</sup> Our outcomes of interest are whether the individual is working, and if so whether they are in the formal sector or informal sector. LFS respondents are asked whether they worked during the previous week, and if not whether they received a salary or wage and whether they have a job to return to. If the individual was not working and had no job to return to, we classified them as not working. Respondents who said they were employed were asked about their type of employment, the categories being: (i) employer; (ii) selfemployed; (iii) unpaid family helper; (iv) government employee; (v) government enterprise employee; and (vi) private employee. We classified (i)-(iii) as informal sector, and (iv)-(v) as formal sector. As for (vi), the private employees, we classified those who earned monthly salary as formal and those who earned hourly or daily wage as informal.

Our UC “treatment” variable is constructed from the province of residence of the respondent, the timing of the UC implementation in the individual’s province, and the date (month and year) of the LFS. We construct variables corresponding to the numbers of months, quarters, half years, and years since implementation of UC in the individual’s province as of the LFS in question. From these we construct the dummy variables in eqn (1).

Our VF variable is constructed from the province of residence of the respondent, the timing of the arrival of the first VF funds, and the date (month and year) of the LFS. We define the VF variable analogously to the UC variable so if, for example, the UC variable is

> 9 We have only the first and the third quarters of LFS 1997 and LFS 1998.
<!-- Start of picture text --> Post-transfer poverty ($1.25), % of populattion People out of poverty due to SP, at $1.25, % of population<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>-40%<br>Source: Authors based on ASPIRE, See Annex<br>Bulgaria Kosovo Belarus Romania Ukraine Latvia Serbia Uruguay Mauritius Poland Malaysia Bosnia and Herzegovina Kazakhstan Pakistan Azerbaijan Egypt, Arab Rep. Macedonia, FYR Turkey Thailand Armenia Argentina Chile Dominican Republic Nepal Mongolia Bangladesh Ghana Peru Ecuador Kyrgyz Republic Mexico Yemen, Rep. Brazil Paraguay Panama El Salvador Bhutan West Bank and Gaza Sri Lanka Georgia Bolivia Cambodia Malawi Philippines Nicaragua Guatemala Vietnam Afghanistan Lao PDR Kenya Timor-Leste Rwanda<br><!-- End of picture text --> There are several countries where absolute poverty measured by $ 1.25 a day standard is low or even zero, and hence the impact of social protection cannot be assessed. For this reason, the annexes give estimates of poverty impact not only for $1.25/day line, but also for a relative line with a cutoff of the bottom quintile.

The poverty headcount, while informative about the scale of poverty, does not characterize the nature of poverty: how deep and severe it might be, how far an average poor person is from the poverty line. The most intuitive measure for this is the total poverty gap- or the sum of the gaps between income and the poverty line for the poor in country.<sup>33</sup> Using again the international poverty line of $1.25 and a simulation of social protection impacts we can see in Figure 4 that the impact of social transfers on this poverty measure is even greater than what we saw for the headcount (on average almost half of the poverty gap is eliminated). Once more countries differ a lot in how successfully they are closing the poverty gap. Some are eliminating it completely; some are making hardly a visible difference.

**FIGURE 4: GRAPH OF POVERTY GAP REDUCTION BY SOCIAL PROTECTION (% OF PRE-TRANSFER POVERTY GAP FILLED, AT $1.25/DAY))**

> 33 Note that this is simply the FGT1 measure of poverty multiplied by the poverty line and the total population in the country—see Annex 1.

14