rafmacalaba/datause-encoder-data huggingface dataset:
|Honduras|2011, 2012, 2013, 2014, 2015,<br>2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares Propósitos<br>Múltiples<br>Encuesta Nacional de|7|LM|
|---|---|---|---|---|
|Mexico|2016, 2018, 2020|Ingresos y Gastos de<br>Hogares|1|UM|
|Panama|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta de Hogares|8|H|
|Paraguay|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares|7|UM|
|Peru|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Nacional de<br>Hogares|8|UM|
|Uruguay|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Continua de<br>Hogares|7|H|

Note: LM: Lower-Middle Income, UM: Upper-Middle Income, and H: High Income Source: SEDLAC (CEDLAS and World Bank) for country classification: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</u> The proposed approach estimates a model of household-level income using a set of time-invariant and deterministic harmonized variables as controls to provide estimates of welfare dynamics. The harmonization procedure requires cleaning and processing each cross-section to ensure that all definitions and variables are identical in each country and year. The full list of available covariates for each country’s data is shown in Table 2.

Table 2: Harmonized variables for LASSO-PMM

|Variable|Definition|
|---|---|
|Welfare aggregate|Per household capita income for international poverty estimations. It is<br>expressed daily basis and deflated using 2017 PPPs|
|Age|Age of the household head (level and squared)|
|Gender|Gender of the household head|
|Education Level|Education of the household head in four education levels: (i) No education<br>(ii) Primary (complete or incomplete) (iii) Secondary (complete or<br>incomplete) (iv) Tertiary (complete or incomplete)|
|Literacy|Literacy status of household head|
|Weight|Surveyweights|

Source: SEDLAC (CEDLAS and World Bank) When estimating regional middle-class thresholds, income distributions are converted into a common, internationally comparable currency unit using exchange rates. PPP conversion factors are preferred to market exchange rates since it incorporates both the relative prices of tradable goods as the market exchange rates and non-tradable services (e.g., getting a haircut) across countries (Jolliffe et al. 2022). PPPs measure how much it costs to purchase a basket of goods and services in one country compared to how much it costs to purchase the same basket of goods and services in terms of the currency units of a reference country, typically the United States. The estimates of PPP and market exchange rates are from the ICP, and the study uses the PPP estimates from the 2011 and 2017 rounds.

11
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Kapteyn, A. & Van Praag, B. M. S. and Van Herwaarden, F. G.(1978) Individual welfare functions and social reference spaces, Economics Letters, 1(2), 173-177.

Kapteyn, A. and van Herwaarden, F.G. (1980) Independent welfare functions and optimal income distribution. Journal of Public Economics 14, 375– 397.

Kapteyn, A., van de Geer, S., van de Stadt, H., Wansbeek, T., (1997). Interdependent preferences: an econometric analysis. Journal of Applied Econometrics 12, 665–686.

Kingdon, G. and Knight, J. (2007) Community, Comparisons and Subjective well-being in a divided society, Journal of Economic Behavior and Organization, 64, 69-90 Knight, J. and Song, L. (2006) Subjective Well-Being and Its Determinants in Rural China. mimeo Knight, J. and Gunatilaka, R. (2008) Income, Aspirations and the hedonic treadmill in a poor society. University of Oxford: Department of Economics.

Knight, J, Song, L., Gunatilaka, R. (2009) Subjective well-being and its determinants in Rural China, China Economic Review, 20, 635-639 Kosicki, G. (1987) A Test of the Relative Income Hypothesis, Southern Economic Journal, Vol. 54, No. 2, pp. 422-434 Kuznets, S. (1942) National Income and Taxable Capacity The American Economic Review , Vol. 32 (1), Part 2, Supplement, Papers and Proceedings of the Fifty-fourth Annual Meeting of the American Economic Association (Mar., 1942), pp. 37-75 Layard, R. (1980) Human satisfaction and public policy, Economic Journal 90, 737– 750.

Layard, R. (2005) Happiness: Lessons from a New Science. London: Allen Lane.

Lelkes, O. (2006) Tasting Freedom: Happiness, Religion and Economic Transition. Journal of Economic Behavior and Organization, 59(2): 173–94.

28
|Household head age at time<br>of follow-up|–0.001|–0.002***|0.002***|–0.000|0.001|
|---|---|---|---|---|---|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|
|Household head uneducated|0.037***|–0.005|–0.049***|–0.031|–0.018|
||(0.014)|(0.017)|(0.017)|(0.026)|(0.020)|
|Constant|0.256***|0.474***|0.862***|0.560***|0.217***|
||(0.034)|(0.041)|(0.038)|(0.061)|(0.041)|
|Observations|4,302|4,302|4,300|2,184|2,979|
|_R_-squared|0.085|0.012|0.045|0.038|0.102|
|Functional form|Linear|Linear|Linear|Linear|Linear|

_Note_ : Robust standard errors are in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ <0.1.

# **References**

- <mark>Abdoulayi, S., G. Angeles, C. Barrington, K. Brugh, S. Handa, K. Kilburn, A. Molotsky, F. Otchere, S. Zietz, M. Tsoka, P. Mvula, J.D. Hoop, T. Palermo, and A. Peterman. 2017.</mark> _<mark>Evaluating the Effectiveness of an Unconditional Social Cash Transfer Programme for The Ultra Poor in Malawi</mark>_ <mark>. New Delhi: International Initiative for Impact Evaluation (3ie).</mark>

- <mark>Aguero, Jorge, Michael Carter, and Ingrid Woolard. 2006. “The Impact of Unconditional Cash Transfers on Nutrition: The South African Child Support Grant." Working Paper 39, International Policy Centre for Inclusive Growth, Brasilia.</mark>

Central Statistics Organization. 2018. _Afghanistan Living Conditions Survey 2016–17._ Kabul.

<mark>Baird, Sarah, Craig McIntosh, and Berk Özler. 2011. “Cash or Condition? Evidence from a Cash Transfer Experiment.”</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>126(4): 1709–53.</mark>

- Banerjee, Abhijit V., Rema Hanna, Gabriel E. Kreindler, and Benjamin A. Olken. 2017.“Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer Programs.” _World Bank Research Observer_ 32(2): 155–84.

- <mark>Calonico, S., M.D. Cattaneo, and R. Titiunik. 2014. “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.”</mark> _<mark>Econometrica</mark>_ <mark>82: 2295–326. doi:10.3982/ECTA11757.</mark>

- Case, Anne, Victoria Hosegood, and Frances Lund. 2005. “The Reach and Impact of Child Support Grants: Evidence from KwaZulu-Natal.” _Development Southern Africa_ 22(4): 467–82.

- <mark>Covarrubias, Katia, Benjamin Davis, and Paul Winters. 2012. “From Protection to Production: Productive Impacts of the Malawi Social Cash Transfer Scheme.”</mark> _<mark>Journal of Development Effectiveness</mark>_ <mark>4(1): 50– 77.</mark>

<mark>Davis, Benjamin, Marie Gaarder, Sudhanshu Handa, and Jenn Yablonski. 2012. “Evaluating the Impact of Cash Transfer Programmes in Sub-Saharan Africa: An Introduction to the Special Issue.”</mark> _<mark>Journal of Development Effectiveness</mark>_ <mark>4(1): 1–8.</mark> 20
as well as physical capital or asset _k._ Each agent can allocate her labor endowment either to productive activities ( _lp_ ) or to rebellion _(lr_ ) so that Wages in the productive sector depend on _h_ and _k_ according to a function with positive first derivatives. Communities or regions are assumed to be characterized by a public good endowment _G1_ , and pre-existing level of grievance, _G2_ . For a community or region where there is widespread discontent arising from a feeling of marginalization or injustice perpetuated by the State, for example, G2 will tend to be high.

The rebel leader selects communities or a region that provides a sufficient economic base such that he can finance rebellion by imposing a tax, _t_ on the community or region (consider _t_ as the rate of expropriation)<sup>17</sup> . For effective expropriation and soldiering, he would need to recruit at least _n1_ (number of) persons from each community (as fighters, informants or agents). In addition, since government forces constitute an active threat to rebel survival, rebellion faces the organizational problem of surmounting an entry threshold. The rebel force must exceed some critical level necessary for survival otherwise there will be rebel exit (Collier 2000).

Collier (2000) argues that the relationship between rebel forces and government forces is proportional, such that _n1 ≥ αn2_ , where _n2_ is the size of government forces and _α_ is a factor of 2004). The two organizations claim to be the legitimate vehicles for the promotion of change with the object to seek, through dialogue, the economic and political development of the people of the Niger Delta. Reports from daily newspapers as well as foreign organizations appear to confirm this allegation. They also reveal the existence (as at the time of investigation) of a rival group said to be as powerful as the state and enjoying the backing of prominent persons in government (Human Rights Watch, 2005; _The Punch_ , Nigeria 30 September, 2004. p6).

However, the assumption of a loot-seeking rebellion is not even necessary for the workability of the model, as it is now commonly agreed that even if a rebel leader is motivated by grievance and not by greed (i.e., rebellion is justice and not loot-seeking), financial resources are needed to prosecute rebellion and the availability of _lootable_ and/or _obstructible_ resources provides an incentive.

17 We can view this taxation as a form of looting. For a region rich in oil, for example, this could mean the direct and indirect costs of such activities as bunkering (stealing of crude oil), vandalization of oil installations, pipelines and other facilities; kidnapping for ransom and other forms of extortion on individuals and the community at large. This could include increased insecurity, reduced economic opportunities, reduced socioeconomic activities as a result of increased military presence. When rebellion is successful, taxation takes the form of control of the resource wealth in which case the tax rate, _t_ , equals unity from the rebel leader’s perspective.

9
What about the effect of other individual and household characteristics? The coefficients on age and age squared show that the probability of volunteering increases with age, albeit at a decreasing rate, and decreases with household size. One possible explanation for the negative coefficient on household size might be that community tasks often require that each household provide a fixed amount of labor-say, one able-bodied worker-regardless of household size. The results in Table 3 also show that men, household heads, those who are married, and those born in their current place of residence are all more likely to volunteer, as are native non-Spanish speakers. Heads of household, married people, and long-term residents may all be more likely to stay in the community in which they cuirently live and may therefore be more willing to invest in the production of local public goods. Higher volunteering rates among the Quechua, Aymara, and other non-Spanish native speakers are probably an indication of cultural or sociological differences: there is a great deal of anthropological work on pattems of cooperation and reciprocity-volunteering, broadly defined-in indigenous communities in rural Peru, especially in the sierra (see, for example, Alberti and Mayer, 1974; Matos Mar, 1976; Aramburui and Ponce Alegre, 1983; Mossbrucker, 1990).15 Note that all of these effects are remarkably consistent across specifications, including the fixed-effects specification.

I turn next to geographic variations in volunteer activity, and to the possible relationship between these differences and differences in government activity. Note, first, that volunteering is much more widespread in the rural sierra and especially the rural selva than in the rural coast (the differences become even larger when the variables measuring ethnicity are excluded from the regression because of the high concentration of indigenous people in the sierra and the selva). Second, the very large increase in the pseudo R-squared in specification (iii) suggests that there are important differences in the level of volunteering across communities-differences which may well be related to underlying differences in social capital, institutions, and within-community homogeneity.

Finally, the results in specification (ii) suggest there is no apparent effect of FONCODES expenditures on the decision to volunteer. That is, FONCODES investments do not appear to crowd volunteer efforts in or out. Measurement error and simultaneity bias may both be sources of conceni in the estimated impact of FONCODES investment. Measurement error is common in analytical work which combines data from household surveys and administrative sources. This is a peculiar form of measurement error, however, since it does not bias the coefficients on the mismeasured variable(s) towards zero. Rather, the estimated coefficients are unbiased but estimated

> 15 Higher volunteering among the indigenous may also be an indication of discrimination in the wageearning market (Macisaac and Patrinos), which could lower the opportunity cost of volunteering for indigenous people.

10
# _3.1.3 Are the Bans Effective in Normalizing Sex Ratios at Birth?_

It is difficult to evaluate the effectiveness of bans in normalizing SRBs since the bans are not randomly assigned and enforced. The non-randomness in the assignment of the “treatment” (the ban in this case) makes the construction of the counterfactual scenario of what might have happened in the absence of the ban difficult. The country-specific studies below outline some of the attempts at estimating this treatment effect (for an overview, see Table 2).

**Table 2.   Summary of studies that examine the impact of a ban on prenatal sex selection on SRB**

|**Author (Date)**|**Ban (details)**|**Data**|**Type of study**|**Observed impact**<br>**on SRB**|
|---|---|---|---|---|
|Nandi and<br>Deolalikar<br>(2013)|Prenatal Diagnostic<br>Techniques Act, India,<br>1994|Village and town<br>level Indian<br>census data, 1991<br>and 2001|Quasi-<br>experimental<br>(difference-in-<br>difference)|Less imbalanced<br>child sex ratio, but<br>can’t discern impact<br>on SRB|
|Park and Cho<br>(1995)|Ban against prenatal sex<br>selection, Korea, 1987<br>(suspension of medical<br>licenses of miscreant<br>doctors in 1990)|Vital statistics<br>and World<br>Fertility Survey|Correlation-<br>based/Trend<br>analysis|Drop in SRB in<br>1991, but this was<br>more likely due to<br>parents’ adherence to<br>the Lunar calendar|
|Guo et al.<br>(2016)|Care for Girls Program,<br>China (cracking down<br>on prenatal sex selection<br>by monitoring second<br>birth-order pregnancies)|2000 and 2010<br>Chinese census|Correlation-<br>based/Trend<br>analysis|Less imbalanced<br>SRB for second-<br>order births, but<br>more imbalanced<br>SRB for first-order<br>births|

# _India_

The Indian government, concerned with the implications of increasing accessibility of prenatal sex diagnostics in the 1980s and 1990s, passed the Prenatal Diagnostic Techniques Act (PNDT) in 1994. The Act went into operation in 1996. It prohibited medical professional from using prenatal diagnostic techniques for sex selection; these techniques could only be used for detecting genetic or sex-linked disorders, or congenital malformations. The Act was amended in 2003 to prohibit the use of pre-conception sex selection techniques as well (Ministry of Health and Family Welfare 1994).

Nandi and Deolalikar (2013) conduct a rigorous ex-post examination of the impact of the PNDT Act. They measure changes to the child sex ratio following the PNDT Act by exploiting spatial variation in the timing of the ban across Indian states. For the state of Maharashtra, the ban on prenatal sex selection was implemented in 1988, nearly a decade before the passage of the national PNDT Act. This allows Nandi and Deolalikar (2013) to use Maharashtra as the pre-treated or control state that is not affected by the national ban. They conduct a difference-in-difference
# **1. Introduction**

Landslides are a natural process of Earth’s geological formation and occur when gravitational force overcomes the resistance force of land materials (Turner, 2018). However, when landslides occur near human settlements and economic activity areas, they can result in losses of lives and livelihoods. Landslides are expected to increase due to the impact of human activities, such as land use or land cover modifications, hill cutting, and climate change (Crozier, 2010). Landslides can be initiated by several factors, such as rainfall, seismic activity, volcanic eruption, snowmelt, stream erosion, coastal erosion, changes in surface water levels, changes in groundwater, and/or human activities (Schuster and Wieczorek, 2002). The Global Landslide Catalog (GLC) reports around 6,800 rainfall-triggered landslide events since 2007, with around 20% of these recorded landslide events located in the South Asia Region (Kirschbaum et al., 2010). GLC reports over 25,000 fatalities that resulted from rainfalltriggered landslides since 2007. Landslides result in direct and indirect costs to humans (Turner, 2018). The direct costs include the loss of lives, properties, and infrastructure, while the indirect costs are associated interruptions to economic activities (Turner, 2018).

Landslide susceptibility mapping is an approach used to provide a spatial classification of potential landslide areas (Fell et al., 2008; Sarkar and Kanungo, 2004). Landslide susceptibility maps are often produced using a heuristic method called “qualitative map combination,” in which the effects of multiple factors that contribute to landslide occurrence are combined using weights (Fell et al., 2008). These weights are based on expert knowledge and are challenging to determine due to varying expert opinions. Such a method has an unpredictable error margin, as individual perceptions can influence expert choices. Behavioral scientists have highlighted notable biases in decisions involving financial or life losses (Tversky 2
# **1. Introduction**

The economic crisis that hit worldwide during 2008-2009 produced a slowdown in economic activity and a significant reduction in the number and quality of jobs. Even though the severity of the crisis has differed by country or region, none has escaped its effects to some extent.

How governments implemented effective policies to spur growth and reduce the negative effects of the crisis on labor markets (for example, incorporating most of the laid off back again into employment) is worth analyzing to draw conclusions and policy lessons for the future. Countries like Germany or Brazil have been more successful than Spain or Mexico in containing the negative effects of the crisis on the labor market. This discrepancy deserves further analyses.

The labor market dynamics of Mexico have been characterized in recent years by low relative unemployment, but high levels of informal jobs, and low-growth, almost stagnant, real wages. These dynamics have been affected by the crisis. Unemployment rates usually were around 3.5 percent and after the crisis increased to about a persistent 5.5 percent. The levels of informality, measured as access to social security benefits through an employment contract, have been high. In 1993, informality was around 63 percent of total employment and increased to about 66 percent recently. This acted as one of the main mechanisms to adjust the labor market. During the crisis, average real wages declined in almost all economic activities and no increase has been recorded during the recovery. Real wages are lower compared to pre-crisis levels. Whether unemployment rates will continue to be relatively high, as a new bottom, and informality can continue absorbing workers in the sector is a matter of interest for the implementation of public policies and regulation reforms aimed at reliving the lasting effects from the 2008-09 crisis.

The aim of this paper is to describe in detail the effects of the crisis on the labor markets in Mexico and discuss the policies implemented by the government to cope with the crisis. In sour analysis, we identify the groups most affected and how policies adopted helped these groups weather the crisis. In addition, we compare public spending plans to fiscal mechanisms and other policies to drawn some conclusions about their effectiveness.

# **2. The Economic Crisis and Mexico’s Labor Market**

Real GDP growth in Mexico has been uneven over the last decade. Whereas in 2000 GDP grew at a rate of 6.6 percent, growth was only 3 percent in 2005, and 3.5 percent in 2007, just before the crisis. Growth decomposition studies (Bergoeing et al., 2002; Faal, 2005) show that moderate output growth in Mexico is mainly due to accumulation of production factors rather than higher levels of productivity. The sharp decline in productivity growth during the 1980s is generally attributed to the impact of macroeconomic instability on economic activity and investment. Subsequent progress over the past decade in attaining macroeconomic stability and opening up the economy to trade and investment flows in the context of bilateral trade agreements has contributed to a steady though 2