rafmacalaba/data-use-mentions-extended huggingface dataset:
communities are likely to help mitigate the impacts of the shocks. In light of these findings, these policy instruments should be given due consideration in the design and implementation of adaptation strategies.

# **5. Concluding Remarks**

Very little empirical evidence exists on the welfare losses that households experience as a consequence of weather shocks. In principle, households at low levels of income are most vulnerable to the impacts of weather extremes given their geographical locations, limited assets and access to resources and services, low human capital and high dependence upon natural resources for income and consumption. While there is wide recognition of the impending threat of climate change upon the poor, limited attention is given to quantifying such effects of climate change and identifying household _adaptation_ strategies and targeted measures that could mitigate the poverty impacts. This paper seeks to make a contribution by analyzing the potential welfare impacts of rainfall shocks in rural Indonesia with a focus on households engaged in family farm businesses, in particular rice farming. It also attempts to identify community characteristics capable of dampening the adverse impact of climate change and extremes. The focus on rice farming is due to the fact that rice is a staple food in Indonesia.

The basic approach adopted here is to exploit cross-sectional variation in the data and link a welfare indicator (i.e. real consumption per capita) or some component thereof (i.e. food versus non-food expenditure) to a weather shock defined on the basis of available rainfall data focusing mainly on rural households. In particular, we consider two types of shocks: delayed onset of monsoon and rain shortfall in the 90 day period following monsoon onset. We find that delay in the monsoon onset does not have a significant impact on the welfare of rural households. However, rice farm households located in areas experiencing low rainfall following the monsoon onset are negatively affected by the low rainfall shock. Nonfood expenditure per capita is the most affected component. This suggests that rice farm households protect their food expenditure in the face of weather shocks. Further study
# INDONESIAN LABOR LEGISLATION IN A COMPARATIVE PERSPECTIVE

# Table of Contents

|||Page Nos.|
|---|---|---|
|I.|INT|RODUCTION ................................................... I1|
|II.|A B<br>RE|RIEF BACKGROUND OF INDUSTRIAL<br>LATIONS IN INDONESIA<br>.2|
|III.|IND<br>PE|ONESIAN LABOR LEGISLATION IN A COMPARATIVE<br>RSPECTIVE<br>.4|
||A.|Collective Labor Relations ..............................<br>4|
||B.|Minimum Wages .............................<br>20|
||C.|Non-Wage Compensation: Employer Mandated<br>Benefits and Social Insurance Systems .23|
||D.|Other Labor Standards: Hours of Work, Leave,<br>Minimum Age of Employment ............................<br>27|
|IV.|CO|NCLUSIONS ............................<br>27|
|REFE|REN|CES<br>..<br>29|
|Table|1:|Union Densities .31|
|Table|2:|Selected Data on Strikes .32
of time are available with cross-sectional surveys. Secondly, an important issue with the examination of income dynamics with genuine panel data is the extent to which non-random attrition biases the results. In particular, one may be concerned that individuals whose incomes suffer large falls or large increases may be much more likely to move houses and leave the sample. While there are structural approaches one can take to attempt to account for attrition (e.g. see Lokshin and Ravallion (2004)), the use of pseudo-panel data is not subject to these same attrition issues. Thirdly, the use of different individuals each period allows for very general forms of serial correlation in the individual measurement errors. The main disadvantage of the use of pseudo-panels instead of genuine panels for the purpose of identifying poverty traps is that while we can still allow for heterogeneity of the intercept at the individual level, we can only evaluate the condition in equation (4) for each cohort at the crossing point(s) determined by the cohort average intercept, rather than at a separate intercept for each individual. This enables us to determine whether the income dynamics of the average individual within a particular cohort give rise to a poverty trap.

# 3 Data

The data come from Mexico’s Encuesta Nacional de Empleo Urbano (ENEU), a quarterly urban employment survey collected by Mexico’s Instituto Nacional de Estadística, Geografía e Informática (INEGI) over the period from the first quarter of 1987 to the second quarter of 2001.<sup>9</sup> Over this period the ENEU expanded coverage from 16 cities in 1987 to 34 cities by the end of 1992 and 44 cities by the second quarter of 2001. We include all 39 cities present by the end of 1994, although our results are robust to restricting the sample to just the 16 cities present in all years. The survey is designed as a rotating panel, with households followed for five consecutive quarters. In addition to information on household demographics and education of all members, each individual
# **Figure 2**

# **Individual distributions of the financial inclusion indicators**

The distribution of each financial inclusion indicator along with the country code for the minimum, maximum and median value. Data averaged over the two rounds of Global Findex (2011, 2014) or over the period 2007–2014 for Enterprise Surveys data.

<!-- Start of picture text --> Borrowed from a financial institution Credit card Saved at a financial institution Account at a financial institution<br>High Inc VEN ARE NZL High Inc LTU SVN ISR High Inc ARG SVK NOR High Inc URY USA NOR<br>Upp Mid TKM CRI IRN Upp Mid MNG BLR TUR Upp Mid TKM ROU THA Upp Mid TKM BLZ MNG<br>UZB<br>Low Mid Low Mid Low Mid Low Mid<br>YEM KSV ARM BTN UKR TJK CMR LKA YEM HND LKA<br>MWI<br>Low Inc Low Inc Low Inc Low Inc<br>CAF SLE KHM MDGZWE NER BEN RWA NER BFA RWA<br>0 10 20 30 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 100<br>Min Median Max Min Median Max Min Median Max Min Median Max<br>Purchased agriculture insurance Debit card Electronic payments Firms with a checking or savings account (%)<br>BLR High Inc High Inc High Inc<br>Upper middle ISR ESP NOR ARG JPN FIN SVK CHL TTO<br>PRY THA<br>MUS<br>Upp Mid Upp Mid Upp Mid<br>SEN TKM MNE IRN TKM IRN IRQ CHN SRB<br>Lower middle<br>GEO ZWB HND<br>Low Mid Low Mid Low Mid<br>MMR HND UKR MMR UKR MMR LKA SDN<br>Low Income AFG MWI<br>BFA ZWE GIN Low Inc NER ZWE Low Inc MDGZWE Low Inc KHM MDG CAF<br>0 10 20 30 40 50 0 20 40 60 80 100 0 20 40 60
and longevity experience of the fund as well as the need to build adequate reserves to enable the payment of rising guaranteed benefits. Annual bonuses may be suspended if net performance is very weak and threatens to undermine the adequacy of reserves. Such policies aim to replicate the benefits paid by fixed real annuities but without committing to do so. Because of this, they do not need to have access to an ample supply of inflation-linked securities.

The **PPM unit-linked annuities** follow a different approach in calculating subsequent annual benefit payments. The Swedish Pensions Agency maintains individual accounts that record changes in account balances. The end-of-year (EOY) balance in any one year is equal to the beginning-of-year (BOY) balance less the benefit payments made during the year, plus investment income, less operating fees, plus survival credits. Survival credits in any one year are determined by multiplying the average account balance during the year (which is given by the BOY balance less half the benefit payments during the year) by the economic mortality risk for the year<sup>16</sup> . Survival credits are given by the remaining balances in the accounts of deceased annuitants in any one year and for each age cohort. The BOY balance in the following year is divided by the new appropriate annuity factor to determine the annual benefits for that year. The annuity factor is based on the AIR, which may change from year to year, and expected mortality. The use of expected mortality protects pensioners in advanced ages from the declining cell numbers, while the small number of survivors does not affect the soundness of the program.

> 16 Half the annual benefits is taken into account because payments are made on a monthly basis.

8
World Bank, Washington DC.

Coudouel, A., Hentschel, J. and Wodon, Q. (2002). Poverty Measurement and Analysis, in the PRSP Sourcebook, World Bank, Washington D.C.

Datt, G. (1998), “Computational Tools for Poverty Measurement and Analysis,” FCND Discussion Paper No. 50.

Deaton, A. (1997). The analysis of household surveys: a microeconometric approach to development policy. Washington, D.C.: The World Bank.

Deaton, A. and Zaidi, S. (2002). Guidelines for Constructing Consumption Aggregates for Welfare Analysis. World Bank. Washington, DC.

Ferreira, F., Chen, S., Dabalen, A., Dikhanov, Y., Hamadeh, N., Jolliffe, D., Narayan, A., Prydz, E. Revenga, A., Sangraula, P., Serajuddin, U. and Yoshida, N. (2015). A global count of the extreme poor in 2012: data issues, methodology and initial results. Policy Research working paper; no. WPS 7432. Washington, D.C.: World Bank Group.

Haughton, J and Khandker, S. (2009) Handbook on poverty and inequality. World Bank, Washington DC.

22
Second, we make use of the United Nations Population Division Migrant Stock data which provides information regarding the stock of international migrants by origin and destination for 232 countries between 1990 and 2020, on five-year based intervals (DESA, 2020). The main source of information collected is extracted from population censuses. When not available, the information has been retrieved through population registers and nationally representative surveys. Finally, we gather information on country-pair characteristics using the ’Gravity’ CEPII database (Conte et al., 2021). From this dataset we use information on the GDP of both origin and destination countries, common religion dummy, common language dummy and a colonial linkages dummy. Information on the share of right wing (i.e. nationalist party) votes is based on the Manifesto Project Database (MPD) and the variable used in regressions built as in Moriconi et al. (2022) – see appendix section B for a detailed discussion.

Some important points for the construction of the estimation data set deserve careful explanation. First, a country-pair in a specific year may have multiple trade agreements in place. To address this issue, for a given country pair in a given year, we keep the highest value regarding the ’Visa’ provisions. Second, we keep every country pair-year combination in the migrant stock database. Namely, if a given country pair-year combination does not have corresponding information in the DTA database, we assume that this specific country pair-year combination does not share a trade agreement with visa-provision (i.e. and ’visa’ dummy is therefore equal to zero). Conversely, every pair-year combination in the DTA database that does not match the migrant stock data is dropped. Indeed, assuming zero-migration for these specific cells would be inaccurate. Finally, every pair-year has been merged with the ’Gravity’ data. We are left with a total of 49,450 country-pair combinations observed in 7 points in time (1990, 1995, 2000, 2005, 2010, 2015 and, 2020). Table
, sampling efficiency, equidistance,

> 12 We cannot use alternative measures, such as the World Bank Country Policy and Institutional Assessment scores, because the series starts only in 2005.

> 13 Obvious candidates for capturing complementary policies and institutions are variables related to doing business, but there is no series that covers the entire period of the analysis.

> 14 Hexagons for which the observed nighttime light was zero were excluded from the analysis. This is done to exclude areas where no economic development is expected, for example, forests and deserts. Since international development organizations tend to have strong environmental safeguards to prevent the depletion of the natural environment, the exclusion of these hexagons is intuitive.

10
the dartboard approach, was introduced by de Bellefon et al. (2021). This method defines cities in relative terms (i.e., by endogenously deriving population density thresholds that are specific to each country), utilizing local density thresholds that are endogenously determined based on a country's spatial population distribution.

Under the DOU approach, the classification of each cell in a gridded population dataset, such as WorldPop and GHSPOP, is based on its belonging to an urban center (city), urban cluster (towns and suburbs), or rural area (Table 1; also see Panel A of Figure A1 in Appendix A for an example of Greater Accra, Ghana).<sup>14</sup> A cell is considered part of an urban center if it is part of a spatially contiguous set of grid cells, where each cell has a population density of at least 1,500 people per km<sup>2</sup> and the aggregate population of the set is at least 50,000. Urban clusters, meanwhile, are defined as sets of grid cells in which each cell has a population density of at least 300 people per km<sup>2</sup> , and the set has an aggregate population of at least 5,000. Rural areas are defined as areas that are not classified as either urban centers or clusters.

The DB approach categorizes areas into three types: cities, which are composed of cores and suburbs; rural towns; and other rural areas (Table 1; also see Panel B of Figure A1 in Appendix A for an example of Greater Accra, Ghana; for a more in-depth description, see Combes et al., 2023). In the DB approach, the classification process begins by screening an area to determine its category. This screening involves identifying a contiguous set of cells for which the population density of each cell exceeds the 95<sup>th</sup> percentile of a counterfactual distribution of grid cells
under certain adhoc assumptions.

Our approach combines several of the methodologies used in the literature. We develop a general equilibrium overlapping generations growth model to study the main determinants of asset laundering and the way that this activity a¤ects the capital stock of an economy. On this basis, we build a two linear structural equation macroeconometric model with a transition equation, which speci...es the determinants of asset laundering, and a measure or indicator equation for an observable variable a¤ected by asset laundering, in this case, the capital stock of the economy. This linear system is then taken to the data for the Colombian economy between 1985 and 2013 using two empirical methods: Calibration and Kalman Filter, where asset laundering is understood as a latent variable to be estimated.

The next section builds the conceptual framework on asset laundering to be used in the theoretical model.

# 3 Conceptual Framework on Asset Laundering

In the literature on asset laundering, there appears to be no consensus on the di¤erence between the terms laundered assets and laundered money. In some cases they are considered to be synonymous while in others the former contains the latter. Most de...nitions, though, focus mostly on criminal 10
2003; Tesliuc and Lindert 2004).<sup>5</sup>

# 3. Data and methods

Estimating a map of poverty and vulnerability requires a household survey and a population census. In the case here, the map is constructed using the Harmonized Survey on Household Living Standards 2018–

> 3 This measure of vulnerability has an intuitive interpretation: the probability of falling into poverty. However, its simplicity comes at the cost of violating desirable axiomatic properties (see Calvo and Dercon 2005). 4 See Gallardo (2018) for an in-depth review of the literature.

> 5 Following Günther and Harttgen (2009), various studies apply multilevel models to estimate the vulnerability to poverty in various countries. Examples include Échevin (2014), who applies a two-level model to study idiosyncratic and covariance shocks in Haiti; Mina and Imai (2017) and Pham, Mukhopadhaya, and Vu (2021), who apply a threelevel model in the Philippines and Viet Nam, respectively, and Skoufias, Vinha, and Beyene (2024), who examine the vulnerability to poverty in the drought-prone regions of Ethiopia.

6
and the Caribbean, 7 percent each are on Europe and Central Asia and Middle East and North Africa, and 6 percent are on the United States (Table 1). The geographic distribution is broadly similar for estimates on the poor’s exposure to climate shocks and the more adverse impact of such shocks on the poor (Table 2).

The majority of studies (57 percent) in our sample focus on general climate change, followed by studies on temperature anomalies (about one third), flooding, droughts, and general natural disasters (about one fifth each), and less than 10 percent of studies focusing on landslides, typhoons/cyclones, or earthquakes (Table 1). These indicators are not mutually exclusive since some studies examine multiple shocks. About one fifth of studies focus on heat, a subset of studies on temperature anomalies. Almost a quarter of estimates in our sample focus on the poor’s exposure to floods, and about a third focus on the more adverse impact of climate shocks on the poor (Table 2). The share differences at the study and estimate levels reflect the estimates from multiple specifications documented in the studies.

We assess the representativeness of the studies in our sample against the reported incidence of regional climate shocks in the global EMDAT (Emergency Events Database) database by performing a two-proportion t-test. EMDAT is a comprehensive global database that collects and provides information on the occurrence and effects of more than 22,000 mass disasters worldwide since 1900. The database includes information on natural disasters (geophysical, meteorological, hydrological, climatological, biological, and extraterrestrial) and technological disasters (industrial, transport, and miscellaneous accidents). The database includes disasters in which 10 or more people died, 100 or more people were affected, a state of emergency was declared, or international assistance was provided. We used EMDAT data from 2000 to 2023 from each region in the world for comparison (Table 3).<sup>5</sup> We find that, in our
- <mark>Islam, A., Palacios Lopez, A., & Amin, M. (2019). Decomposing the labour productivity gap between migrant-owned and native-owned firms in Sub-Saharan Africa.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>55</mark>_ <mark>(9), 2065-2082.</mark>

- <mark>Islam, Asif, Gaddis, Isis, Palacios-Lopez, Amparo, Amin, Mohammad. (2020). The Labor Productivity Gap between Female and Male-Managed Firms in the Formal Private Sector. Forthcoming</mark> _<mark>Feminist Economics</mark>_ <mark>.</mark>

- <mark>Islam, A., Galinato, G. I. and Zhang, W. (2021). Can government spending boost firm sales?</mark> _<mark>Kyklos</mark>_ <mark>, 74(4), 488-511.</mark>

- <mark>Jackson, K. (2013). Diversity and the distribution of public goods in Sub-Saharan Africa.</mark> _<mark>Journal of African Economies</mark>_ <mark>, 22(3), 437-462.</mark>

- <mark>Jensen, J. B., McGuckin, R. H., & Stiroh, K. J. (2001). The impact of vintage and survival on productivity: Evidence from cohorts of U.S. manufacturing plants.</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>, 83(2), 323-332.</mark>

- <mark>Jetten, J., Spears, R., & Postmes, T. (2004). Intergroup distinctiveness and differentiation: a metaanalytic integration.</mark> _
sample to sample variation. If there is a substantial lack of common support simply because of sampling error, then reweighting can also not solve this. To make progress Blanchet et al. (2022) use a replacement step, creating duplicates of observations in the surveys and assigning to each observation above the merging point the average income for its population share (the population share is determined by each individual’s weight and the population size) in the tax data. This replacement step also preserves the distribution of other variables and their relationship with income in the survey data.

Alvaredo’s (2011) analysis for Argentina assumed that the top 1% was completely missing from the household surveys, and therefore tax data should be used for the top 1%. Blanchet et al. (2022) point out that this is an extreme form of reweighting where the weights of the survey respondents are rescaled, so they represent the bottom 99% and then tax data completely represents the top 1%. Alvaredo (2011) showed that the Gini was 6-7 percentage points higher when correcting for missing top incomes using this method for Argentina.

Chancel et al. (2023) estimate inequality and provide distributional national accounts for African countries between 1990 and 2017. In doing so the authors attempt to overcome several challenges, as discussed above, including a lack of income data in many African countries, poor quality of national accounts and the assumed under-representation of the top tail of the distribution. Their starting point is not raw survey data, but tabulations on consumption per capita from the World Bank’s Povcalnet database, from which they impute consumption percentiles. They do use household survey microdata for five African countries, because these surveys asked respondents about income and consumption, and so are used to determine the relationship between the two over the distribution of consumption, meaning consumption can then be used to impute incomes where only consumption data is available from Povcalnet.

Chancel et al. (2023) then use tax data from South Africa and Côte d’
the World Bank Investment Climate Assessments (ICA) that have been conducted in the last few years. About a quarter of the firms on average complain about the lack of external financing, with large variations though, from less than seven percent for Latvia and Lithuania, to more than 50 percent for several countries and a high of 60 percent for Brazil.

Somewhat similar to the households’ analysis is the answer to the question what are the unbanked firms? To the extent we know, profiles are as expected, with size of the firm 10
group may be negatively affected by the program and impact estimates may thus overstate its true effect. In the context here, displacement effects are probably not a great concern during the program and in the first years after the program since the subsidy was paid for maintaining existing jobs instead of creating new jobs. In addition, the crisis resulted in layoffs in both eligible and ineligible industries, so that firms in eligible industries had a pool of unemployed workers with previous experience in eligible industries they could draw on when they started to hire back workers in 2009 and 2010. However, once employment in eligible industries reached pre‐crisis levels (in 2011), it becomes conceptually more likely that new hires may have come from workers in ineligible industries.

I try to assess whether the wage subsidy program had displacement effects using the data from the ENOE, described in section 4.4. The ENOE has a panel nature and aims to follow each household for five consecutive quarters. I first examine whether eligible industries tend to hire away individuals from

> measurement equipment (industry code 334519). In 2009, manufacture of measurement equipment made up only 4.6 percent of the total number of employees in code 334.

> 11 Less than 1 percent of individuals say they have access to medical care through government institutions other than IMSS or that they don’t know whether they have access to such care.

12