rafmacalaba/datause-encoder-data huggingface dataset:
pension system provisions and contribution rates (and other forms of financial saving in the case of housing affordability) and saving for old age.

The paper proceeds as follows: Section 2 reviews the existing literature. Section 3 describes the data and presents the descriptive statistics for saving for old age around the world. Section 4 presents the empirical analysis of saving for old age, in terms of empirical strategy and estimation results with sets of individual, macroeconomic and pension-system characteristics. Section 5 concludes.

# **2. Review of existing literature**

Research has long shown that retirees suffer from a variety of economic hardships, even in developed economies that enjoy relatively strong pension systems. In their study of the United States, Bernheim, et al. (2001) show that consumption drops rapidly at the time of retirement, and decreases even more post-retirement. Using UK data, Bardasi et al. (2000) report that on average, economic well-being declines sharply in the years preceding retirement, and continues to do so through retirement and the years following it,<sup>1</sup> with women facing a swifter decline than men. Among adults in Germany, satisfaction with current household income decreases substantially upon retirement, while satisfaction with free time increases, according to Bonsang and Klein (2011). Retirement has an overall negative effect on life satisfaction, with larger effects for involuntary retirement, and insignificant effects for voluntary retirement.

High levels of poverty among the elderly compound the negative economic consequences of a retirement crisis. In the US, Clark and Quinn (1999) report that the elderly remain disproportionally represented among the near poor and certain older Americans still face substantial risk of economic deprivation, even though their average economic well-being has significantly improved compared to older generations (also see Sabelhaus and Manchester, 1995). Crystal and Shea (1990) also show that resources among the elderly are distributed even more unequally than among the rest of the population.

> 1 For evidence against the negative impact of retirement on well-being, see e.g. Kerwin (2002).

4
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33
> The relative changes are derived from _DPCMREL_ i,c = _PCMPCM_ ( _pre_ ( _crisis_ − _crisis_ )i,c )i,c − 1

> for the price-cost margins and _DESTREL_ i,c = _ESTEST_ ( _pre_ ( _crisis_ − _crisis_ )i,c )i,c − 1 for the EST. The

dummies control for industry-fixed effects. On the country level, we use either dummies or country-level variables. Note that country dummies control, among others, for the severity of the crisis and the consequent impact on average demand. As a robustness check, we include country-level variables to control for a misspecification due to possible omitted institutional differences which impact the dependent variables and are correlated with the interaction terms in Equation (1). Pre - crisis level _Y_ i,c is either _PCM_ ( _pre_ − _crisis_<sup>)</sup> i,c or _EST_ ( _pre_ − _crisis_<sup>)</sup> i,c and controls for country-industry differences in the level of PCM (EST) before the crisis. Note that using pre-crisis values helps in avoiding endogeneity issues. The interaction term consists of the external dependence measure of _young_ firms, taken from RZ, and a pre-crisis institution index. This is the average of the institution index of interest in the pre-crisis period, being law and order, accountability, corruption or a composite of the three. Institutions are better for higher values of the indices. We also use the pre-crisis values of the indices to avoid endogeneity problems.

According to Hypothesis 1, we expect the coefficient of the interaction term, β 2 , to be negative and significant only when we use a corruption index in the interaction term. This means that for more dependent industries in less corrupt countries, the drop of its PCM during a crisis is significantly deeper. This implies that the depressing effect of weakening demand was not compensated for by the uplifting effect of a significant drop in output and competition, causing a larger slide of the PCM. Hence we can interpret this 13
proximately twelve times larger than what was previously estimated.

While these studies provide important insights, the reliance on cross-country data does not allow to adequately address institutional and other differences across countries that matter for the economic impact of climate change but are typically unobservable, raising endogeneity concerns (see, for example, Durlauf, Johnson, and Temple 2009; Hauk and Wacziarg 2009). Cross-country data also neglect subnational variation in (the impact of) climate change which has been found to be substantial (Bettarelli et al. 2024).

Recent literature has extended the level of granularity of analyses to the sub-national level, leading to more accurate estimates. Kotz, Levermann, and Wenz (2024) use a sample of 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes. They suggest a reduction in global output of 19% by 2050, compared to a scenario without climate impact. Burke and Tanutama (2019) assemble panel data on economic output from over 11,000 districts across 37 countries and find that district-level growth declines for higher average temperatures. Carleton et al. (2022) use subnational data from 40 countries to investigate the age-specific mortality-temperature relationships and suggest a nonlinear effect, whereby extreme cold and hot temperatures increase mortality rates.

We contribute to this literature by using more granular spatial data combined with firmlevel information. The more granular spatial data can help address endogeneity concerns since the effect of climate change can vary at a very granular spatial level such as within provinces or districts due to correlations of local geographies with climate and economic activity—more districts with higher elevations, for example, typically have lower average temperatures and economic activity while coastlines have milder climates and higher activity. Moreover, firmlevel data allow assessing how the impact of climate change differs across firms given their ability to adapt. Young or small firms, for example, often face tighter financial constraints or higher costs to access construction permits needed to invest into more resilient buildings. At the firm-level, Somanathan et al. (2021) find that rising heat days in India between 5
28 and 47.5 percent reduction in their homicide rate respectively. Roraima, Sergipe follow them, and Mato Grosso do Sul. Yet, it is important to bear in mind that these states had some of the highest homicide rates in the country, and their rates remain higher than the value predicted by GDP (Figure 6).

**Figure 5: Homicide Rate Change, 2007–2011**

**Figure 6: Homicide Rate and GDP per Capita, 2011**

<!-- Start of picture text --> PB<br>AM AL<br>RN BA<br>GO AL<br>TO CE SE PA<br>MA NE<br>SC PI SP ACRSSOMG NRBRAPRORRMWDFPRMTMS SE ES MA PB CE PARNSEPEBAACAPTO RRAMROGOMGMS MTPRESRSRJ DF<br>RJ<br>PE PI SC SP<br>10 20 30 40 50 60 8 8.5 9 9.5 10<br>Homicide Rate (per 100,000 inhabitants) 2007 LN GDP per Capita (constant 2000)<br>95% CI Fitted values 95% CI Fitted values<br>Difference Homicide Rate (per 100,000 inhabitants) 2007-2011 Homicide Rate (per 100,000 inhabitants)<br>Source : Ministry of Health, SVS 2013. Source : IBGE, Ministry of Health, SVS 2013.<br>Figure 7: Homicide Rate in Bahia, Ceará and Figure 8: Homicide Rate in Minas Gerais and Rio de<br>Pernambuco (per 100,000 inhabitants), 2002–2011 Janeiro (per 100,000 inhabitants), 2002–2011<br>PE: PPV - Bonus MG: Performance MG: 2 nd stage<br>Agreements– 1 st stage<br>RJ: PBM<br>Agreements - Bonus<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Year Year<br>Bahia Ceará Minas Gerais Rio de Janeiro<br>Northeast Pernambuco Southeast<br>20 80<br>10 60<br>0 40<br>-10 20<br>-20 0<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>Homicide Rate (per 100,000 inhabitants) Homicide Rate (per 100,000 inhabitants)<br>0 0<br><!-- End of picture text --> _Source_ : Ministry of Health, SVS 2013.

**Figure 7: Homicide Rate in Bahia, Ceará and Pernambuco (per 100,000 inhabitants), 2002–2011**

**Figure 8: Homicide Rate in Minas Gerais and Rio de Janeiro (per 100,000 inhabitants), 2002–2011**

_Source_ : Ministry of Health, SVS 2013.

_Source_ : Ministry of Health, SVS 2013.

When examining regional patterns, the declining trend in Pernambuco contrasts with the increasing incidence of homicide in the rest of the region, including in reformer states such as Ceará. Pernambuco had a homicide rate of 53 in 2007, but by 2011 it had dropped to 39.2. The change in trend corresponds well with the introduction of the Pact for Life policy and a performance-related reward for state employees in the security sector (Figure 8). Although the state’s homicide rate is rapidly converging to the (growing) regional average, Pernambuco’s homicide rate is still well above the national average.

In the southeast, Minas Gerais’ homicide rate has remained below the regional average and below the level predicted for its GPD per capita. Currently, the state has the second-lowest rate 18
<!-- Start of picture text --> 27 ; 0.8 By 80<br>18 oF 18 fF 70<br>16 16<br>14 06;<br>3 G0 =<br>| ° 14) ra<br>3 0.5 e 6" | 50 id<br>o 1] ;oa & 5 414 a g<br>2 = = =)<br>08 | os & 0.8 20 O<br>o 0.6 0.2 a °*B+ 1-29 Lael&<br>o4 —— O4 + <<br>02 + o1 oz + 10<br>a T T oO oe Crm<br>1990 1995 2000 2005 2010 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text -->

ees Modelled incidence (%), adults 15-49, from Spectrum software Prevalence (%), females 15-24 (source: national DHS/AIS reports) men PreValence (5), females 15-24 (source: national ANC reports) mens ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013
Figure 1: Aggregate Headcount Poverty Rates – GHS (National Poverty Line) a) <!-- Start of picture text --> Aggregate Poverty Rates - GHS (National Poverty Line)<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>child 0-4 child 5-9 child 10-14 child 15-17 adult 18-59 adult 60above adult child<br>Wave 1 Wave 2 Wave 3<br><!-- End of picture text --> b)

# **AGGREGATE POVERTY RATES -GHS (NATIONAL POVERTY LINE)**

<!-- Start of picture text --> adult child child north child south child rural child urban<br>W A V E 1 W A VE 2 W A V E 3<br>65%<br>53% 51% 54% 54% 59%<br>41% 43% 46%<br>29% 23% 20% 31% 27% 21% 33% 20%<br>14%<br><!-- End of picture text --> Source: Authors’ estimations based on GHS Wave 1-3. Note: Individuals with missing age were dropped from the analysis.

Figure 2 a): Rural- Urban Headcount Poverty Rates – GHS (National Poverty Line) Figure 2 b): North - South Headcount Poverty Rates – GHS (National Poverty Line) <!-- Start of picture text --> Rural-Urban Poverty Rates - GHS North-South Poverty Rates - GHS<br>(National Poverty Line) (National Poverty Line)<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Wave 1_R Wave 2_R Wave 3_R Wave 1_U Wave 2_U Wave 3_U Wave 1_N Wave 2_N Wave 3_N Wave 1_S Wave 2_S Wave 3_S<br><!-- End of picture text --> Source: Authors’ estimations based on GHS Wave 1-3. Note: Individuals with missing age were dropped from the analysis.

14
# **Figure 3: Price Indexes (nominal, 2005 = 100)**

<!-- Start of picture text --> Food and crude<br>oil price indexes<br>250<br>200<br>150<br>100<br>50<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>Source : World Bank<br><!-- End of picture text -->

**Figure 4: Stock-to-use Ratio**

<!-- Start of picture text --> 1997-2004 2005-12<br><!-- End of picture text --> — 30 —
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Four of the five economies with the most rapid per capita growth were in SAR and EAP, where per capita growth averaged more than 5.7 percent in 2016-18 and is expected to remain above 5 percent growth in the next three years. SSA and LAC, however, lagged behind other regions.

In SSA, where the majority of the world’s poor live, average per capita output contracted in 2016-18 and is expected to remain near-zero (0.6 percent) in 2019-21. SSA’s three largest economies have witnessed negative per capita growth since 2015-16. Some metal exporters and countries affected by fragility, conflict, and violence have also had weak per capita growth. In contrast, other SSA economies have maintained robust per capita income growth.

Average per capita growth is expected to remain near-zero (0.8 percent) over the next three years. In the region’s two largest economies, the weakness of the last three years will remain. In Iran, U.S. sanctions will weigh on growth. In Saudi Arabia, OPEC restrictions on oil production constrain prospects.

The weakness in EMDE growth in the past five years has set back convergence with advanced-economy per capita incomes. In 2019, per capita income gaps with advanced economies widened in about one-third of EMDEs, and in about two-thirds and one-half in MNA and LAC, respectively.

# 2.5 Poverty and Inequality

The world has made significant strides in reducing the number of poor and the severity of poverty over the past two decades (World Bank 2018b). In 1999, 1.729 billion people lived on $1.90 or less per day (the international extreme poverty line), concentrated in EAP, SAR, and SSA (Figure 5). In 2015, their number had declined by more than half to 736 million; the latest available datapoint. Much of the success in eradicating global poverty came from China and India. In China, the number of extreme poor has fallen from 503 million in 1999 to under 10 million by 2015. As a result, the share of extreme poor in EAP declined to just 2.3 percent of the population in 2015, from 38 percent of the population in 1999. In India, too, the number of people living in extreme poverty declined by 260 million to 176 million in 2015. As a result, the share of extreme poor in South Asia declined to 12.4 percent of the population in 2015 from 39 percent of the population in 1999.

In contrast, in MNA and SSA, rapid population growth has swelled the number of extreme poor, even though, in SSA, they now account for a smaller portion of the total population. In SSA, the number of poor rose by 14 million since 2008 and 32 million since 1999. The countries with the largest increases in the absolute number of extreme poor since 2008 are South Sudan, Madagascar, Nigeria, Malawi and South Africa. In SSA, where 41 percent of the population live in extreme poverty, this share is five times as high as in other EMDEs, on average.

Since the global recession, there is evidence that the rate of poverty reduction has slowed further (World Bank 2018b).<sup>6</sup> Between 2011 and 2013, poverty declined by 1.25

> 6 Studies that looked at the negative impact of the global recession include Chen and Ravallion (2010);
# **1. Introduction**

In 2001, the Government of Thailand launched the Thailand Village and Urban Revolving Fund (VF) program. This was an important component of the electoral platform of newly-elected Prime Minister Thaksin Shinawatra, and envisaged providing a million baht (about $22,500 at the exchange rate of the time) to every village and urban community in Thailand as working capital for locally-run rotating credit associations.<sup>1</sup> The stated purpose of the fund was to “stimulate the rural economy,” with the stated intention of targeting “new activities such as processing and packaging” (Thaksinomics 2006). An element of the platform of the government elected in July 2011 is to add another million baht per village to the Village Funds.

The program was put in place rapidly; by the end of May 2005 the VF committees had lent a total of 259 billion baht ($6.9 billion at an exchange rate of Baht 37.6/$) to 17.8 million borrowers, some of whom had by then borrowed more than once (Arevart 2005). At the time, the Thailand Village Fund was the largest single microfinance scheme in the world.

The first purpose of this paper is to explain the performance of the Village Funds themselves. In this context we are interested in the extent to which they have maintained and/or expanded their lending capacity, their loan recovery and profit rates, and the extent to which their loans go to a wide swath of society (“outreach”). These are important if one is to assess the sustainability of the VF model of microcredit. The second goal of the paper is to weigh the costs and benefits of the Village Fund program, which may potentially serve as a guide to the desirability of expanding it further.

In the following sections, we first summarize the main features of Village Fund lending since 2004, using data from the Socio-Economic Surveys of 2004, 2009, and 2010, and show that the Village Fund remains important by any standard. We then describe how Village Funds, which are highly decentralized, actually operate at a practical level, drawing heavily on the findings of a survey of over 3,000 Village Funds undertaken in 2010. We then develop a model of Village Fund Behavior from which we derive a number of testable propositions. In a subsequent section we refine our measures of Village Fund performance, which we then try to explain using econometric models that are based on our theoretical discussion. The

> 1 The average exchange rate during 2001 was Bht44.51/$, which implies that a million baht are equivalent to $22,468.  The exchange rate as of mid-October 2006 was Bht37.57/$, which  would  value a million baht at $26,617.

2