rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

In 2019, Sri Lanka reached the status of an upper middle-income country,<sup>1</sup> just crossing the target rate for gross national income (GNI) per capita of USD 3,996. The national poverty headcount ratio has also continued to decline, with extreme poverty restricted to a few geographical areas of the country. Despite this, a number of Sri Lankans still live just above the extreme poverty line. It is estimated that 8.7 percent of the population survives on USD 3.20 per day.<sup>2</sup> <mark>Against this backdrop</mark> , ensuring inclusive growth will be crucial to sustaining Sri Lanka in the upper-middle income bracket in the years to come.<sup>3</sup> Inclusive growth is a long-term concept, where the idea is to encourage productive employment rather than simply income redistribution (see Ianchovichina and Lundstrom, 2009). Inclusive growth entails improving the pace of growth, expanding the economy’s size, and providing a level playing field for investments, all leading to more productive employment opportunities.

Financial inclusion<sup>4</sup> is an important enabler of inclusive growth as it helps people to invest in the future, manage financial shocks, and smooth household consumption, all of which can contribute to reductions in poverty and inequality. It is a means through which people at both low and high levels of income can be integrated into the formal financial system, thereby contributing to the formal economy (see Martinez, (2011) and Zulfiqar et. al. (2016)). For businesses, financial inclusion enables critical investments and access to capital. Therefore, access to and usage of formal finance contributes to inclusive growth from multiple angles (see Banerjee & Newman, (1993); Galor & Zeira, (1993); Aghion & Bolton, (1997), Beck, Levine and Loayza (2000); Klapper, Laeven, and Rajan (2006); Beck, Demirgüç-Kunt, and Levine (2007); Demirgüç-Kunt and Levine (2009); World Bank (2008) and Demirgüç-Kunt et al. (2017)).

> 1 The World Bank defines middle-income countries in two categories: (1) lower-middle-income economies with a GNI per capita between USD 1,026 and USD 3,995 and (2) upper-middle-income economies with a GNI per capita between USD 3,996 and USD 12,375. Source: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519world-bank-country-and-lending-groups.

> 2 https://www.worldbank.org/en/country/srilanka/overview#4

> 3 Inclusive growth refers to both (a) the rapid ‘pace’ of growth leading to poverty reduction as well as (b) the ‘pattern’ of growth to span across sectors and be inclusive of the country’s labor force (Ianchovichina, E. and Lundstrom, S., 2009).

> 4 Financial inclusion means that individuals and businesses have access to useful and affordable financial products and services that meet their needs – transaction accounts, payments, savings, credit, and insurance – delivered in a responsible and sustainable way (https://www.worldbank.org/en/topic/financialinclusion/overview).

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15 is disrupted, i.e. we consider all values of _ν ∈_ [0 _,_ 1]. Later we discuss which values are most likely for each country group, depending on school closures, technology, etc.

# **3.1.2. Employment Shock**

**Incidence** The employment shock affects mainly workers with sub-college education and the young. We assume that, if the unemployment rate among adults without college education rises by a certain amount _x_ , then unemployment among college-educated adults rises by one half of that amount (0 _._ 5 _x_ ). Youth unemployment is more severely affected so we assume that if unemployment among adults without college rises by _x_ youth unemployment rises by 2 _x_ . We explain in detail in Web Appx .Section D how we arrived at these factors, based on ILO (2020) data over the COVID-19 pandemic.

**Severity** We examine an increase in overall unemployment rates ranging from zero to ten percentage points. This is achieved by varying _x_ between 0 and the value that leads to overall unemployment increasing by 10 percentage points in each economy. Web Appx. Section D explains in detail how we arrived at this range, but note that the ILO (2020) nowcasting model estimates that total working hours (including unemployment, inactivity and reduced hours) lost worldwide in 2020 were 8 _._ 8 percent, varying from 6 _._ 7 percent in LIC up to 9 _._ 5 percent in MIC, making 10 percent a reasonable upper bound. **Duration** Hall and Kudlyak (2021) find that recoveries from unemployment shocks regularly occur following a proportional factor _ρ ∈_ (0 _,_ 1) – that is, given an initial employment shock _x_ 0, thereafter _xt_ +1 = _xt × ρ_ . Based on employment data from across the OECD, we calculate a proportional factor of _ρ_ = 0 _._ 7143 (see Web Appx. Section D).

Of course such an economy would never fully recover: even though lim _t→∞ xt_ , we would still have that _xt >_ 0 _∀t_ . Having a date at which the recovery is complete is necessary to distinguish cleanly between the direct impact of the shock through loss of employment and the long run impact through lost human capital. Based on these criteria, we proceed as follows. First, we assume that the full recovery takes 10 years. Assuming a decay factor of 0 _._ 7143, by ten years there would be very little residual deviation from steady state GDP (0 _._ 7143<sup>10</sup> ≈ 0 _._ 03).<sup>32</sup> Then, we subtract this deviation from _xt_ for all _t <_ 10 – so the last factor is now zero. We set _xt_ = 0 for _t ≥_ 10. Finally, we multiply all the factors by a number (slightly larger than one) to ensure that the initial

> 32In addition, the employment recovery from the Great Recession of 2008 and onwards, which was a smaller employment shock, took over ten years to be completed: see Web Appx. Section D.
of a shift in expectations, people may have become more frustrated with difficult-to-measure factors related to quality, such as the deterioration in the quality of public services, the ability to get good quality jobs, and institutional and environmental quality. Worsening of other subjective indicators, such as the ability to voice concerns and demand accountability and the incidence of corruption and cronyism, may have also contributed to deterioration in well-being.

Motivated by the need to understand the ‘unhappy development’ paradox in developing MENA, this paper empirically tests which factors are associated with life dissatisfaction in MENA countries in the years immediately preceding the Arab Spring uprisings (2009-10), taking into account objective and perceptions data regarding different aspects of life and society. In addition, we compare the extent to which the factors associated with life dissatisfaction are also associated with the Arab Spring social upheaval in developing MENA.

The paper adds to the literature in three ways. To our knowledge, we are the first to examine empirically the relative importance of different explanations provided for the declining life satisfaction in developing MENA in the wake of the Arab Spring. In particular, we examine several explanations or hypotheses for the fall in life satisfaction in developing MENA countries, including dissatisfaction with: (1) the political system of autocracy and limited civil freedoms, (2) the standard of living, (3) the high unemployment and poor quality jobs, and (4) corruption and crony capitalism. Second, we investigate systematically the factors behind the decline in life satisfaction by decomposing the decline into two components: a first-order effect associated with changes in the prevalence of dissatisfied individuals and a second-order effect associated with changes in the relative importance of these factors or perception domains for life satisfaction. In other words, this decomposition allows us to determine whether life satisfaction declined because a greater percentage of people became more dissatisfied with certain domain satisfactions or whether the relative importance of the domain satisfaction for subjective well-being increased. Third, we compare the factors related to unhappiness in developing MENA with the perceived reasons for the Arab Spring uprisings. We find that the main perceived reasons for the uprisings are the factors associated significantly and negatively with subjective well-being levels in developing MENA during this period. Our findings suggest that perceptions provide valuable information about public preferences and needs, which are typically not reflected in objective data (Veenhoven 2002). In other words, we make the case that both objective and subjective (or perceptions) data matter for understanding the root causes of political violence (cf. Okulicz-Kozaryn 2011).

The remainder of this paper is organized as follows. Section 2 presents the potential root causes of dissatisfaction with life in developing MENA. Section 3 discusses the concepts, methodology, and 5
stock prices. An increase of one standard deviation in exposure to the energy sector increases CDS spreads by 10.4 percent (column 3) and reduces stock prices by 3.2 percent (column 4) after the shock. One reason why banks with higher exposure to the energy sector were more affected by the shock is that their borrowers were in distress. The results in column 5 confirm this, as the delinquency rate after the shock increased substantially, given the ex-ante bank exposure. An increase of one standard deviation in exposure to the energy sector leads to an increase in delinquencies in the portfolios of banks by about 0.16 percentage point (roughly 8 percent).

# **4.2. Impact on Credit to Energy Borrowers**

The results suggest that banks with higher ex-ante exposure to the energy sector expanded their lending to the energy sector after the collapse of energy prices. In this section, we use loanlevel data on the universe of loans to energy sector borrowers to document how the credit terms of firms in the energy sector changed in response to the energy price shock.

Table 3, panel A, summarizes the results of our benchmark equation 2 on three credit outcomes: (i) total lending, (ii) lending for working capital, and (iii) lending for investment projects. All the regressions include _Bank*Firm_ and _Month_ fixed effects, and the regressions displayed in columns 2, 4, and 6 further include fixed effects at the _Firm*Month_ level. The inclusion of the latter limits our sample to firms that borrowed from more than one bank at a given point in time. However, this helps us isolate time-varying changes in the demand for credit of borrowers in the energy sector. This is important, as the decline in energy prices directly impacted producers’ revenues, forcing them to demand more external funds.

Columns 1 and 2 corroborate the earlier finding that banks with higher ex-ante exposure to the energy sector channeled more credit to the sector. Once we control for time-varying changes in the demand for credit, we find that banks that were more exposed ex-ante injected more credit in energy borrowers. An increase of one standard deviation in ex-ante exposure to the energy sector leads to an increase in the value of loans to firms in the energy sector of around 18 percent. Columns 3 to 6 show that the increase in credit was mainly for working capital, reflecting that distressed energy firms financed their working capital needs rather than starting new investment projects. An increase of one standard deviation in ex-ante exposure to the energy sector leads to an increase in lending for working capital of almost 85 percent, but it has no impact on lending for investment.

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Columns (9)-(12) show that countries with financial sector strategies are less vulnerable to banking crises or experience lower output losses from banking crises. More comprehensive strategies can reduce the cost of financial crises even further. For instance, a country that was hit by a banking crisis and did not have a strategy suffered on average some 23% GDP loss,<sup>15</sup> but had it used a quality strategy with a rating of 9 (the maximum in our sample), the loss would be almost halved (-1.011*9 = -9.099 percentage point of GDP).

The coefficients on the control variables provide interesting insights into the effect of macroeconomic and institutional variables on stability. Good governance can help reduce the volatility of both variables, whereas financial openness is associated with lower volatility only of deposits to GDP. High inflation as well as interest rates can increase the volatility of credit to GDP.

Time dummies signal year-specific effects across the countries in our panel. All time dummies are positive and significant in the regression that explains the volatility of credit to GDP, with the years 2000-2003 displaying the largest effects, while for the volatility of deposits to GDP only the 2007-2011 dummies positively have affected the volatility, very likely due to the conditions during the financial crisis.

# **4.e. The impact of strategies can partly work through improvements in the regulatory framework**

Table 6 presents the results of regressions that measure the effect of strategies on the quality of regulatory policies governing finance (each regression relates one strategy indicator to one regulatory indicator). We use the distance to the frontier DTF<sup>16</sup> measure for the three regulatory

> 15 Median output loss during banking crises calculated using Laeven and Valancia (2013) data set. The corresponding average output loss is about 30.1% of GDP.

> 16 An economy’s distance to the frontier is the difference between a financial sector outcome for that economy and

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Zambia.<sup>15</sup> The opportunities reported in table 1 cover two aspects of well-being: education and health care.<sup>16</sup> An important consideration in the selection of opportunities is the extent to which quality of service is considered. Basic goods and services are not usually homogeneous: their quality varies tremendously. This is particularly true for education and health care analyzed in the HOI framework. A relatively simple approach to measuring education quality is to focus on timely progression through school. While going to school provides a sense of inclusion, timely progression may reflect children’s adequate progress. This is, of course, no substitute for a more direct measure of learning, such as standardized test scores. Unfortunately, information on test scores—even when available—cannot be readily matched to household survey data. For health care opportunities, quality is partly accounted for in the definition of the opportunity itself, by differentiating public compared to private provision or—when information allows— defining access more specifically as timely and affordable access to medical attention when demanded, as in the case of Paraguay or Tajikistan (table 1).<sup>17</sup> Circumstances are related to inherent characteristics of the child and his or her household for which the child has no control over. Circumstances may include those that are specific to every child— gender, age, birth order, orphan condition—and the household—such as number of children, household per capita incomes, presence of both parents (or household head and his/her spouse/partner), and geographical location. Furthermore, religion, language, and ethnicity (of the household head) are sometimes included in the analysis. The characteristics of the household head constitute a proxy for the features of the child’s parents that, sometimes, are not easily identifiable for each specific child within a household from the household survey.

> 15 The selection of these countries responds to an analytical demand from a country and should not be taken to indicate either a favorable or unfavorable status of their equality of opportunities.

> 16 In addition to housing/habitational opportunities, this constitutes a typical set of opportunities used in HOI literature. However, some studies have also experimented with other sets of opportunities, such as normal anthropometric indicators (normal body mass index, normal weight for height, and normal height for age) as a health outcome opportunity; adequate toys and adequate psychomotor development as early childhood development opportunities; and playing sports and maintaining safe sexual practices as youth development opportunities.

> 17 In the case of housing opportunities, for example, most household surveys allow discriminating across different types of safety of water and sanitation services. This is not the case, however, for electricity provision; household surveys do not capture quality issues such as frequency and severity of blackouts or disruptions in service.

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The linear liabilities-to-GDP variables, however, are now estimated with a coefficient of -0.008 that is significant at 5 percent in the CDS regression 9 of Panel B, while the quadratic liabilitiesto-GDP variable obtains a coefficient of 0.003 that is insignificant in this regression. These point estimates suggest that essentially all banks (with liabilities to GDP less than 2.7) can reduce expected losses on their liabilities by increasing their systemic size, as evidence of TBTF.

The results in both panels of Table 7 suggest that the relationships between the market-tobook ratio and the CDS spread on the one hand and systemic size on the other may be non-linear. One way to deal with this is to introduce a categorical variable indicating whether systemic size exceeds a certain threshold level. The regressions in Table 8 include the Big0.5 variable, which equals one if the liabilities-to-GDP ratio exceeds 0.5 while it is zero otherwise. The table again contains two panels, with Panel A including only the bank and banking-system size and risk variables, and Panel B in addition including a range of control variables. In regressions 1-5 of either panel, the market-to-book value is the dependent variable, while the CDS spread is the dependent variable in regressions 6-10. Regressions 5 and 10 in either panel differ from the other regressions in that the fiscal balance rather than the public debt ratio is the included public finance variable. This leads to a reduced sample size in these regressions, as fiscal balance information is available from the IMF and OECD only for recent years for many countries.

The Big0.5 variable enters the market-to-book regression 1 and the CDS regression 6 with negative but insignificant coefficients in Panel A of Table 8. In regressions 2 and 6, an interaction term of Big0.5 with the public debt ratio is included as well, yielding insignificant coefficients in both regressions. Regressions 3 and 7 instead include an interaction term of the Big0.5 variable with the bank stock risk variable. Now the Big0.5 variable obtains a negative coefficient of -0.454 that is significant at the 5 percent level in the market-to-book regression, while its interaction with bank stock risk obtains a positive coefficient of 0.763 that is significant at 10 percent. This suggests that systemically large banks see their valuation increase relatively more with higher bank risk at the margin, as bank risk apparently adds to these banks’ TBTF status.<sup>8</sup>

> 8 In line with this, we see in Table 6 that systemically large banks have a higher average stock market volatility, even if the difference from smaller banks is not statistically significant. In a study of bank risk, Gonzalez (2005) finds that regulatory restrictions increase banks’ risk-taking incentives by reducing their charter value.

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3 these counties, but also at the impacts on _all_ health facilities (township health centers and county hospitals) in these counties. Our results are based on a comparison of changes before and after the program’s introduction between households and facilities covered by the program and those not covered by it. We couple this double-difference or difference-in-differences approach with matching methods to reduce the possible biases due to the two groups having different preprogram characteristics that may influence both changes in outcomes after the program’s introduction and the household’s or facility’s coverage status. In our analysis of households, we look at impacts not only for the sample as a whole but also for selected income deciles, allowing us to explore possible differential impacts between poor households and others further up the income distribution.

The paper is organized as follows. Section II provides a brief description of the NCMS. Section III outlines our methods. Section IV presents our data. Section V presents the estimation results of the model we use to estimate the propensity score for our analysis of household impacts, and presents the results of our balancing tests. Section VI presents our estimates of the program’s impacts, and the final section (VII) contains a summary and discussion.

# II. **THE NEW COOPERATIVE MEDICAL SCHEME**<sup>**8**</sup>

NCMS differs from the old CMS in several key respects. It is a voluntary scheme.<sup>9</sup> However, to make it fairer and financially more attractive to low-risk households, contributions are supplemented by government subsidies. Another key difference between NCMS and the old CMS is that the new scheme is to operate at the county level rather than at the village or township level, thereby providing for a larger risk pool and for economies of scale in organization and management.<sup>10</sup> Counties are being given considerable discretion in the design of NCMS—the risks covered, the emphasis on inpatient and outpatient expenses, the use of demand-side and supply-side cost-sharing, and so on. One reason for this was simply an acknowledgement that local choice on design issues is an integral feature of China’s highly

> 8 This section draws on a county program survey that was done along with the household survey on which this paper is based. More details about the survey are provided in the data section below. For information about design and implementation of the NCMS, also see Mao (2005).

> 9 At least in part, this decision was motivated by widespread dissatisfaction in rural areas with a proliferation of fees and taxes. In order to reduce the tax and fee burden of rural residents, the government has eliminated a number of rural taxes and reduced others (Yep 2004; Lin 2005) (Tao and Liu 2005). In this context, it was seen as difficult to introduce a new mandatory charge. 10 Most rural counties have a population ranging from 200,000 to 300,000 people.