rafmacalaba/datause-encoder-data huggingface dataset:
increases in the budgetary allowance for the program. Green Card expenditures rose significantly in real terms between 2004 and 2008.

The stated aim of the program was to improve access and quality of essential health services for the uninsured, mostly poor. Apart from raising both the level and quality of care on aggregate, the very concept of insurance, with a defined bundle of inpatient and outpatient health services as well as prescription drugs, was to provide households with uninterrupted access to vital health services, including in times of household level income shocks. The Green Card program, which had started out as a stand-alone program with a separate budget and was the main flagship social protection program of the Turkish government in the past decade, was—as of January 2012—fully integrated into the Universal Health Insurance Scheme covering all Turkish citizens (under Law 3816).

This paper assesses the Green Card program along several dimensions. First, we look at who was reached by the massive coverage expansion between 2003 and 2008. Using repeated cross-section household survey date, we report both average and marginal incidence changes. Second, we assess whether the program had a significant impact on improving the financial protection of households by lowering out-of-pocket expenditures which would have been both impoverishing as well as catastrophic in nature. Third, using a special household survey which was collected during the economic crisis in Turkey in 2008 and 2009, we evaluate whether the Green Card non-contributory health insurance program protected health care utilization during the economic crisis period during which households faced income shocks.

The structure of our paper is as follows: We start in Section 2 by providing information on the institutional set-up and targeting mechanism of the program. Section 3 describes the data used in this paper. Section 4 <mark>presents the results as to who benefited from the massive expansion of the program that occurred between 2003 and 2008. Section 5 reports our results on the financial protection impact of the program while S</mark> ection 6 includes an assessment of the program with respect to protecting the health utilization of the poor in Turkey through the Global Financial Crisis period in 2008-2009. Section 7 concludes.

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diaries (which track the financial lives of poor households at short intervals) indicates strong demand for microcredit, but as part of a patchwork portfolio of products from formal and informal providers that are stitched together because the incomes of the poor are small and irregular, and needs to access reasonably large sums (for health emergencies, weddings, funerals) inevitably arise (Collins, et al., 2009). In short, while microcredit might not be a magic bullet in alleviating poverty, generally it has been shown to be a useful tool for poor households in managing their financial lives.<sup>2</sup> While microcredit products have proved useful to poor households, the industry (or at least a sizable part of it) has moved beyond the relatively rigid group liability products introduced by Grameen. For example, Beck et al. (2011) note that African MFIs have shifted away from group liability lending mechanisms and, in West Africa, only individual liability loans are used. The trend toward individual liability lending has also coincided with greater reliance on commercial sources of funding (see discussion of commercialization in Morduch, 2000; Yunus, 2007; Cull et al., 2009, 2011a). While there is little direct evidence, it stands to reason that the market-based returns that commercial funding requires provide a strong incentive for MFIs to make larger loans to somewhat wealthier borrowers. Those loans are more cost-effective per dollar lent and thus contribute to greater financial sustainability for the commercially-oriented MFIs that specialize in them (Cull et al., 2009).

The increasing commercialization of microfinance has fueled debates about mis-targeting and mission drift, from serving the poor and promoting social inclusion to pursuit of profitability (Yunus, 2007). While there is some evidence that MFIs benefit somewhat wealthier households rather than the poorest in some developing country contexts (Coleman, 2006; Kondo, et al., 2008; Takahashi, et al., 2010),<sup>3</sup> evidence from studies that use larger data sets from MFIs in many developing countries emphasize the diversity of approaches to outreach and profitability within the industry (Cull et al., 2007, 2009). In particular, NGO-based MFIs place greater

> 2 And experimental evidence on microsavings products has shown them to be effective in helping poor entrepreneurs to protect profits and grow their businesses (see, e.g., Dupas and Robinson, 2013 for evidence from Kenya).

> 3 For example, Coleman (2006) finds that participants in a credit program run by two NGO microfinance institutions in Northeast Thailand were significantly wealthier than nonparticipants prior to the program intervention. The difference in wealth was largely explained by the value of female-owned land across households.

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imports of computers and parts falls by about as much as the domestic and export prices.

The effects of these quality change adjustments over the 1953 to 1991 period as a whole, calculated from indexes with 1975 weights, _Ls_ shown in Table 7, for SITC 7 and for all manufactures.

Table 7 Effect of Quality Corrections on Price Indexes for Total Exports of Manufactures by Developed Countries to the World and to Developing Countries, 1991/1953, 1975 Weights

||World|Developing Countries|
|---|---|---|
|SITC 7|||
|Unadjusted Index|488.0|484.6|
|Adjusted for U.S. only|446.4|377.2|
|Adjusted for all countries|340.8|264.9|
|AllManufactures|||
|Unadjusted Index|485.2|453.6|
|Adjusted for U.S. only|458.4|407.8|
|AdJxsted for all countries|397.4|332.9|

Source: Appendix Tables 1, 2, 6, and 7.

The most conservative adjustment, assuming no quality change outside the U.S.., indicates an upward bias in the price indexes for developed-country exports to the world of about .15 per cent per year for all manufactures and .23 per cent per year for machinery and transport equipment (SITC 7) alone. If we adopt the assumption that in the 11 products with quality adjustments prices in other countries followed those of the U.S., the adjustment is much larger: over half of one per cent per year for all manufactures and almost one per cent per year for machinery and transport equipment. The adjustment is even larger for price indexes of exports to developing countries.

The average declines in the adjusted indexes relative to the unadjusted
# 5. Results

## **Five Dimensions of Poverty**

The divergent trends for five different dimensions of poverty illustrate that the progress of development in Armenia has been uneven.<sup>7</sup> Between 2010 and 2015, the share of the population deprived of labor decreased, and between 2010 and 2012 the share deprived of basic needs went up but then fell back (Figure 15). Both trends track slight improvements in Armenia’s economic situation, which is also reflected in a decline in the number living below the upper national poverty line, from 35.8 percent in 2010 to 29.8 percent in 2015. For the health dimension, it appears that individuals experienced improvements in their health status in 2011 and 2012, but thereafter their status worsened. As for housing, the percentage of Armenians deprived of adequate housing continuously decreased until 2013 and then increased again in 2014.

**Figure 15. Nonmonetary Dimensions of Poverty, 2010–15, Percent of Population**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>Labor<br>Housing<br>30%<br>Health<br>Basic Needs<br>Education<br>20%<br>10%<br>0%<br>2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source:_ ILCS data.

Improvements in housing were mostly driven by better access to public services and goods, such as hot running water. The indicators for subjective housing conditions and adequate housing have both been relatively stable over time (Figure 16), though the percentage deprived of healthy heating (those who use wood rather than electricity or gas for heating) has gone up, tracking the rise in utility prices. Whereas

> 7 As discussed in Section 2, because deprivations are determined at the household level, calculated shares correspond to “the share of the population living in deprived households,” shortened for simplicity to “the share of the population”.

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more detail in the next section, Jones (1995) criticism of endogenous growth models applies here. That is, growth rates cannot be made a function of non-stationary parameters unless cointegration between the variables is accepted.

# **3.2 Some Implications for Empirical Testing**

Distinguishing between the role of education as a factor of production, and as a factor that facilitates technology absorption and the production of knowledge, is significant. Any policy measure which raises the level of human capital may only have a oneand-for-all effect in the first framework, but will increase the growth rate of the economy forever in the second one. In such cases, the estimated increase in productivity is not simply a phenomenon in the transitional period since an increase in the flow of education leads to a gradual increase in human capital stock. Implicit is the claim that by increasing the level of education the rate of economic growth will increase over time. Empirically, however, there is no consensus over which is the appropriate approach.

# **_Observational Equivalence_**

A main problem for empirical testing at the macro level emerges from observational equivalence. This means that, despite a number of different ways of hypothesizing how human capital can affect growth, empirical analysis can yield similar predictions regarding the relationship between some human capital variables and some variables of income growth. In other words, apart from data uncertainty, the empirical research seeking to test these alternatives has been hampered by the use of relatively similar econometric specifications. Insofar, macro regressions do not readily allow testing one theory against another. Rather they tend to emphasize an expanded set of variables as suggested by the literature. Consequently, Romer (1990b) argues that the role of an endogenous growth framework is not to generate testable predictions, but rather to guide the process of data analysis.

# **_The Jones Critique_**

A second criticism, especially relevant for the present study, is the seminal contribution of Jones (1995). Testing endogenous growth models in the context of time series implies establishing a relationship between a variable that is usually stationary? without drift? such as income growth, and a variable which is usually non-stationary, such as years of schooling. In other words, his results fundamentally call into question the implicit prediction of many endogenous growth models suggesting output growth should exhibit large permanent increases. Time series data over a very long time period for the United States and other OECD countries reveal that the growth rates of GDP per capita in these countries exhibit little persistent changes, and can be characterized by more or less constant mean.

This observation imposes a testable prediction. According to endogenous growth models permanent changes in certain policy variables, such as schooling, or the number of scientists and engineers engaged in research and development, should have

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years 1979-1981, also in the table, show that the- tax effect disappears for U.S. loans, and is reduced to around 21 per cent for non-U.S. loans. The vanishing tax effect for U.S. loans cannot be due to a change in the tax law, as at that time there had not yet been substantial changes in U.S. tax regulations. Of course, the effect to some extent may be due to rumblings of future changes. More likely, the tax effect declined in importance because (i) banks already had accumulated huge potential tax credits and (ii) in the wake of the second oil crisis bank taxable income was declining.

To explore the latter explanation further, Table 7 reports regression results for different time periods of loans origination for all creditors. The four periods roughly correspond to the periods leading up to and following the oil crises of 1973 and 1979. The actual years these oil crises arose were included in the pre-oil crisis periods, as bank taxable income can be assumed to respond with a lag to a downturn of the economy. Regardless of the exact cut-off points of the specific periods, what emerges is the result that the tax effect on interest rate spreads basically disappeared in the years following each of the two oil crises. Apparently, in those years banks did not value the prospect of tax credits, as taxable income was sufficiently sheltered either because there was no taxable income or because other sheltering methods were sufficient.

To support this, Table 8 provides same data on U.S. bank profitability and taxes for the years 1970-1985. In particular, the table provide data on gross income as a percentage of total assets, and of U.S. tax liability as a percentage of gross income for all banks traded on the New York Stock Exchange, and separately for the top 10 U.S. banks. The table shows that both measures were relatively low during the years 1973-1974 in the wake of 13
<!-- Start of picture text --> Japan<br>Netherlands<br>Doensall<br>Ia<br>aal<br>Slovak RepublicI<br>Canada<br>Czech Republic<br>Denmark<br>United States<br>Republic of Korea i<br>Seay fs,<br>AS ee,|<br>GT<br><!-- End of picture text -->

<!-- Start of picture text --> PolandFrance ——etEEEEEE ||<br>Armenia i<br>Spain ‘<br>Italy i<br>Georgia i<br>Vietnam |<br>COM Dia 0<br>BO V6 t |<br>CS i 1<br>Chao ee | a<br>0 50 100 150 200 250 300<br>= — = PIAAC Average (National) = = = STEP Average (Urban)<br><!-- End of picture text -->
formal manufacturing sector. As shown in Figure 8 adapted from their paper, older firms in the unorganized manufacturing sector employ fewer people than firms younger than 5 years old.

Farazi (2013) analyzes the World Bank informal enterprise surveys covering 2,500 firms across 13 countries in Latin America and Sub-Saharan Africa and finds that the biggest operational challenge identified by informal firms is lack of access to finance. Relative to small firms, informal microenterprises show lower rates of use of bank accounts and rely less on banks and more on MFIs for working capital financing. The usual concerns with high operational costs relative to small loan amounts and lack of collateral that inhibits banks from lending to SMEs is further exacerbated in the case of microenterprises. de Mel, McKenzie, and Woodruff (2011) find that only about 10 percent of microenterprises receive bank loans in Sri Lanka. There is evidence that innovative lending techniques can increase lending to the informal sector. Bruhn and Love (2013) analyze the opening of Banco Azteca in Mexico, which targeted previously underserved low-income clients, by relying on synergies with a large retail chain owned by the same parent company, Grupo Elektra. Bruhn and Love show that the Banco Azteca’s opening promoted the creation and survival of informal businesses and led to increase in total employment and earnings among low-income individuals.

However, their study and others (Khandker, Samad and Ali 2013; McKenzie and Woodruff 2008, Udry and Anagol 2006) show that the real returns to capital are substantially higher than market interest rates among microenterprises. The high returns suggest that these entrepreneurs should be able to grow by reinvesting profits 25
**Table 2. Impact of Pandemic on Student Mathematics Achievement**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Departure from linear trend|-12.20***|-5.27***|-8.70***|-4.74***|-7.50***|
|in 2022|(0.43)|(0.79)|(1.03)|(1.14)|(0.88)|
|UNESCO: number of weeks<br>with full closures||-0.44***<br>(0.04)||||
|UNESCO: number of weeks<br>with full or partial closures|||-0.08***<br>(0.02)|||
|PISA: country average<br>student-provided number of<br>weeks of closures||||-0.32***<br>(0.04)||
|PISA: country average|||||-0.21***|
|principal-provided number of<br>weeks of closures|||||(0.03)|
|_Additional controls_||||||
|Individual level: gender,<br>immigrant background, SES|Yes|Yes|Yes|Yes|Yes|
|Country average: gender,<br>immigrant background, SES|Yes|Yes|Yes|Yes|Yes|
|•<br>country fixed effects|Yes|Yes|Yes|Yes|Yes|
|•<br>country*time|Yes|Yes|Yes|Yes|Yes|
|N|2,896,916|2,896,916|2,896,916|2,791,234|2,791,234|

Standard errors in parentheses. * p<0.05; ** p<0.01; *** p<0.001. Full results available in the Annex.

Two things are worth noting when looking at the estimates in Figure 1. First, the learning losses increase with the length of school closures. This shows that the achievement decline is associated with school closures and not only the overall impact of the pandemic on students. Second, the four measures provide similar results when comparing relative standing of countries in terms of the length of school closures. Countries with the shortest closures experienced relatively small losses, 9%-12% of a SD, or to 4-6 months of learning. Countries with average length of school closures experienced losses of 13%-14% SD, or 7 months of learning. Countries with the longest closures, experienced losses of 17%-23% SD, or 9-12 months of learning.

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males proposing risky sex. If male utility in the absence of risky sex is smaller than his outside option, he may not enter into partnerships at all. We note in Table A8 that males in the _Boys_ treatment arm are significantly more likely to spend time on sports and leisure at endline than males in control communities—even though the soccer intervention ended more than 6 months prior to the endline survey. Thus, some proportion of the decline in sexual activity could be offset by an increase in male engagement in sports.

Figure 4 presents a summary of treatment effects across outcomes for the _Boys_ treatment ( _β_ 1 from equations 1 and 2), highlighting improved violence attitudes and SRH perceptions for males and reductions in sexual activity. The findings are broadly compatible with IPV reductions being driven by changes in both the expressive and the instrumental motivations for violence.

# **6.4** _Goal_ **Treatment Mechanisms**

# **Applying the model**

The _Goal_ treatment aims to strengthen females’ commitment to adopt safe sexual behaviors to remain healthy. In the model, this would cause a rightward shift in the distribution of _Rf_ , such that _G_<sup>_′_</sup> _f_<sup>(</sup><sup>_Rf_)</sup><sup>_>Gf_(</sup><sup>_Rf_).Thiscanleadtomoreorlessviolence.Ontheone</sup> hand, some of the females who would have played Y to risky sex in the absence of the intervention—and therefore would not be exposed to violence—will play N in the presence of the intervention, running the risk of a violent response. By increasing the probability that the female plays no and not exit (N, NE), given by ∆ � _Gf_ ( _Rf_<sup>_∗∗_)</sup><sup>_−Gf_(</sup><sup>_R_</sup> _f_<sup>_∗_)</sup> �, the intervention could increase violence.

On the other hand, some of the females who would not have exited a violent relationship in the absence of the intervention—thereby being exposed to violence—will now exit the relationship (escaping violence). By increasing the probability that the female plays no and exits (N, E), given by ∆ �1 _− Gf_ ( _Rf_<sup>_∗∗_)</sup> �, the intervention can reduce violence. The net effect on violence will depend on the relative change of these two probabilities, and is ultimately an empirical question.

The location of _Rf_<sup>_∗∗_</sup> determines whether the increase in the probability of leaving 28
The 1985-1994 Global Real Estate Cycle. Page II the peak year of 1993, inflows were very large: USD 24.7 bn representing 7.6 percent of GNP. No data on real estate prices were obtained on Mexico in this review.

Figure 5. Surges of Capital Inflows into Mexico, Spain and Thailand <!-- Start of picture text --> 1 4.0-<br>12.0 - - - - - - - - - - - - - - - - - - - - - - - - - - -<br>10.0 - - - - - - - - - - - - - - - - - - - - - - - - -<br>8.0<br>C0 .<br>C _ _*<br>a2.n<br>Three Previous Years Year 2 Year 4<br>Year of Surge<br>IOSPAIN (1987) ETHAILAND (1988) OMEXIC0 (1969)<br><!-- End of picture text --> Source: Schadler _et al_ [1993] Other cases of recent real estate booms like Colombia (1990-93) are coincidental to the global boom but have distinct causes. Like in many other cases, the surge of capital inflows into Colombia was associated with the opening of the economy 1990 and major domestic financial reforms starting the same year. However, Colombian authorities are much less concemed in such one-time adjustments than with permanent large capital inflows of the "Dutch Disease" variety caused by the opening of the large Cusiana oil fields and the attendant increase in permanent income and wealth. The new real estate price boom has been much sharper than any previous local cycle in the post W.W.Il period. [Renaud, 1994].

# III. DOMESTIC FACTORS AFFECTING REAL ESTATE VOLATILITY

Differentiating the quantitative effects of international factors from domestic ones in the synchronous national real estate cycles which we observe in this first global real estate cycle could prove difficult. The explosive phase of international capital exports by Japan coincides in individual countries with major episode of capital market liberalization, financial deregulation, occasional macroeconomic enmrs induced by the stock market crash of October 1987, as well as instances of significant deregulation of the real estate market Synchronism in office real estate cycles across coumtries may have been fed by
but a positive effect on labor taxation. Although the sources of variation and identification involved are different in our three our results are strategies empirical specifications, consistent across them and robust to a range of sensitivity checks.

To better understand these results, we study potential mechanisms using event studies and the instrumental variable research designs. Consistent with the tax-capacity hypothesis, we find that trade liberalization leads to a rise in the fraction of domestic product that originates from the corporate sector (at the expense of the non-corporate business sector) and to an increase in salaried employment (at the expense of self-employment). These changes lead to a growing fraction of output being produced and income being earned in sectors that are more visible and easier to tax. We also find that the positive impact of trade on capital taxation, in addition to being concentrated in developing countries, is stronger in populous countries and in countries with restrictions on capital flows. This finding is consistent with the notion that large countries and countries managing their capital accounts are less exposed to the race-to-the-bottom effect that has pushed capital taxation down in high-income countries. Last, trade liberalization is actually associated with a decline in statutory corporate tax rates across all countries, but more so in high-income countries. On net, the trade-induced increase in tax capacity dominates the statutory tax rate reduction in developing countries, and vice-versa in rich countries.

The rest of the paper proceeds as follows. In Section 2, we relate our work to the existing literature. Section 3 describes the methodology and data collection. Section 4 presents our findings on the evolution of effective tax rates over the long-run. In Section 5, we present graphical evidence on the association between trade openness and effective tax rates. Section 6 studies the impacts of major trade liberalization events. Section 7 presents instrumental variable estimates of the effect of trade liberalization and investigates heterogeneity and mechanisms. In Section 8 we analyze episodes of capital liberalization. Section 9 concludes. The paper is supplemented by an Online Appendix that provides step-by-step details of data construction and additional results.

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