rafmacalaba/datause-encoder-data huggingface dataset:
was designed in collaboration with the bank with the intent to raise the repayment rate, so that repayment by the deadline is the natural outcome to examine. Moreover, it is important to note that we are limited in our ability to evaluate outcomes measured after this deadline. Text messages were always sent on the sixteenth day of the month. However, after the eighteenth day of the month, delinquent customers are reported to the credit registry and may receive phone calls from the bank, so that we no longer have full experimental control over the sample. In particular, the bank’s actions after the deadline might interact with a customer’s treatment status, so that the impact of our intervention on outcomes other than repayment is not causal and must be interpreted with caution.<sup>33</sup>

## **2.5 Estimation**

Since treatment status was randomly assigned, our identification strategy is straightforward. We identify experimental treatment effects, using regression models of the form: where _Yi_ is an indicator for customer _i_ repaying an amount equal to or greater than the required minimum payment within the deadline. The variables _Ic,i_ are indicators for customer _i_ being in category _c_ , where _c_ indicates the experimental treatment condition to which investor _i_ was assigned. In all of regressions, the omitted category is the control group that received only a basic reminder but no second text message two days prior to the deadline. Finally, in some specifications we include control variables: **X** _i_ is a vector of controls that includes either month fixed effects only, or month as well as a set of customer and account characteristics.

# **3 Main Results**

## **3.1 Moral Incentives**

We begin by comparing raw minimum repayment rates, shown in Figure 3. Since the moral incentive group was oversampled relative to the control in the last wave of the experiment and no placebo messages were sent during that month, we exclude these observations from the figure to keep all treatments arms comparable. Compared to the control group, the share of customers making at least the minimum payment increased by 18% (from 34% to 41%) under the moral incentive treatment condition. The difference in repayment rates is significant at the 1 percent level (pvalue=0.001). Table 2 displays the results in regression format. In column (1), we report the effect of the moral incentive treatment compared only to the control group across all waves and without

> 33For example, the bank’s collection department might expend greater effort on calling customers from a group that had a lower average repayment rate as a result of the intervention.

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changes in the south may reflect a continuing process of land consolidation through quasi-market processes that predate the reforms.

One test of PILE takes the form of a “proof by contradiction”: we show that, under certain assumptions, PILE implies things that we do not find in the data. To see why, consider a stylized version of PILE according to which initially non-poor farmers become poor after abandoning their land. This would clearly put upward pressure on the poverty rate among both the landless and those with land. For rural Vietnam, the impact on the poverty rate among the landless will exceed that for the group with land. This is readily verified if we note that moving one non-poor household out of the group of farmers will increase the poverty rate by _L_ > 0 _L_ > 0 _L_ > 0 _L_ > 0 _L_ > 0 _dH_ = _H_ / _N_ (where _H_ and _N_ denote the headcount index for those with land and the number of such respectively) while adding one poor household to the group of landless will increase the poverty rate in that group by _dH L_ = 0 = 1( − _H L_ = 0 /) _N L_ = 0 (in obvious notation) We find that 1( − _H L_ = 0 /) _H L_ = 0 > _N L_ = 0 / _N L_ > 0 in all years, using the estimates from rural Vietnam _L_ = 0 _L_ = 0 _L_ > 0 _L_ > 0 from Table 1, implying that _dH_ / _H_ > _dH_ / _H_ . The proportionate increase in the poverty rate will be higher for the landless. On top of this effect of rising landlessness, we can allow for an independent trend due to other factors. For lack of a more plausible assumption, we assume that the proportionate rate of poverty reduction due to other factors is the same between the two groups. Under PILE, we would then expect that poverty will fall less rapidly among the landless, given that rising landlessness will put a break on their rate of poverty reduction. That implication is not borne out by the data for Vietnam as a whole; indeed, the trend rate of poverty reduction between 1993 and 2004 is slightly higher for the landless (Table 1). However, while this version of PILE does not offer a convincing explanation of these data, the above test cannot reject PILE for the MD, where the rate of decline in poverty is lower for the landless.

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stimulate innovation are unlikely to yield a high return if not complemented with a sufficient stock of advanced human capital (De Ferranti et al. 2003). Therefore, educational systems need to be of high quality and ensure that graduates have skills relevant to the labor market.

Human capital formation may be a strategic priority even before a country faces unmet demand from industry. Evidence suggests that technologies not only increase demand for skills, but new technologies are adopted more extensively in organizations with a high share of skilled workers (Doms et al. 1997). Large increases in the supply of workers with university training in the 1970s contributed to the rapid technological change of the time and accelerated the demand for skills (Acemoglu 1998). South Korea, for example, started to expand higher education significantly in 1960s, which not only enabled but also contributed to the rapid progress in later years (World Bank 1999).

# **2.1 University education in Latin America**

Evidence suggests that jobs presupposing university education have grown faster than jobs requiring lower skill levels in Latin America. Moreover, new technologies have increased the relative productivity of educated workers in the region. As a result, the relative wages of workers with higher education are on the rise everywhere in Latin America. In Argentina, for example, returns to university education rose 50 percent between 1992 and 2002, while returns to primary and secondary education remained largely unchanged (Patrinos and Vegas 2005).

Countries in Latin America have responded to increased demand for advanced skills by expanding and diversifying the supply of university education. In 2001, 23 percent of Latin Americans 18-24 were enrolled in postsecondary institutions. This represents an annual growth rate in enrollment of more than 2 percent since 1985. Uruguay, Argentina and Peru and notably Chile are among the regional leaders, with enrollment rates of more than 30 percent. Despite impressive growth, however, Latin America still lags behind leading economies. Among the OECD countries the average higher education enrollment rate is currently 69 percent (Figure 1).

**Figure 1. Gross higher education enrollment in Latin America and the OECD, 1965-2003**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>OECD Chile Uruguay Argentina Peru Colombia Mexico El Brazil Honduras<br>Salvador<br>1965 1975 1985 1995 2003<br>Tertiary gross enrollment rate<br><!-- End of picture text --> Source: World Bank (2006) and for Argentina: INDEC (2003) and SPU (2003) 7
networks, and other infrastructure. Estimates suggest that between a third and two-thirds of education and health services now completely bypass the state (van de Walle, 2001, 100). The rise in the number of NGOs from less than 200 in 1909 to nearly 1000 in 1956 to over 20,000 currently points to their growing importance in public goods provision (Union of International Associations (UIA), 2005).<sup>3</sup> The World Bank, international donor institutions and governments are encouraging non-state actors to play a larger role in the provision of public goods and social welfare in developing countries and emphasizing publicprivate partnerships. Their rationale for expanding non-state welfare provision generally emphasizes the inability of state institutions to meet basic welfare needs (Devarajan and Reinikka, 2004; Edwards and Hulme, 1996; Marcussen, 1996) or the greater ability of community-groups and NGOs to identify and tailor projects to local-level needs (Mansuri and Rao, 2004; Platteau, 2004). Increasingly, governments are contracting service provision out to private actors in order to create competition where government agencies are viewed as corrupt or inefficient (Smith and Lipsky, 1993).

There is some evidence that foreign aid has harmful consequences for a country’s political development (Azam, 2001; Brautigam, 2000; Kaldor, 1963; Knack, 2001). Governments dependent on foreign aid also have fewer incentives to build or improve domestic revenue collection, are more accountable to external donors than to domestic parliaments and constituencies, and are therefore less likely to build accountable political institutions (Moss, Pettersson and van de Walle, 2006). One study found that local governments that rely on transfers from external donors or the national government for revenue are less responsive to their constituencies development needs than local governments that depend on local tax revenue (Gibson and Hoffman, 2005).

In Zambia, for example, World Vision among other international NGOs has been providing a substantial number of services for Zambia’s citizens. World Vision estimates that about one-quarter of Zambia’s population are benefiting from this work. In 2008 to 2009, I carried out a unique survey throughout the Southern, Eastern and Copperbelt provinces.<sup>4</sup> In a ward in

> 3The amount of discretionary funding that high-income countries have given to NGOs to promote international development assistance has risen from a negligible amount before 1980 to nearly $2 billion in 2004 (OECD, 2010). Currently, the NGO sector currently constitutes a 1.1 trillion dollar industry (Edwards, 2004, 21)

> 4This fieldwork was carried out as part of my field work for my PhD dissertation. The

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that the long-run relationship can be estimated from the cross-country variation in the data. Although for efficiency purposes time-series data are also used, the within-country variation is quite small relative to the cross-country variation. Therefore, identification of long-run parameters comes from the comparison across countries. Figure 1 illustrates this point for the relationship between informal employment and GDP per capita.

The second assumption is that the speed of adjustment to the (equilibrium) trend relationship is homogeneous across countries. This implies a stable dynamic process for informal employment that is common to all countries, including those whose limited time-series data would not clearly reveal such a process. Finally, the third assumption is that the short-run response of informal employment to cyclical movements in aggregate output is heterogeneous across countries but in a systematic way that links this response to specific country characteristics. Next we describe how these assumptions affect the long- and short-run specifications of the empirical model.

# Long Run

First we examine whether in the long run informal employment is determined by the flexibility of the formal business environment, the quality of public services available to formal enterprises (e.g., the police and judicial system), and the enforcement of taxes and business regulations. The first two factors should determine the opportunity cost of informality, while the last one would represent its direct cost. Since informal – employment is also related to other features of underdevelopment such as the lack of education, rudimentary infrastructure, and laggard technology – we also relate the level of informal employment to a country’s per capita GDP. In light of these considerations, a straightforward representation of the long-run regression equation is given by: 12
The most extensive study of investment of pension reserves in Latin America was a study by Mesa-Lago (1991) published by the World Bank. Returns data were taken from this study for Costa Rica, Jamaica, Mexico, Peru and Ecuador. Venezuelan rates of return were taken from Marquez (1992). Trinidad and Tobago reported their returns in the report on the Sixth Meeting of Heads of ISSA Member Organizations in the English-Speaking Caribbean, Geneva 1988 which took place in Bermuda. Chilean returns were taken from Iglesias and Acufia (1992). The U.S. Social Security Administration provided OASDI Trust Fund rates of return which are not published in the annual Statistical Bulletin of the SSA. The Canada Pension Plan Advisory Board published the returns of the CPP and QPP in their 1991 Report.

# C. **Administrative Costs**

Concepts and Definitions. Pension administration involves several functions, the most important of which are collection, record-keeping, benefit distribution and management of reserves (when these exist). This production function in the provision of public pensions is analyzed in more depth in James and Palacios (1995) and problems associated with comparisons of administrative costs of public pension schemes are discussed in Valdes-Prieto (1994), James and Palacios (1995), and Ping, Sunden and Mitchell (1994). Costs of public schemes are often understated in official reports because some cost items (such as capital expenditure and depreciation and collection costs) are omitted or borne by other agencies. Individuals also bear non-trivial compliance/evasion costs on the tax collection side and time costs on the benefit application side neither of which are included in official statistics.

Issue Brief 5 also points out that the ratio of costs to benefit expenditures is biased against new or "immature" schemes while the ratio of costs to contribution revenues is biased against schemes with relatively low contribution rates, high floors and low ceilings on taxable earnings. A preferable approach is to compare administrative costs per participant in the pension system. This measure is presented in the two figures in Issue Brief 5 as well as in Table 6.4 and is normalized by per capita income. In all cases, administrative costs are those which apply to the old age/survivors/invalidity programs for the main scheme. Administrative costs in private schemes particular in the Chilean AFP system, are discussed in a later section.

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rule is: where _FPMkt_ is the amount of resources allocated to state _k_ in year _t_ , and _θi_ is the FPM coefficient of municipality _i_ based on its population size.

Due to sample size restrictions, we limit our attention to municipalities with less than 50,940 inhabitants. These municipalities account for about 90% of Brazilian municipalities and 1/3 of the population. The provision of public goods and services in these locations is primarily financed by intergovernmental transfers from the federal and state governments _−_ local taxes represent only about 6% of fiscal revenue. The second column of Table 1 displays the FPM coefficients applied to each population interval. We focus our attention on the initial seven thresholds: 10,189; 13,585; 16,981; 23,773; 30,564; 37,356; and 44,148. The intervals between the initial three thresholds are equal to 3,396, whereas the intervals between the subsequent thresholds amount to twice as much (6,792). For symmetry, we exclude municipalities with fewer than 6,793 inhabitants. In light of these restrictions, it is important to emphasize that the results reported below are specific to the municipalities that are included in the sample, which excludes both very large municipalities and very small ones.

The coefficient _θi_ is assigned to a given municipality by _Tribunal de Contas Uni ̃ao_ (TCU) on the basis of population estimates for the previous year. These population estimates are calculated yearly by _Instituto Brasileiro de Geografia e Estat ́ıstica_ (IBGE), the national statistical institute. IBGE is independent from the government and employs a top-down approach in producing these estimates: municipal-level estimates must be consistent with the state-level estimates; the latter must in turn be consistent with population estimates of the whole country (which draw on birth and mortality rates, and net immigration between Censuses).

However, IBGE population estimates for a given year do not perfectly predict the FPM transfers that each municipality effectively receives in the subsequent year. As noted by Brollo et al. (2013), reasons for such discrepancies may include: (i) imperfect adjustments 7
∂Lit v v ∂Qit ∂Xitv = zitPit −λit ∂Xitv (2) v θitv zitμit = αitv (3) where μit is the firm’s mark-up and αitv = QPititv XPititv , or the ratio of the expenditure on input v to sales.3 The expression θitv denotes the elasticity of output with respect to input v, which would need to be estimated from a production function. Equation (3) is similar to De Loecker and Warzynski (2012), but with a multiplicative adjustment cost.4 Suppose we have firm-level input and output data, including data on input expenditures and revenue. We v could then use equation (3) to infer the product of the price mark-up and the adjustment cost, zitμit, but it would require estimating a production function to measure the elasticity of output with respect to input v.

However, it is possible to measure the change in input adjustment costs over time without estimating a production function. We make two assumptions: A1. The production function is Cobb-Douglas. This implies time invariant output elasticity.

A2. The production function of firm i stays fixed over the study period.

These assumptions are common in studies using firm-level panel data to estimate production functions. If we were to estimate the production function, we would have to make additional assumptions to address the endogeneity of input use.

Taking ratios of equation (3) for two inputs, we get The ratio of input expenditures is inversely proportional to the ratio of the input adjustment costs.

1 To simplify, set zit = 1. In other words, we are redefining the input 2 adjustment cost relative to that on 2 input 1. If zit > 1, this would mean that, in sum, factor market distortions are taxing input 2 relative to input 1. Note that the ‘implicit input tax’ τ in the two-input model in Hsieh and Klenow (2009) is defined in this relative sense: an input market distortion that favours one factor of production over the other.

Taking logs of equation (4) we have: Since the production function is fixed over time, taking the difference between two consecutive time periods 1 and 2, we have

> 3 De Loecker and Warzynski (2012) define the output markup as the output price to marginal cost ratio. Given this definition, the optimal cost-minimization expression in Equation 3 is not dependent on the form of (static) price competition among firms.

> 4 If there are no adjustment costs to either input, then the mark-up computed from either input should be the same. This also implies αθitit1 1 αθitit2 2 = 1 6
of markets, some cities are no longer viable. Remote mono-industrial settlements have been especially hard hit.<sup>33</sup>

**Figure 4.25. Space per capita in ECA capital cities and comparator countries**

(Line shows trend for comparator countries only) <!-- Start of picture text --> 35<br>BG<br>30<br>HU<br>25 GE AL BA RU BY PL<br>MD<br>KZ<br>LT<br>20 UZ AZ RO<br>KG<br>15<br>AM TM<br>TJ<br>10<br>5<br>0<br>0 2000 4000 6000 8000 10000 12000 14000<br>GDP per capita PPP (2001)<br>AL=Albania; AM=Armenia; AZ=Azerbaijan; BA=Bosnia-Herzegovina; BG=Bulgaria; BY=Belarus;<br>GE=Georgia; HU=Hungary; KG=Kyrgyz; KZ=Kazakhstan; LT=Lithuania; MD=Moldova; PL=Poland;<br>RO=Romania; RU=Russia; TJ=Tajikistan; TM=Turkmenistan; UZ=Uzbekistan<br>Space per capita (m2)<br><!-- End of picture text --> Source: WDI for GDP per capita levels; UN Habitat Global Urban Indicators for space per capita in comparator countries and household surveys for space per capita in transition countries (see Table 2.1).

Centrally planned cities also suffer from spatial misallocation of the capital stock (i.e., buildings), which resulted from decades of construction without reference to land values. The figure below compares residential density by distance from the city center in Paris (shown by a line) and Moscow (shown by shaded bars). In Paris, residential density is greatest near the city center, where land values are highest, and decreases with distance. In Moscow quite the opposite is true and the construction of large numbers of high-rise apartment buildings on the fringes of the city mean the greatest residential density is found on the least valuable land. The resulting spatial misallocation of housing creates costs for residents (who need to commute longer distances) and the city (which has to provide city services to remote locations). Perhaps more importantly, the construction of large amounts of poor quality housing in remote locations provides a concentration of cheap housing stock that is likely to be increasingly filled with the poor as better off residents move to better locations.

> 33 The non-viability of cities explains the tremendous out migration from cities in the far north and east in Russia. See also,  the Tomsk case study referred to in Box 4.1.

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reliance on subsidized funds. We count $2.6 billion in subsidized funds (in purchasing power parity-adjusted dollars) fueling the institutions. Of this, the microfinance nongovernmental organizations take a share that is disproportionate in terms of the number of customers reached and, especially, in terms of their assets. Banks absorb subsidies too, but in much smaller quantities.

# _How widespread is profitability?_

The data on profitability start with an important finding: earning profits does not imply being a “for-profit” bank. Most microfinance institutions in our sample that have total revenues exceeding total costs in fact have “non-profit” status. They are earning profits in an accounting sense, but as non-profits they cannot distribute those profits to investors. The distinction is important, as it means that the microfinance industry’s drive toward profitability does not necessarily imply a drive toward “commercialization,” where the latter status reflects institutions that operate as legal for-profit entities with the possibility of profit-sharing by investors. If anything, the profit data here signal the strength and growth of nongovernmental organizations.

Figure 1 sets the scene with a plot relating profitability and the extent of non-commercial funding. The measure of profitability on the vertical axis is the “financial self-sufficiency ratio,” a measure of an institution’s ability to generate sufficient revenue to cover its costs. The financial self-sufficiency ratio is adjusted financial revenue divided by the sum of adjusted financial expenses, adjusted net loan loss provision expenses, and adjusted operating expenses ( _MicroBanking Bulletin_ , 2005, p. 57). It indicates the institution’s ability to operate without ongoing subsidy, including soft loans and grants. Values below one indicate that it is not doing so. The horizontal axis gives the “non-commercial funding ratio,” which is defined as the sum of donations plus non-commercial borrowing plus equity, divided by total funds. The ratio is zero if all funds come from either commercial borrowing or deposit-taking. The ratio is 1 if the institution draws funds from neither source, instead relying on donations, borrowing at belowmarket interest rates or equity.<sup>4</sup> The gently downward sloping line shows a weak link between

> 4 Here, donations are defined as: donated equity from prior years + donations to subsidize financial services + an inkind subsidy adjustment.  Equity is the sum of  paid-in capital, reserves, and other equity accounts; it does not include retained earnings or net income.  Commercial borrowing refers to borrowing at commercial interest rates (though in practice it can be hard to determine where the market would set those rates).  Non-commercial

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