rafmacalaba/datause-encoder-data huggingface dataset:
To check whether individuals (or their parents) tried to manipulate the forcing variable to being eligible to participate in the formal labor force or to pretend they complied with the law, we perform the McCrary test. We also run some placebo tests to check whether our estimates might be confounded.

# _The McCrary Density Test_

The McCrary density test consists of verifying whether there is evidence of perfect manipulation of the forcing variable around the cut-off point. The test compares the density distributions of the forcing variable around the threshold (McCrary 2008).

Although our birth data comes from household surveys conducted by the Brazilian Census Bureau, which is not related to the public institutions responsible for the surveillance and enforcement of the law, one could still think of the possibility that a family misreports dates of birth to the surveyor, particularly in cases where children are working illegally. If this occurs systematically, one would observe a discontinuity in the density function of the forcing variable around the cut-off point and this would call into question the plausibility of our identification strategy.

We illustrate the results graphically with age defined in weeks and days. The reason for using age in days is to make it as continuous as possible given that the McCrary test is non-parametric (see Lee and Card 2008 for this point).

The figures 2 and 3 show the McCrary density test for age in weeks and age in days respectively. Both figures indicate that there is no discontinuity in the forcing variable around the threshold, that is, there is no evidence of perfect manipulation of the forcing variable.

# _Balance Check_

Another important check of the plausibility of the RDD consists of a balance check comparing observed characteristics of children around the cut-off point. Under the assumption that the law change can be seen as a natural experiment, we should observe ‘treated’ and ‘control’ boys with similar observed characteristics, on average. We perform a test for difference in means and the results are presented in table 3.

In addition to control variable we use in our regressions, we also compare children in terms of household income per capita. We find no systematic differences of the mean values of these variables between the two unaffected and affected cohorts. This result 19
WPS3795 overall bank lending in both developed and developing nations (Berger, Hasan, and Klapper 2004).

# **B. Foreign-owned versus domestically-owned institutions**

Foreign-owned institutions may have a comparative advantage in transactions lending and a disadvantage in relationship lending in part because these institutions are typically large. In developing nations, foreign institutions headquartered in developed nations may have an additional advantage in transactions lending because of access to better information technologies for collecting and assessing hard information.<sup>9</sup> Foreign institutions may also face additional hurdles in relationship lending because of difficulties in processing and transmitting soft information over greater distances, through more managerial layers, and coping with multiple economic, cultural, language, and regulatory environments (e.g., Buch 2003).

There is little empirical evidence on SME lending by foreign-owned institutions in developed nations. Some research finds that these institutions tend to have a wholesale orientation (e.g., DeYoung and Nolle 1996), and in some cases tend to specialize in serving multinational corporations headquartered in their home nation (e.g., Goldberg and Saunders 1981). Presumably, these institutions use transactions technologies applied to hard information.

There is more evidence on foreign-owned institutions and SME credit availability in developing nations. In most of the studies, foreign banks are associated with greater SME credit availability (e.g., Beck, Demirguc-Kunt, and Maksimovic 2004a, Berger, Hasan, and Klapper 2004, Clarke, Cull, Martinez Peria, and Sanchez 2005). However, some find that foreign-owned banks may have more difficulty in supplying credit to SMEs than to large firms (e.g., Berger, Klapper, and Udell 2001, Mian 2005). This relative preference for large firms over SMEs has also been found for foreign lenders entering formerly socialist countries (Giannetti and Ongena 2005). Again, the lending technologies are generally not identified. Although foreign institutions almost surely use transactions technologies, it is usually not known which of these technologies are employed or the opacity of the borrowers served.<sup>10</sup>

> 9 For example, some foreign-owned institutions use a simplified form of small business credit scoring to lend to SMEs in developing nations based on the SME’s industry or provide home-nation training for loan officers stationed in developing nations (Berger, Hasan, and Klapper 2004).

> 10 There is also research on the efficiency of foreign versus domestic institutions, but the efficiency differences may or not be linked to lending technologies and SME credits.  The research in developed nations suggests that foreign institutions are less efficient on average (e.g., DeYoung and Nolle 1996, Berger, DeYoung, Genay, and Udell 2000).

11
The figure conveys three messages. First, Sub-Saharan Africa consistently lags behind the comparator regions, in terms of both quantity and quality of infrastructure. Second, over the last fifteen years progress in Africa has been slower than in other developing regions, and as a result Africa has fallen further behind in both dimensions. Third, in the case of infrastructure quality, the region’s performance has worsened also in absolute terms, not just relative to that of other regions.

Of course, these cross-regional comparisons conceal a great deal of heterogeneity within Africa in terms of infrastructure performance. A closer look at the country-specific data reveals a sharp contrast between the performance of some low-income economies (such as Niger or Togo), which lag well behind the rest of low-income developing countries, and that of the region’s richer economies (South Africa, Botswana, Mauritius), which are roughly on par with countries of similar income levels in other developing regions. The appendix documents these differences across sub-regions of Africa, and offers extensive details on various dimensions of infrastructure performance, including that of universality of access to infrastructure services– an area in which Africa also lags significantly behind other regions.

# **3.1.2 Econometric methodology**<sup>11</sup>

Our empirical strategy is based on estimation of simple equations relating growth and inequality to a set of standard controls, augmented by the synthetic measures of the quantity and quality of infrastructure, in a panel data setting. This poses some wellknown problems: (a) the presence of unobserved country-specific effects and common time effects, and (b) potential endogeneity of the regressors. To address these issues, we employ the generalized method of moments (GMM) developed by Arellano and Bond (1991) and Arellano and Bover (1995) for dynamic panel data models. Specifically, we deal with common factors through the inclusion of period-specific dummies, and unobserved country effects are handled by differencing. To control for endogeneity we rely on instrumental variables.

This approach permits relaxing the assumption of strong exogeneity of the explanatory variables by allowing them to be correlated with current and previous

> 11 This section draws from Calderón and Servén (2008).

15
This distinction between real GDP on the expenditure and output-side is recognized by the United Nations 1993 System of National Accounts (SNA). The former is called real Gross Domestic Income (GDI), while the latter is real GDP. One definition<sup>3</sup> of real GDI is: Real GDI = (Nominal GDP)/(Domestic absorption price index), (1) as compared to, Real GDP = (Nominal GDP)/(GDP price index). (2) Nominal GDP, of course, is domestic absorption (C+I+G) adjusted for the trade balance (X–M). The “domestic absorption price index” in (1) is constructed over the components of (C+I+G). By _excluding_ export and import prices from this price index, changes in the terms of trade (which affect nominal GDP) are then reflected in real GDI, as demonstrated by Diewert and Morrison (1986). An improvement in the terms of trade, for example, would cause real GDI to grow faster than real GDP. Kohli (2004) has shown that this pattern holds for Switzerland, due to its terms of trade improvement. We shall avoid the term “real GDI,” because it is suggestive of the incomeapproach to measuring GDP (through adding up the earnings of factors) which we do not use. Instead, we use real GDP<sup>e</sup> to reflect the expenditure-side concept like (1). This is essentially the same as “real expenditure”, except that by using nominal GDP rather than nominal expenditures in the numerator of (1) we are adjusting for the net foreign balance.

Now we come to the key question motivating this paper: which concept does the PWT use as “real GDP” – the output-side measure real GDP<sup>o</sup> , or the expenditure-side measure real GDP<sup>e</sup> ? It turns out that the answer is unclear: the ICP constructs real GDP<sup>e</sup> in _benchmark years_ , but then to interpolate between these years, the PWT must reconcile these changes in real GDP<sup>e</sup> with the national accounts data reports on countries real GDP growth. Since national accounts real GDP growth is closer to real GDP<sup>o</sup> , which is being compared to benchmark estimates of real GDP<sup>e</sup> , the distinction between these becomes lost in the reconciliation.<sup>4</sup> In other words, the

> 3  See <u>http://unstats.un.org/unsd/sna1993/introduction.asp</u> , paragraph 16.154.  The other definitions of real GDI depend on the deflator used for (X-M).  Other than the domestic absorption price index, we might use the import price index, export price index, or an average of these, as recommended by Geary; see Neary (1997).

> 4  The process of reconciling the initial PWT estimates (in benchmark years) and the national accounts data for each country is described in Summers and Heston (1991, pp. 339-341).

6
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**Table 15. Oil Prices and Fiscal Balances of Fuel Exporters—Country Fixed Effects**

|Dependent variable:|Fiscal balance/GDP<br>(%)|Total revenues/GDP<br>(%)|Total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Lagged dependent<br>variable|0.379***<br>(4.87)|0.539***<br>(4.76)|0.498***<br>(7.25)|
|Ln (real $ oil price)|16.01***|11.92***|-3.44|
||(6.31)|(6.28)|(-1.39)|
|Lagged ln (real $ oil|-9.62***|-4.92**|6.31***|
|price)|(-4.08)|(-2.18)|(3.96)|
|Political stability|0.182**|0.133**|-0.0135|
|(0 to 100)|(2.18)|(2.16)|(-0.23)|
|Time trend|-0.644**|-0.661***|-0.223|
|(=0 in 2000)|(-2.39)|(-3.40)|(-0.99)|
|n|297|305|297|
|No. of countries|30|30|30|
|RMSE|4.29|4.02|3.85|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel > 25 percent of total exports. Country fixed effects are included in the regression. RMSE = root mean square error, or sample standard deviation.

28
Table C2: Drivers of Total Debt Inflows Before and After the Global Financial Crisis, by Sector - Emerging Markets (Quarterly AHKS data, missing filled from Public Sources)

||P|re-GFC: 200|2q4-2007|q4|P|ost-GFC: 20|08q1-2014q|4|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total|(6)<br>Public|(7)<br>Banks|(8)<br>Corp.|
|log(VIX_t−_1)|-3.269<sup>_∗∗∗_</sup>|0.271|-1.595<sup>_∗∗_</sup>|-1.945<sup>_∗∗∗_</sup>|-0.927|1.465|-2.047<sup>_∗∗_</sup>|-0.345|
||(0.813)|(0.430)|(0.586)|(0.380)|(1.022)|(1.132)|(0.780)|(0.282)|
|GDP Growth_it−_1|0.00421<br>(0.0171)|-0.00331<br>(0.00843)|0.0152<br>(0.0148)|-0.00764<br>(0.00885)|0.0717<sup>_∗∗∗_</sup><br>(0.0197)|-0.0377<sup>_∗∗∗_</sup><br>(0.0135)|0.0747<sup>_∗∗∗_</sup><br>(0.0173)|0.0348<sup>_∗∗∗_</sup><br>(0.00876)|
|Observations|588|588|588|588|784|784|784|784|
|_R_<sup>2</sup>|0.037|0.001|0.018|0.073|0.025|0.018|0.072|0.045|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Total Debt is the sum of Portfolio Debt and Other Investment Debt inflow data, constructed by AHKS as described in Section 2. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01
5 both the subsidy and the tax "are a first-best policy" to "the extent that they are both targeted to the emissions."<sup>7</sup> The situation is different when there are positive externalities to be gained rather than merely negative externalities to be avoided.<sup>8</sup> Some examples of activities that can generate positive externalities are new technologies and privately provided ecosystem services, such as carbon sinks and flood control. Granting subsidies can be an appropriate government instrument to incentivize behavior that generates positive externalities. Indeed, the United Nations (UN) Environment Programme (UNEP) has declared that "public financing is essential for the transition to a green economy and more than justified by the positive externalities that would be generated" (UN Environment Programme 2011, p. 614). The grant of subsidies to promote innovation can be compared to the grant of intellectual property rights, and policy makers should weight the costs and benefits of each approach versus the other approach.

Besides negative spillovers, the existence of sectoral market failures can be a justification for government intervention. For example, the World Bank has observed market challenges in renewable energy "such as knowledge externalities, latent comparative advantage and increasing returns, information asymmetries, capital market imperfections, and the coordination needed across industries to permit a technological transition" (World Bank 2012, p. 66). Some of these problems are caused by poor macro and microeconomic policies, but others may be inherent to an industry at a particular stage of development. Although the use of a subsidy is not an obvious corrective to something like information asymmetries, a subsidy could make sense to respond to certain challenges such as lack of investment capital. For that problem, one form of subsidy is a government loan at below-market rates. Conceptually, such a loan would be warranted when the social rate of return for 7World Trade Organization 2006, p. 101.

8Some analysts have suggested that the avoidance of a negative externality, such as a carbon abatement, is a positive externality.
distribution. For example, the variance of treatment effects is the second (centered) moment of this distribution and provides a measure of the dispersion of the treatment i.e., how much they vary across individuals. In the fictional RCT in Part 2, the varife) b ons and can be estimated treat ent andco tates simultaneously. As t he data do not identify , Without additional assumptions. Consider the ata in Table 1, but more realistically, suppose we do not k h —~(Y (Y (Y Thus, the va ce of treatment effec fi face an analogo s iden fication problem when estimating the entire distribution of individual treatme effecs r other features of this distribution: while features of the marginal distributions an be calculated from the data, parameters that describe the relationship are necessary for point identification of the distribution of treatment point esti ates a discussed in Section 4.2. Bounds are often simple to obtain, but inference can be difficult. They also tend to be too wide to be informative and narrowing them requires additional assumptions. Second, we can estimate the (parameters of the) relationship between counterfactual outcomes, as we discuss in Section 4.3. This requires further assumptions and modeling, such as amending the RCT with a model of participation choice.

Bounding the Distribution of Treatment Effects While the data cannot identify higher order moments of the treatment effects distribution without additional assumptions, they can identify a range of values that must include the tion. This is distinct from the variance of the estimate of the average treatment effect due to sampling that is To keep the example simple, we abstract from sampling variation throughout.
Policy ReseaRch WoRking PaPeR 4882

# **Abstract**

The authors use a sample of 133 countries to investigate the link between the abundance of natural resources and micro-economic reforms. Previous studies suggest that natural resource abundance gives rise to governments that are less accountable to the public and states that are oligarchic, and that it leads to the erosion of social capital. These factors are likely to hamper economic reforms. The authors test this hypothesis using data on micro-economic reforms from the World Bank’s Doing Business database. The results provide a robust support for the “resource curse” view: a move from the 75th percentile to the 25th percentile on resource abundance equals 10.9 percentage points more reform. This is a large effect given that the mean probability of reform in the sample is 57.1 percent.

This paper—a product of the Enterprise Analysis Unit, Financial and Private Sector Vice Presidency—is part of a larger effort in the department to is part of a larger effort in the department to better understand the functioning of the private sector. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mamin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- Over-fishing in the lower portions of the river basin shifts fishing activity upstream.

- Declining stocks of mature timber in the lower sub-basins shift demands to the upper sub-basins.

- Loss of forest cover in the upper sub-basins aggravates erosion and flood hazards downstream.

The breadth of the Fraser Basin Council’s agenda is reflected in, and reflects, some of its organizational characteristics. As noted earlier, the council is composed of 36 members, an expansion from the predecessor FBMB. Furthermore, Fraser Basin Council seats are deliberately distributed so that no sector of basin interests or level of government has a majority of members and so non-water as well as water-related basin interests are represented. Table 4 provides the breakdown of Fraser Basin Council representation.

**Table 4. Composition of Fraser Basin Council members**

|Number of<br>members|Category of representationor interest|
|---|---|
|3|Federal government agencies, typically one from DFO, one from EC and<br>onefromthe10 other relevant departments.|
|3|Provincial government agencies, typically one from Ministry of<br>Sustainable Resource Management, one from Ministry of Water, Land and<br>Air Protection, and one from Land and Water B.C. or Ministry of Health<br>Services|
|8|Regional districts—these are local government units larger than the scale<br>of a municipality but below the province level. The Fraser basin<br>encompasses eight regional districts. Regional district representatives may<br>be from regional district governments or from municipalities or other local<br>government units withinaregionaldistrict|
|8|First Nations representatives—one from each of the eight language groups<br>in the basin, chosen by the tribal councils of the First Nations associated<br>withthatlanguage group|
|10|Regional representatives—two non-government individuals from each of<br>thefiveregions oftheFraser Basindesignated by theFraser BasinCouncil|
|1|Basin-widerepresentative witha perspective oneconomic sustainability|
|1|Basin-widerepresentative witha perspective onsocialsustainability|
|1|Basin-wide<br>representative<br>with<br>a<br>perspective<br>on<br>environmental<br>sustainability|
|1|Impartialchairwho serves as president ofthe council|
|36|Total|

Source: Calbick et al. 2004 The membership structure conveys the diversity of represented interests fairly well, but not entirely. Several members wear “multiple hats,” representing for instance not only a geographic region in the basin but also an interest sector (e.g., a regional 21