rafmacalaba/data-use-mentions-extended huggingface dataset:
conditional convergence in urban municipalities in the 1990s, at an annual rate of 2 percent.

The conditional model shifts the convergence rates upward in most cases relative to the absolute model. The only exception is 2005–10 when the magnitude of income convergence remained virtually unchanged. A plausible explanation is that the coefficient of initial per capita public spending was negative during those years when the economy was hit by various adverse shocks. The expectation, confirmed in the remaining cases, is that the point estimate of the variable is positive and significant,

> 9The focus is on total public spending only because no sizable differences in the rates of convergence appear if particular components of public spending or revenues are used instead, and this reduces the sample significantly because no disaggregated public finance data are available for all municipalities (see tables 2–11 and 17–26 in the ancillary file). Moreover, to exploit the panel dataset of municipalities and control for time-invariant factors, conditional convergence is estimated using fixed effects models, which consistently confirm convergence, as in the standard ordinary least squares model. Random effects specifications also produce coefficients with the same signs. As extra robustness checks, 5-year and 10-year averages are used for the public spending variables and generalized method of moments (GMM) techniques. The results are again consistent; that is, poor municipalities converge at a faster rate when compared to rich municipalities.

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|0.117***|0.452**|0.174|
||(0.00882)|(0.0108)|(0.178)|(0.236)|
|Constant|7.084***|7.713***|34.40***|34.25***|
||(0.0915)|(0.131)|(1.793)|(2.987)|
|N|6,143|6,143|6,143|6,143|
|R-2|0.250|0.360|0.300|0.496|
|Municipalities|2,154|2,154|2,154|2,154|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Fixed effects model by year and municipality for the years 2005, 2010, and 2014. Weighted results show the same model adding importance by size of municipal population.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total occupied and active economic units in each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1.

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# 4.3.2 Sample Balance (re‐interviewed vs. lost treatments)

As noted above, attrition rates in the follow‐up survey were quite high. Of the 743 individuals in the treatment group, only 338 individuals (or about 45%) were successfully re‐contacted and interviewed. This will bias the estimate impacts of the program if changes in youths’ socioeconomic conditions have different effects on the probability that treatments and controls were re‐interviewed. For example, if successful graduates of the program were more likely than successful controls to respond to an interview request, because they felt gratitude towards the program, the benefits of the program could be overstated. Similarly, the benefits of the program could also be overstated if less successful program graduates were less likely to be successfully re‐interviewed because they are ashamed of their current status, or are less favorably disposed to the program.

While the extent of this attrition bias is unobservable, one can gain a measure of insight into the potential for bias by comparing the mean baseline characteristics of participants who were re‐interviewed with those who were not. Similar characteristics among these groups would provide a degree of confidence that observable baseline characteristics, at least, are not systematically related to the probability that participants were successfully re‐interviewed in the follow‐up survey.

The results of this comparison are reported in Table 4B. Column 1 shows results for participants who were not re‐interviewed, Column 2 shows results for participants who were, and FUS, and Column 3 shows the difference. Again, we find that these two groups are very similar in their basic baseline characteristics. They are only slightly different in the mean age, with re‐interviewed treatments being on average 9 months (0.75 years) older, at the time of the baseline, than non‐interviewed treatments. Furthermore, regressions of the main outcomes of interest, measured at baseline, on a dummy for attrition show no significant difference in outcomes at the baseline between the
3. _The data·_ This· study uses income statement and balance sheet data of commercial banks from the BankScope data base provided by IBCA (for a complete list of data sources and variable definitions, see the Appendix). Coverage by IBCA is comprehensive, with banks included roughly accounting for 90 percent of the assets of banks in each country. We start with the entire universe· of commercial banks, with the exception that for France, Germany and the United States only several hundred commercial banks listed as 'large' are included. To ensure reasonable coverage for individual countries, we include only countri~s where there are at least three banks in a country for a given year. This yields a data set covering 80 colintries during the years 1988-1995, with about 7900 individual commercial bank observations. This data set includes all OECD countries, as well as many developing countries and economies in transition. For a list of countries, see Table 1.

First, we consider the extent of foreign bank penetration in national banking markets. Table 1 presents two measures of foreign bank penetration: the share of banks that: is foreign-owned, and the share of foreign bank assets in total bank assets. The number penetration measure· is an appropriate measure, if the number of domestic and · foreign banks determines competitive conditions. This is the case, if ·domestic banking firms adjust their operations as soon as foreign entry occurs to prevent the foreign entrants from ever capturing significant market share. Alternatively, the share penetration measure is appropriate, if foreign banks start to have an impact only after they have gairied sub~tantial size. Foreign banks may indeed have to be sizable for' there to be any significant transfer of banking technology to the domestic banking sector. Note that either penetration n;ieasure is a measure of actual foreign banking penetration, and thus does not capture the disciplining effects on domestic banks of potential foreign bank entry. The threat of foreign bank entry, however, may not be credible in the absence of
**Figure 5:  Decomposition of Changes in Poverty,1994-2002**

<!-- Start of picture text --> 3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>-4%<br>-5% total poverty change<br>due to grow th<br>-6% due to inequality<br>1994-1998 1998-2002 1994-2002<br><!-- End of picture text --> Note: The 2002/1994 change is approx. a sum of 1998 –2002 and 1994-1998. _Source:_ Staff calculations based on GUS data..

For further illustrative purposes, one can look at the consumption level and its dynamics at different parts of the consumption distribution. It is interesting to compare mean incomes/consumption across the ranked population. Inverting the cumulative distribution of consumption at a p-th percentile, one can infer consumption levels at this percentile. This line – depicted in the left panel of Figure 6 – is called a quintile function, or, sometimes, a Pen’s parade (Pen, 1971). For example, individuals at the 70th percentile had in 2002 a consumption level of almost PLN750 per month.

The left panel of Figure 6 presents three curves for three different years (1994, 1998, and 2002). Between 1994 and 1998 the line shifted upward almost in parallel, implying a rise in consumption across the entire population. However, between 1998 and 2002 the line generally shifted downward (except for the higher percentiles). The magnitude of this decline was clearly greater for the lower percentiles. The consumption level of the poor returned to, or fell below, its 1994 level. The right panel in Figure 6 presents the same story but from a dynamic perspective. The period 1995-98 was generally favorable for all the consumption groups,<sup>8</sup> but in 1999 an
by a factor of 10. Of course, as we have shown above, the RM estimates are not interpretable, as they do not correspond either to the PK model or to any formal econometric model of program evaluation that RM or anyone else has ever set out, not to mention that the linear LIML estimation strategy RM adopt just makes the estimates even more strange.

Can one show that, in general, the linear LIML does not “work” in the PK model as RM have formulated it, or is it simply a reflection of some defect in the PK data? In our Table 8 below, we reproduce the simulation results of our Table 1 (above) but add the linear LIML estimates to the set of estimates reported. These are exactly the same simulation datasets described earlier. Recall that the errors are normally distributed and the instruments are not “weak” in these simulations. The linear LIML estimates of column (4) of our Table 8, the only new column in this table as compared to Table 1, have clearly bizarre point estimates (means of 200 simulations are presented) and huge standard deviations. It is no surprise that RM get outlandish parameter values and large standard errors in their Table 6 when they estimate the PK model with their linear LIML setup. Our Table 9 reports the estimates from one of the simulation datasets. The model estimates an effect of female credit from group 1 to be -244.388 when the true value is 0.75, and the male credit effect of group 3 to be 177.334 when the true value is 0.25. This should not be surprising given the large standard deviations reported in column (4) of our Table 9. Almost anything can result when an erroneous econometric model is applied to an artificially altered data matrix.

# **7.  Sources of identification in Pitt and Khandker:  Some new results**

In this section, we further examine two aspects of our instrumental variable approach that have been attacked by RM.
constant-elasticity regressions of these indicators on gross national expenditure (or absorption) per capita and the simulated evolution of GNE per capita under the _base_ run (Quijada 2012). The definitions of individual elasticities, which drive the relative importance of the different determinants in Table 2.1 (indicated by decompositions) was informed by a survey of cross-country studies, complemented by judgment calls (Lofgren 2010); in this application, roughly 30-40 percent of the changes in MDG and education indicators is attributed to changes in services and private per-capita consumption with the remainder due to the other determinants (cf. Table 2.1). Econometric estimation of the details of these relationships remains very challenging for a number of reasons: causality may go in both directions (higher incomes improve health and better health improves incomes) and include complex time lags; many of the variables (determinants and outcomes) are highly correlated; relationships may vary over time and space; and data is imperfect. To provide an example with regard to the latter point: it near impossible to determine the levels of real supplies of government education or health services in the absence of relevant price indices and a separation of spending data into current and capital.

This MAMS application includes links between growth in TFP and growth in government capital stocks in agriculture and public infrastructure; drawing on available evidence (see for example Foster and Briceño-Garmendia, 2010, p. 71; IMF 2008, p. 20; and World Bank 2007a, p. 65), the

> 23 Cross-country data on total private and government capital stocks (with values expressed as shares of GDP) constitute a source of checks on initial estimates. As an additional check on initial capital stocks and depreciation rates, dynamically one would expect a growing developing economy with investment rates that match historical data to see its private capital stocks grow faster than its labor force, i.e. to experience a process of capital deepening in its private sector (as well as in the economy in general).

> 24 Israel Osorio-Rodarte
. There are no significant differences between the treatment and control groups and population (see Appendix B), and **Table 1** demonstrates there are no significant differences in characteristics between the pooled treatment and control groups.<sup>16</sup>

> 13 Baseline could only be completed in 765 of the 924 schools owing to budgetary and logistical constraints. The authors do not find any significant differences for key school-level variables in the reduced 765 school sample using administrative school census data. Results available on request.

> 14 This population excludes private schools, but includes schools owned by religious institutions which are accredited and financed by Government and considered part of the public school system.

> 15 Malawi has 28 local government authorities, including districts and municipalities. Some of these are subdivided into two or three components for the purposes of education management, producing a total of 34 education districts. In this paper, we use ’district’ to refer to these education districts.

> 16 There are similarly no significant differences between the treatment and control groups within either cohort (see Appendix B)

17
In our basic specifications, we use _gdppc_ as the primary indicator for the conditional effect of media on agricultural protection because the employment share data are of poor quality, which precludes a consistent comparison across countries and over time, especially for developing countries (see Timmer and de Vries 2007 for a discussion). One reason for the poor quality is the differences in national definitions of “agricultural labor.” Another reason is that the yearly “observations” in the FAO labor statistics are linear interpolations between census data collected once every decade. We understand that there are also problems with the measurement of _gdppc_ because national accounts data are noisy over short time horizons (see Deaton 2005), but we believe the data problems are less important for _gdppc_ than for _emps_ . However, we perform a series of robustness checks using _emps_ as indicator.

Both variables (the level of development and employment share) are also included as control variables because both have been identified as major determinants of agricultural protection outside the media effect.

# **_Other control variables_**

In addition to the variables discussed above, in the empirical specifications, we include controls that are likely to affect the level of agricultural protection, as suggested by previous studies. Standard control variables in studies on the political economy of agricultural policies are indicators of comparative advantage, trade status, terms of trade effects, and political institutions (see Olper 2007; Swinnen 2010; Olper and Raimondi 2012). To control for comparative advantage, we include agricultural land per capita, _landpc_ , and the agricultural export share, _exps_ , measured as net exports over production. These two variables are based on data from FAO and the World Bank’s Agricultural 15
Provincial Leaders’ Factional Affiliations and County’s Guerrilla Presence.** Here we rely on information from two primary sources: (1). “History of the Communist Party in Fujian Province, 1926-1987”; (2). “Recollections on Yangtz-River Detachment”. We use these two primary sources to determine whether a county was assigned cadres affiliated with the FA3 or with the YRD.

Moreover, we hand-collected the resume of every member of the Fujian Provincial Communist Party Standing Committee from 1950-1993. We identify if a member belongs to the FA3 faction or the YRD faction based on their working experiences listed on their resumes.

To determine whether a local guerrilla force had strong presence in the counties during the pre-Communist liberation period, we hand check various county gazettes (as of May 1948).

**County-Level Development Performance from 1952-1998.** We examine various measures of development performance at the county level from 1952 to 1998. First, measures related to economic growth and other economic outcomes are gathered from “Statistical Information on 50 Years of Fujian Province” and “Regional

16
the Education Assessment System (SAEB).<sup>10</sup> Students take the test at the end of the school year (between October and November). In 2007, children took the test between November 5 and 20; and in 2009 between October 19 and 31 (approximately, two months after the school shutdowns). For fifth-graders, proficiency in reading has a scale ranging from 0 to 325, and math has a scale ranging from 0 to 350 (SAEB scale). The student’s proficiency can be classified as insufficient, basic, or advanced.

In addition to answering reading and math questions, students filled out a socioeconomic questionnaire with information on their parents’ educational attainment; incentive from the family to pursue an education; time watching TV, time spent on the internet, time dedicated to reading books, and to homework; if they already dropped out or repeated a grade; and if they attended kindergarten.

Data from the School Census and _Prova Brasil_ are used to calculate the National Education Development Index (IDEB), the most important educational indicator in Brazil, that monitors students’ grade promotion and learning levels.<sup>11</sup> Brazilian State and municipal governments use this indicator to monitor the quality of public education and compare the schools’ performance within and between municipalities.

# **4 Empirical strategy**

To estimate the treatment effects of the 2009 school closures due to the H1N1 outbreak on fifthgraders proficiency in reading and math, we explore the policy variation between and within the municipalities of the state of São Paulo. We focus the analysis on grade five because only five of the 13 municipalities that opted for extending the winter break had proficiency scores available for grade nine in 2007, our ’baseline’ year.

To estimate the impact of the shutdowns on learning, we first look at the impacts on locally-managed schools. To do so, we explore the policy variation between municipalities

> 10SAEB stands for _Sistema de Avaliação da Educação Básica._

> 11
production of commodities linked to forest destruction (which exceed by a factor of 100 or more the amount spent on measures to combat deforestation) and the deforestation rate, with improvements in the Amazon biome being undone in recent years (CBD 2020).<sup>6</sup> It is expected that new targets will be committed following the UN Convention on Biological Diversity (CBD) in October 2021 (COP15), which has the objective of increasing the extent of protected areas, promoting the sustainable use of ecosystems and lessening the causes of biodiversity loss.

Given the potentially significant macrofinancial implications of biodiversity loss in Brazil, it is important that both banks and Banco Central do Brasil (BCB), the central bank, build the capacity to understand and in time manage the risks associated with the loss of biodiversity. As the Dasgupta Review of the Economics of Biodiversity notes, the “financial system is critical to supporting a more sustainable engagement with nature” (Dasgupta 2021). Brazil’s financial system is bank-based, with banks’ assets accounting for about two-thirds of total financial system assets. Therefore, banks can play a pivotal role in managing and mitigating the risks and uncertainty resulting from the unsustainable engagement with nature. At the same time, BCB, within its mandate to ensure a solid and efficient financial system, has an interest that banks address nature-related risks adequately and proactively, learning and building on the advances on climate-related financial risks. Leveraging on its participation in the Central Banks and Supervisors Network for Greening the Financial System (NGFS), BCB is implementing a sustainability agenda as a part of its institutional work program, which could give renewed emphasis to nature-related financial risks.<sup>7</sup> This paper explores how and to what extent Brazilian banks are exposed to the loss of biodiversity through their lending to non-financial corporates. First, we describe the extent to which the banking sector is indirectly dependent on ecosystem services, which is our proxy for physical risks. Using the ENCORE database, which
Table 3 shows the model coefficients and the mean of the regressors in third quarter IE-LFS 2019/20 and AWMS R3. Overall, the training process identified different variables for the urban and rural models, even when the variables available during variable selection process were the same.<sup>7</sup> When both models identify the same variable, their coefficients differ, further validating the decision to estimate independent models. Consumption dummies are an important component of the models, comprising five out of seventeen variables in the urban model, and seven out of twenty-five in the rural model. Changes in the distribution of variables between the third quarter IE-LFS survey and the AWMS R3 indicate the direction of the poverty estimates. In the urban sample, there is a noticeable increase in average household size and a decrease in certain asset holdings, except for small livestock ownership. However, there is an uptick in the consumption of key items. Typically, the patterns observed in the first two categories are indicative of declining household welfare, whereas the trend in the third category suggests an improvement in welfare. In rural areas, the household demographic trends hint at a slight decrease in welfare. In contrast, the data related to asset ownership, dwelling conditions, and consumption patterns indicate significant improvements in welfare between the two surveys.<sup>8</sup>

> 7 Given the urban population is concentrated in the Kabul region, region 1 in table 3, that was the only regional dummy available during the tuning of the model.

> 8 An additional model was estimated, which included quarter-average satellite measures of rainfall, temperature, NDVI, and nightlights at the province level. Meteorological variables were included as shocks, using 2 standard deviations from the historical mean, while nightlights were used as levels. The model estimates and predictions are qualitatively the same. Results are available upon request.

8