rafmacalaba/data-use-mentions-extended huggingface dataset:
data is from the _Sistema de Avaliação da Educação Básica_ (SAEB), a national assessment implemented biannually. In additional to _Ceará_ , we consider the following adjacent states: _Piauí_ , _Rio Grande do Norte_ , _Paraíba_ and _Pernambuco_ . The definition of border considers the first _three_ municipalities on each side of _Ceará_ ’s border. Panel A considers 5<sup>_th_</sup> graders. Panel B is for 9<sup>_th_</sup> graders. The baseline period of 2005 and 2007 is when neither _Ceará_ nor adjacent states had implemented _RBF_ incentives. The column " _RBF_ " considers 2009 in Panel A, because the _RBF_ law started on January 1, 2009. For 5<sup>_th_</sup> grades, technical assistance ( _TA_ ) initiated in 2011, presented in column " _RBF_ + _TA_ " in Panel A. In Panel B, the column ‘’ _RBF_ ( _re_ )" presents the statistics for 9<sup>_th_</sup> grade when the RBF formula was re-calibrated in 2013, but _TA_ was offered only to 5<sup>_th_</sup> grade classes. In Panels A and B, periods of re-calibration and TA are shown in columns " _RBF_ ( _re_ ) + _TA_ ".

at the municipal level from the _Instituto Brasileiro de Geografia e Estatística_ (IBGE) and live birth records from the Ministry of Health.<sup>13</sup> To investigate potential RBF mechanisms, we use municipal public spending data provided by the National Treasury Bureau ( _Secretaria do Tesouro_

> 13Municipal data on live births can be obtained at the url: `http://www2.datasus.gov.br/DATASUS/index. php?area=0205` .

14
using the World Development Indicators (WDI) database, almost one-fifth (i.e., 28) have only one poverty data point and as many as 29 countries do not have any poverty data point in the same period. Furthermore, poorer countries have fewer surveys: a 10-percent increase in a country’s household consumption level is associated with almost one-third (i.e., 0.3) more surveys (Dang, Jolliffe, and Carletto, 2019). The ongoing Covid-19 pandemic could increase poverty and further exacerbate these data deprivations and digital divides for poor countries (Naude and Vinuesa, 2020).

> 2 Imputation techniques are regularly used by international organizations and national statistical agencies to fill in missing data gaps such as education statistics (UOE, 2020) and income data (US Census Bureau, 2017).

> 3 The poverty-mapping technique combines a household consumption survey and a non-consumption census, which allows us to provide poverty estimates at a more disaggregated level than available in the household survey.

2
is precisely what we want for the purposes of constructing synthetic control firms, namely we would like some very large importing firms that are not participants and some firms that trade the same goods as the treated firm. The SCM uses data from non-IHC firms to construct a synthetic control firm that looks like an IHC firm, but that is possible only if the non-IHC firms demonstrate enough variation in their characteristics that they are capable of reproducing the IHC firms in terms of import composition and dynamics.

**<u>Table 5.  Annual Import Values in 2010 for IHC and Non-IHC Firms</u>**

||Distrib<br>|ution of firm i<br>|mport values in 2<br>|010 in Serbian<br>|Dinars<br>||
|---|---|---|---|---|---|---|
||Mean|Median|Standard<br>deviation|Minimum|Maximum|Number<br>of firms|
|IHC firms by the end|||||||
|of 2013|1,354 million|654 million|1,656 million|53 million|6,013 million|32|
|Non-IHC firms by the<br>end of 2013|49 million|2.7 million|881 million|50|93,633 million|22,485|
|All firms|51 million|2.7 million|884 million|50|93,633 million|22,517|
|Ratio IHC/Non-IHC|27|244|1.88|1.1 million|0.06||

Source: Authors’ calculations based on Serbian customs data.

# **4.3 Implementation Details**

In Section 3 we described the SCM in abstract terms. In practice our application of the SCM faced a number of challenges. We describe here briefly the details of implementation.<sup
by the local mayor’s office or directly by FMSD.<sup>4</sup> The remaining pool of potential clients, about 20%, were called up by credit officers directly or visited the branch.

Once potential clients were registered, credit officers followed-up with a visit to assess eligibility, and to make randomized offer. All prospective clients also received a leaflet with information about the loan (see Appendix B for a sample of the flex product flyer in Spanish). Loan applications were collected by credit officers and reviewed by the credit committee. Clients with approved loans received additional explanations from a dedicated staff when the loan was disbursed either during the branch visit or over the phone.

Recruitment into the study took place continuously over 18 months. Overall, 22.4% of potential clients were assigned to a flexible offer (see Appendix A for further details). Panel A of Appendix Table 3 confirms that the randomized assignment of offer types was balanced overall with respect to the recruitment process and branch location (the p-value of a joint test of equality of means is 0.23).

# **Randomization of second stage (switch to flexible loans)**

Approved standard loans were randomly switched to flexible loans at disbursement, with a target probability of 50%, based on the observed distribution of the last three digits of the national identification document using the loan data set of our partner microcredit institution. In total, 1,893 standard loan offers were accepted and 971 (51%) of them were converted to flexible contracts as part of the second stage randomization.

Clients learned about the switch when their credit officer called them about the approval of their application and gave a short explanation of the new flexible loan. All clients in the Std→Flex group accepted the switch to the flexible loan.

We test for balance in the second stage randomization by looking at the sample of new clients that initially received a standard offer. Using a combination of data from the recruitment process, data collected
region or country, respectively. For ease of exposition, we group funds into two categories: “global funds” and “specialized funds.” Global funds encompass world funds and foreign funds. All other fund types are called specialized funds, investing in a subset of the assets available to global funds. Naturally, funds with a wider investment scope (global funds) are always able to invest in the stocks held by specialized funds, but not vice versa.

We also collect data on the time series of return/price data on mutual funds. Since these are open-ended funds, the value of each fund each day reflects the value of their underlying holdings or the net asset value (NAV). To be able to compare returns across funds within families, we restrict our focus to large families with several types of funds. We thus use daily returns at the fund level between September 1989 and June 2006 for 36 mutual fund families, as reported in Table 1. We work with a total of 722,885 daily observations, encompassing all returns for all funds in our sample. We include all funds within a given family of funds. On average, each family has ten different mutual funds.<sup>21</sup>

# **3. How Do Mutual Funds Allocate Their Portfolios Globally?**

The U.S. mutual fund industry’s activity in international capital markets has expanded sharply since the early 1990s. For example, in 1991 there were fewer than 170 mutual funds established to invest in international equity, while in 2005 there were almost 700 funds. While the number of global funds increased steadily until the early 2000s, that of specialized funds increased until 1998 and declined since then (Figure 2). This was likely driven by the Asian and Russian crises that seem to have generated a desire to hold funds that can invest more freely around the world. At the end of 2005, there were 490 global funds and 186 specialized funds. In terms of assets under management
Table 1: Sub-Saharan Africa: Nine Highest-Income Countries

|Countries|GDP per capita (USD) -2019|
|---|---|
|Gabon|7,767.00|
|South Africa|6,001.40|
|Namibia|4,957.50|
|Angola|2,790.70|
|Nigeria|2,229.90|
|Ghana|2,202.10|
|Kenya|1,816.50|
|Zambia|1,305.10|
|Lesotho|1,118.10|

and 25.9 percentage points (DHS), respectively. It follows that the effect of greater investments in road networks tends to be larger for more-developed economies. However, the differential effect is smaller in the DHS sample, which, likely offers a more accurate picture because of the greater number of countries included in it (27 countries) than in the LSMS sample (7 countries).

In areas with high access to road networks, being 10 km closer to an electric grid increases the probability of working by 25.0 percentage points in more-developed countries, compared to 5.5 percentage points for less-developed countries in the LSMS sample. In the DHS sample, the corresponding estimates are 11.0 versus 13.7 percentage points, respectively. Again, the effect tends to be larger in more-developed economies.

In locations that are far enough from road and electricity networks, increasing proximity to both has a positive effect, but this effect is stronger in more-developed countries. These findings imply that the complementary role of roads and electricity is stronger in more-developed African economies.

Results from the OLS regressions (reported in Appendix Table 13) confirm these findings. Corresponding coefficients have similar signs, although the OLS estimates are smaller. Overall, the findings show that investment in infrastructure promotes job creation, and that the impacts are larger in more economically advanced African countries. This differential effect tends to validate the idea that the nature of jobs varies
August 15 and September 8, 2017. During this second round, 581 of the 717 households in the conflict telephone survey were re-interviewed (only the 717 were attempted to be reached).<sup>27</sup> Due to the attrition, we present the results using this sample as additional results instead of as part of our main analysis. However, because the additional survey round was collected in autumn 2017, this sample allows us to employ the conflict information from 2016 and spring 2017 in the analysis, which provides an interesting robustness check.

> 27 The attrition rates from the first to the second telephone survey rounds are 16 percent, 21 percent, and 19 percent for the North East, the North Central, and the South South, respectively. The geographical distribution of this sample is the following: 147 households in the North East, 219 in the North Central, and 216 in the South South.

24
market (returns to education) and school. The next section provides a discussion on the data sets used for the empirical analysis. Section 4 reports and discusses the estimates of the investment and mobility equations for the urban households, along with an analysis of how the estimated parameters can help sort out the mechanisms driving the observed pattern of educational investment and intergenerational mobility. Section (5) then discusses the corresponding results for the rural households. Section (6) concludes with a summary of the main findings and the methodological contributions of the paper.

# **(2) Related Literature**

The literature on intergenerational mobility in developed countries is well-developed, with many fundamental theoretical and empirical contributions. For excellent surveys of the literature, please see Solon (1999), Black and Devereux (2011), and Bjorklund and Salvanes (2011). The focus of this literature has been on intergenerational (permanent) income persistence, and a lot of effort has been devoted to understanding the biases that arise from measurement error and life-cycle effects. Also, most of the studies deal with father-son linkages, and, as noted above, research on women, and in particular on gender bias is lacking.

In contrast, research on intergenerational economic mobility in developing countries remains relatively neglected. This partly reflects the data constraints. Good quality income data for long enough time periods to calculate permanent income remain rare. As a result, the focus of the research on developing countries has been on intergenerational educational persistence. For a discussion on the methodological challenges and data constraints in research on intergenerational mobility in developing countries, see Emran and Shilpi (2019). Recent surveys of this literature include Iversen et al. (2019), Behrman (2019) and Torche (2019). For cross-country evidence, please see Hertz et al. (2008), Narayan et al. (2018) and Bhalotra and Rawlings (2013). The recent studies on India include Azam and Bhatt (2015), Azam (2016), Emran and
$2.9 million per year on the school feeding program.

# **3. Data Sources**

The primary data source is the Survey of Living Conditions and Household Budgets (SLCHBS) conducted by the Central Statistical Office (CSO), the country’s national statistics office, between April and May of 2018–19. This survey encompassed a sample of approximately 1,689 households. The primary objective of this questionnaire was to collect information related to household socioeconomic and living conditions as well as the household budget in order to construct the consumption variable (as the survey did not collect income information), data that are crucial for measuring poverty in Grenada. It should be noted that this survey did not gather information regarding pensions received or any social security contributions, neither by individuals nor the government. The survey comprised two distinct parts, one focusing on household conditions and another for obtaining individual characteristics of each household member. The person interviewed was one adult member (older than 18 years old), though the survey collected information on every single household member.<sup>7</sup> The use of administrative data on taxable wages for the PIT complements the survey information. The source of this data set was the Inland Revenue Division (IRD), a division within Grenada's Ministry of Finance (MOF). This data set covers wages for different workers and is disaggregated into two parts: the estimation of the share of the wage allocated for pension funds, and the other being the gross income, which is the taxable part of the wage received.

> 6 Fees are waived for some services provided to children, the elderly, and the indigent. All fees and co-payments were not included in the analysis given the low amounts reported in the SLCHBS of 2017–18.

> 7 To evaluate the accuracy of the data for fiscal interventions, a macroeconomic validation is presented in Appendix B3.

6
5), approaches to operationalize and measure are manifold, particularly across regions. This diversity is also reflected in the various coding schemes applied across various data sources. While there have been recent calls for a globally comparable (land) tenure module to be integrated into HH surveys (e.g., Holden et al. 2016), uniform data on security of tenure are hitherto absent within most multi-purpose HH surveys. Some (more recent) HCES include detailed questions on property titles or documents that HHs hold regarding their occupation status. In some African countries, some surveys include country-specific questions on e.g., (customary) certificate of occupancy, right to occupancy, or whether HHs hold formal title deeds

> 3  For the 64 countries included in the _AHI_ , data on shared sanitation facilities were not available for the majority of countries (Albania, Angola, Argentina, Belarus, Brazil, Burkina Faso, Burundi, Chad, Côte d’Ivoire, Colombia, Costa Rica, Democratic Republic of Congo, Djibouti, Dominican Republic, Arab Republic of Egypt, Ethiopia, Guinea, Guinea-Bissau, Haiti, Indonesia, Kazakhstan, Lao PDR, Madagascar, Mexico, Mozambique, Myanmar, Nepal, Nicaragua, Pakistan, Philippines, Peru, Romania, Russian Federation, Senegal, Sierra Leone, Sri Lanka, Togo, Tunisia, Turkey, Ukraine, Uzbekistan, Vietnam, and Zambia). This means we will not be able to identify HHs that share sanitation facilities and therefore likely share a housing unit with other HHs. As comparability across countries and a uniform coding scheme of the seven adequacy dimensions is our paramount objective; therefore, we opt not to differentiate between shared and non-shared sanitation facilities.

> 4  Other agencies like Eurostat have a more nuanced classification defining an HH to be overcrowded if it does not have at least one room for the household; one room for each couple in the household; one room for each person aged 18+; one room per pair of single people of the same gender between 12-17
Another feature of the _SP_ program also helps with the identification strategy; the program was launched in 2002, but was rolled out across municipalities over time. This variation in locationtime of _SP_ coverage provides a cleaner test than a simple before-after comparison.

Studies show that there has been significant uptake of the program, although the impact of _SP_ on health outcomes is mixed. King et al. (2007) assessed a sample representing 118,569 households in seven Mexican states, and measured outcomes in a 2005 baseline survey (August, 2005, to September, 2005) and follow-up survey 10 months later (July, 2006, to August, 2006). The treatment consisted of encouragement to enroll in a health-insurance Program and upgraded medical facilities. Participant states also received funds to improve health facilities and to provide medications for services in treated clusters. They estimated intention to treat and complier average causal effects non-parametrically. They find that the Program resources reached the poor. However, the Program did not show some other expected health effects, possibly due to the short duration of treatment (10 months).<sup>4</sup> Scott (2006) also does an impact evaluation of _SP_ using data from 2004 and finds that there was significant uptake. However, he finds that the selection of beneficiaries does not target the poorest states and households as effectively as its stated objectives. This raises the possibility that the program could indeed be affecting the decision of whether to work in the formal or informal sector. If the program successfully targeted those in the lowest income deciles one might expect more limited impact on formality as these households are already significantly less likely to participate in the formal sector. However, this evidence that non-poor households are participating in _SP_ would suggest that it is affecting a portion of the population that might well be considering shifts between the formal and informal sectors.

# **3. Literature Review**

There have been several studies that examine the impact on informality of large social programs that may affect the incentive
However, the price of nontradable staple food prices did decline. Moreover, the fact that the imputed value of home consumption likely fell in real terms must be factored into the analysis. When it is, and when account is taken of the fact that real cocoa prices did not increase substantially, there is reason to think that in the short run following the devaluation, poverty would have increased, or at least failed to decrease, among a substantial segment of the rural population.

# Household survey results

Two household expenditure surveys are available that allow us to trace the short-run impact of the devaluation on poverty. The pre-devaluation survey was carried out between June and November, 1993, except Abidjan, which was carried out in March-April 1992. A total of 9600 households were surveyed. The post-devaluation survey, with a much smaller sample of 1000 households, was carried out in April-May 1995. With several exceptions, we maintained the INS data as it was processed, cleaned, and aggregated. The data take into account the change that was made to the consumption of home production data to correct an error made in computer data entry, as described in Appendix C. We also re-estimated the housing expenditure variable and changed the definition of agricultural workers to correct some inconsistencies, described in Appendix A.

In view of the fact that GDP growth per capita was negative in 1994, one would expect that the devaluation would not have had a strong impact on reducing poverty by early 1995, when survey results are available. Overall, the survey results, based on the cleaned INS data set (table 2), show that the incidence of poverty increased from 32.3 percent in 1993 to 38.6 percent in 1995. Given the various shortcomings of the surveys-- which are discussed below--the increase in poverty is probably overstated.

While overall there was little change in mean household expenditure and poverty, there were big differences in the changes in mean expenditure and poverty across regions and socioeconomic groups. Within urban and rural areas there
<!-- Start of picture text --> Gross NPA to Gross Advances Ratio<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Public Private SCBs<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Data is from Reserve Bank of India. Years refer to respective fiscal years.

According to experts, the distorted incentive structure coupled with political compulsions has resulted in allocative and operational inefficiencies of a public sector led banking sector, and to periodic loan write-offs, e.g. to the agriculture sector, and under recovery of the corporate credit. Reconsidering the ownership balance, and the incentive and governance structure may be important in order to alter the perennial cycle of allocative and operational inefficiencies.

Under its current balance sheet situation, the banking sector does not seem well equipped to help finance higher growth or investment rate, and suitable reforms will be needed to reverse this equilibrium.<sup>44</sup> In this direction, the government announced an unprecedented recapitalization of public sector banks on October 24<sup>th</sup> , 2017. The proposed measures include recapitalization of approximately INR 2.11 trillion (around $32 billion) over the next two years. The government proposed to fund it through budgetary provisions amounting to approximately INR 180 billion; recapitalization bonds to the tune of INR 1.35 trillion; and direct capital raising by banks from the market by diluting government share (an estimated INR 580 billion). The recapitalization ensured continuity and stability in the system, but needs to be followed by wider reforms.

The government also implemented and strengthened a new Insolvency and Bankruptcy Code. While an important step toward changing the credit culture, the policy will take time to help clean the balance
indexes, we also report their standard deviations in the control group at the respective follow-up.<sup>18</sup>

# **3.4 Empirical specification**

We conducted two separate analyses for the short-term (n=606) and medium-term (n=619) follow-up samples. It is worth mentioning that individuals in these two samples were not necessarily the same. In particular, 42% of those who completed the medium-term survey also

> would count the entire difference between the two visits as a visit to the first website. To correct for the related measurement error we assumed that all visits that lasted more than 2 hours were capturing time spent away from our websites. Hence, we discarded them by replacing the values with missing data.

> 17For missing values, we also followed Kling et al. (2007). Particularly, if a respondent has a non-missing value for at least one of the variables in an index, we impute any missing values for the other variables using the random assignment group mean. This implies that differences between treatment and control means of an index coincide with the average of treatment and control means of the variables in that index (when divided by their standard deviations).

> 18Impact effects are divided by standard deviations derived from the control group at follow-up. An increase of two standard deviations, for example, would move someone from having average knowledge to being in the top 5 percent of the group.

10