rafmacalaba/data-use-mentions-extended huggingface dataset:
fora would tell us how much additional legal security the TPP offers, it would not tell us whether the agreement produced explicit liberalization. The third and most meaningful comparison is between TPP commitments and actual policy in the TPP members.<sup>3</sup> In order to make each of these comparisons, we created a new public database of information on the TPP countries legal commitments and applied policies in five major services sectors – financial, basic telecommunications, retail distribution, transport and selected professional services.<sup>4</sup> This database includes: information on TPP countries’ commitments under the TPP and under earlier multilateral (WTO) and preferential trade agreements; and information on their applied policies in 2008, just after the TPP negotiations began, and in 2015, just before the negotiations concluded.<sup>5</sup> Our main findings can be summarized as follows. First of all, TPP commitments seldom go beyond countries’ applied policies and, therefore, the explicit liberalization resulting from the agreement in these five services sectors is limited to a few members and a few areas. We can distinguish between three categories of liberalization in terms of timing: over the course of the

> 1 <u>https://www.worldbank.org/content/dam/Worldbank/GEP/GEP2016a/Global-Economic-Prospects-January-2016Spillovers-amid-weak-growth.pdf.  The United States International Trade Commission (2016) has estimated that the</u> US services sector would see a gain of $42.3 billion (0.1 percent) in output thanks to the TPP.

> 2 https://ustr.gov/sites/default/files/TPP-Overall-US-Benefits-Fact-Sheet.pdf

> 3 While several attempts have been made to compare the GATS with the PTAs (Roy, 2011 and Marchetti et al, 2013, USITC, 2016), a comparison of PTAs with applied policies is relatively rare (Gootiiz and Mattoo, 2015). 4 Available from the website of the WB Services Trade Restrictions Database:
In contrast, mean years of teaching is negatively correlated with physical health and communication and general knowledge—although the effect sizes are relatively small, ranging from -0.056 to -0.117 standard deviations. In addition, experience in early childhood education and receiving teacher training yielded null results. For the case of teacher training, follow-up assessments of the training experience suggest that the intent of the model was inconsistently and infrequently followed. Often, in practice, training was didactic or overly theoretical. Followup on-site supervision has been expected but very rarely occurred.

It is also important to note, that teacher characteristics used in this study are averaged at the center-level, which means we cannot attribute student outcomes directly to their teachers. As a result, we interpret the small but negative coefficient on years of teaching as well as the null results of ECED experience and teacher training as follows: policies focused solely on hiring teachers with more experience and training will not be sufficient to improve children’s development in rural Indonesia. Rather, policies must address the quality of professional development activities for teachers in order to ensure the effectiveness of early childhood education programs. Training, in particular, should be multifaceted: including not just group training but coaching, observations of quality programs, and regular teacher cluster meetings on meaningful topics.

The mixed results on teacher characteristics in our study is not surprising given that prior studies on teacher qualification have also shown mixed evidence in relation to children’s developmental outcomes. The few studies that have successfully produced statistically significant results often employ a value-added model (Hanushek and Rivkin, 2006). A limitation of our teacher level data is that we cannot match the teacher observations with the student EDI. As a result, the measure of teacher characteristics used in this study is averaged at the center-level, which is a less precise measure of teacher quality than those derived from matched teacher-student datasets. Future studies of early childhood quality in developing countries could benefit from matched teacher-student data to improve the estimates of teacher quality and
scores. The results of the intervention are persistent. In the _regional_ sample, 14 months after the intervention those students from schools assigned to treatment that took the census evaluation improved their scores by 10%, 12% and 10% in _Mathematics_ , _Reading Comprehension_ , and _History, Economics and Geography_ (compared to the control group). Our results shed light on the potential of growth-mindset interventions. Given its per capita ITT cost, this puts the ‘ _¡Expande tu Mente!_ ’ package among the most highly cost-effective educational interventions in the developing world. At the same time, our results highlight that while growth-mindset interventions work, they do not work in all contexts. In _metropolitan_ areas, where schools and classrooms are larger and students have higher scores, our intervention failed to be effective.

The rest of the paper is organized as follows. Sections 2 and 3 describe the intervention and its conceptual pathways (respectively). In section 4, we describe the experimental design and the implementation of the intervention in the field. Sections 5 and 6 present the data and the empirical strategy, and sections 7 and 8 report our main results and robustness checks. Section 8 concludes.

# **2.** **<u>The intervention</u>**

The intervention, known as ‘ _¡Expande tu Mente!_ ’ in Peru (‘Grow Your Mind!’; from here onwards, GYM)) was a collaboration between the authors and the Peru Ministry of Education (MINEDU). Within MINEDU, the research team worked in coordination with _MineduLAB_ , the cost-effectiveness laboratory of the Ministry, and the Direction of Secondary Education. The main objective of the intervention was to instill in students the growth-mindset to improve their motivation, perseverance, effort at school, and, ultimately, academic performance (see the theory of change in **Section 3** ). The basic education level in Peru is composed of a pre-school level, six grades at the primary level and five
Table 5: IV results, robustness checks

||Omitting strength|Non-seasonal inversion|State clustering|Without winsored|
|---|---|---|---|---|
|Mean PM2_._5|-0.0090<sup>_∗_</sup>|-0.0094|-0.0075<sup>_∗∗_</sup>|-0.0085<sup>_∗∗_</sup>|
||(0.0050)|(0.0062)|(0.0032)|(0.0037)|
|N|6,142|5,575|6,142|6,069|
|**Weather controls:**|||||
|Temperature|Y|Y|Y|Y|
|Precipitation|Y|Y|Y|Y|
|Relative humidity|Y|Y|Y|Y|
|Surface pressure|Y|Y|Y|Y|
|Wind speed|Y|Y|Y|Y|
|Wind direction|Y|Y|Y|Y|
|**Fixed efects:**|||||
|Fiscal year|Y|Y|Y|Y|

Notes: The outcome is the first difference within a district of the log of annual GDP measured at the district level. We instrument for annual average of PM2 _._ 5 measured at the district level during a fiscal year from April to March and weighted by population with inversions and inversion strength. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversion strength is the average difference in temperature when there is an inversion. All inversion data and weather
# **2. Data and empirical approach**

The data on 124 microfinance institutions (MFIs) in 49 developing countries were collected by the Microfinance Information Exchange (or the MIX), a not-for-profit private organization that aims to promote information exchange in the microfinance industry.<sup>6</sup> The database contains one observation per institution from 1999 to 2002. Seventy percent of the observations are from 2002. These data, collected for publication in the _MicroBanking Bulletin_ (MBB), have been adjusted to help ensure comparability across institutions. The adjustments, which are summarized in Appendix A, include an inflation adjustment, a reclassification of some long-term liabilities as equity, an adjustment for the cost of subsidized funding, an adjustment for current-year cash donations to cover operating expenses, an in-kind subsidy adjustment for donated goods and services, loan loss reserve and provisioning adjustments, some adjustments for writeoffs, and the reversal of any interest income accrued on non-performing loans.

The institutions have been selected based in large part on the quality and extent of their data. The data set is thus not representative of all microfinance institutions. They do, however, collectively serve a large fraction of microfinance customers worldwide. A sense of the skewed size distribution of microfinance is given by Honohan’s (2004) analysis of data provided by the Microcredit Summit organization, a data set whose top end largely overlaps the data here. Honohan (2004, p. 3) finds that “the largest 30

> 6 Information on the MIX is available at their website www.themix.org.  Data for institutions is available via the MIX MARKET, a global, web-based, microfinance information platform accessible via the MIX’s website. The MFI data include outreach and impact data, financial data, audited financial statements, and general information. The MIX MARKET, previously known as “The Virtual Microfinance Market,” began as a project of the United Nations Conference on Trade and Development (UNCTAD). The project was supported by the government of
1 Introduction Individual welfare is a multidimensional concept. Economists tend to favour measures of well-being that are closely oriented on economic theory, such as consumption or income. Yet, going beyond these standard measures is useful for various reasons. Following Sen (1999), increasing economic resources is essential as a means to enhancing individual welfare, but it is just an instrument and not the desired outcome. The overarching objective is the expansion of real freedoms, or capabilities, enjoyed by the members of the society.

Even a wider array of objective measures is unlikely to capture the notion of well-being adequately. Interpersonal comparisons of welfare cannot be made by looking solely at the “revealed” preferences of people as evident from their demand behaviour (Pollack and Wales (1979) and Browning (1992)). This has severe consequences. As is well know from Arrow’s Impossibility Theorem (Arrow (1950)), a social welfare function cannot be constructed in the absence of interpersonal comparisons of individual welfare. In addition, participatory poverty assessments around the world have shown that a good life or well-being has both material and psychological dimensions. It includes aspects of “empowerment”, or the effective inclusion of people in making decisions on economic, social, cultural and political issues. ? Lack of voice, discrimination, social barriers, mistreatment by public officials, alienation from service providers, and language barriers all affect the ability of individuals to achieve their potential and create a sense of powerlessness. While economic well-being and empowerment are related, they represent separate and imperfectly correlated concepts.

separate consideration of economic well-being and power is especially important when nations embrace systemic institutional reforms. The fall of the Berlin Wall in 1989 gave rise to political and economic liberalization in Eastern Europe, the Former Soviet Union, and in most countries of Africa and many nations in East Asia. Such reforms are likely to have different impacts on economic and power status. Not everybody benefits in the same way, and some even lose out, as traditional values or authority structures
This decomposition clarifies how partial analyses of productivity performance within individual sectors (such as manufacturing or agriculture) can be misleading when there are large differences in labor productivities ( _pi,t_ ) across economic activities. In particular, a high rate of productivity growth within an industry can have quite ambiguous implications for overall economic performance if the industry’s share of employment shrinks rather than expands. For example, if the displaced labor ends up in activities with lower productivity, economywide growth will suffer and may even turn negative.

Armed with the results of the decomposition, the authors of each of the chapters then use a variety of strategies to gain a deeper understanding of the country-specific factors that played a role in facilitating (or impeding) structural change. For example, in Chapter 1 of this book, Mitra and Ahsan use state-level data on employment shares by industry, tariffs, education, and labor regulations to explore the correlates of structural change across states in India.

# **Country Studies: Data and Measurement  Issues**

Here, too, in an effort to maintain consistency, all of the country studies use national accounts data and labor force statistics to compute measures of sectoral employment and value-added for nine broad sectors of the economy. The authors also draw on several complementary datasets to conduct more detailed analyses of the underlying correlates of structural change and withinsector productivity growth. Country-specific data appendixes appear at the end of each of the country chapters. These appendixes document the sources of data, as well as any inconsistencies in the data and how these were handled. Nevertheless, several measurement issues common to all of the studies warrant clarification.

**Informality.** A big question with national output and employment data in developing countries is how well they account for the informal sector. The coverage of the informal sector in national accounts data varies from country to country (Timmer and de Vries 2009). While all countries make an effort to track the informal sector, obviously the quality of the data can vary greatly. In contrast
total workers, which could occur if these enterprises are competing with the farm sector for labor, and positive rainfall shocks increase the demand for farm labor (see Colmer (2019)).

Table 4 shows the impact of precipitation shocks on qualitative measures of enterprise performance and credit usage. The NSS survey asks enterprises whether they are expanding, stagnating or shrinking, and whether the enterprise is registered with state or local authorities. We combine stagnation and shrinkage into a single category and run linear probability models to identify the impact of rainfall shocks on these binary outcome measures. As seen from columns (1)-(3), a positive rainfall shock has a significant positive impact on the likelihood of enterprises registering with a local or state body, and a weak negative impact on their probability of stagnating or shrinking. We however find no impact of precipitation shocks on enterprise expansion.

Columns (4)-(9) of Table 4 identifies the impact of rainfall shocks on enterprise credit. Our survey captures credit both along extensive and intensive margins, and also identifies – the source of credit bank, or non-bank. Across both the extensive and intensive margins, we find little impact of a positive rainfall shock on access to credit. While the coefficient on non-bank credit along the extensive margin is positive, the confidence intervals are too large to draw any meaningful conclusions.

> 8 This mean enterprise revenue (value addition) when enterprises are faced with a zero shock is INR 35514 (INR 10028). Based on the coefficients from Table 3, this implies an increase of INR 0.257*35514 (INR 0.303*10028) on enterprise revenues (value-addition).

> 9 The observations in the last three columns are smaller as we exclude own-account enterprises from these regressions as these enterprises by definition do not hire any workers.

11
information coming from statistical households’ surveys. Labor growth is exogenous. Capital accumulation derives from savings/investment decisions. The model has a vintage structure for capital that allows for putty/semi-putty assumptions with sluggish mobility of installed capital.

The model allows for a wide range of productivity assumptions that include autonomous improvements in energy efficiency that can differ across agents and energy carriers. The model can be calibrated to different SAMs that follow a standard set of conventions in representing the economic structure. The model is implemented in the General Algebraic Modeling System (GAMS) software and an aggregation facility is used as a front-end to the model to allow for full aggregation flexibility.

# **_Model Description_**

The IRENCGE-DF model is a recursive dynamic computable general equilibrium (CGE) model. Each year, a scenario is solved as a static equilibrium, with dynamic equations linking exogenous factors (such as population growth and capital accumulation) across years with, in addition, update equations for productivity factors. Each static equilibrium relies on a relatively standard set of equation specifications.

Production is modeled using a series of nested constant-elasticity-of-substitution (CES) functions designed to capture the substitutions and complements across the different inputs, notably capital and labor, but also with a focus on energy as energy policies are one of the key objectives of the IRENCGE-DF model. Energy is assumed to be a near-complement with capital in the short-run, but a substitute in the long-run. Thus, rising energy prices tend to lead to rising production costs in the short-run when substitution is low, but a long-run response would lead to energy-saving technologies that dampen the cost-push factor. This feature of the model is embodied in a vintage capital structure that captures the semi-putty/putty relations across inputs with more elastic long-run behavior as compared to the short-run.

The model allows for both multi-input and multi-output production. The former, for example, would allow for electricity supply to be produced by multiple activities—thermal, hydro, solar and other renewable forms of electricity production. The latter allows for a single
percentage goes to zero). One percent decrease in the share of products that are licensed increase product creation rate by six percent. The last column of the table shows how licensing reform affected the total number of varieties produced by firms. It uses log number of products as the dependent variable and applies the same GMM-SYS method. The result shows that the reform increased product scope by around 3 percent.

In the estimations, coefficients of tariff rates and FDI indicator are not significant. However Goldberg et al. (2010b) find significant increase in product growth rate caused by the reduction in input tariffs. My analysis differs from their study in several ways. In their model input tariff rates are used as instruments for a conventional price index and a variety index. They show that both indices affect product growth. According to their model lower tariffs lead to lower prices of inputs which result in increased imported varieties. Higher use of varieties is associated with an expansion of firm scope. In the estimations of product growth they do not control for the licensing reform. Moreover the model presented in their study does not show the dynamics of product growth.

FDI reform is another factor that can affect innovation performances of firms. It can reduce the cost of innovation by increasing R&D capacity or lowering capital and transactions costs. Using the Prowess database, Vishwasrao and Bosshardt (2001) find that foreign ownership is among the variables that impact a firm’s probability of adoption new technology. However they do not use the detailed product information as their measure of technology adoption. The estimation results presented in Table 5 include all these factors and thus provide a more complete set of controls of policy reforms during the time period included in the analysis. Among the three major reform areas, licensing reform is the only one that significantly increases product creation rates.

At the bottom of the table I present Sargan tests of over-identifying restrictions to test the validity of instruments. For consistent estimation, the error term should be serially uncorrelated. In the GMM estimator
<!-- Start of picture text --> Corresponding Correlation with Question asked to village | Regressions with average rank of maize<br>Skill Index corresponding skill index informant yield as dependent variable<br>Cognitive 0.43 Level of education 5.08*** 1.49<br>(1.24) (1.48)<br>Non-cognitive 0.22 Active/ Motivated 3.45* 3.03<br>(1.77) (1.91)<br>Technical 0.15 Agricultural knowledge 5.44*** 4.83***<br>(1.49) (1.50)<br>Controls Vil. FE Vil. FE All<br><!-- End of picture text -->

|Corresponding<br>Skill Index|Correlation with<br>corresponding skill index|Question asked tovillage<br>informant|| Regressions <br>yield a|with average r<br>s dependent va|ank of maize<br>riable|
|---|---|---|---|---|---|
|Cognitive|0.43|Level of education||5.08***|1.49|
|||||(1.24)|(1.48)|
|Non-cognitive|0.22|Active/Motivated||3.45*|3.03|
|||||(1.77)|(1.91)|
|Technical|0.15|Agricultural knowledge||5.44***|4.83***|
|||||(1.49)|(1.50)|
|||Controls|Vil. FE|Vil. FE|All|
|||Observations|887|887|887|
|||
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 5 comparability is hampered by inter-country differences in defining muncipios or equivalent lower-level administrative districts. For instance, the GPW3 data for Peru is based on 1895 third-level units, Bolivia on just 100 second-level. All intercountry comparisons should be approached with caution.

||**Landscan**|**GPW 3**|**GRUMP v1**|
|---|---|---|---|
|**Produced by**|Oak<br>Ridge<br>National<br>Laboratory|CIESIN and CIAT|CIESIN,<br>IFPRI,<br>World<br>Bank,<br>and<br>CIAT|
|**Nominal year of**<br>**data**|2002|2000|2000|
|**Grid cell size**|0.5’|2.5’|1 km|
|**Typical level of**<br>**unit for which**<br>**input**<br>**census**<br>**data**<br>**was**<br>**obtained**|province/state|municipio/county,<br>varies by country|municipio/county|
|**Within-unit**<br>**allocation**<br>**of**<br>**population**|model based on road<br>proximity, slope, land<br>cover, urban lights|homogeneous|reported<br>urban<br>population allocated<br>to cells of known<br>light areas and used<br>as<br>a<br>means<br>for<br>reallocating<br>population estimates<br>of<br>administrative<br>areas|

#
budget actuals. The best sources for this information are the ministries of planning and finance, the parliament, and published budget laws.

- _Operator level._ Financial variables are collected from SOEs, public corporations, or parastatals, and special funds, following fiscal data template 11. The best source for this information is the public operator itself, be it a corporation or a special fund.

# Fiscal data template at the national level

## **Macroeconomic parameters for normalization (template 8)**

Template 8 is used to guide data collection at the national level. Template 8 collects basic macroeconomic data for normalization (for example, GDP, exchange rates), most of which are available through internationally validated secondary sources (the World Bank and IMF Web sites, for example). The template should be completed for the same years for which data for templates 10 and 11 have been collected (that is, the most recent five years).

## **Template 8  Macroeconomic parameters for normalization**

|**Policy**<br>**code**|**Series code**|**Variable**|**Definition**|**Unit**|
|---|---|---|---|---|
|struc<br>IN)|XXXXXXX|Gross domesticproduct||LCU per year|
|infra<br>re (N|XXXXXXX|Average exchange rate||LCU per year|
|Non<br>tu|XXXXXXX|Population||LCU per year|

_Source: Authors’ elaboration_ . _Note:_ LCU = local currency units.

# Relevant definitions: Functional and economic classifications

The following definitions and clarifications may be useful when completing all the templates related to fiscal data (see templates 9 and 10).

## **The functional classification of expenditures or COFOG**

Using the GFSM 2001 as the starting point for this exercise provides a methodological platform