rafmacalaba/datause-encoder-data huggingface dataset:
the country. Given these constraints, so far, there is very limited empirical research on virtually any macroeconomic topic in Guatemala.

The following paragraphs describe the data needed for the analysis that follows. These are measures for the human and physical capital stock and the labor force, and quality indices for human and physical capital. Information other than that reported in this section is listed in the Appendix. The time series are mainly from Banco de Guatemala, and, in the case of educational statistics, from the Ministry of Education and the United Nations Educational, Scientific and Cultural Organization.

# **4.1 Human Capital Stock**

The human capital stock of Guatemala is defined by average years of schooling evident in the labor force.<sup>12</sup> In line with most empirical analyses, this study assumes that years of schooling provides a reasonable approximation of the human capital stock, although it should be briefly stressed that the indicator is incomplete for several reasons.

_(1) Education as proxy variable._ Human capital is multifaceted and includes a complex set of human attributes. As a consequence, the genuine level of human capital is hard to measure in quantitative form. At best, average years of schooling can be regarded as a proxy for the component of the human capital stock obtained in schools. Therefore, in a later robustness test, life expectancy at birth will be included in the regressions. Life expectancy is commonly viewed as a companion indicator to educational capital that captures the effect of health.

_(2) Quality changes._ Average years of schooling measurements do not take into account quality changes within the education system. Quality changes may complicate comparison of schooling effects on growth over time as well as making comparisons with other countries difficult. Bratsberg and Terrel (2002), CIEN (2002) and the World Bank (1995c) argue that the quality of the education system in Guatemala is rather low, and may not have shown much improvement over time.

_(3) Aggregation bias._ Average years of schooling raise human capital by an equal amount regardless of whether a person is enrolled in a primary, secondary or tertiary school. This is an important point because by defining human capital by average years of schooling, one implicitly gives the same weight to any year of schooling acquired by a person. This completely disregards the findings of the microeconomic literature on wage differentials. For example, Psacharopoulos and Patrinos (2002) suggest that the rates of return to education could be decreasing with the acquisition

> 12 The use of labor force instead of total population data is due to problems regarding the Guatemalan population data for the 1980s. By contrast, the labor force proxy used here is assumed to take into account some of the effects of the civil war, i.e. migration and displacement.

-16-
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Senegal > Telecom investments > Macro results**

||||Reduction in<br>h bli|Te|lecom<br>I|Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|1,500,000.00|0.13|0.63|0.62|0.65|0.74|
|EV|Equivalent variation||0.13|0.58|0.58|0.64|–1.30|
|s|Wage|1.00|–0.79|1.47|1.45|1.55|1.96|
|yg|Government income|539,000.00|0.42|5.13|5.13|5.13|5.13|
|g|Total government expenditure|295,000.00|–4.61|3.98|3.98|3.98|3.98|
|ye|Firm income|935,000.00|1.02|0.22|0.22|0.19|0.04|
|It|Total private investment|860,000.00|0.97|0.21|0.05|–0.45|–0.02|
|GDP|GDP|2,100,000.00|0.53|0.53|0.53|0.53|0.53|
|e|Nominal exchange rate|1.00|–0.35|0.05|–1.10|–4.27|–0.64|

# **Senegal > Telecom investments > Sectoral results**

||||Reduction in<br>|T|elecom<br>|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign<br>aid|Income<br>tax|
||Crop agriculture|252,000.00|0.40|0.34|0.35|0.37|0.28|
||Export agriculture|221,000.00|1.21|–0.13|–0.13|–0.22|0.06|
|Va<br>|Industries|361,000.00|0.78|–0.43|–0.40|–0.53|–0.10|
|Value added or<br>output|Construction|91200.00|2.97|2.25|2.26|2.56|2.42|
||Private services|995,000.00|0.90|0.49|0.46|0.38|0.29|
||Public services|180,000.00|–3.85|2.92|2.96|3.59|3.11|
||Crop agriculture|1.02|–0.14|0.16|–0.01|–0.78|–1.44|
||Export agriculture|1.02|–0.54|0.24|0.15|–0.66|–0.27|
|pq|Industries|1.14|–0.32|1.34|1.36|–1.79|–0.43|
|Market prices|Construction|1.01|5.80|6.12|6.06|5.41|5.81|
||Private services|1.02|–1.10|–0.26|–0.40|–1.26|–1.21|
||Public services|1.00|–0.79|1.04|0.99|0.38|0.84|
||Crop agriculture|1.00|0.78|0.21|0.66|1.68|–1.74|
|r|Export agriculture|1.00|1.14|–0.05|–0.06|–0.19|0.93|
|Rental rate of<br>|Industries|1.00|1.30|–1.44|–1.32|–1.77|0.39|
|capital|Construction|1.00|15.83|13.72|13.77|15.80|15.34|
||Private services|1.00|–0.38|–0.43|–0.59|–0.98|–1.09|

39
These numbers fit relatively well the average probabilities shown above and show that the model supports a scenario in which government-led renegotiations following a previous renegotiation are only a small subset of all renegotiations at the initiative of the government.

### Stationarity of the βs.

Another important point to consider is the question of the stationarity of the βs in a multiperiod model. When there are successive renegotiations, it could be questioned whether such a stationarity assumption is realistic. Given the complexity of the model, however, we leave this issue for further analysis (see for example Laffont and Martimort, 2002) and content ourselves with observing that this problem is made less relevant by the fact that overall successive renegotiations are a rather small subset of the total.

Moreover, note that empirically this provides justification for the way projects are handled in our data base. They leave the sample when they are renegotiated, and enter as a new, different project if the concession carries on with significant modifications, thus avoiding the problems of confusing projects which distinct underlying technological characteristics.

# 3 Empirical Evidence

## 3.1 The Data

We use an original data set, developed by the World Bank, which describes the characteristics of nearly 1,000 concessions awarded in Latin American and Caribbean countries from 1982 to 2000<sup>19</sup> . As in GLS, and to keep results strictly comparable, we again focus on water and transport concessions in five countries (Argentina, Brazil, Chile, Colombia and Mexico), between 1989 and 2000. We have information on the general details of the projects (sector, activity, year of award, award criteria, size and duration of the concession), on the institutional and regulatory context, the type of regulatory framework put in place (price cap or rate of return), as well as the evolution of the main economic variables (growth, exchange rate, inflation) and the timing of national and local elections. Table 1 presents the full list and definitions of variables used in the analysis below and gives summary statistics.

> 19See GLS (2003) and Guasch (2004) for additional details on the data set.

21
15

||**Pooled regr**<br>|**ession: 2007-2010**<br>||
|---|---|---|---|
|**Dependent variable:**<br>**Log (aid for trade/GDP)**|Indicator<br>based on a<br>simple<br>average|Indicator based on a<br>weighted average-<br>weights derived from<br>the coefficients of<br>the cross-section<br>gravity regressions|Indicator based on a weighted<br>average- weights derived from<br>the coefficients of the pooled<br>gravity regression|
|Ln (GDP per capita)|-0.707***|-0.547***|-0.548***|
|Rule of law|0.919***|1.021***|1.020***|
|TCio.|-0.915***|-0.871***|-0.871***|
|N|116|116|116|
|Year dummies|YES|YES|YES|

The figure below (Figure 3) shows the results for the year 2007 and 2010 based on the regression provided in Table 4, column 2. Similar countries receive again relatively lower aid for trade compared to their potential need, after controlling for their level of development and the quality of the legal system.

Figure 3. Aid-for-trade flows against trade capacity indicator-- low-income countries Pooled 2007-2010 regressions controlling for GDP per capita and quality of the legal system <!-- Start of picture text --> GIN<br>NIC<br>MNG BOL MMLI OZ SENKENUGA<br>MOZ TZAKEN<br>NIC BOL HND KGZ KGZ CMR<br>MNG TZA RWA E TH BEN TGO UGA<br>RWAGHA<br>BEN ZM MLIB CMRSEN NPBFA L CIV BFAGHAETHA ZE NPL LKA<br>PZ AKMBAZE H ND SDN PAKLKA<br>NER CIV<br>NER GIN<br>SDN<br>NGA TGO NGA<br>INDIND<br>-1 -.5 0 .5 1<br>Demand for aid for trade (weighted index cross sectional gravity)<br>2007 2010<br>Fitted values pooled aid for trade regression<br>Regression exlcude middle-income countries<br>2<br>1<br>0<br>-1<br>-2<br><!-- End of picture text -->
**40**

Burundi 1: PFP - Projectlons and A*tuals <!-- Start of picture text -->
*<br>_2_  _ _  _  _  _  _2<br>Y*w  ~ ~  ~<br>-Pmeoens  4 A.tsm<br><!-- End of picture text -->

Burundl 3: PFP - Projections and Actuate <!-- Start of picture text --> M . D* ~ijt-.<br>aI _ I2<br>- 1 06 SO SO St 03<br>Yer<br>- +MIaelo U<br><!-- End of picture text --> Burundl 5. PFP Projectlons and Actuahe <!-- Start of picture text --> &a &pt aw anw " " "<br>-.. ,-: , .'4't -<br>Y, .<br>~Pvft 1m +Aoj4ft -<br>Srn,. Pef dlens- PPPS U/SI. _1/.040<br>Adude. ANO MEW/sAUuY BdeW<br>Pt J.m D* Deeoww+A<br><!-- End of picture text --> Burundi 2: PFP - Prolojtions and Aotuals <!-- Start of picture text --> 4<br>of ea__<br>~ ~ -" +t WAWTQ t *4uobd<br>TOT * of Tr*l<br>Burundl 4<br>PFP Projections and Actuals<br>S<br>4o\\D<br>S O. 6? 55 5 05 St 02<br>Yew<br>-Projetei +Aeluai.<br>Burundl 6: PFP - Prolections and Actuals<br>660<br>YeW<br><!-- End of picture text --> Burundl 6: PFP - Prolections and Actuals
level analyses, and the datasets include numerous characteristics of the responding firms, while taking care to preserve firm anonymity to encourage candid responses.<sup>3</sup> Household surveys addressing corruption issues are not quite so well developed as firm surveys. Beginning in 2003, Transparency International annually has sponsored the Global Corruption Barometer (GCB), conducted with assistance from Gallup International’s survey network. The number of countries covered has expanded from 44 in 2003, to 64 in 2004, and 69 in 2005. The questions changed almost entirely from 2003 to 2004, but much of the 2004 content remained in the 2005 survey. The World Values Surveys (WVS), International Crime Victimization Surveys (ICVS), “Voice of the People” surveys by Gallup International, and several regional “Barometer” surveys have also included questions on households’ experiences with, or attitudes toward, corruption. Most of these household surveys suffer from greater comparability problems than does the BEEPS. For example, the surveys administered by Gallup International (including the GCB) cover only urban households in many countries. Unlike the BEEPS, results from the WVS and regional “Barometer” surveys are made public only with long lags, limiting their value in diagnosing problems and designing policy responses.

Expert assessments of corruption have been most widely used for comparisons across countries and over time. A large and growing number of organizations provide such assessments. Their methods differ in several potentially important ways.

First, they differ in the degree to which assessments are “centralized.” The centralized type is exemplified by Nations in Transit (NIT) and by the International Country Risk Guide (ICRG). Corruption ratings from these sources are informed by a

> 3 Information and data for the World Bank’s investment climate assessment surveys are available at http://iresearch.worldbank.org/ics/jsp/index.jsp.

8
data elsewhere, we do so for informational purposes only, and any comparisons must consider this caveat.

# **Data description**

Table 2 presents key descriptive statistics of the survey data. Between 2015 and 2019, the survey collected an average of 204,000 observations annually for men and 240,000 for women. However, in 2020, the year most impacted by the pandemic, the number of observations for both groups dropped sharply to 123,000 and 142,000, respectively. By 2021, the data collected had partially recovered, but the observations remained below pre-pandemic levels (188,000 for men and 219,000 for women).

Demographic variables such as average age, marital status, and residential area show a slight difference between the groups, while poverty is more prevalent among women. For both groups, the average age is 36 years, and 22% of men live in rural areas, while on average, 19% of women do. Additionally, 56% of men are married or living in a union, and 58% of women are. Finally, 31% of men live in low-income households, while 37% of women do. A further breakdown of these categories reveals that 21% of the population are young people between 18-24 years old, approximately 42.6% are between 25-44 years old, and 36.5% are between 45-59 years old.

In terms of children in the household, women live with more children than men. On average, for every man, there are 0.3 children aged 0-3 years in the household, 0.2 children aged +3-6 years, and 0.40 children aged +6-12 years. In comparison, women have, on average, 0.3 children aged 0- 3 years in the household, 0.2 children aged +3-6 years, and 0.45 children aged +6-12 years. Moreover, 21% of men live in households with children aged 0-3 years, while 26% of women do. For +6-12 years, the numbers are 17% for men and 21% for women. Finally, in households with children aged +6-12 years, the numbers are 25% for men and 30% for women.

Human capital variables such as years of education and experience show slight differences between the groups. On average, men have 9.6 years of schooling, women have 10.2 years, and both groups have over 20 years of experience. A disaggregation of these categories shows a difference in the higher education component, with approximately 20% of men having higher education while around 25% of women do. However, in non-education or just elementary school, men account for 35%, while women account for 30%. Experience is strongly centered on the group with +15 years of experience, where 57% of both groups are.

The survey reveals significant heterogeneity between men and women in the labor component. Between 2015 and 2021, for the population aged 18-59 years, 82% of men were employed, 8.4% were unemployed, and 9% were inactive. In contrast, only 56% of women were employed, 11% were unemployed, and 33% were inactive. Additionally, women have a higher rate of part-time employment (32%) compared to men (12%), indicating that women work almost two times fewer hours per week (23 hours) on average than men (40.3 hours).
11 However, for smaller labor-exporting countries, like<sup>Brazil or Sri Lanka,the actual</sup> number of migrants to any particular OECD country might<sup>not be recorded.This makes</sup> it difficult to accurately compare and contrast migration<sup>rates between differentlabor-</sup> exporting countries.

IV. Brain Drain to the United States: Migration Rates and<sup>EducationalLevels</sup> This section presents estimates of immigrants in the United<sup>States by educational</sup> attainment, both in absolute number and as a fraction<sup>of theindividualsin thelabor-</sup> exporting country with the same level of educational<sup>attainment.As explained in the</sup> previous section, following the reporting conventions of<sup>the U.S.Census Bureau,these</sup> figures consider as immigrants all "foreign-born" individuals<sup>residing in the United</sup> States. Thus, these figures may well include substantial<sup>numbers of people who are not</sup> usually regarded as either migrants or as part of the<sup>brain drain, suchas individuals who</sup> migrated to the United States as children and have few<sup>ties with their original country of</sup> birth.

Table 4 presents immigration data to the United States for<sup>all 24 study countries.</sup> It is striking to note that one single country<sup>-</sup> Mexico<sup>-dominatesmigration to the United</sup> States. In our sample about 50 percent of all immigrants<sup>in the United Statescomefrom</sup> Mexico!1<sup>2</sup> The second largest source of immigrants is<sup>the China, PR,which supplies less</sup> than 10 percent of total migrants.

Table 4 shows the key role that education plays in migration.<sup>In the sample as a</sup> whole, about 67 percent of all immigrants have a secondary<sup>education or higher.This</sup>

> 1<sup>2</sup> According to the 2000 U.S. Population Census, of<sup>the total foreign-born populationimthe UnitedStates</sup> (31,107,000), Mexico accounts for 29.5 percent (9,177,000)
Table 3. Impact of China shocks on exports of selected BRI countries.

||Supply|shock|Deman|d shock|
|---|---|---|---|---|
|Country|1995-2015|2005-2015|1995-2015|2005-2015|
|Cambodia|35.46|43.36|161.33***|79.6|
||(31.89)|(46.32)|(27.40)|(189.09)|
|Indonesia|-12.87|-31.78*|39.76***|36.97***|
||(9.82)|(17.98)|(15.02)|(13.73)|
|Lao|-63.83|-55.18|-189.1|59.5|
||(43.05)|(44.35)|(641.80)|(363.26)|
|Malaysia|7.21|-83.48**|19.03|29.57*|
||(12.01)|(40.64)|(16.24)|(16.19)|
|Myanmar|-30.59|-35.05|21.16|-12.52|
||(49.83)|(102.48)|(15.78)|(97.22)|
|Philippines|-13.07|-70.54***|40.7|61.96|
||(14.32)|(24.35)|(30.56)|(38.34)|
|Thailand|-1.34|-40.84**|32.35***|6.79|
||(10.38)|(17.74)|(9.34)|(23.90)|
|Vietnam|-1.58|-36.92***|38.18|4.23|
||(15.27)|(11.04)|(45.67)|(13.05)|
|Mongolia|94.95|-129.03|126.42|512.76***|
||(66.45)|(190.87)|(104.73)|(130.44)|
|Average BRI country|-21.75***|-52.82***|42.52***|44.95***|
||(3.88)|(8.31)|(7.27)|(16.94)|

Source: Bastos (2018). Notes: Robust standard errors clustered by country‐industry, *** p<0.01, ** p<0.05, * p<0.1. Reported coefficients are obtained using the 2SLS estimation method.

The China effect on trade depends on the complementarity of the trade structure of developing East Asian countries with China and the similarity of their export destinations with China. Bastos (2018) also offers an intuitive approach – validated by the previous econometric results ‐ on how to identify countries that are more likely to be impacted by China’s rise. First, countries that share a similarity of exports with China in both the exported products and the destination markets are likely to face competition pressure from China in the markets abroad (Figure 12a). Indeed, for Vietnam, Thailand, Malaysia and the Philippines, which have the highest similarity index among the BRI countries in 2015, the econometric analysis yields negative and significant supply shock coefficients in the regressions covering the period 2005‐2015. Second, countries whose exports are similar to China’s imports and who also have a large share of their exports going to China are likely to benefit from an increase in Chinese imports (Figure 12b). For Instance, Mongolia, a country in this category, is found to have a positive and significant demand shock effect during the period 2005‐2015. Third, countries that share a similarity of production structure (proxied by exports) with China and also have a large share of imports sourced from China are likely to be affected negatively in their domestic markets by Chinese competition (Figure 12c).

23
**Figure 1: Rural and urban welfare distributions (in logarithm)**

<!-- Start of picture text --> 0.8<br>0.7<br>Urban<br>0.6<br>0.5<br>0.4<br>Rural<br>0.3<br>National<br>0.2<br>0.1<br>0<br>11 12 13 14 15 16 17<br>-0.1<br>Log of per eq adult expenditure<br>National Urban Rural<br>Density<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.

# **Figure 2a: Budget share of rice expenditure**

<!-- Start of picture text --> 0.14<br>0.12 Urban Rural<br>0.1<br>0.08<br>National<br>0.06<br>0.04<br>0.02<br>0<br>10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5<br>Log of per eq adult expenditure<br>National Urban Rural<br>Rice Share in Total Expenditure<br><!-- End of picture text --> Source: Authors’ estimation using ELIM 2006.
3

# CAPITAL MARKET IMPERFECTIONS BEFORE AND AFTER FINANCIAL LIBERALIZATION: A Euler Equation Approach to Panel Data for Ecuadorian Firms

## **I.- INTRODUCTION**

During the 80's Ecuador introduced financial reforms to facilitate capital accumulation and growth. These reforms consisted mainly of the removal of administrative controls on the interest rate, and the elimination or scaling down of directed credit programs. Whereas there has been a growing literature and research effort in assessing the effect of liberalization on the financia! markets, less is known about the effects of these reforms on firms. The studies that exist are often descriptive in nature and have concentrated on the larger macro picture. The purpose of this paper is to provide some evidence on the effects of liberalization by focusing on individual firms as the unit of analysis. The question we address here is whether financial reform has relaxed the constraints faced by firms in obtaining external funds for investment. Our empirical investigation is based on a rich panel data set for Ecuador, containing balance sheets and profit and losses statements for 420 manufacturing companies over the period 1983-1988.

In order to evaluate the effects of financial reform on firms' capital accumulation decision, we must improve our understanding of the relationship between firms' financial and real choices, in the presence of imperfections in the capital markets. Such imperfections are due to the existence of administrative controls and regulations, and, at a deeper lev_l, to asymmetric information problems. A growing body of literature has examined how informational asymmetries and the risk of bankruptcy may restrict access to outside funding and the implications thereof for a firm's investment decisions, even in the absence of administrative credit rationing.' The empirical work in this field has mainly tried to assess whether there is a significant departure from investment mnodels derived in the absence of information asymmetries and incentives problems, and whether the importance of such departure varies across firms with different characteristics. Most of the empirical work has used data from developed couniries, for which panel data on firms were available, and it I At a theoretical level see the contributions by Stiglitz and Weiss (1981, 1987), Wette (1984), Myers and Majluf (1984), Bemanke and Gertler (1989), Gertler and Hubbard (1988), Greenwald and Stiglitz (1988).