rafmacalaba/datause-encoder-data huggingface dataset:
# **B Simulations’ data**

The project data used for the simulations in Section 7 stems from CCR NovaDutra, a highway franchise in Brazil that was in place between 1996 and 2020. Yearly revenue, operational cost and investment data in current values can be seen in Table 10.

Table 10: CCR Nova Dutra – Yearly Revenue (pqt), Investment (It) and Cost (ct)

|Year|pqt|It|ct|
|---|---|---|---|
|1996|82,791|114,950|17,108|
|1997|202,782|133,364|44,662|
|1998|225,373|20,2307|53,149|
|1999|243,209|131,520|128,332|
|2000|281,570|73,650|85,060|
|2001|314,398|75,917|114,949|
|2002|353,779|33,661|157,616|
|2003|394,573|49,846|62,152|
|2004|500,107|70,063|81,315|
|2005|551,542|88,297|65,218|
|2006|611,791|75,866|79,655|
|2007|665,577|100,411|86,238|
|2008|748,324|159,720|92,432|
|2009|782,241|172,692|160,625|
|2010|900,052|203,472|79,348|
|2011|993,056|229,810|83,078|
|2012|1,050,626|197,592|82,509|
|2013|1,113,905|219,078|79,813|
|2014|1,150,439|241,056|77,840|
|2015|1,168,369|137,177|87,125|
|2016|1,210,658|89,337|87,664|
|2017|1,297,371|165,574|84,563|
|2018|1,350,917|172,908|93,001|
|2019|1,429,118|39,262|211,450|
|2020|1,320,042|102,489|143,212|
|Sum|18,942,610|3,280,019|2,338,114|

Source: own elaboration with data from Brazil’s Ministry of Infrastructure. Note: To keep notation of investment as a one-time disbursement (I0), one could consider investment to be zero and include it as part of operational cost ct.

To perform simulations for each of the eight countries, we use national Consumer Price Index rates as discount factor δt and inflation adjusted US Dollar exchange rates Xt. These values are shown in Tables 11 and 12.

46
<!-- Start of picture text --> 6,000<br>5,000 @ Sustainability-Linked Bonds<br>4,000 7 Sustainability-Linkedabit Ts Loans<br>5 @ Social Bonds<br>3,000 = Sustainability Bonds<br>PF<br>2,000 = Green Bonds<br>= Green Loans<br>1,000<br>0<br>2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text -->
**6b. Characteristics of lending By type**

<!-- Start of picture text --> Commercial Development<br>Risk-sharing with priv. banks Loan term > mkt<br>Interest rate < mkt<br>6c. Non-lending support<br>By type<br>Commercial Development<br>Consulting/tec. assistance Support for EU funds<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

**6c. Non-lending support By type**

**By granular region**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>Risk-sharing with priv. banks Loan term > mkt<br>Interest rate < mkt<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

**By granular region**

<!-- Start of picture text --> Benchmark Europe EAST ECA WEST ECA<br>Consulting/tec. assistance Support for EU funds<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> About 60 percent of Development SOFIs offer products that deviate from market conditions (i.e. in terms of maturity or rates) compared to just 20 percent of Commercial SOFIs (Figure 6b). Interestingly, this appears to prevail more in Benchmark Europe and West ECA rather than East ECA. There is some correlation between SOFIs that provide products that deviate from current market conditions and SOFIs that have a government guarantee on their debt. Of the 13 SOFIs that are characterized by this relationship, 12 are Development SOFIs, suggesting that benefits are possibly passed on to the ultimate beneficiary. Development SOFIs are also almost twice as likely to risk-share with private banks compared to Commercial SOFIs. Technical assistance is offered by just 25 percent of all SOFIs, but about 45 percent provide application assistance to enterprises that seek EU funding support (Figure 6c).

20
1. A change in the U.S. Treasury bill rate.

2. A change in U.S. government expenditures.

3. A change in the U.S. personal income tax rate.

4. A change in the U.S. federal deficit.

Each of these alternatives represent major changes that have taken place in the U.S. macroeconomy over the past decade. Conceivably, major adjustments in these policy instruments could again be exercised. For example, the present U.S. federal deficit that is looming so large in political debate in Washington could be resolved by any one or a combination of these measures.

The first step in the calculation of macroeconomic effects on agriculture here is to simulate the FAIRMODEL to determine the effect of increasing each of these policy instruments on the various macroeconomic transmission variables that affect the agriculture sector. These transmission variables include inflation (as reflected by the GNP price deflator), the interest rate (the three-month U.S. Treasury bill rate), disposable income, the price level of nonfarm sales (the GNP price deflator for nonfarm total sales), and the U.S. federal deficit.

Estimated Effects of U.S. Macroeconcmic Policy on U.S. Agriculture Using the estimated effects of U.S. macroeconomic policy variables on the macroeconomic transmission variables, the US.AGMKTS model is used here to calculate the resulting effects on major IJ.S. agricultural prices and trade. To generate estimates that can be interpreted as elasticities, the macroeconomic simulations are done with each of the alternative macroeconomic policy instruments increased by 1.0 jercetnt from historically observed levels. The resulting effects on macroeconomic transmission variables are then fed directly into the USAGMKTS model to estimate effects on U.S. agricultural prices and trade which have elasticity interpretations. Note, however, that 14
_permanent_ effects on the rate of economic growth. Empirically, however, neither in the United States nor in other OECD countries does economic growth seem to exhibit such an effect. Incidentally, albeit for different reasons than in the OECD countries, these stationarity properties seem to be equally true for schooling and income growth in the Guatemalan data, as demonstrated in the Augmented Dickey-Fuller (ADF) tests in the Appendix.

# **3.3 More Evidence on Education and Growth**

Empirical studies usually take the form of regression analysis and typically look at many more explanatory variables than human capital. A large number of papers have found one or more variables that correlate with growth. In fact, their number is very large and the question arises which combinations of these variables are actually robust. In the context of the present study, some of these findings will be outlined next.

# **_Weak Correlations_**

While there is strong theoretical support for a key role of human capital in growth, Sala-i-Martin (2002), Easterly (2001) and in particularly Pritchett (2001) argue that the empirical relationship between education and growth is weak.<sup>9</sup> However, more specifically, Temple (2001) points out that fragile correlation in cross-country data may be due to measurement error and influential exceptions. Also, some kinds of relationships are more robust that others. For example, what is less clear and weak is the relationship of educational _growth rates_ on output growth, the role of different education levels, and differences in effects of female and male education on growth. By contrast, some measures of health seem to be positively correlated with growth. In addition to human capital, many other factors have been found to be important for growth. Following Barro (2001), these include institutions, such as free markets, secure property rights and the rule of law. Similarly, more open economies and countries with lower initial inequality appear to experience higher growth.

# **_Conditional Convergence_**

One much debated prediction of neoclassical growth models is that of convergence. Poor countries should grow relatively faster than rich countries if countries are similar with respect to their structural parameters for preferences and technology. The cross-country studies by Mankiw et al. (1992), Barro and Sala-i-Martin (1995) and Barro (2001) find some evidence of convergence, albeit in a modified form. More

> 9 Pritchett (2001) uses measures of the _growth_ rate of human capital and finds a _negative_ impact on output growth. Easterly (2001) argues that human capital accumulation is not a panacea. He emphasizes indirect ways that explain technological progress and factor accumulation by looking at the features of economies that facilitate them, such as government policies and structural issues.

-11-
21 related foreign lobbying by Caribbean principals. Unlike previous work on lobbying, although complementary to it, we argue that the mechanism of effect is not quid pro quo bargaining, the resolution of information asymmetries. In this sense, we are close to Rauch and Trindade (2002, 2003) who have emphasized the role that informational barriers play in restricting trade, and how private networks can overcome those barriers. Our case emphasizes a similar mechanism at work but involving trans-boundary business-government networks.

We offer robust findings that, in fact, lobbying both increases the level of tourism, as well as reduces the price elasticity of demand, suggesting an ability of destinations to differentiate their product. Both are of magnitudes that suggest important policy implications for developing economies. Our results are among the first in the literature and therefore deserve scrutiny. Gathering time-varying data on US Department of State website postings on Caribbean countries would provide direct evidence on (i) whether the postings affect tourism, and (ii) whether the postings respond to lobbying. Of theoretical relevance is the question of whether, as more islands participate in the lobbying game, lobbying competition among them may be used strategically by policymakers being lobbied to capture rents without benefiting any lobby. While that outcome is likely with quid-pro-quo lobbying (Grossman and Helpman 1994), it remains to be demonstrated with informational lobbying.
To evaluate global imbalances we examine the trade balance as a share of GDP across regions and income groups, and also whether the countries tend to be surplus or deficit countries. Figure 7 and 8 show movements in the aggregate trade deficit and surplus for international borrowers and lenders, respectively. Specifically, Figure 7 is the aggregate trade deficit relative to GDP over time of all the countries that had a deficit before the downturn.<sup>8</sup> Similarly, Figure 8 is the aggregate trade surplus relative to GDP over time of all the countries that had a surplus before the downturn. On aggregate, there is only a temporary improvement, and the position quickly returns to where it was before the episode. This suggests that there is not an overall rebalancing between pre-downturn surplus countries and pre-downturn deficit countries.

However, the pictures are very different when we look across regions (Figures 9 and 10) or income groups (Figures 11 and 12). We find that in Latin America, East Asia, Europe and the Middle East and North Africa there is a tendency to reduce sharp deficits following downturns. These are regions where the imbalances were deemed dangerous, and the government put policies in place to ensure they did not reemerge. These included savings incentives and maintaining an undervalued exchange rate to strengthen the BOP position. In addition, weakened firm fundamentals during a global downturn may induce a drop in investment.

In contrast, North America tends to show a relatively stable account that improves slightly during the downturn, but worsens in its aftermath. This may reflect the flexibility of the U.S. economy and the safety of dollar assets. This may also be related to the

> 8 Specifically, we use the value of deficit or surplus four years before the episode to characterize country as a surplus or deficit country.  Data are from the IMF BOP Statistics for a balanced sample of countries reporting.

10
# Intergenerational Education Mobility in Africa: Has Progress Been Inclusive?<sup>*</sup>

Th ́eophile T. Azomahou<sup>(</sup><sup>_a,b_)</sup> and Eleni A. Yitbarek<sup>(</sup><sup>_a_)</sup> ( _a_ ) Maastricht University and UNU-MERIT Boschstraat 24, 6211 AX Maastricht, the Netherlands 65 Boulevard F. Mitterrand, F-63009 Clermont-Ferrand, France Key words: Intergenerational persistence, education, Africa, gender gap JEL Classification: I24, I28, J24

> * Acknowledgments: This study is done as background research for the World Bank flagship report “Poverty in a Rising Africa” led by the World Bank’s Africa Chief Economist Office. We are deeply indebted to Kathleen Beegle and Isis Gaddis to their constructive discussions and helpful comments throughout the study. We thank Dede Raissa for translating the French survey questionnaires. Fruitful discussions with participants of UNU-WIDER, Development Conference–Human capital and growth, 6-7 June 2016, Helsinki, are also gratefully acknowledged.

>  Corresponding Author: E.A. Yitbarek, e-mail: e.yitbarek@maastrichtuniversity.nl.
contracts, firms might be induced to behave more efficiently to increase their chance to be awarded the contract again later on. Conversely, when protected by minimum income guarantee, they might make less efforts. Price caps or private financing can also be expected to have incentive effects on the behavior of firms.

The problem we intend to tackle is to disentangle these two dimensions, in order to assess the real incentive effect of each specific aspect of the contract. More precisely, we use a two stage process aimed at controlling the self-selection effect of each of the variables suspected to be endogenous.<sup>28</sup>

> To do this, we need to find suitable instruments. We take as instruments: sectors,

> corruption, bureaucratic quality, rule of law, and existence of regulatory body, which are obviously exogenous in the sense that they are not determined by the risk of potential renegotiations. Nevertheless, finding instrumental variables that would not enter the equation explaining the probability of renegotiation appears very difficult:' virtually any contract characteristics and any aspects of the institutional and macroeconomic environment can be argued to have an impact on the probability of renegotiation. Appendix 2

> presents the "reduced form" equation, including only truly exogenous variables.

The correct treatment of endogeneity in non linear probit models is still an open question (see Wooldridge, 2002). One of the available strategy is the following. We run probit estimates (OLS in the case of contracts' duration) of the six variables we want to instrument, using the static sample of the 307 concessions. Note that these first stage regressions are fairly satisfactory (see Appendix 3). We then take the predicted values of each of these variables and reintroduce them in the probit panel. Finally, we estimate the equations with these instrumented variables. The results are in Table 7.

> 28We test for endogeneity, by running regressions augmented with the residuals of the first stage

> regressions. Endogeneity is confirmed for the price cap, the arbitration and the minimum income variables.

> 29Although the existence of one of these variables not entering the equation explaining renegotiation

> is doubtful, identification is still ensured by the nonlinearities of the model.

27
Table S.19: Children’s educational efficiency - GPA z-scores: specification <u>(3) (OLS)</u>

|Dependent variables:|Bengali|English|Math|General<br>|Islam<br>|Bangladesh<br>|
|---|---|---|---|---|---|---|
|||||science|studies|general<br>studies|
||(a)|(b)|(c)|(d)|(e)|(f)|
|All products|-2.285***|-1.545**|-2.363***|-2.445***|-1.832***|-1.635***|
||(0.747)|(0.637)|(0.582)|(0.665)|(0.643)|(0.566)|
|One product|-0.511|0.248|0.108|-0.605|0.066|-0.152|
||(0.742)|(0.620)|(0.788)|(0.655)|(0.810)|(0.694)|
|Study at school|-0.003*|-0.002|-0.004**|-0.001|-0.001|-0.003*|
|(per-day minutes)|(0.002)|(0.001)|(0.002)|(0.002)|(0.002)|(0.001)|
|Study at home|-0.001|-0.001|0.000|-0.002|-0.003|-0.002|
|(day, per-day minutes)|(0.002)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|
|Study at home|-0.001|0.002|0.001|-0.001|-0.000|0.001|
|(night, per-day minutes)|(0.002)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|
|One if attend<br>|0.125<br>|0.032<br>|0.021<br>|0.131<br>|0.187<br>|0.179<br>|
|(February)|(0.127)|(0.134)|(0.144)|(0.116)|(0.126)|(0.136)|
|One if attend|-0.242*|-0.237**|-0.194|-0.175|-0.203|-0.100|
|(April)|(0.120)|(0.112)|(0.123)|(0.119)|(0.127)|(0.134)|
|One if attend|-0.142|-0.269*|-0.023|-0.148|-0.163|-0.276**|
|(August)|(0.108)|(0.137)|(0.105)|(0.120)|(0.118)|(0.136)|
|All products|||||||
|_×_ Study at|0.008**|0.005*|0.008***|0.007***|0.005|0.008***|
|school|(0.003)|(0.003)|(0.003)|(0.002)|(0.003)|(0.002)|
|_×_ Study at|0.009**|0.007|0.006|0.011***|0.009**|0.004|
|home (day)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|_×_ Study at|-0.003|-0.005|-0.001|-0.003|-0.002|-0.004|
|home (night)|(0.004)|(0.004)|(0.003)|(0.003)|(0.004)|(0.004)|
|_×_ One if attend|0.372*|0.308|0.459**|0.282|0.175|0.212|
|(February)|(0.206)|(0.215)|(0.219)|(0.185)|(0.193)|(0.202)|
|_×_ One if attend|-0.168|-0.069|-0.008|-0.103|-0.108|-0.239|
|(April)|(0.195)|(0.221)|(0.205)|(0.181)|(0.180)|(0.183)|
|_×_ One if attend<br>|0.120<br>|0.516**<br>|-0.084<br>|0.242<br>|0.129<br>|0.157<br>|
|(August)|(0.186)|(0.215)|(0.157)|(0.223)|(0.196)|(0.201)|
|One product|||||||
|_×_ Study at|0.001|-0.000|0.001|-0.001|-0.001|0.000|
|school|(0.004)|(0.004)|(0.003)|(0.003)|(0.004)|(0.004)|
|_×_ Study at|-0.000|-0.002|-0.009*|0.000|-0.003|-0.001|
|home (day)|(0.005)|(0.005)|(0.005)|(0.005)|(0.006)|(0.005)|
|_×_ Study at|-0.000|-0.004|0.001|0.001|0.000|-0.004|
|home (night)|(0.004)|(0.004)|(0.003)|(0.003)|(0.003)|(0.003)|
|_×_ One if attend|0.222|0.288|0.425*|0.190|0.147|0.130|
|(February)|(0.202)|(0.242)|(0.215)|(0.234)|(0.220)|(0.220)|
|_×_ One if attend|0.085|0.110|0.120|-0.067|0.020|0.052|
|(April)|(0.274)|(0.251)|(0.255)|(0.239)|(0.268)|(0.246)|
|_×_ One if attend|0.506***|0.358*|0.307|0.544***|0.409*|0.724***|
|(August)|(0.179)|(0.178)|(0.201)|(0.197)|(0.211)|(0.212)|
|R-squared|0.180|0.164|0.225|0.209|0.163|0.181|
|No. of obs.|618|618|618|618|618|618|
|Baseline control|No|No|No|No|No|No|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade.

64
<!-- Start of picture text --> Figure 13: Drought scenario - Fiscal Accounts<br>3.0 ppts ppts 3.0 1.0 ppts ppts 1.0<br>Total expenditures<br>Debt-GDP ratio 0.8 (%GDP) 0.8<br>Fiscal balance (% GDP) Total revenues<br>2.0 2.0<br>0.6 (%GDP) 0.6<br>Social services<br>transfers (%GDP)<br>0.4 0.4<br>1.0 1.0<br>0.2 0.2<br>0.0 0.0<br>0.0 0.0<br>-0.2 -0.2<br>-1.0 -1.0 -0.4 -0.4<br>2020 2025 2030 2035 2040 2020 2025 2030 2035 2040<br>Source: World Bank analysis.<br>Note: Percentage point deviation from baseline shown<br><!-- End of picture text -->

**External accounts deteriorate due to the fall in exports and the increase in (nominal) imports** , as the exchange rate depreciation raises import prices by more than the reduction in import demand due to lower economic activity (Figure 14).

<!-- Start of picture text --> Figure 14: Drought scenario – Current Account<br>2.0 % % 2.0 1.0 ppts ppts 1.0<br>1.0 1.0<br>0.0 0.0<br>0.0 0.0<br>-1.0 -1.0<br>-2.0 -2.0<br>Current Account<br>-3.0 -3.0 Balance (%GDP)<br>-1.0 -1.0<br>Real effective<br>-4.0 -4.0<br>exchange rate<br>Nominal effective<br>-5.0 -5.0<br>exchange rate<br>-6.0 -6.0 -2.0 -2.0<br>2020 2025 2030 2035 2040 2020 2025 2030 2035 2040<br><!-- End of picture text --> Source: World Bank analysis. Note: Percent deviation from baseline shown for exchange rates, with a fall indicating a depreciation. Percentage point deviation from baseline shown for the Current Account Balance.

# **Poverty and vulnerability indicators increase with the drought, and remain above the baseline scenario**

**after six years** . Poverty measured by the international upper middle-income line of US$6.85 per day at purchasing power parity (PPP) increases 0.5 percentage points in the year of the shock. Poverty would still be 0.3 percentage points higher than in the baseline scenario in the projected horizon due to attrition. The new trend would be similar to the baseline, but at a higher level. Vulnerability, defined as not poor

21
_T = c hi/a (_ **_1/i)_** Thus, we have an adjustment cost function which is convex in the hiring rate, h, but also depends on the employment level, _1._ This dependence of the "training and hiring costs" on the employment level is the only difference to the previous model formulation. It is, however, straightforward to incorporate the more general adjustment cost function into the efficiency wage model resulting into three steady state equations in the three unknowns _w_ * ,p _*_ , *. We plan to conduct quantitative policy analysis for such an extended model with a general matching technology in the future. The efficiency wage model developed in the last sections can be thought of as the special case in which a = 1 since then we have T = _c h,_ that is, an adjustment cost function which only depends on the hiring rate. Incidentally, this function is linear, a property we assume in the quantitative section 4.

# **3. Microeconomic Data Analysis**

This section uses micro-economic data from Mexico to estimate the quit-rate function and to test the model's predictions about the determinants of quitting behavior. The quit-rate function estimated here is used in Section 4 for macroeconomic policy analysis.

# _a) Data Description_

The National Urban Employment Survey (ENEU) conducts extensive quarterly household interviews in the 16 major metropolitan areas and is available from 1987 to 1993. The sample is selected to be geographically and socio-economically representative. The statistical agency (INEGI) expanded it significantly over the period by adding municipalities, however, we include only those present in every year of the survey to prevent changes in composition. The questionnaire is extensive in its coverage of participation in the labor market, wages, hours worked, etc. that are traditionally found in such employment surveys. INEGI's treatment of sample design, collection, and data cleaning is careful. Surveys and documentation of methodology are available on request.

The ENEU is structured so as to track a fifth of each sample across a five quarter period. To construct the panels, workers were matched by position in an identified household, level of education, age and sex to ensure against generating spurious transitions. Using just the first 12