rafmacalaba/datause-encoder-data huggingface dataset:
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# 3.2 Framework and Some Assumptions

We assume that there are two important players in the CPE: the Planner and the firms.a The latter are comprised of identical Workers; there is no distinct management. There is a representative Worker's utility function. With no unemployment in the CPE, this function includes effort and the expected real wage. An increased expected real wage (W/P) increases consumption possibilities either now or in the future and utility is hence a positive function of the real wage. The relevant price index is the consumer price index 1. We assume that the Workers form their expectation of this period's price level in terms of the previous period so that PI can be considered an exogenous variable.

Utility is a negative function of the effort (Z) delivered.3 The underlying argument is that the Workers face a trade off between work and leisure and prefer less hard work. For convenience we choose an additive fo.mulation of the utility function.

Uw - u(Z) + v(W/PF) We assume that u'(Z) < 0, u"(Z) < 0, v'(W) > 0, and v"(W) < 0. That u"(Z) < 0 follows from the fact that the disutility associated with a high effort increases with more effort. The indifference curves will be upward sloping in the 'effort - wages space.

The Planner's role is to set economic targets and achieve them. In doing so, the Planer might be concerned about a number of objectives, including the volume of investment, private consumption, growth, income equality, price stability, and the degree of excess demand on the goods markets. This can be treated in the following way. The Planner has multiple objectives, the rec ncilation of which consistently imposes a tension on the system. To achieve the objectives, the Planner allocates the whole labor force, n, to production. The output price level from the last period, P, is maintained. Finally, the Planner sets a certain minimum wage calculated per time unit. We call this basis wage, Wis.

# 2 In section 3-5 a closed economy is assumed.

2 It can be argued that in an economy with chronic excess demand for goods the real wage is not a good measure for the consumption possibilities of a certain nominal wage. However we have chosen not to include any measure of the rationing at the goods market. The reason is that there is no a priori knowledge of the way excess demand on the goods market influences the workers' desired wage. One argument is that a rationed goods market will lead to less wage pressure, cf. Ellis and Fender (1985), while another view is that the workers will compensate for the present goods rationing by demanding .aigher wages (for later effective consumption or for purchase of goods at the black markets). Charemza and Gronicki (1983) call this last argument "wage illusion".

> 2 A formulation of the worker's utility function close to the one chosen here can be found in Sokolovskii (1987), p5.
ON THE IMPORTANCE AND EXTENT OF RENT SHARING IN LEE MULTI-FIBRE ARRANGEMENT: EVIDENCE FROM US-HONG KONG TRADE IN APPAREL by Kala Krishna The Fletcher School of Law and Diplomacy, Tufts University and NBER and Ling Hui Tan Harvard University

* We are grateful to the World Bank for partial<sup>research support. We would also</sup> like to thank Ronald Chan, Carl Hamilton, Paula<sup>Holmes, Peter Ngan, and Tom Prusa</sup> for sharing their data with us, and Phillip Swagel<sup>for helpful comments.</sup>
also collected. For the same reason, household members under 10 years of age were removed from the dataset so as to reflect the demographic profile of the household 10 years ago. However, because we did not have dates on marriages of household members, we were unable to identify which of them joined the household more recently.

# _5.3 Implementation of PSM_

The implementation of the propensity score matching procedure involves several steps. We first estimate the propensity score for international migration using a probit model including both determinants of migration and factors that affect the consumption outcomes. Heckman et al. (1997, 1998) emphasize that the quality of the match can be improved by ensuring that matches are formed only where the distribution of the density of the propensity scores overlap between “treatment” and “comparison” observations, or where the propensity score densities have “common support.” Common support can be improved by dropping treatment observations whose estimated propensity score is greater than the maximum or less than the minimum of the comparison group propensity scores. Similarly, comparison group observations with a propensity score below the minimum or above the maximum of the treatment observations can be dropped. A shortcoming of this approach identified by Heckman et al. (1997) is that treatment observations near these cut-off points face a potential comparison group with propensity scores that are either all lower or all higher than that of the treatment observation. To account for this problem, we modified this “min/max” approach to identifying a region of common support to include comparison group observations that had lower propensity score than the minimum propensity score of the treatment group, but were nevertheless very close to it (those that had scores above the 95<sup>th</sup> percentile of the “min” group) and comparison group observations that had higher score than the maximum in the treatment group but were very close 17
The quality-adjustment factor in our study is calculated in the following way: where _m_ corresponds to the productivity of a tertiary degree coming from a “zero-quality” institution; _Q_ corresponds to the average quality score of universities in country _c_ , ranging from 0 to 100; _β_ is a productivity-adjustment factor that transforms the quality score into productivity units; and _m_ is scaled in a way that quality adjustment ( _QAc_ ) equals 1 if _Qc_ equals 100.

The measure of quality corresponds to the information collected by global university rankings. These rankings, published by private, for-profit companies, have grown in number over the years. They are usually based on an underlying score that is usually a weighted average of scores on different aspects of higher education (the volume and quality of research, research influence, the quality of teaching, international outlook, links to industry). These rankings do not include all higher education institutions (universities need to send their information, usually at a cost, to the publishers), and they use different methodologies. Our analysis relies on a combination of several of these ranking, including the scores from the Times Higher Education (THE) ranking; the Quacquarelly Symonds (QS) ranking; the Academic Ranking of World Universities (ARWU, also known as the “Shanghai” ranking); the Center for World University Rankings (CWUR); the U.S. News Global Universities Ranking; and the U-Multirank ranking (a nonnumeric, userdefined ranking). These rankings contain information on 400–1,000 universities in 43 countries in Europe and Central Asia. We generate a country-level average by averaging the scores for all the universities in a given country included in each ranking, yielding six values for each country (one for each ranking source). As detailed later, we normalize each of them, and then take the average of them as the aggregate quality score.

# _University rankings_

Table 1 describes the six university rankings used in this analysis. The CWUR includes the largest number of universities (2,000); the ARWU/Shanghai includes the smallest number (1,000). The rankings include 385–1,040 higher education institutions in Europe and Central Asia. The total number of countries covered ranges from 63 to 98; the number of countries in Europe and Central Asia ranges from 32 to 43. Five of the six rankings (THE, QS, ARWU, CWUR, and U.S. News rankings) have scores that (theoretically) range from 0 to 100, although no institution included in any of the rankings has a score of 0. The U-Multirank is a nonnumeric, multidimensional, user-defined ranking. To use it, we imputed numeric values (ranging from 0 to 100) to the letter-based scores assigned. The CWUR has the highest minimum score (65.8) and the lowest dispersion (5.07). The ARWU/Shanghai overall score is reported only for the world’s top 100 universities.

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traded sectors based on ISIC code classifications of the economic activity.<sup>4</sup> Output per capita is proxied by GDP per capita, and output per capita of the foreign country is a trade-weighted average of GDP per capita of the domestic country's trading partners. _TOT_ is the ratio of export to import prices. Data are taken from IMF, the World Bank, OECD, and national central banks.

The equilibrium RER is obtained by multiplying the estimated coefficients of the long-run RER equation by the permanent values of the RER fundamentals. These permanent components are computed using the band-pass filter, and the RER misalignment is the difference between the actual and equilibrium levels of the RER<sup>5</sup> . According to our definition of RER, positive (negative) deviations imply a real exchange rate over- (under-) valuation. We use two different set of estimated coefficients to compute the RER misalignment. While we compute RER misalignments using the Johansen time series cointegration estimator, for the sake of robustness we also compute the RER misalignments using the PMG estimator for non-stationary panel data series.

# **2.2 The Determinants of the Likelihood and Sustainability of Real Exchange Rate Undervaluations**

After defining the real exchange rate misalignments, we examine the ability of economic policies to affect the probability and magnitude of RER undervaluations. We include policy variables such as exchange rate regimes, capital controls, foreign exchange market intervention, trade openness, liability dollarization and fiscal discipline.

_Exchange Rate Regimes._ We approximate the exchange rate regime _de facto_ in place in the country by the database developed by Reinhart and Rogoff (2004) and updated by Ilzetzky, Reinhart and Rogoff (2009). These authors have developed a new system to classify historical exchange rate regimes. In contrast to previous

> 4 The sign of the coefficient of relative labor productivity at Home (relative to the Foreign) country will be positive (negative) if the surge in aggregate labor productivity is explain by shocks to tradables (non-tradables).

> 5 The coefficient estimate of the ratio of net foreign assets (NFA) to GDP may be subject to issues of reverse causality as it can be argued that the NFA position of the country is sensitive to valuation effects arising from changes in the real exchange rate. In spite of the detrending the NFA position (using band-pass filtering techniques), this permament component of NFA is still determined by the exchange rate. For instance, a real depreciation will increase the absolute value of the stock of net foreign debt assets over GDP. Therefore, net debtors would see their NFA worsening with a depreciation, which captures the correlation implicit in the model but for the wrong reasons.

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**Table  2.5 Volume and intensity of treated wastewater use for irrigation in selected high-income countries, ranked by intensity**

|**Country**|**Treated wastewater used for**<br>**irrigation—volume**<br>**(m**<sup>**3**</sup>**/d)**|**Treated wastewater used for**<br>**irrigation—intensity of use**<br>**(m**<sup>**3**</sup>**/d per million capita)**|
|---|---|---|
|Kuwait|431,520|225,455|
|Qatar|80,000|141,593|
|Israel|767,123|127,007|
|Cyprus|68,493|87,364|
|United Arab Emirates|200,000|76,746|
|Malta|26,000|66,667|
|Bahrain|27,000|42,188|
|Saudi Arabia|594,521|29,221|
|Oman|67,000|26,399|
|USA, California|814,271|23,745|
|Spain|931,507|23,340|
|USA, Florida|300,940|16,390|
|Italy|741,262|12,885|
|Australia *|103,000|10,853|
|Greece|20,030|1,888|
|France|19,178|324|
|Japan|35,300|278|

_Sources:_ Adapted from Jimenez and Asano, 2008b; California data from SWRCB, 2003; Florida data from FDEP, 2009; Australia data from www.recycledwater.com.au.

* The volume figure corresponds to wastewater irrigation of 4 cities only: Melbourne, Adelaide, Sydney, and Alice Springs.  Intensity is calculated utilizing the populations of the four cities (9.49 million).

# _Operational Involvement of the World Bank_

During the last decade the World Bank’s operational involvement in improving wastewater use in agriculture has mostly focused on lower-middle-income countries. A portfolio review based on an analysis of Project Appraisal Documents (PADs) approved between fiscal year (FY) 1999 and 2009 identified 26 projects that address wastewater treatment and (possible) use in agriculture. They are listed in Annex 3. Of the 26 projects, 17 include wastewater reuse in agriculture under project components: Eight projects plan the preparation of a reuse strategy, a related feasibility study, or the establishment of pilot sites; and nine projects provide support for treated wastewater use in agriculture. The latter projects are shown in Box 2.9. Overall, lending in direct support for wastewater use in agriculture amounted to less than 0.5 percent of the total lending for wastewater and sewerage projects during FY99-09.

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<!-- Start of picture text --> Figure 6: Actual and simulated GDP per capita<br>3,600<br>3,400<br>3,200<br>3,000<br>2,800<br>2,600<br>2,400<br>2,200<br>2,000<br>Actual GDP per capita<br>GDP per capita growth at the observed average (0.7% pa)<br>GDP per capita growth in the no-collapse scenario (1.7%pa)<br>$3,469<br>$2,639<br>$2,579<br>$2,092<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> In 1995-2005, the average growth rate for countries during acceleration episodes was 3.8%, the second highest average among the three ten year periods. Interestingly, it was in 1975-1984, a period of very modest regional economic growth, that average growth during accelerations reached its highest rate. This reflects a compositional effect at work. In the last decade even long stagnant economies such as the Central African Republic, Ethiopia, Mali, Mozambique, Sierra Leone, and Tanzania, experienced some sustained growth, pushing down the averages during acceleration episodes, whereas in 1975-1984 the high average growth rate was mainly due to a small number of growth accelerations over all and very rapid growth in the Republic of Congo.

The average (negative) growth rate for countries experiencing growth decelerations in 1995-2005 was less than half that in previous decades, contributing to the more positive overall economic performance of the period. Economic declines had both the highest frequency – double that of the next highest decade – and the greatest impact on countries during the period 1985-1995.

Over the entire 30 year period richer countries have had more growth accelerations and poorer countries more growth collapses. This is of course to some 11
distortions, investment dynamics are qualitatively similar, and intricately related to the protracted adjustment in _TFP._ However, when innovation is endogenous, the economy postpones investment so as to invest in innovation at the same time it preserves consumption smoothing.

# C Chile's Growth Acceleration 1985-1996

In earlier versions of the manuscript, we considered Chile's growth acceleration between 1985 and 2011 as a complementary case study to that of China. Considering Chile's acceleration was motivated by the availability of rm-level data covering the acceleration period, a key ingredient for a tight calibration of the pace of reversal of distortions in the model. However, while the Chilean acceleration surpasses the criterion for counting as a sustained growth acceleration, it is one that is very contaminated by cyclical elements, driven by the strong recovery the economy was undergoing after a deep recession in the early 1980s. Moreover, as shown in the growth accounting exercise depicted in gure C.1, it is only in the early years that the acceleration was fueled by rapid and sustained _TFP_ growth, the ingredient of the acceleration that our theory seeks to account for, whereas it was physical and capital accumulation that became the primary driving forces in the second half of the period. For this reason, we decided to focus on China's growth acceleration as case study for the evaluation of our model and to leave the characterization of Chile's less clear-cut acceleration episode for this appendix.

## C.1 Calibration of Chile's Growth Acceleration

We think of Chile's economy prior to its growth take-o as subject to idiosyncratic distortions, and model its acceleration as driven a protracted alleviation of these distortions. Based on Chile's ENIA (Encuesta Nacional Industrial Anual), a yearly industrial survey covering the universe of manufacturing plants with 10 or more workers<sup>23</sup> , we estimate the productivity-elasticity of idiosyncratic distortions. As before, the productivity elasticity is estimated as the regression coecient between the _log_ ( _TFPR_ ) and _log_ ( _TFPQ_ ) , where _TFPR_ and _TFPQ_ are measured exactly as in Hsieh and Klenow (2009). Similarly to how we proceed in China's case study,

> 23We work with the version of the ENIA that is provided in Chen and Irarrazabal (2015)'s replication material, downloadable from https://www.economicdynamics.org/codes/13/1361/pack_nalversion.zip

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# **III.  Some Stylized Facts About Distance and Trade**

The simplest way to describe the effect of distance on trade over time is by calculating the average distance that world trade travels. Average distance, ADIST, is defined as follows: Where _tradeij_ is trade between _i_ to j, _trade_ is total world trade, and _distanceij_ is distance between _i_ and _j_ . _ADIST_ can be calculated at the aggregate level, the country level, or the industry level.

Figure 1 shows the average distance that aggregate trade travels from 1980 to 2000, using import (cif) and export (fob) data from the IMF Direction of Trade Statistics. We include 166 countries that report trade all years in this period.<sup>4</sup> Both the import and export data show that the average distance that trade travels has declined slightly since 1980. Figure 1 also reports confidence bands of two standard deviations from the mean, showing that the variance has remained roughly constant over this period.

The disparity between imports and exports could explain some of the dispute in earlier work about whether distance is becoming more important. It is generally believed that the import data are more accurate because customs authorities require that imports are carefully recorded. In much of what follows, for parsimony, we report the results using only the import data.

Next, we examine industry-level data since 1985. We use data for 768 industries at the 4-digit SITC Revision 2 level from the COMTRADE database. While the data 7
Do Export Promotion Agencies Promote New Exporters?<sup>_∗_</sup>

# Marcio Cruz<sup>_†_</sup>

**JEL Classification:** D04, D22 F13, F14

**Keywords:** Trade, Export Promotion Agencies; New Exporters; Impact Evaluation; Brazil.

> _∗_ I thank Jean-Louis Arcand, Nicolas Berman, Marcelo Olarreaga, Leonardo Iacovone, Maurizio Bussolo, Christian Volpe Martincus, Jer ́onimo Carballo, Richard Baldwin, Jonathan Eaton, Gordon Hanson, Johannes Biesebroeck, Mark Dutz, Leila Rafei, Aleksandra Zaronina, Ieva Zumbyte, participants of the Development Therapy in Geneva (November, 2011) and participants of the IDB Workshop on Internationalization of SMEs in WashingtonDC (October, 2013) for their comments. I also thank the team of DISET-IPEA in Bras ́ılia, particularly Fernanda De Negri, Jo ̃ao De Negri, Bruno Oliveira and Lucas Rocha, for granting access to data from the Brazilian Ministry of Labor and for comments received in a seminar (February, 2013). I also thank Marcos L ́elis, Rˆomulo Clezar and all the team of Intelligence Unit of Apex-Brasil for granting access to Apex’s data and for their help in understanding the program. The data was used under confidentiality agreement.

> _†_ Graduate Institute of International and Development Studies, Federal University of Paran ́a and the World Bank. Email: marcio.cruz@graduateinstitute.ch.
## **Box 3:  Micro Housing Finance in Colombia**

During the banking crisis, banks suffered considerable losses from residential mortgages with non-performing loan ratios exceeding 20%. As a result, banks remain reluctant to lend to the sector. The Government has taken a number of measures to promote lending to the sector, these include: (i) a rediscounting program with a state owned second-tier financial intermediary (FINDETER) for banks, credit cooperatives and finance companies to offer mortgage and real estate micro-credits to lower income communities; (ii) a voluntary agreement between Government and the banking sector to allocate a small portion of bank funding to the microhousing finance sector (alternatively, banks can purchase bonds issued by FINDETER whose proceeds are used jointly with FINDETER funds for FINDETER’s micro-credit and social housing programs); and (iii) an insurance program established by the Government’s National Guarantee Fund to cover financial institutions against losses from housing micro-credit and social mortgage loans. Under the FINDETER social mortgage program, mortgage loans at subsidized interest rates are available for households which earn less than four minimum legal monthly salaries. Social mortgage loans are only available for the construction of new housing.

# **2.2 COST BARRIERS**

10. The demand survey has identified lack of resources and high costs of bank accounts as key reasons why people do not use formal financial services. The following pages take a look at the costs of different banking services in the formal sector based on the limited material available to date. Sources include a survey by the Superintendencia Bancaria of financial services of 26 banks as of June 2002 and subsequent update, as well as interviews with five financial institutions in Bogotá, including four banks (Bancolombia, Colmena, Megabanco and _Caja Social_ ) and one financial corporation (Finamerica). A more comprehensive supply side analysis is recommended to validate the initial findings presented here.

11. **Access to financial services for low income clients increases transaction costs.** Serving low income clients increases transaction costs, as smaller transaction amounts reduce margins above fixed technological infrastructure costs<sup>30</sup> . On this basis, a comprehensive analysis of cost barriers to financial access should include an analysis from a client affordability perspective and an analysis of banks’ cost structures. In the absence of detailed information on the latter, the following mainly focuses on the aspect of affordability.

12. **Even basic savings accounts are hard to afford for low income clients.** As demand studies in other countries have shown, low income clients are primarily interested in savings accounts as most important financial services. In the absence of any credit risk, they should also be the most affordable financial service for any type of client. In Bogotá, banks which explicitly focus on lower income clients, such as _Banco Caja Social_ or Colmena, require a minimum deposit of COP50,000 to

> 30 Interview with Patrick Tissot, Banco Caja Social, February 2004

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