rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4550

# **Abstract**

There is increasing empirical evidence that vertical product differentiation is an important determinant of international trade. However, the economic literature so far has solely focused on the case in which quality trade stems from differences between countries. No studies investigate the role of quality trade between similar economies. This paper first develops a simple theoretical trade model that includes vertical product differentiation in a heterogeneous-firm framework. The model yields three main predictions for trade between similar economies. First, exported goods are of higher quality than goods sold on the domestic market. Second, larger economies have on average higher export qualities compared with smaller economies. Third, with increasing trade costs higher quality goods are exchanged. For all three effects, strong empirical support is found using detailed export trade data of the United States and 15 European Union countries.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the group to improve our knowledge on trade costs, trade facilitation, and economic development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at helblem@who.int.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table S.17: Children’s educational efficiency - GPA z-scores: specification <u>(1) (OLS)</u>

|Dependent variables:|Bengali|English|Math|General<br>science|Islam<br>studies|Bangladesh<br>general<br>studies|
|---|---|---|---|---|---|---|
||(a)|(b)|(c)|(d)|(e)|(f)|
|Study at|-0.001|-0.000|-0.002|0.001|-0.000|-0.001|
|school|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Study at school|0.001|0.001|0.001|0.002|-0.001|0.000|
|(per-day minutes)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Study at home|-0.002|-0.001|0.000|-0.002|-0.001|-0.002|
|(day, per-day minutes)|(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.002)|
|Study at home|0.215**|0.134|0.150|0.182**|0.213**|0.197*|
|(night, per-day minutes)|(0.101)|(0.092)|(0.101)|(0.085)|(0.099)|(0.115)|
|One if attend|0.215**|0.134|0.150|0.182**|0.213**|0.197*|
|(February)|(0.101)|(0.092)|(0.101)|(0.085)|(0.099)|(0.115)|
|One if attend|-0.245***|-0.206*|-0.148|-0.205**|-0.207**|-0.129|
|(April)|(0.090)|(0.102)|(0.093)|(0.089)|(0.077)|(0.125)|
|One if attend|0.039|-0.001|0.044|0.086|-0.006|-0.030|
|(August)|(0.074)|(0.096)|(0.070)|(0.079)|(0.088)|(0.097)|
|R-squared|0.135|0.130|0.175|0.166|0.131|0.139|
|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.

62
- They are the anchor price reference to all other financial instruments along the yield curve and across different risk classes.

- They provide financial intermediaries with instruments (e.g. repos, short-term derivatives) to manage risk and maturities in their balance sheet so that they can be reallocated according to the preferences and perceptions of different market participants.

- They enable intermediaries to finance their debt securities portfolios through repos.

- They are a key channel for the Central Bank to implement monetary policy through indirect, market-based instruments.

- They facilitate financial intermediaries’ hedging of forward positions in the foreign exchange market and are therefore instrumental to support liberalized foreign exchange regimes.

Thus, the lack of efficient money markets is a major obstacle for the efficient price formation and resource allocation in domestic financial markets, as well as for providing tools to support macroeconomic and financial stability.

**Developing efficient money markets is still a major challenge in most emerging markets around the world given their links to complex structural and institutional reforms.** They include the liberalization of the capital account and the development of foreign exchange markets; the ability of the Central Bank to improve its liquidity forecast (autonomous factors being a major difficulty<sup>17</sup> ) and to conduct timely and effective liquidity management operations; as well as the existence of a critical mass of sound and business-oriented financial intermediaries operating in the money market.

**Money markets in MENA countries are shallow and most trades take place between banks and the Central Bank and not between banks.** 18 This is the result of three interconnected factors:

- i) excess structural liquidity that is ineffectively sterilized; ii) Central Banks’ sterilization instruments that are not supportive of market development and efficiency;

- iii) poor money market operational arrangements.

**The preconditions for money markets to function properly are missing, depriving banks of the ability to actively manage liquidity.** Hence they use more costly and less

> 17 Usually the net position of the government is the most volatile autonomous factor to predict. As this component is beyond the Central Bank’s control, this illustrates the importance of broader institutional coordination to build a more effective framework for liquidity forecasting.

> 18 These features are well documented in FSAPs and specialized papers for individual countries in the region. See FSAPs for Tunisia (2006), Morocco (2008) and Egypt (2007); for Jordan in Vandenbussche, Blazsek, and Watt, (2009)

16
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Our analysis finds that household income shocks affect both male and female students in Brazil at both levels of schooling. However, the increased likelihood of school exit from tertiary education in the presence of a shock in Argentina is only present for young men. The coefficients for Mexican students continue to be insignificant, though a decrease in likelihood of male exit from tertiary school is found statistically significant at the 90 percent confidence level.

We also consider a model where the main earner becomes unemployed. Though this type of shock is less common (2% frequency rate vs. about 29% for the income shock), it implies more long-term employment uncertainty and a larger decrease in the main earner’s labor income. As such, it has the potential to result in more severe household adjustments. The effect of an unemployment shock is significant for Argentinian and Mexican students enrolled in secondary school only. It is not significant for students in tertiary school in any of the three countries and does not affect secondary enrollment in Brazil (Table 4). The lack of relationship between unemployment shock and enrollment in Brazil may reflect the country’s relatively higher rates of access to unemployment benefits; these may be sheltering households from experiencing severe income hardships in the presence of unemployment.

_Table 4. School exit hazard and parental unemployment shock, by country_

||Argen|tina|Bra|zil|Mex|ico|
|---|---|---|---|---|---|---|
|||Pt||Pt||Pt|
||Secondary|os<br>Secondary|Secondary|os<br>Secondary|Secondary|os<br>Secondary|
|Unemployment|0.380***|0.233|0.0568|0.160|0.302***|-0.005|
|shock<br>(quarter=q)|(0.128)|(0.214)|(0.037)|(0.098)|(0.098)|(0.134)|
|Ml d|0.311***|0.136*|0.102***|0.091***|0.209***|-0.152**|
|ae ummy|(0.047)|(0.073)|(0.013)|(0.030)|(0.043)|(0.062)|
|Age main|-0.006**|-0.019***|-0.002***|-0.006***|-0.017***|-0.014***|
|earner<br>(quarter=q-4)|(0.003)|(0.004)|(0.001)|(0.001)|(0.002)|(0.003)|
|Female main|0.138**|0.109|0.022|0.017|-0.012|0.079|
|earner<br>(quarter=q-4)|(0.056)|(0.076)|(0.015)|(0.032)|(0.049)|(0.065)|
|Years of|-0.077***|-0.066***|-0.028***|-0.046***|-0.113***|-0.040***|
|education main|||||||
|earner|(0.008)|(0.011)|(0.002)|(0.005)|(0.006)|(0.008)|
|(quarter=q-4)|||||||
|Employed|0.385***|0.408***|0.164***|0.131***|0.299***|0.334***|
|dummy<br>(quarter=q-4)|(0.090)|(0.085)|(0.016)|(0.033)|(0.052)|(0.078)|
|Quintile|-0.086***|-0.122***|-0.064***|-0.091***|-0.038**|0.022|
|(quarter=q-4)|(0.024)|(0.032)|(0.007)|(0.015)|(0.020)|(0.029)|
|Observations|19,066|9,643|48,381|19,698|30,292|14,197|
|Pseudo-R2|0.0356|0.0324|0.0507|0.0582|0.0340|0.0126|

21
5 world price rises above the Fairtrade minimum price, the Fairtrade price becomes: the world price plus US$0.05/lb. Finally, note from Table 1 that not all Fairtrade coffee is organic, but a higher minimum price prevails if it is.

**Table 1 The Fairtrade Minimum Price for Coffee (US cents/lb)**

|**Type of Coffee**|**Conventional (U**<br>**Central America,**<br>**Mexico, Africa,**<br>**Asia**|**S cents/lb)**<br>**South**<br>**America,**<br>**Caribbean**|**Certified Organic**<br>**Central America,**<br>**Mexico, Africa,**<br>**Asia**|**(US cents/lb)**<br>**South**<br>**America,**<br>**Caribbean**|
|---|---|---|---|---|
|washedArabica|126|124|141|139|
|non-washedArabica|120|120|135|135|
|washedRobusta|110|110|125|125|
|non-washed Robusta|106|106|121|121|

_Source: Adapted from FLO 2004_ The minimum prices in Table 1, along with a commitment to trade for more than one season and the advance of credit, stipulate the requirements of _buyers_ . Producers, or rather their organizations, are required to retain US$0.05/lb out of the minimum price (referred to as the ‘Fairtrade premium’) and to “administer the Fairtrade premium in a way which is transparent for beneficiaries and FLO. Decisions on the use of the premium are taken democratically by the members” (FLO 2004, p.4). In practice, producer organizations can decide to retain more than US$0.05/lb and tend to spend this on organizational capacity building, social programs, services for members, specific infrastructure projects and/or quality programs (Ronchi 2002a; Ronchi 2002b).

The formal standards for Fairtrade coffee (see footnote 6) make it clear that producer organizations must meet a further host of labor and quality standards.<sup>9</sup> In turn, Fairtrade organizations like Twin, Equal Exchange, Oxfam (until 2003), Traidcraft and others, provide a large variety of producer support activities in order to help producer groups meet the quantity and quality requirements for export, not only to Fairtrade markets, but to conventional markets as well. This reduces their dependence on limited Fairtrade coffee demand<sup>10</sup> and enables them to access conventional premium markets as well (e.g. organic).

> 9 These requirements provoke critics of the Fairtrade system to point out that Fairtrade seems to be reserved for smallholder producer organizations that are already strong and independent.  The system is endogenous, however: Many of the FLO-registered producer organizations can meet the stringent criteria only as a result of the capacity building and producer support they receive from NGO Fairtrade organizations, as described above.

> 10There is an excess producing capacity of Fairtrade coffee of some 80% (Giovannucci _, et al._ 2003).
- Gertler, P., M. Gonzalez-Navarro, T. Gracner, and A. D. Rothenberg (2022): “Road Maintenance and Local Economic Development: Evidence from Indonesia’s Highways,” Working Paper 30454, National Bureau of Economic Research.

- Glaeser, E. L. and G. A. Ponzetto (2018): “The Political Economy of Transportation Investment,” _Economics of Transportation_ , 13, 4–26.

- Herrendorf, B., R. Rogerson, and Ákos Valentinyi (2014): “Chapter 6 - Growth and Structural Transformation,” in _Handbook of Economic Growth_ , ed. by P. Aghion and S. N. Durlauf, Elsevier, vol. 2 of _Handbook of Economic Growth_ , 855 – 941.

- IPUMS-I (2019): “Integrated Public Use Microdata Series, International: Version 7.2 [dataset],” Minnesota Population Center, Minneapolis, MN, https://doi.org/10.18128/D020.V7.2, (last accessed 11 December 2019).

- Jedwab, R. and A. Storeygard (2021): “The Average and Heterogeneous Effects of Transportation Investments: Evidence from Sub-Saharan Africa 1960 - 2010,” _Journal of the European Economic Association_ , 20, 1–38.

- Kanbur, R. and A. J. Venables, eds. (2005): _Spatial Inequality and Development_ , WIDER Studies in Development Economics, Oxford: Oxford University Press.

- Kelly, M., J. Mokyr, and C. Ó Gráda (2023): “The Mechanics of the Industrial Revolution,” _Journal of Political Economy_ , 131, 59–94.

- Kochenderfer, M. J. and T. A. Wheeler (2019): _Algorithms for Optimization_ , MIT Press.

- Lipscomb, M., A. M. Mobarak, and T. Barham (2013): “Development Effects of Electrification: Evidence from the Topographic Placement of Hydropower Plants in Brazil,” _American Economic Journal: Applied Economics_ , 5, 200–231.

- Michaels, G., F. Rauch, and S. J. Redding (2011): “Technical Note: An Eaton and Kortum (2002) Model of Urbanization and Structural Transformation,” Working Paper.

- Moneke, N. (2019): “Can Big Push Infrastructure Unlock Development? Evidence from Ethiopia,” Working Paper.

- Morten, M. and J. Oliveira (2023): “The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City,” _American Economic Journal: Applied Economics_ .

- Paiva, G. (2010): “Brazil Infrastructure: Paving the Way,” Morgan Stanley Blue Paper, Morgan Stanley.

- Perez-Sebastián, F. and J. Steinbuks (2017): “Public Infrastructure and Structural Transformation,” World Bank Policy Research Working Paper 8285.

41
10 Note that this procedure gives exactly the same weight to growth rates in every year. Thus, it assumes complete symmetry between negative and positive growth rates which means that the adverse impact of a negative growth rate in a year cancels out by a positive growth rate of the same magnitude in another year, It must be further noted that GMGR gives the same weight to log(l + rt) for all t which is not the same thing as giving equal weights to each rt. Thus, the two methods differ with respect to their welfare implications. This issue will be discussed further in Section 6.

# 4. GROWTH RATES OF GNP PER CAPITA IN SEVENTY-SEVEN DEVELOPING COUNTRIES. 1970-87

The alternative procedures for computing growth rates (discussed in the previous sEction) will now be applied to the data from eighty-three developing countries. These data were taken from the World Bank Data Files by means of the ANDREX system. Gross national product (GNP) per capita was used to compare the economic performance of countries over the period 1970-87. All growth rates w-re computed in constant prices using the local currencies. The numerical results are presented in Table 1. The first column in the Table provides growth rates computed by the least-squares procedure which gives maximum weight to growth rates around the 1979 year. The remaining four columns in the Table present growth rates computed by four alternative procedures proposed in the paper.

It can be seen from the Table that the growth performance of countries vary widely. Most African countries have performed extremely badly. In a large number of these countries, growth in GNP was less than the growth in population. The deterioration in growth rates is even worse in the low-income
# **5 Conclusion and policy implications**

While there has been considerable debate on the impact of user fees for both the use of health services and the incidence of sickness in the population, few studies have utilized micro-data from before and after a significant policy intervention to assess this impact. The abolition of user fees in Uganda (in March 2001), together with the availability of two large household data sets from before and after the policy change, respectively, allows us to examine the impact of this policy on the demand for health care and on the incidence of sickness - and the economic impact of being sick.

In line with administrative data that point toward significant, though regionally differentiated, increases in the delivery of services, we find that the abolition of user fees reduced the incidence of rationing, particularly by the poor. While the impact on the choice of health facilities was less pronounced, there was a significant impact on the probability of being sick, which was greater for children than for adults. This provided particular benefits to the poor by eliminating, among others, asset bias in the incidence of sickness. In fact, using the regressions to provide a rough initial estimate of the value of the time saved as a result of the policy change suggests that the social benefits exceed the likely amount of revenue foregone and the significant economic benefits were concentrated among the poor. Although this does not imply that the abolition of user fees solved all the problems affecting the health sector in Uganda, it is not inconsistent with the notion that the monetary cost of treatment did constitute a barrier for the poor.

Our results highlight that the abolition of fees improved access as well as outcomes, something that is likely to be at least in part attributable to the increase in health sector funding, as well as measures to improve the supply of health services that were contemporaneous to the elimination of user fees. This illustrates the potential of an approach that removes obstacles to supply health services in a coordinated manner. Maintenance of these gains, which would eventually help Uganda to improve health sector performance, will require continued attention to the quality of service provision in the public sector. This will require the establishment of a system to continuously monitor, publicize, and reward the quality of service delivery and client satisfaction in different regions and health units. Having addressed the issue of access in a radical fashion, the challenge facing Uganda is to devise mechanisms that would provide both market and non-market incentives to maintain and improve the quality of health care.

19
28 Table 4: Impact of US and Doha partial reform of cotton subsidies and tariffs on economic welfare and net incomes of cotton farmers, by region <u>(equivalent variation in income in 2001 US$m, and percent change in value added)</u>

||Change|in econom|icwelfa|re($m)|Change in|net income|s of far|mers(%)|
|---|---|---|---|---|---|---|---|---|
||Compl-<br>iance by<br>|Doha p<br>refor<br>|artial<br>m<sup>a</sup>|Full<br>reform|Compliance<br>by US to|Doha p<br>refor<br>|artial<br>m<sup>a</sup>|Full<br>reform|
||US to|Due to|||<br>|Due to|||
||WTO DS<br>panel<sup>b</sup>|<br>trade<br>measures|Total||WTO DS<br>panel<sup>b</sup>|<br>trade<br>measures|Total||
|**High-income countries**|**210**|**-9**|**280**|**465**|**-4.1**|**0.1**|**-5.4**|**-15.4**|
|Australia|33|1|41|137|7.7|0.3|7.5|22.2|
|United States|229|0|231|429|-6.6|0.1|-6.0|-17.9|
|EU25|-29|-9|42|14|4.2|0.6|-20.9|-53.3|
|Japan|3|-1|-4|-24|1.1|0|0.5|1.5|
|Korea-Taiwan|-18|0|-20|-61|2.5|0|2.5|6.9|
|**Developing countries**|**-94**|**7**|**-88**|**-182**|**1.2**|**-0.1**|**1.7**|**4.3**|
|**_E. Europe & C. Asia_**|**_-10_**|**_0_**|**_-12_**|**_-14_**|**_1.1_**|**_0.1_**|**_2.1_**|**_3.3_**|
|Turkey|-20|-1|-29|-86|0.9|0.1|1.5|2.1|
|Other ECA|10|1|17|72|1.4|0.2|3.1|5.3|
|**_East Asia_**|**_-23_**|**_-2_**|**_-33_**|**_-83_**|**_0.5_**|**_0_**|**_0.7_**|**_1.9_**|
|China|19|0|15|50|0.4|0|0.6|1.5|
|**_South Asia_**|**_-31_**|**_-1_**|**_-36_**|**_-96_**|**_0.7_**|**_0_**|**_0.9_**|**_0.7_**|
|Bangladesh|-5|0|-6|-11|1.1|0.1|1.3|5|
|India|-23|0|-27|-85|0.6|0|0.7|-0.4|
|Pakistan|-4|-1|-5|-7|0.8|0.1|1.0|3.0|
|**_M. East &North Africa_**|**_2_**|**_7_**|**_8_**|**_19_**|**_1.8_**|**_-1.7_**|**_1.3_**|**_6.1_**|
|**_Sub-Saharan Africa_**|**_20_**|**_3_**|**_35_**|**_147_**|**_5.0_**|**_0.6_**|**_8.2_**|**_30.6_**|
|South Africa|0|0|0|-1|6.5|0.3|8.5|20.6|
|Mozambique|0|0|0|2|3.6|0.6|9.2|17.5|
|Zambia|0|0|0|0|1.0|0.2|2.1|3.6|
|Uganda|1|0|1|4|4.8|0.4|8.6|25.6|
|Other Sthn & E. Africa|3|0|5|17|3.7|0.3|6.1|19.7|
|Nigeria|0|0|0|-1|5.0|0.2|6.3|21.3|
|Other Sub-Sah. Africa|16|3|29|126|5.6|0.7|9.4|36.8|
|**_Latin America & Car._**|**_-52_**|**_0_**|**_-50_**|**_-155_**|**_3.0_**|**_0.1_**|**_3.6_**|**_9.4_**|
|Argentina|-1|0|0|7|2.2|0.1|2.8|10.7|
|Brazil|0|0|2|13|2.3|0.2|3.4|10.3|
|Mexico|-35|0|-36|-128|4.1|0|4.4|10.5|
|**World**|**116**|**-2**|**192**|**283**|**-0.4**|**0**|**-0.5**|**-1.8**|

a ‘Trade measures’ consist of removal of all export subsidies and removal of tariffs on highincome countries’ imports of cotton from LDCs; ‘Total’ adds a one-third cut in domestic support in high-income countries. b Reduction by one-third in cotton production subsidies (average 2000-2002) in U.S. alone. Source: Authors’ GTAP model simulation results
to construct an export productivity measure defined as exports per worker. Since exports and exports per worker are heavily skewed and contain many zeroes, our pre-analysis plan stated that we would take the inverse hyperbolic sine transformation of these outcomes. Finally, we also construct an overall export performance index, defined as the average of standardized z-scores of these different export measures. Appendix C defines each outcome in more detail.

We supplement these export and employment data with a combination of data from the program, a survey, and with linking the firms to other government datasets in Colombia. We use data from the application forms to describe the characteristics of firms at baseline, for stratifying the random assignment, and for balance checks. Program records provide information on take-up and usage of the intervention. A follow-up survey (described in section 5.2) is used to examine impacts of the program on export-specific and general management practices. Finally, in Appendix E we report impacts on secondary outcomes of interest such as sales, employment, survival, and productivity, by using data from 20182020 annual filings of firms in the Mercantile Registry (RUES) and to data from 2016 to 2019 in the Annual Manufacturing Survey (EAM). We did not elicit priors for these secondary outcomes, and so provide only frequentist and not Bayesian impact evaluation results for those outcomes.

# **4.2 Estimation of Treatment Effects using Frequentist Methods**

Our frequentist estimation follows the approach standard in the literature. We use the following pre-specified Ancova linear regression specification to estimate the intention-totreat effect. Our estimating equation for the ITT impact on outcome _Y_ of firm _i_ being assigned to treatment versus being assigned to control takes the form: where _Yi,t−s_ is the _s_ th pre-intervention lag of the outcome of interest; _δj_ are randomization strata fixed effects (following Bruhn and McKenzie (2009)); and _β_ is the intent-to-treat effect. Robust (Eicker-White) standard errors are then used.

In addition to the standard hypothesis test that the average effect of being offered the treatment is zero, _β_ = 0, we can also use the elicited priors to test additional hypotheses. We provide tests of the treatment effect being equal to the medians of the prior distributions of policymakers, academics, and firms.

The key assumption underlying equation 4.1 is the stable unit treatment value assumption (SUTVA). This will be violated if treated firms compete directly for export 15