rafmacalaba/datause-encoder-data huggingface dataset:
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**achieving total protection of** about **-60 percent** In **the** cases of **Pakistan (wheat) and**

**Zambia** (corn).

Tahle 2: Direct, Indirect, and **Total Nominal** Protection **Rates for** Imported **Food** Products (percent)

|Country|Product|Direct|1975-79--<br>Indirect|Total|Direct|Indirect|Total|
|---|---|---|---|---|---|---|---|
|Brazil<br>|Wheat<br>|35|-32|3|-7|-14|-21|
|Chile<br>|Wheat|11|22|33|9|-7|2|
|Colombia|Wheat|5|-25|-20|9|-34|-25|
|Cote d'lvoire<br>|Rice<br>|8<br>|-33<br>|-25<br>|16<br>|**-26**<br>|-10|
|Dominican Re<br>|pub. Rice<br>|20<br>|-18<br>|2<br>|26<br>|-19|7|
|<br>Egypt<br>|<br>Wheat|-19|-18|-37|-21|-14|**-35**|
|<br>Ghana<br>|Rice<br>|79<br>|**-66**<br>|13|118|**-89**|29|
|Korea<br>|Rice<br>|91<br>|-18<br>|73|86|-12|74|
|Malaysia|Rice|38|-4|34|68|-10|58|
|<br>Morocco<br>|Wheat<br>|-7<br>|-12|-19|0|-8|**-8**|
|Pakistan|Wheat|-13|**-48**|**-61**|-21|-35|-56|
|Philippines|Corn|18|-27|-9|26|-28|-2|
|<br>Portugal<br>|Wheat<br>|15<br>|-5|10|26|-13|13|
|<br>Sri Lanka<br>|Rice<br>|18|-35|-17|11|-31|20|
|Turkey|Wheat|28|-40|-12|-3|-35|-38|
|<br>Zambia|Corn|-13|-42|-55|-9|-57|**-66**|
|Average||20|-25|-5|21|-27|-6|

Source Krueger, Schiff, and Valdes, 1988, "Agricultural Incentives in Developing Countries," World Bank Economic Review 2(3):p.263.

Note: Argentina and Thailand are not included because their main food products are exported.

Turkey was a net exporter of wheat in some years, and in the Dominican Republic rice was not traded in some years.

The Direct nomninal protection rate is defined as the difference Detween the total and the indirect nominal protection rates, or equivalently as the ratio of (1) the difference between the relative producer price and the relative border price, and (2) the relative adjusted border price measured at the equilibrium exchange rate and in the absence of all trade policies.

While discrimination against agriculture Is well known, the KSV results provlde quantitative measures of the degree of bias agalnst agriculture. In the face of such massive direct and indirect taxation of agricultural commodities, It Is not surprising that farmers resent additional taxation. Nor is It surprlsing that many governments are reluctant **to** Impose additional taxes on farmers. In **fact,** many **of** their direct agricultural
# **Annex 3: Variables uded in the analysis**

# **Variables taken directly from the questionnaire**

**GovrBdy** =V31.  This variable distinguishes between level of governance of the RBO.   With Values ranging from 1-5, with higher ones expressing more decentralization.  It can be used as a dummy variable.

**PltclCost** =V37.  This variable measures the political/transaction cost of the decentralization process.  It is expressed on a scale from 1-5, with 5 being the highest.  It can serve as an independent as well as a dependent variable.  As an independent variable it may explain low success or longer period of decentralization.  As a dependent variable it may be positively correlated with variables expressing the complexity of the basin.

**%BgtBsn** =V182.  This variable measures the share of the budget obtained from basin stakeholders. It is expected that the higher _BgtBsn_ the more likely the decentralization success.

**%BgtExtr** =V180.  The share of the basin’s budget allocated by external (government) agency.  It is expected the higher the _%BgtExtr_ the more likely the decentralization process to succeed.

**%BgtSrcs=** PC(V180, V182, V183).  Share of budget from sources other than Government and Basin stakeholders.  It is expected that the more diverse the budget sources the more difficult it will be to implement the decentralization.

**FormsDispute1** =V44. The forums that are available to hear/solve disputes.

**FormsDisput2** =V45.  This variable might have a pivotal role in explaining success of decentralization.  It measures, using a dichotomy variable Y=1; N=0 the existence of dispute resolution institutions.  It is expected that existence of such institutions be positively correlated with higher level of success of decentralization.

**GovrBdy** =V31.  This variable distinguishes between level of governance of the RBO, with values ranging from 1-5, with higher ones expressing more centralization (5 = “Federal”,4 = “State Authority”’3 = “State owned company”,2 = “Regional Authority”,1 = “Regional Board/Council/Committee”).

**MtdCreatn** =V33.  This variable indicates the way the RBO was created.  N/A=0, Bottom Up=1, and Top Down=2.  We expect that a bottom up method of creation is associated with more successful decentralization.

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comparisons of a limnited number of specific services; the other is that it does not involve the problem that differences in services and security regulations between the two countries cause a difference in the costs of the services. Thus the second approach was employed in the comparison.

42. The use of inputs as output indicators in intemational companisons for the comparison-resistant service sectors involves an underlying assumption about the productivity differential between professional personnel worling in these sectors of the two countries. Kravis, et al. discussed this issue in general in their report on Phases **m** of the ICP (Cravis, et al. 1982) and Gordon, et al. discussed medical care service comparison between China and the United States in particular in their study (Gordon, et al. 1990). Gordon, et al. also suggested that the staiscs in the UNDP's _Human Develpment Report 1990_ (UNDP, 1990) on ifi **e** xpectancy, mortality rates in child birth, inoculation aganst isease and other indicators of health could be taken as evidence of the high quantity and quality of care provided by China's 4.97 million health care workers to its populaton of over 1 bilion people.

43. The diference between the two countries in the length of training peiod and in the quality of medical education leads us to believe that the average quality of service could be quite different between the two countries. At this stage, an assumpfion has to be nude that there are no differences in quality and productivity between the medical professionals of the two countries. This underlying assumption and the sensitivity of the final companson reslts to altemative assumptions will be analyzed in section VI. ANALYSIS OF ERRORS.

44. The items of comparison include eamings of physicians, eamings of dentists, and earnings of nurses, physiotherapists, technicians, midwives, and pharmacists, etc. AU esfimated earings are disposable incomes because they are below the minimum income level to pay tax according to the 19
# **Figure 22. SSA: Investment growth slowdown**

_Investment growth has slowed sharply from about 8 percent in 2010 to near-zero in 2015, despite significant public investment until 2014. The slowdown has reflected a severe terms-oftrade deterioration in commodity exporters as well as long-standing structural bottlenecks and political tensions._

# **A. Investment growth**

# **B. Output growth**

<!-- Start of picture text --> Percent Percent<br>14 1990-2008 avg 2003-08 avg 14 1991-2008 avg 2003-08 avg<br>12<br>10 10<br>8<br>6 6<br>4<br>2 2<br>0<br>-2<br>-2<br>-4<br>Overall Com. exp. Com. imp.<br>Overall Com. exp. Com. imp. EMDE SSA EMDE<br>C. Share of SSA EMDEs with weak D. Contributions to investment growth<br>investment growth<br>Percent Percentage points<br>75 Below long-term average 20 Public Private<br>Contracting 15<br>10<br>50<br>5<br>0<br>-5<br>25<br>Commodity exporters South Africa and<br>0 excl. South Africa and Nigeria<br>2010 2011 2012 2013 2014 2015 Nigeria<br>E. Terms of trade change F. Political stability<br>Percent ICRG index, 0-100 (100=best)<br>15 Energy exporters 65<br>Non-energy commodity exporters<br>10<br>60<br>5<br>0 55 Energy exporters<br>Non-energy commodity exporters<br>-5<br>50<br>-10<br>45<br>-15<br>-20 40<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>Sources: Haver Analytics; Oxford Economics; World Economic Outlook, International Monetary Fund; World<br>Bank Development Indicators; PRS Group; World Bank.<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> A. Weighted averages.

C. Long-term averages are country-specific and refer to available data over 1990-2008.

E. F. Investment-weighted averages.

46
decline. As total rents are a combination of scale and scope, they are potentially at their maximum level for middle-income countries.<sup>12</sup>

# **What We Got Right, and What We Missed**

With the benefit of ten years of hindsight, we are even more convinced today that there is a “practice gap” between the Solow and the endogenous growth models. The former appears suitable for describing the growth problem and guiding policy making in low-income countries, but one of its central features—the exogeneity of technology—is a clear defect in any discussion about middle-income country prospects. Endogenous growth models seek to unpack the technological discussion, but relate more to creating new technology for frontier economies than to the process of adapting and diffusing technology in a way that allows firms in middle-income countries to catch-up to the frontier. So policy makers in middle-income countries must straddle both models. They must ensure that capital investments remain efficient even after an economy transitions through the Lewis turning point. They must focus on the transition from productivity growth stemming from inter-sectoral resource reallocations to intra-sectoral catch-up technological growth (moving up the value chain). And they must manage a transition to more mature institutions.

What does this mean in practice? We think that the six propositions we elaborated ten years ago are still reasonable. They are:

1. _Trade and technology_ . We argued that middle-income countries were likely to retain export competitiveness in a few sectors where they could reap scale economies. Scale economies are to be found in selected products, such as scientific instruments, electrical machinery, nonelectrical machinery, iron and steel, and pharmaceuticals. They can also be created through services such as efficient logistics that are needed for firms to be competitive in global value chains, often benefiting even more from agglomeration effects in cities. These principles still seem valid. Global trade is increasingly within-firm; parts and components have been the most rapidly rising components of global trade. Firms participating in global value chains also see substantial productivity gains as a result.

The evidence on the importance of the link between trade and growth is stronger than ever, but globally, the elasticity of trade with respect to growth appears to have slowed in recent years. Partly this is cyclical, but structural factors are also at play.<sup>13</sup> There has been greater protectionism in major economies, although the WTO estimates that only about 4 percent of merchandise exports have been affected. There has also been a consolidation of regional supply chains in East Asia, with China substituting more domestic parts for foreign

> 12 Riedel, comments at 37th PAFTAD conference, Singapore, June 2015

> 13 World Bank, Global Economic Prospects, January 2015

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# **_Absence of primary‐school teachers:  Comparative cross‐country findings_**

Given these issues, what do we know about teacher attendance in developing countries? One study, in which one of us was involved, set out to bypass the problem of faulty administrative records by measuring attendance through direct observation of teachers during surprise visits to primary schools in 2002‐03 (Chaudhury, Hammer, Kremer, Muralidharan, and Rogers, 2006). The research team used this same methodology across six countries on three continents, in each case in a random nationally representative sample of primary schools, which made cross‐ country comparisons possible. Teachers were counted as absent only if they were ordinarily scheduled to teach at the time of the surprise visit.<sup>2</sup>

**Table 1:  Absence rates of primary‐school teachers** , 2002‐03

||**Absence rate (%)**|
|---|---|
|Six-countrystudy:||
|Bangladesh|16|
|Ecuador|14|
|India|25|
|Indonesia|19|
|Peru|11|
|Uganda|27|
|Other comparable surveys:||
|Papua New Guinea|15|
|Zambia|18|

Note: Estimates are taken from surprise visits to representative random samples of schools in 2002‐03.

Sources: Chaudhury _et al_ , 2006 (for the six‐country study); World Bank, 2004 (PNG); Das _et al,_ 2007 (Zambia) Absence rates in these six countries averaged 19 percent (unweighted average), and they ranged from manageable to alarming, depending on the country (see Table 1). In Peru, the highest‐income country in the sample, some 11 percent of full‐time

> 2 This applied also to head teachers, who were included in the absence calculations only for times when they were scheduled to be teaching class.
We focus on three performance measures. _Return on Equity (ROE)_ is defined as profits relative to equity, while _Return on Assets (ROA)_ is profits relative to total assets. _Overhead costs/assets_ are the total administrative costs relative to total assets. The first two indicators are measures of profitability, while the third one is a measure of cost efficiency.

There is a wide variation in performance and efficiency across banks and over time (Table 4 Panel A). ROE and ROA vary between high negative and positive outliers, with averages of –13 and zero percent respectively. In the case of the return on equity, however, this is driven by the negative outliers; the median is 3%.<sup>36</sup> Overhead costs relative to total assets vary between zero and 69%, with a mean of 2% and a median of 1%. Banks with higher overhead costs show lower profitability (Table 4 Panel B).<sup>37</sup> To assess the effect of privatization on bank performance, while controlling for other bank characteristics, we utilize the following regression<sup>38</sup> : Performancei,t = α + β 1 Assetsi,t-1 + β 2Fixed Assetsi,t + β 3Savings Depositsi,t + β 4Eventually Federalized and Privatizedi,t + β 5Federalized before Privatizationi,t + β 6Privatized after Federalizationi,t + β 7 Time since Privatization after Federalization + β 8Eventually Privatizedi,t + β 9Privatizedi,t + β 10 Time since Privatization + β 11Eventually Federalizedi,t + β 12Federalizedi,t + β 13 Time since Federalization + β 14Eventually Restructuredi,t + β 15 Restructured + β1 6 Time since Restructuring + β 17Non-participating State Banksi,t + β 18Federal Banki,t + β 19Foreign Banki,t + ε i,t

> 36 The high negative outliers in the case of ROE are also explained by the following adjustment we make in the case of negative equity: we set equity at 1% of assets to avoid that a bank with negative equity and losses shows a positive ROE.

> 37 Note that these are quarterly data.  For annualized data, these would have to be multiplied by four.  Since we use a linear estimation model, there is only a scaling effect.

> 38 Similar models are employed by Berger et al. (2005) and Beck et al. (2005).

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How sizeable are these effects? At the level of the 10<sup>th</sup> percentile, one standard deviation increase in top Gini (from the average value of 0.266 to 0.296), reduces annual per capita growth rate by almost 0.5 percentage points. This is a sizeable effect since the average annual growth over 50 years at that point of income distribution was less than 1 percent (Table 1). But inequality among the poor affects positively growth rate of the rich. The standard deviation of Gini bottom is 0.025 and its increase would raise the growth rate of the rich by about 0.2 percentage point per annum, which is about 1/10<sup>th</sup> of the average growth rate of the rich over the 50-year period. The adjusted _R_<sup>2</sup> is, as before, between 0.73 and 0.88 and greater for higher income percentiles.

What are the channels whereby inequality among the rich may affect negatively the growth rate of the poor, and inequality among the poor positively the growth rate of the rich? We cannot test them, so we are reduced to simply discussing some hypotheses. Consider first the role of high inequality among the rich. A possibility which seems to us most compelling is that inequality in general, and among the rich in particular (where a given Gini coefficient implies much larger absolute gaps in income) is an indicator of societal fragmentation. We view it similarly to the ethnolinguistic fragmentation that was interpreted as a cause of conflicts and in many cases was found to correlate with civil strife (see the review in Hegre and Sambanis, 2006). Alike ethnic fragmentation that creates “horizontal” cleavages between the groups, income fragmentation creates “vertical” cleavages between the poor, middle class and the rich. These cleavages particularly strongly, and negatively, affect the poor. High inequality among the rich means that some of them must be extremely rich. This might promote “social separatism” whereby the rich prefer to opt out of publicly-funded and publicly-provided education, health, urban infrastructure and other services because their private equivalents may be of better quality and signal the wealth and power of those who can afford them. One example of this is the vastly different preferences of the rich (top 1%) and the rest of the population when it comes to the cuts in Medicare, education and infrastructure spending as a way to reduce federal deficit; according to the survey data reported by Page, Bartels and Seawright (2011; quoted in West, p. 10, forthcoming); 58% of the rich are in favor of the cuts versus only 21% among 22
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# SECTION 2: THE APPROPRIATE CONCEPT OF MARGINAL COST

2.1 The purpose of this section is to lay out the considerations that should determine the marginal cost concept to be used as a basis for the determination of public sector prtc'-. The main issue here is whether short-run marginal cost should be used, or whether the marginal cost concept should also include some allowance for capital costs. World Bank practice in this area is not uniform: Walters (1968) recommends the use of short-run marginal cost as a basis of road user charges, while Munasinghe (1981) suggests the use of long-run marginal cost as the basis of electricity charges.

2.2 Although Saunders, Warford and Mann (1977) show that there are several ways of defining marginal cost to include capital costs, the discussion here will concentrate on the basic issue of whether they should be included at all. This section therefore starts with ai. explanation of the advantages of short-run marginal cost and discusses its application to transport pricing. The main argument against short-run marginal cost, price variability, is then analyzed. Finally, the relationship of pricing to investment decisions is considered.

A. The Advantages of Short-Run Marginal Cost 2.3 The argument for using short-run marginal cost as a basis for public sector pricing is most easily seen in a situation where the capital stock ia fixed an.1 the only way in which output can be altered is by changing the level of variable inputs. The social cost of producing one more unit of a good (or the benefit in producing one less unit) is then clearly the cost of the extra variable inputs required to produce an extra unit of output, in other words the short-run marginal cost. Allocative
transport, communications and financial services. The<sup>shareoftheservicesector</sup> averaged 22% in 1996-98 (and growing fast). Assuming the same productivity<sup>gainsfrom</sup> NAFTA in services as in manufacturing, the impact on GDP would<sup>have beentwiceas</sup> large as that reported above.

# 7. Conclusion

Recent theoretical models of economic growth have highlighted<sup>the importanceof</sup> trade as a channel of technology diffusion. Empirical<sup>studiesof theimpactof North-</sup> South trade-related technology diffusion on total factor productivity<sup>(TFP)havebeen</sup> undertaken at the aggregate level. This paper examines this issue<sup>at the industry level.</sup> The paper focuses on Mexico. It examines the separate effects<sup>on</sup> Mexico's<sup>TFP</sup> of foreign R&D from the US and Canada, on the one hand, and from<sup>therestofthe</sup> OECD, on the other. We find that the impact of foreign R&D on<sup>theTFPof Mexico's</sup> manufacturing sector is large for imports from Mexico's NAFTA neighbors<sup>but notfor</sup> imports from the rest of the OECD.

Based on the estimated- TFP equation, we show that NAFTA<sup>hasledtoan</sup> increase in TFP in Mexico's manufacturing sector of 5.5%<sup>to7.5%.Given theplausible</sup> assumption that it has had negligible effects on the joint<sup>economiesoftheUS and</sup> Canada, NAFTA has resulted in some convergence of Mexico's economy<sup>to thoseof the</sup> US and Canada.

15
significant in all specifications. Some caution is, however, appropriate here given the much larger size of the estimated coefficients when compared to the OLS results. Optimistically interpreted, this could be an indication of reverse causality: IPAs are more likely to be established in countries with low FDI inflows. OLS estimation fails to take this into account and thus underestimates the magnitude of the effects. Pessimistically interpreted, the increase in the coefficients in the IV estimations could indicate that the exclusion restrictions are not fulfilled and that the instruments affect FDI inflows directly. Given the challenge of finding truly exogenous instruments in a panel of countries, we do not want to overemphasize the instrumental variable results.<sup>31</sup>

# **4.4 Agency characteristics**

In response to the difficulties in teasing out the true effect of investment promotion, we next turn to asking questions about the effectiveness of different agency structures. We believe that finding patterns consistent with the case study evidence collected by investment promotion professionals and conventional wisdom will give us more faith in the results we have found so far.

The first question we analyze is whether the agency’s legal status affects its performance. According to the case study work by Wells and Wint (2000), the nature of investment promotion activities suggests that quasi-governmental agencies may be best positioned to fulfill this function. On the one hand, there are several reasons why investment promotion should be performed by governments. First, the results of investment promotion activities may be difficult to translate into direct profits. While there is a market for consulting firms assisting companies in finding potential investment destinations, image building activities cannot be translated into profits that are readily captured by private companies. Second, agencies linked to the government may find it easier to assist investors in getting regulatory approvals, channel their complaints to the government and lobby authorities on behalf of foreign companies. On the other hand, the key ingredients of investment promotion—marketing a country as an attractive FDI location and investor servicing—are closer in nature to activities that are better performed by the private

> 31 To further investigate the possible endogeneity of IPA establishment to FDI inflows we also estimated several probit models. The dependent variable was equal to one if an IPA was established in country c at time t, and zero if there was no IPA in country c at time t. On the right hand side, we included the log of FDI inflows as well as other controls from Table 16. Neither contemporary FDI inflows nor their first, second or third lags appeared to be statistically significant in any of the specifications. As the IFS database contains very few observations on FDI stocks, we did not repeat the exercise with FDI stocks as an explanatory variable.

26