rafmacalaba/datause-encoder-data huggingface dataset:
presented at the bottom of table 4 and they are related to the full version of the model, containing eqs. (1)-(10).

The discussion of the results is best carried out by looking at the different national cases. Each national case will be considered by looking both at its sectoral trade competitiveness (parameters entering eqs, (1)-(4) and (7)-(8) ) and at the features of the knowledge accumulation process (parameters entering eq (5)). Results will be compared with the structure of revealed comparative advantages (RCA) in the four Pavitt macrosectors as reported in table 2 33so as to check to what extent our estimates are

**Table 2 Revealed Comparative Advantages: France , Germany, Italy, Japan,** UK. **Pavitt macrosectors**

||France|German<br>y|Italy|Japan|UK|
|---|---|---|---|---|---|
|Traditional|0.96|0.96|2.2|0.66|0.87|
|Scale Int.|1.21|1.37|0.89|1.62|0.98|
|Spec Sup.|1.4|2.06|1.87|2.37|1.62|
|Science B.|1.1|1.03|0.72|1.96|1.39|

Source: see Appendix 2 consistent with the distribution of RCA's over the sample period. Before looking at the specific cases the following general comments can be made: i) results appear to be consistent with theoretical expectations about the importance of "price" and "non price" determinants of trade performance in the four macrosectors<sup>34</sup> ; ii) results also seem to be generally consistent with the distribution of RCA in the sense that the country cases exhibit high and

> 33 RCA are defined as (Xi/Xiw)/(Xj/Xw), where X= exports, i=sector, j=country, w=world. A value greater that I indicates the presence of a revealed comparative advantage in sector i.

> 3 Amable and Verspagen (1995) also find some consistency between theoretical expectations and empirical results about the determinants of sectoral export share performance when sectors are grouped according to Pavitt's taxonomy. In our case attempts to find effects of relative knowledge on the export share performance in traditional goods never led to significant results.

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comparison of the average test scores of _all_ scholarship recipients and non-recipients, without conditioning on school attainment (as in Table 3 above), would find small or no program effects on learning, because the marginal children kept in school did not learn much.

# E. <u>Non-learning outcomes</u>

Education may confer benefits other than in terms of learning outcomes. In this section we discuss CSP program effects on knowledge about health practices; on expectations about the future; and on adolescent mental health. These results are presented in Table 6.

As part of the household survey, we asked children who had applied for CSP scholarships whether they agreed or disagreed with five statements about the health consequences of smoking.<sup>24</sup> For example, the first of these questions states that “smoking cigarettes can be harmful to one’s health”, and the second states that “the smoke from other people’s cigarettes is harmful to you”. The survey also asked respondents whether they had heard of HIV/AIDS and, conditional on an affirmative answer, what could be done to avoid infection. In this case, the question was open-ended, and respondents were asked to list their answers. Both the smoking and HIV/AIDS questions were asked of all applicants, regardless whether they were enrolled in school.

The first panel of Table 6 shows that, among non-recipients, schooling is associated with better knowledge of HIV transmission. CSP recipients were also more likely to provide correct responses to the HIV/AIDS questions than non-recipients: the coefficient is 0.120 (with a standard error of 0.057). On the other hand, there is no association between schooling and knowledge of the harmful effects of smoking and no effect of the program on this outcome.25

> 24 A small number of respondents answered “no opinion”, “don’t know” or “refuse”.  The number of missing observations for each outcome is: 40 (AIDS); 0 (Cigarettes); 1 (Good home); 105 (Marriage age); 123 (Age for first child); 0 (Current economic ladder); 14 (Future economic ladder); 34 (Mental health scale 1); 6 (Mental health scale 2). The responses are no more or less likely to be missing for recipients, and the likelihood of missing status is unrelated to the dropout-risk score.

> 25 Virtually all respondents had heard of HIV/AIDS, and there is no program effect on the likelihood of having heard of the disease. Also, because CSP recipients were more likely to answer questions about HIV/AIDS, even when the responses were not correct, we also generated a dependent variable for the share of responses that were correct

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# **Clientelism in the Public Sector: Why Public Service Reforms Fail and What to Do about It**

Tessa Bold Ezequiel Molina Abla Safir<sup>1</sup> JEL classification codes: H0, I0 Keywords: Public Service Delivery, Public Sector Governance 1 Bold: Institute for International Economic Studies, Stockholm University; Molina: Education Global Practice, World Bank; Safir: Social Protection and Jobs Global Practice, World Bank.

This paper is a background paper for the World Development Report 2017 Governance and the Law. We thank Kathleen Beegle for very useful comments.

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.
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 15. Financial flows to Botswana’s infrastructure, average 2001 to 2007**

$ millions per year

||O&M<br>Public sector|Public<br>sector|Ca<br>ODA|pital expenditur<br>Non-OECD<br>financiers|e<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|ICT|80|56|0|0|19|76|155|
|Irrigation|—|—|0|0|0|0|0|
|Power (*)|101|35|1|0|0|35|137|
|Transport|106|140|0|9|0|149|256|
|WSS|95|172|1|0|0|172|268|
|Total|383|402|1|9|19|433|815|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology.

— = Not available.

(*) Table only considers period 2001-7, therefore it excludes the recent spending on Morupule

**Figure 16. Botswana’s existing infrastructure spending level is adequate**

<!-- Start of picture text --> SSA<br>LIC-NoFragile<br>SADC<br>MIC<br>LIC-Fragile<br>Resource-Rich<br>Cape Verde<br>Ethiopia<br>Kenya<br>Uganda<br>Botswana Capex O&M<br>South Africa<br>Congo, Dem. Rep.<br>% of GDP 0 5 10 15 20 25<br><!-- End of picture text --> _Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ LIC = low-income country; MIC = middle-income country; SADC = Southern African Development Community; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M= operations and maintenance; CAPEX = capital expenditure.

Despite its strong economy and good investment climate, during the mid-2000s, Botswana captured private investment commitments worth around only 0.2 percent of GDP, predominantly in the ICT sector. Most of Botswana’s peers, many of them with much less favorable economic fundamentals, have done significantly better in this area. Countries such as the Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal have all captured between 1.8 and 2.5 percent of GDP, while the most successful country in this regard—Guinea-Bissau—has captured in excess of 3.0 percent of GDP.

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Table 5: **Effects of Inpres Schools on Absolute Mobility in Primary Completion of Children: Absolute Mobility is Measured by Expected Primary Completion (EPC)**

|||**S**|**ons**|||**Da**|**ughters**||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||**Highest**|**Intensity(=1)**|**Mean Int**|**ensity(=0.215)**|**Highest**|**Intensity(=1)**|**Mean In**|**tensity(=0.215)**|
|||**Normalized**||**Normalized**||**Normalized**||**Normalized**|
|∆_EPC_0|0.195<br>(0.042)|20.70%|0.042<br>(0.009)|4.45%|0.197<br>(0.047)|20.91%|0.042<br>(0.010)|4.50%|
|∆_EPC_6|0.042<br>(0.033)|4.46%|0.009<br>(0.007)|0.96%|0.132<br>(0.034)|14.01%|0.028<br>(0.007)|3.01%|
|∆_EPC_9|0.023<br>(0.038)|2.44%|0.005<br>(0.008)|0.52%|0.130<br>(0.034)|13.80%|0.028<br>(0.007)|2.97%|
|∆_EPC_12|0.042<br>(0.040)|4.46%|0.009<br>(0.009)|0.96%|0.147<br>(0.032)|15.61%|0.032<br>(0.007)|3.36%|
|∆_EPC_16|0.126<br>(0.045)|13.38%|0.027<br>(0.010)|2.88%|0.201<br>(0.043)|21.34%|0.043<br>(0.009)|4.59%|

Notes: Robust standard errors are in parentheses, clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Absolute Mobility is measured by expected probability of primary completion (EPC) conditional on father’s schooling. ∆ _EPCy_ = _θ_ 4 _× Inpres_ + _θ_ 7 _× Inpres × Eiy_<sup>_p_+</sup><sup>_θ_11</sup><sup>_× Inpres ×_(</sup><sup>_E_</sup> _iy_<sup>_p_)2, where</sup><sup>_E_</sup> _iy_<sup>_p_represents father’s years of schooling for</sup><sup>_y_= 0</sup><sup>_,_6</sup><sup>_,_9</sup><sup>_,_12</sup><sup>_,_16.</sup> _EPCy_ are calculated at _y_ father’s years of schooling, where _y_ = 0 _,_ 6 _,_ 9 _,_ 12 _,_ 16. The EPC values are based on coefficients reported in Table 3. The variable Inpres intensity measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The Normalized EPC is the EPC value relative to EPC of children for father’s with average years of education (5.57 years), comparison cohort (born between 1957 to 1962), and zero Inpres intensity for the full sample (combined sample of sons and daughters). Normalized EPC values are reported in percentage. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

42
gives rise to the confusing reporting of unemployment rates for Turkey ranging (for 1984) from 13 to 21 percent.

We obviously have to make up our minds here about what we are asking for and that presumably must mean that we have to specify an analytical point-of-view. It is imporc..an **t** o notice that the SPO distinguishes between "unemployment" and "labor surplus". The notion of unemployment points in the direction of labor market analysis in terms of demand and supply where a crucial problem is to measure the degree of market clearing, so-called involuntary unemployment and its role in wage formation, in a Phillips relation, say, and the standard statistic here is that of labor "not employed and seeking employment at going market wage", typically obtained through special labor force surveys. The notion of labor surplus points in the direction of development theoretical analysis of potential production at full employment, defined either as a target or as labor supply under certain ideal (target) conditions. The problem is whether we are aiming at descriptive, analytical-empirical or prescriptive, normative analysis. While the SPO estimates of "'agricultural labor surplus at peak season" presumably are of the normative type it is not clear how the SPO defines and estimates urban and rural unemploympnt.<sup>1</sup> I shall here concentrate upon the unemployment estimates. Although apparently now anchored upon the single national labor force survey of 1985 (Table 1.3), they may for other years be

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Columns 1 and 2 show the selection and intensity regressions for the sample of all importers. Again, importer standards have a negative and statistically significant coefficient in the probit regression indicating that higher standards are associated with a lower probability of having positive trade. The restrictiveness index is omitted in the second stage OLS regression as it is used as the exclusion restriction in the two stage HMR model. The coefficients on the exporters’ income groups have the expected signs and magnitudes; they are increasing in income, in other words, higher income countries export more. The omitted control is the group of high income countries and thus the negative coefficients on the income groups’ coefficients. The interaction terms between income groups and the restrictiveness index are all statistically indifferent from zero, except for the interaction between product standards and low income countries in the intensity regression which is negative and statistically significant. The results indicate that on average, stricter product standards are associated with a lower likelihood that exporters will target a given destination no matter from what income group. However, the results also show that stricter standards could be associated with fewer exports from low income countries. This result could partially be explained by import rejections at the border due to standards requirements not being met which would affect the intensive margin of trade. Even though most exporting countries inspect products prior to shipment, many developing countries have financial constraints which limit the effectiveness of these procedures and in many cases testing and inspection facilities are inadequate. Border agencies in the importing country can reject the entrance of any shipment not meeting the required standards.

Columns 3 and 4 of Table 6 display the results for the estimations of the HMR model on the restricted sample of exports only to BRICS markets. The results show that the marginal effect of BRICS standards on trade is greater in absolute terms than those imposed by other importing countries.<sup>13</sup> The coefficient for the standards restrictiveness index for the BRICS sample is statistically greater than the one for non-BRICS countries displayed in Column 5. The interaction terms between the standards restrictiveness index and the four income groups are not statistically different from zero. The income group effects have the expected sign and their magnitudes are increasing in income. The source for the negative effect on the intensity of trade from low

> 13 The average BRICS restrictiveness measure is 0.0323 whereas the non-BRICS average restrictiveness measure is 0.0889. The BRICS marginal effect is greater but the average standard is lower than for non-BRICS. The resulting average effect of standards is still greater for the BRICS but not that much greater as the marginal effect would suggest.

14
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where _w_ is an element of the spatial-weighting matrix. _λ_ and _ρ_ are spatial autoregressive parameters in the dependent variable and error term, respectively. _ε_ is an idiosyncratic error distributed independently and identically. Under the normality assumption, this can be estimated by the maximum likelihood estimation procedure (e.g., Anselin, 1988; Amaral and Anselin, 2011).<sup>3</sup> For the spatial weighting matrix, inverse distances between two locations _i_ and _j_ are used. The distance is calculated using the Euclidean distance between the two locations. The intuition is that two locations are more closely related to each other, if they are located closely. This follows Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things (Tobler 1970).”

# **IV. DATA**

The summary statistics are shown in **Table 1** . Our primary data source is the SPAM, which is, as discussed above, a model to disaggregate the national and subnational agricultural production and land data into the 10km x 10km pixel level. The model generates data for each of 42 crops, which are aggregated with regional production prices used from FAOSTAT ( **Table 2** ). Thus, our dependent variable, _Yit_ , is the total value of crops produced at each location _i_ at time _t_ ( **Figure 2** ).

> 3 For estimation we applied a STATA command **_spreg_** developed by Drukker, Prucha and Raciborski (2013).
appearance of double-digit inflation is clustered in countries that are also experiencing currency crashes and in some cases are already in debt distress.

Figure 2. Share of EMDEs with double-digit inflation rates: 1980-2020 (in percent) <!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Percent of EMDEs<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> _Sources_ : World Economic Outlook, October 2020 and author’s calculations. _Notes:_ Annual average consumer price inflation.

Currency crashes, on which I have been comparatively silent, are an integral part of inflation dynamics, as the short-lived spike of the Global Financial Crisis highlights in Figure 3. The larger concern is whether currency weakness in a growing number of EMDEs becomes a more persistent feature of the post-COVID-19 landscape that feeds into both inflation and foreign currency debt servicing difficulties for governments and corporates.

# **IV. The Stages of Debt and Financial Crises and Their Resolution**

Turning to the resolution of banking and sovereign debt crises, Laeven and Valencia (2013) document that banking crises often take 3-4 years to resolve.<sup>25</sup> Sovereign external debt crises (default spells) take longer, with a post-World War II mean and median of 7.9 and 5 years,

> 25 Encouragingly, they also document that a significant share of episodes were resolved in a year.

18
additional year of schooling increases income per worker by approximately 18.4 percent.<sup>27</sup> This number suggests that the macro return to schooling in Guatemala is rather high, but it compares favorably with earlier microeconomic evidence. For example, the World Bank (1995) reports a private return to schooling of 14.9 percent for Guatemala.<sup>28</sup> There is evidence for much lower returns in the informal sectors and for decreasing patterns over time, but the magnitude of the coefficient is echoed in Funkhouser (1997). An estimate from Haeussler (1993) based on 1989 survey and Ministry of Education data suggests that, depending on the schooling level and underlying assumptions, the social return to schooling lies in a band between 13-19 percent. Finally, these results also confirm the cross-country evidence from Cohen and Soto (2001). They essentially find that in macro and micro regressions the effect of education on income is of similar magnitude.

# **6. Robustness Check and Additional Explanatory Variables**

This section seeks to answer some basic questions. How much confidence should be placed on the previous results? Evidently, given certain data restrictions and distortions caused by the civil war, a key issue is if the previous findings can be used to derive firm policy conclusions. In addition, another important aspect is considered: does the conditioning information set cause the schooling coefficients to change?

In order to answer these questions, the following paragraphs are organized as follows. Section 6.1 tests the stability of the variables. By comparing the results with alternative sources, section 6.2 includes time trends, and analyzes the reliability of the human and physical capital stock data. Section 6.3, the bulk of the analysis, includes additional variables explaining growth. An overview of the alternative data is presented in Figure 7. Additional variables are the quality of capital, trade openness, terms of trade, and imported capital goods. This section also examines the effect of life expectancy as a companion indicator for human capital. In addition the role of military expenditures is analyzed, which, among others things, may serve as a proxy for bad governance in Guatemala. Section 6.4 closes with a brief summary of the findings.

## **6.1 Stability of Coefficients**

Given the distortions of the economy by the civil strife and other events, it is imperative to evaluate the stability of the coefficients. For example, comparing data

> 27 According to the Table, the implicit return to schooling can be calculated as follows: (0.034/0.240)/(1-0.240) ̃0.184. Discrepancies are due to rounding.

> 28 Based on ENCOVI (2000) survey data the World Bank (2003a) reports an overall rate of return of 6 percent.

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county, in the second one, at the level of a specific bilateral trade relation and in the third one, at the level of a single exporting sector within a bilateral flow. We also derive the accounting methods for other relevant perspectives. We argue that each perspective is conceptually suited to addressing a different class of empirical issues. For instance, the country-level approach is the proper one when allocating a country’s GDP across the different trade flows, whereas the bilateral perspective is best suited to evaluating the extent to which the GDP of a country is involved in the commercial interchange between two economies.

We show how the main methodologies proposed in other contributions fit into this framework. Then, on the one hand, we reconcile a large part of the existing literature under a unique comprehensive scheme; on the other hand, we show the main shortcomings and limitations that affect some of the techniques that have been proposed in the literature.

In general, the detailed breakdowns of bilateral/sectoral exports we present find a broad scope for empirical investigations on global production networks both at macro and micro level. Indeed, there is a fast expanding empirical literature that uses tools similar to those proposed in this paper. Our contribution to this literature is twofold: i) we extend the set of possible measures, including a new indicator of GVC-related trade, in order to address a wider range of empirical issues; ii) we improve the accuracy of some of the existing measures. These improvements are likely to become increasingly relevant from a quantitative point of view since the inter-country input-output data will become more and more detailed, eventually relaxing some of the simplifying assumptions that characterize current databases (de Gortari, 2018).

Finally, an aspect that is currently debated in the literature is how to measure the impact of trade policies on countries’ productions by taking all international supply linkages into account. The ‘sectoral-bilateral’ perspective of value-added accounting proposed in this paper offers a promising conceptual framework to address this issue, since it provides a precise measurement of the GDP of a country that is exposed to a trade policy implemented by any economy for a given sector and a given trade partner. We exploit this measure to derive an indicator of the effective incidence of tariffs on value-added. This may contribute to development of more refined measures of the spillovers of trade policies in global value chains using ICIO data.

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