rafmacalaba/datause-encoder-data huggingface dataset:
# **5.2 Entry, costs, and competition**

We now estimate specifications of Equation 5 which include cost proxies, _Xjkdt_ , and show results in Table 4. As expected, lower costs are positively associated with entry for both public and private HEIs. Larger institutions (where size is measured by the institution’s enrollment share in the department) are more likely to open new programs in any field, and institutions that are more specialized in a particular field (as measured by the fraction of their students enrolled in the field) are more likely to open programs in it. Comparing the entry elasticities with respect to these variables for private and public HEIs, we find that private HEIs are more sensitive to specialization than public HEIs, consistent with their lack of public funding—in the absence of public funding, they must avoid the fixed cost of opening programs outside their field of specialization. Meanwhile, public HEIs tend to be more responsive to size than private HEIs, consistent with their larger size.

Table 4: Program Entry and Proxies for Cost Dependent Variable : Indicator for Opening a New Program <u>(Narrow</u> Entrant Definition)

||All SCPs|Private SCPs|Public SCPs|
|---|---|---|---|
||(1)|(2)|(3)|
|L.GDP by Field|0.0012***|0.0014***|0.0004*|
||(0.0001)|(0.0001)|(0.0002)|
|L.HEI share in Dept.|0.1067***|0.1310***|0.0879**|
||(0.0188)|(0.0236)|(0.0286)|
|L.Field share in HEI|0.1456***|0.1397***|0.1571***|
||(0.0091)|(0.0098)|(0.0216)|
|Constant|-0.0090**|-0.0150***|0.0039|
||(0.0034)|(0.0037)|(0.0083)|
|N. of Observations|62,822|48,527|14,295|
|Mean of Dep. Variable|0.0203|0.0194|0.0236|
|Elasticities:||||
|HEI share in Dept.|0.18|0.16|0.26|
|Field share in HEI|0.31|0.31|0.28|

Note: Each column presents the coefficients from an OLS regression where the dependent variable is an indicator for whether institution _j_ opens a new program in department _d_ and field _k_ in year _t_ for 2004-2019. GDP-by-field is in billions of COP; HEI share in the department and field share within the HEI are enrollment shares and range between zero and one. All independent variables are lagged one year. All regressions include department, year, and field-category fixed effects. *,**, and *** denotes significance at the 10, 5 and 1% level. Standard errors are clustered at the institution-year level.

Table 5 shows estimates of a specification of Equation 5 including market structure variables, _Mkdt_ , to assess the relationship between SCP entry and the presence of competitors. These regressions control for entry cost proxies because an institution’s 23
aggregate sales in 2019.<sup>78</sup> Appendix Table 29 presents the results of this aggregation. We first consider the set of credit program users (Panel A).<sup>79</sup> The change in indebtedness for these firms takes into account both program and non-program debt.

Consistent with the results in the paper, riskier firms experience larger within-group changes in indebtedness: the leverage of high-risk firms increases by 11.58 percentage points, while the leverage of low-risk firms increases by 8.84 percentage points (Column 1). Nevertheless, riskier firms represent a smaller share of aggregate activity: high-risk firms represent only 6.1% of countrywide sales compared to the low-risk firms that represent 35.6% of aggregate sales (Column 2). As a result, the contribution of high-risk firms as a group to overall indebtedness is smaller (0.71 percentage points) than the contribution of low-risk firms (3.15 percentage points) (Column 3). The small weight of riskier firms in the aggregate, therefore, mitigates the micro selection by riskier firms documented in the paper.

Summing across all risk groups shows that the indebtedness of firms that use the credit program increases by 9.71 percentage points. Those firms experience an increase of 3.6% of GDP in guaranteed credit (Table 7) and a decline in non-guaranteed credit. Extending the analysis to include all active firms (users and non-users) by risk confirms that the higher the risk, the larger the within-change in debt (Appendix Table 29, Panel B). But the higher the risk, the smaller their weight in the economy, attenuating the increase in aggregate risk.<sup>80</sup>

# **Appendix D: Model Details**

This appendix gives further details of the model in Section 7, including equations, calibration, and analysis/intuition. In addition to producing the quantitative results, the model motivates and justifies the empirical predictors of risk in our default probability model.

## **D.1 Firms**

For convenience, we reproduce the firm’s profit equation, (7), which depends on its given state including productivity, _z_ , equity, _e_ , and labor, _l_ :

> 78In alternative estimations, we use value added instead of sales, obtaining similar results. However, the magnitude of change in debt relative to value added is larger than that relative to sales (as sales provide a measure of gross output). We report the estimates relative to sales to link it better to the micro part. Moreover, unlike value added, most firms in the economy report sales.

> 79The results reported in this table are only for credit-program users and therefore are not directly comparable with the numbers in Figure 2.

> 80In unreported results, we decompose indebtedness into two additional margins: banking status and size. Across the different partitions of firms, large increases in firm leverage within groups occur for firms with a relatively small weight at the aggregate level.

4
Figure 1: Total Household Credit to Households, 1995-2007 <!-- Start of picture text --> 60<br>40<br>20<br>0<br>1995 1997 1999 2001 2003 2005 2007<br>Bulgaria Croatia Czech Republic Estonia<br>Hungary Latvia Lithuania Poland<br>Romania Slovak Republic Slovenia Turkey<br>EU-15 and Cyprus<br>(% of GDP)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2: Mortgage Credit to Households,1995-2007<br>80<br>60<br>40<br>20<br>0<br>1995 1997 1999 2001 2003 2005 2007<br>Bulgaria Croatia Czech Republic Estonia<br>Hungary Latvia Lithuania Poland<br>Slovak Republic Slovenia Turkey EU-15 and Cyprus<br>(% of Total Household Credit)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3: Credit to Enterprises, 1995-2007<br>100<br>80<br>60<br>40<br>1995 1997 1999 2001 2003 2005 2007<br>Bulgaria Croatia Czech Republic Estonia<br>Hungary Latvia Lithuania Poland<br>Romania Slovak Republic Slovenia Turkey<br>EU-15 and Cyprus<br>(% of Total Privare Sector Credit)<br><!-- End of picture text --> 16
figure of 1,977 million.”<sup>34</sup> In the same report, ANAS states that all of the concessionaires guaranteed to fill this gap, by allocating “investment reserves” ( _reserve vincolate di capitale_ ) to the missing part and implementing such investments in the next fiscal year. Although this is a soft, constructive and fine-free solution, it allows concessionaires to speculate on this permanent one-year delay and can provide them with a fictional financial capacity given by the non-synchronized movement between investment and proceeds.

Table 4: Autostrade Ebit<sup>35</sup>

|**Year**|**Million euro**|**% of stock**|
|---|---|---|
|||**capitalization**|
|**2004**|1,454|0.1290|
|**2005**|1,536|0.1326|
|**2006**|1,606|0.1248|
|**2007**|1,643|0.1108|
|**2008**|1,616|0.2157|

_Source: Autostrade and Atlantia balance sheets between 2004 and 2008._ A further aspect that may hinder competition is the concession renewal mechanism. In fact, the current state of the art makes it sufficient for the current concession holder to agree with ANAS on a future investment plan, in order to secure the renewal of the concession for the duration of the current concession. This risks petrifying the market and avoiding the entrance of more competitive concessionaires.

Another major problem in the highway regulation is the overlap of too many independent and political authorities, which by themselves are neither specifically designed for this complex process nor to collect sufficient independent information. This ends up creating a confusing and dissuasive business environment. It is enough to think that before Autostrade was given the right to obtain a tariff adjustment for the difference between the expected and realized inflation rate, two years and some six authorities were involved in the decision process (ANAS, Consiglio di Stato<sup>36</sup> , Autorità per la vigilanza sui contratti pubblici di lavori, servizi e forniture, NARS, MEF and MIT<sup>37</sup> ) before a decisive statement by the Consiglio di Stato solved the situation.

34 The reports states, in Italian, that “La spesa per investimenti, nelle more delle approvazioni dei progetti di bilancio per l’anno 2008, sostenuta dalle società concessionarie autostradali per investimenti, rilevata dai dati di contabilità analitica al 31 dicembre 2008, è risultata pari a 1.674 €/Milioni (escluso CAS), a fronte di una previsione di piano di 1.977 €/milioni corrispondente all’86percent,” refer to ANAS 2008 reports, available at <u>http://www.stradeanas.it/index.php?/content/index/arg/dati_di_bilancio</u> 35 For these data, please refer to the section bilanci in the Atlantia website. Data for 2009 are not ready yet. <u>http://www.atlantia.it/it/bilanci/2006.html</u> 36 <u>http://ricerca.repubblica.it/repubblica/archivio/repubblica/2002/11/21/autostrade-vince-la-partita-delle-tarifferecuperera.html</u> 37 Please refer to Autorità per la vigilanza sui contratti pubblici di lavori, servizi e forniture, Relazione 2007, Pag 172, 176, 177.

13
# **The Falling Price of Cement in Africa**<sup>∗</sup>

Fabrizio Leone<sup>_†_</sup> , Rocco Macchiavello<sup>_‡_</sup> , and Tristan Reed<sup>_§_</sup>

> _†_ Universite ́ Libre de Bruxelles, ECARES

> _‡_ London School of Economics, BREAD, CEPR

> _§_ World Bank Development Economics, Research Group

Originally published in the Policy Research Working Paper Series on _June 2021._ This version is updated on _June 2022._ To obtain the originally published version, please email prwp@worldbank.org.

## <u>JEL Classifcation:</u> E31, L13, L61, O12

Keywords: International price dispersion, market power, market size, markup, cement, Africa.

> ∗Many thanks to David Atkin, Ana Paula Cusolito, Penny Goldberg, Phil Haile, Aart Kraay, Eduardo Morales, Tommaso Porzio, Bob Rjikers, Mike Sinkinson, Daria Taglioni, Catherine Thomas, seminar participants at the Africa and North America meetings of the Econometric Society, the International Industrial Organization Conference, the World Bank Development Research Group, and Yale University for helpful questions and comments, and to Jan Gabriel Oledan for exceptional research assistance. Reed acknowledges a grant from the Knowledge for Change III Program. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank or their affiliated organizations, or those of the Executive Directors of the the World Bank, their Managements, or the governments they represent. Address for correspondence: Tristan Reed, treed@worldbank.org.
-4-

quantified estimates of the costs that military spending imposes on the economic welfare of their citizens and to have convincing evidence of the improvements in living standards that can result over the long run from military spending cuts.

Such a quantification is attempted in this paper. We address several questions. Is there a Peace Dividend from military spending cuts? If so, how large might it eventually be? More specifically, how much is productive investment likely to increase in response to military spending cuts, and how strongly will the associated improvements in the efficiency of resource allocation increase long-run capacity output, relative to the level it would have attained if the fraction of GNP absorbed by military spending had remained unchanged? Our paper extends a standard neoclassical growth model to take account of important linkages between military spending, productive investment, and the long-run growth of per capita capacity output. It implements an econometric technique for obtaining empirical estimates of the model from a panel of time-series and cross-section data for a large sample of developed and developing countries.

We then use the estimated model in simulation experiments designed to gauge the size of the Peace Dividend--that is, the impact of cuts in military spending on economic growth performance--in a number of major geographic regions of the world. To summarize, our estimation and simulation analyses suggest that these Peace Dividend effects would take some time to emerge, but would eventually be large, especially for countries in regions--such as Eastern Europe, North Africa and the Middle East--where levels of military spending have traditionally been high.
<!-- Start of picture text --> yr<br>“ ad ~<br>a Ss<br>ty<br>es<br>Ag 5 lee Ml (14.57,15.79]<br>< AP (13.35,14.57]<br>y MO) (12.12,13.35]<br>{ (10.90,12.12]<br>(9.68, 10.90]<br>(8.46,9.68]<br>ie 1 (6.02,7.24](7.24,8.46]<br> (4.80,6.02]<br>HB (3.57,4.80]<br>Wl (2.35,3.57]<br><!-- End of picture text -->
117

||1975|1980|1985|
|---|---|---|---|
|51El Salvador|,|.||
|5XGuatemala|--|||
|5 L Guyana|9.83|10.51||
|5 LHaiti||_||
|5 1 Honduras|21.33|||
|5NJamaica|19.33|-|-|
|5MMexico|18.52|18.00|11.55|
|5 M Nicaragua|14.72|11.59|-|
|5MPanama|16.88|13.37|15.95|
|5 M Paraguay|13.34|12.88|-|
|5 N Peru|20.56|15.59|16.16|
|5 M Suriname|16.29|||
|5 M Trinidad and Tobago|-|11.58|-|
|5 1 Uruguay|11.45|8.83|6.42|
|5 M Venezuela|16.49|19.92|19.70|
|6 Eastern Europe|1.11|1.50|1.13|
|(14)|1.11|1.50|1.13|
|6 1 Bulgaria|-|||
|6 M Czechoslovakia||||
|6NGerman Democratic Republic||||
|6MHungary||-|1.60|
|6 N Poland||-||
|6 M Romania|2.21|3.01|1.80|
|6 M Yugoslavia|0.00*|0.00*|0.00*|

Note: The Regional averages are only for the group of countries that have data for that year; L - low income; M - middle income.

Source: INF Government Financial Statistics; Consolidated Central Government Expenditure.
In theory, the solution to this problem is to come up with a regulatory model that minimizes the information requirements. This is what regulation such as price caps or comparable tools deliver, at least until prices need to be revised for any reason. They also provide a strong incentive to operators to cut costs since the difference between actual costs and the price cap defines the rate of return. When technical, legal or financial capacity to regulate is limited, in the short to medium run, these regulatory approaches are usually preferable to rate-of-return or cost-plus regulation. They are easier to implement without initial access to serious cost accounting. Their starting point could take the form of a benchmarking exercise relying on comparable operators in comparable contexts.

In practice, the simplest solution is thus to allow authorities to buy time to put all the regulatory tools in place (i.e. accounting system, asset valuation, cost of capital assessment and efficiency measurement methodologies and matching databases). These are needed to compute a fair average price and/or subsidy and it can be done with a reasonably modest amount of basic cost accounting data. The approach also reduces the risks of corruption allowing artificial cost inflations to justify higher prices and profit sharing between corrupt officials and operators.

# **_2.2 What happens when credibility is limited?_**

Government and regulatory credibility is an issue in the water sector. Of all infrastructure sectors, water is the one in which contracts are most frequently renegotiated, indeed over the last 25 more than 85 percent of contracts have been subject to changes, demonstrating the limited ability of all parties involved in a large-scale water transaction to make a long-term commitment. Contract renegotiation may be desirable when new information is identified that deserves a different treatment. However, in most cases, this information should have been identified before the contract was awarded, suggesting a strategic use of information gaps by one or more parties involved. Operators complain that renegotiation is evidence of a non-respect of promises to firms. Users argue that it reflects the regulators’ limited willingness to enforce contracts.

The real issue is the fact that when commitment and credibility are low, all firms will have an incentive to pretend to be of the high-cost variety, even If they are not. For the good firms, the main outcomes are higher risks of underinvestment, higher scope for rents and higher than required average prices. There are also higher risks of a “ratchet effect” (that is, if the firm reveals its type to be low cost, it knows the regulator will be more demanding, so there is an added incentive not to reveal information). Empirical evidence suggests that price caps are more highly associated with renegotiation than costplus regulation. This is also the prediction of theory and hence cost-plus regulation may seem to be the best form of regulation. This is because higher risks linked to a lower credibility and commitment 8
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10 Unsurprisingly some of the first applications of DEA and SFA in health were on the hospital sector (e.g., (Wagstaff 1989) and (Ray 1991)), and there has been a good deal of work undertaken since then: EconLit contains 56 publications with “hospital” and “frontier” in the abstract). According to Hollingsworth and Street (2006), however, this work has had a relatively modest impact on policymakers.

Our data are from Vietnam‟s official public hospital inventory, the same dataset used by Weaver and Deolalikar (2004) in their study of economies of scale and scope in Vietnamese hospitals. By the standards of low- and middle-income countries, this is an unusually good dataset. However, it does lack detailed information on patients treated, distinguishing only between inpatients, surgery cases and outpatients and not between different departments, let alone different diseases and treatments. In what follows we have included only district hospitals that have between 50 and 500 beds. We have excluded central hospitals run directly by the health ministry, and level-1 and level-2 hospitals (more complex hospitals). Our sample consists of 795 hospitals. Our data are for three years: 1998, 1999 and 2000. The expenditure data cover recurrent costs.

# III. **THE SINGLE OUTPUT CASE**

We start with the simplest case, where we have just one output, or one dimension of quality. (We allow for multiple outputs in the next section.)

# **Methods**

We assume labor and nonlabor inputs are combined to produce an output _y_ at a cost _C_ . Costs can exceed their feasible minimum because the input bundle used does not yield the maximum possible output (technical inefficiency), or because inputs are used in the wrong proportions given their prices and marginal products (allocative inefficiency), or both. We do not try to disentangle the two, instead presenting an estimate of overall inefficiency.

Suppose we have data from multiple service-delivery units. We can then generate a scatterplot of _C_ (or average cost) against _y_ —the space of the standard total (or average) cost curve chart in a microeconomics textbook. In services like education and health, it is important
half of the average income level in sub-Saharan Africa, Rwanda remains a rather poor country.<sup>4</sup> For our analysis it is important to highlight that aggregate exports have increased rapidly over the last decade. Figure 1 shows the evolution of both exports and imports, calculated by aggregating over all firms in the firm-level sample that we use in our subsequent analysis (which is described in the next section). Both series are normalized at 1 in 2008, the starting period of the data in the firm-level sample. We use two alternative deflators to convert the nominal values that we observe in the firm-level data into real values.<sup>5</sup> Exports approximately tripled from 2010 to 2014 in real value. Imports also expanded rapidly, but they increased only by 120%.

**Figure 1: Evolution of aggregate exports and imports**

<!-- Start of picture text --> (a) exports<br>4<br>2<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Total exports (nominal) Total exports (real - US deflator)<br>ratio (with WDI Goods exports) Total exports (real - WDI deflator)<br>ratio (with WDI Merchandise exports)<br>(b) imports<br>4<br>2<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Total imports (nominal) Total imports (real - US deflator)<br>ratio (with WDI Goods & Services imp.) Total imports (real - WDI deflator)<br><!-- End of picture text --> Source: Total exports and imports are constructed by aggregating over the firm-level custom’s data. The denominator in the ratios and the WDI deflator are constructed using series downloaded from the World Bank’s online World Development Indicators.

4 The average GNI per capita for all of sub-Saharan Africa, excluding a few high-income countries, was almost double, at 3,680 (current PPP) in 2017.

5 Only between 2014 and 2016 do the two deflators differ noticeably with the prices in the WDI deflator rising more rapidly than using the US deflator to adjust the trade values that are reported in USD.

4