rafmacalaba/datause-encoder-data huggingface dataset:
the capital-output ratio, by 2016, the estimates presented here are less than 1% different from those generated by H&S. Moreover, over the period 1995 to 2005, the average compound growth rate of the capital-output ratio differed by just 0.2% per year (2.9% against 2.7%).

**Figure 13 Two estimates of the capital-output ratio**

This paper and Holz and Sun <!-- Start of picture text --> 3.5 20.0<br>Level<br>3.0 15.0<br>2.5 10.0<br>2.0 5.0<br>% difference<br>1.5 0.0<br>1.0 -5.0<br>1995 2000 2005 2010 2015<br>Holz & Sun This paper<br><!-- End of picture text --> _Source_ : Holz and Sun (2017) and author estimates

# **_Estimates by Wu et al._**

While the estimates in this paper agree with those of Holz and Sun, there are substantial differences relative to the estimates published by Wu _et al._ (2016) and Bailliu _et al._ In the former case, the nonresidential capital stock is shown as growing more rapidly than the estimates in this paper (Wu _et al._ put the average annual compound growth between 1990 and 2010 at 15% against an estimate of 12% in this paper). While the estimates for the non-residential capital stock in Bailliu et al. for the period 2004 to 2014 are similar to those in this paper, their estimates for both the growth and level of the total capital stock are lower than those in this paper and grow less rapidly – indicating a difference in the estimate of the capital stock in the housing sector. By 2014, the capital stock in this paper is estimated to be 5% higher than the estimates in the Penn World Tables.

# **_International comparisons of capital-output ratios_**

The extent to which there has possibly been over-investment in China can be judged domestically by the rates of return, but cross-country comparisons of capital-output ratios can also provide evidence to judge the level of Chinese investment. The OECD publishes data on the non-residential capital stock covering 28 of its member countries for the past two decades and data for somewhat fewer countries for the previous decade. The OECD data show that the average non-residential capital-output ratio for its members was around 2.6 for the five years ending 2016. However, this estimate is based on a capital-services definition of the capital stock rather than the balance sheet estimate definition used in this paper. While the comparable capital-output ratio for China has steadily increased over the 20 years prior to 2016, as infrastructure investment rose, its level is nonetheless slightly below that in the OECD area at 2.3 (Figure 14).

21
to finance, as emphasized by the G20 Principle for Financial Inclusion _._ Household and enterprise-level survey questions can provide governments and regulators with the data they need for designing reforms and then monitoring the effectiveness of those reform measures. For example, OIC governments can encourage Islamic banks to launch financially inclusive accounts, shifting G2P payments (increased transaction volume) through such accounts to enhance their viability.

# **V.3. Policy Recommendation III - Ensure a Level Playing Field for Islamic Microfinance, SME, and Micro** **_Takaful_**

**The lack of** **_Shariah_ -compliant micro-finance services is constraining financial inclusion to a proportion of the population.** The growth of Islamic microfinance will depend to a large degree on whether financial institutions can develop sufficiently attractive financial products and services, which are competitive with conventional products in terms of pricing, transparency, processing time, and burden on the client. This will be determined in part by their capacity and systems, but regulators can also provide a more level playing field for Islamic microfinance. This extends beyond financial sector regulation to the tax code, as Islamic financing that involves additional transactions such as passing on a property title, may have tax implications such as capital gains tax, which are not applicable in conventional deals. OIC governments should also extend access to a broader range of financial services through existing branch networks of Islamic banks, which can offer a unique network of existing outlets as a delivery mechanism for _Shariah_ -compliant financial services.

**When designing the financial inclusion reform plan, OIC governments should take three points into their consideration specifically** i) allowing banks to expand access through agents and use of technology (e.g. mobile phones), ii) providing a _Shariah_ -compliant finance company model for microfinance and microinsurance, and iii) removing interest rate caps for microcredit and strengthen customer protection laws.

**Competition policy can also contribute to further SME lending.** (Rocha 2010) suggests that private banks are more efficient in lending technologies and are able to generate and manage a significant SME portfolio, even within weak enabling environments.  The entry of these banks into OIC countries could contribute to more SME lending, both directly and through spillover effects.  Therefore, the policy implication is to ensure that entry requirements for Islamic financial institutions are not overly restrictive and that Islamic banking markets remain contestable.<sup>35</sup> In addition, the impact of the entry of foreign banks on _Shariah_ -compliant SME lending can be magnified if these new foreign banks have access to substantive credit information on potential SME borrowers.

> 35 Azoategui, Martinez Peria, and Rocha (2010) show that there is a higher rejection of banking licenses in MENA than in other regions.

45
- 25 -

Table 14: Pakistan Factors Underlvinz the Changes in the Total Debt-GDP Ratio. 1973-1991 (%)

|Year<br>1973-1981|Atd<br>-29.5|z<br>-77.1|ii<br>8.1|(r-g)zd<br>-94.6|e<br>_d_<br>-3.9|
|---|---|---|---|---|---|
|198 1-1991|34.5|-52.3|16.3|-25.1|23.6|

Source: Own estimates

- Although the debt dynamics component remained favorable, it is inportant to note the substantial difference in the impact over the two periods. Indeed, the differential effects of. the debt dynamics component on the total debt-GDP ratio over the past two decades is. a key result that needs emphasis. The reason that Pakistan has avoided'an explosive debt sitation in the face of large fiscal deficits over the past twenty years is largely explained by the fact that real interest rates have been negative for a substntial period. However, the interest rate on both external and domestic debt has been turning positive. Continued high growth of the economy has helped absorb the adverse effect of this change. But unlike the 1970s, this cushior is weakening. Indeed, if real interest rates had been positive-say 3 % in real terms over the past twenty years-the total debt-toGDP ratio would have expanded by an additional 40.5 percentage points between 1973 and 1991, reaching 125% in 1991.

28. Can Pakistan run large fiscal deficits as in the past and avoid a debt crisis by financing these deficits through seignorage or inflation tax? In order to estimate the relationship between seignorage and inflation, we first need to estimate the demand for real money balances. The following specification was used to esfimate the demand function: where Ln = natural log _A_ = constant _BM_ Base or high-powered money _P_ = price level y = real income X = inflaton rate The specification allows the elasticity of demand for real balances to vary along the demand curve. The estimated results are shown in Table 15. The coefficients are of the right sign. The long-term elasticity of real balances with respect to real income is close to one.
# **Annex II: Benchmark Dataset (parameters) Description and Sources**

# A2.1 Activity and Structure-Related Data Compilation

A2.1 Activity and Structure-Related Data Compilation _The Volume of brick production_ [ ] rr,gg **Table A2.1** : Volume of brick production (NN (tt) **)** in 2018 and the number of operational brick kilns in 2019 by province rr,gg NN<sup>ab</sup>

|Province|Brick Prod<br>|uction byb<br>|rickquality <br>|<sup>a</sup> (million<br>|)<br>|Numbe<br>|r of kilns by <br>|employee<br>|size<sup>b</sup><br>|
|---|---|---|---|---|---|---|---|---|---|
||Grade A|Grade B|Grade C|Others|Total|Small|Medium|Large|Total|
|Koshi|140|63|39|20|262|7|36|42|85|
|Madesh|452|149|106|18|725|16|151|125|292|
|Bagmati|499|361|63|2|925|10|35|160|205|
|Gandaki|90|34|30|-|154|26|20|16|62|
|Lumbini|455|208|93|15|771|35|81|110|226|
|Karnali<sup>c</sup>|-|-|4|-|4|-|2|-|2|
|Sudurpachim<sup>c</sup>|95|61|37|5|198|16|39|39|94|
|Total|1731|876|372|60|3039|110|364|492|966|

Source: (ILO et al., 2020) Notes: a Grade A is well baked, shiny, standard size, uniformly yellow or red, of regular texture, and uniformly shaped. Such bricks are free from pebbles, gravel, or organic materials. Grade B bricks are slightly over-cooked and may have irregular shapes and textures in comparison to Grade A bricks. Grade C bricks are crumbling, small pieces, overlooked, and irregular in shape. Others include tiles and other materials.

b Small kilns have employees of less than 50, medium kilns have employees between 50 and 100, and large kilns have employees of more than 100.

c Brick production data for Karnali is not available. We assume the type of technology of two operational kilns in Karnali is CK and they produce Grade C bricks. Note that out of 41 million Grade C bricks produced in Karnali and Sudurpachim provinces combined, we assume 4.2 million [i.e., (202 ⁄ 96) × 2)] Grade C bricks are produced in Karnali, and the remaining 36.8 million Grade C bricks are produced in Sudurpachim. The value, 202, is the total number of bricks produced (Karnali + Sudurpachim), the value, 96, is the total number of kilns (Karnali + Sudurpachim), and the value, 2, is the total number of kilns in Karnali. _The percentage weight by brick quality type_ [ ~~w~~<sup>]</sup> jj,gg **Table A2.2** : Estimated percentage weight by brick quality type ~~(w~~ type jj,gg<sup>) and the number of brick kilns by technology</sup>

|Technology type (j)|Percentage<br>|weight by bric<br>|k quality type (<br>|g)<sup>a</sup><br>|Brick kiln <br>|<sup>b</sup><br>|
|---|---|---|---|---|---|---|
||Grade A|Grade B|Grade C|Others|Number<br>(_Nj_)|Share (%)|
|CK||74.1|100.0||125|9.3|
|FC BTK|75.0|23.8|||899|66.6|
|ZZ FC BTK|24.1||||289|21.4|
|VSBK||2.1|||25|1.9|
|HK|0.7|||72.7|8|0.6|
|TK|0.3|||27.3|3|0.2|
|Total|100.0|100.0|100.0|100.0|1,349|100.0|

Notes: 44
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Table 14 shows these results. The coefficients are almost identical to those in tables 10, 11 and 12 and therefore we do not have any reason to believe that the results discussed in section 5.2 are biased by the effects of the civil violence in 2006.

# **_5.3.2. Migration biases in the analysis of school outcomes in 2001 and 2007_**

A final important concern in our analysis is the fact that some individuals have migrated at some point in their lives, thereby introducing a potential selection bias in the results. The 2001 and 2007 datasets provide information on their place of birth and their place of current residence.<sup>28</sup> If both coincide, then we can infer that they either did not migrate or migrated but only temporarily. There is, however, a group of individuals that migrated to a different place from the place of birth. The percentage of those that migrated represents 13% of the sample in 2001, and 19% and 24% respectively in the two samples of 2007.<sup>29</sup> The data does not allow us to establish when this migration occurred, or whether these individuals migrated for conflict related reasons. But a form of selectivity bias may occur if individuals did not choose the new place of residence randomly (the so called ―spatial sorting‖, see Kondylis 2010). For instance, if we find that those that migrated went to areas in which economic conditions are typically better (for instance, urban areas), our results would be likely to exhibit a downward bias in the effect of violence on education.

We have estimated the impact of a potential spatial sorting effect in tables 15 and 16 (for 2001) and table 17 (for 2007). These tables show the estimated results for the sample of individuals that never moved from the place of birth to the place of current residence for 2001 and 2007, respectively. These calculations test if the results in section 5.1 and 5.2 hold if we restrict the sample to those that did not move. The hypothesis we test is the following: if the coefficient on violence for those that did not migrate is similar to the one that we get when estimating the whole sample, then we can be quite confident in the validity of our results and argue that migration does not affect our estimates.

Calculations using the 2001 sample show that children that do not move have slightly lower attendance rates than the entire sample (71.1 percent versus 72.1 percent, respectively). This difference is not statistically significant. As a result, the coefficients obtained on the non-migrant sample (tables 15 and 16) do not differ from the results shown in tables 6 and 7. We are able

> 28 The 2001 survey also provides information on the place of residence before 1999. 29 1977-1992 and 1968-1984. 31
# Abbreviations and acronyms

|AEZ|agroecological zone|
|---|---|
|CIESIN|Center for International Earth Science Information Network|
|ESRI|Environmental Systems Research Institute|
|FAO|Food and Agriculture Organization|
|FAOSTAT|Food and Agriculture Organization Statistical Databases|
|GDP|gross domestic product|
|GIS|geographic information system|
|GPW–UR|Gridded Population of the World, version 3, with Urban Reallocation|
|GRUMP|Global Rural-Urban Mapping Project|
|IFPRI|International Food Policy Research Institute|
|IIASA|International Institute for Applied Systems Analysis|
|kg/ha|kilogram per hectare|
|km/hr|kilometer per hour|
|LUTs|land utilization types|
|ORNL|Oak Ridge National Laboratory|
|SI|crop suitability index|
|SPAM|spatial crop allocation model|
|SRTM|Shuttle Radar Topography Mission|
|UNEP|United Nations Environment Programme|

## **About the authors**

Paul Dorosh, Hyoung-Gun Wang, and Emily Schmidt are economists at the World Bank. Liang You is an economist with the International Food Policy Research Institute. The corresponding author is Hyoung-Gun Wang, hwang4@worldbank.org.

v
# **The (Non-) Effect of Violence on Education:**

**Evidence from the “War on Drugs” in Mexico**

Fernanda Márquez-Padilla<sup>*</sup> , Francisco Pérez-Arce<sup>†</sup> , Carlos Rodríguez-Castelán<sup>‡</sup>

**Keywords: Violence,** Crime, Education, Fixed Effects, Mexico **JEL codes:** C23, D74, H75, I21, O54

The authors would like to thank Janet Currie and Wendy Cunningham for their thoughtful comments.

> * Princeton University: E-mail: fmarquez@princeton.edu

> † RAND. E-mail: fperezar@rand.org

> ‡ World Bank. E-mail: crodriguezc@worldbank.org
# **5. Concluding Remarks**

The contents of PTAs continue to expand in both scope and depth, increasing the number of trade agreements that devote significant coverage to a wide assortment of regulatory environments extending beyond traditional trade policy and market access considerations. Intellectual property rights in particular have become an area of growing focus in this context, as the ever-increasing importance of the global knowledge economy has sharpened the incentives for policy makers and PTA negotiators to implement more rigorous and consistent systems of IPRs protections across countries. While a nascent literature exists exploring the effects of these developments on IPA member countries’ trade in goods, it is striking that little work to date has investigated whether such PTAs impact the dissemination of intellectual property between and beyond agreement member countries.

To explore this question, we empirically assess the role played by the proliferation of PTAs possessing substantive and legally binding language on IPRs, as defined by the World Bank’s database on the content of deep trade agreements and which we denote as IPAs, in shaping international flows of innovation as measured by cross-border patenting. To this end, we quantify the impacts of countries’ accession to IPAs in a structural gravity setting analogous to the widely used structural gravity model of trade. We employ detailed information on bilateral international patent applications at national patent offices, a data set which covers hundreds of countries over our sample period of 1995–2015. We further consider heterogeneity in the impacts of IPA accession as originating from the attributes of the agreements, delineating between “treatment” definitions that alternatively define IPAs as those possessing legally enforceable provisions on IPRs (WTO LE2 agreements), IPRs-related PTAs negotiated by the United States or EU/EFTA, and finally, IPAs possessing three or more TRIPS-Plus IPRs provisions (CORETRIPS3 agreements).

Our baseline analysis yields evidence that IPAs encourage heightened levels of total bilateral patent flows to member countries, as well as consistent increases in patent applications when broken down across a number of IPRs-intensive, high-technology industry clusters. However, the exact interpretation of this finding hinges on the nature of the IPA under consideration. For WTO LE2 agreements and US/EU/EFTA-negotiated agreements, these impacts manifest as increased patenting volumes to IPA-member countries originating from nonIPA-member countries, with no apparent rise in within-agreement patent flows. In striking 27
Excel file) to all matches and validate each match only if names, activity and neighborhood were consistent in both databases.

The “reclink” command allows inputting different weights to match on each of the three variables used (surname, first name and gender). In order not to rely on the weights used, we reiterate the process with different weights until no additional matches were found.

Since it is possible that the first name in the program data corresponds to the second name in the GUFE data, we also reiterate the whole process for all combinations of first to fifth names. Surname and first name were inverted in one of the two databases. So we also reiterate the process with other combination of surname and first names.

# **Checking that the matching method is working:**

To assess whether our matching method is working efficiently, we used the following methods:

1. First we looked at whether we could find additional matches using a more usual method of matching. That is looking at the two lists (sorted by surnames) and trying to find each business of the GUFE data in the program database. So it means looking mainly at businesses with surnames starting with the same letters. We were not able to find any additional matches.

2. Secondly, we looked at the proportion of business which formalized with the _entreprenant_ status during the first 3 months after program launch. Indeed, most businesses which formalized with the _entreprenant_ status should be also in the program data (in theory they are the only businesses aware of this new status). We matched 78% (119/153) of the newly registered _entreprenants_ .

3. We then took the 34 businesses which formalized with the _entreprenant_ status and were not matched with the program data and we tried harder to match these businesses. We took the program data and looked at all the businesses in the same neighborhood as the unmatched businesses. We were able to find 6 new matches. These 6 matches are very imperfect matches with surnames somewhat different and first name sometimes different. For two cases, the match was not done with our main method because the surname is missing in the program data.

As a result of this process, we use a conservative definition of a match between the business names in the two databases as “two businesses with a close surname, and at least one close 57
change in the relative price of capital, or an increase in productivity<sup>8</sup> . It has been increasingly recognized that poor infrastructure, particularly in low-income countries, may be an important cause for these frictions<sup>9</sup> . For instance, an expansion in the road network may not only reduce congestion on highways and facilitate the shipment of goods across regions (thereby reducing unit production costs, as noted earlier) but also reduce expenses associated with the construction of a new factory or the transportation of heavy equipment for installation to a new, remote production site. In large and sparsely populated countries, the impact on the cost of investment can be fairly substantial. Thus, by lowering not only production costs but also adjustment costs related to investment, public capital in infrastructure will tend to raise expected rates of return and therefore stimulate private capital formation. This positive effect may be particularly important for small firms. As documented by Tybout (2000) and Bigsten et al. (2005) for instance, in low-income countries the size distribution of firms is often heavily skewed to the right, with a high proportion of very small firms. These firms tend to be especially affected by adjustment costs. Indeed, in the study of Uganda by Reinikka and Svenson (2002) mentioned earlier, only a small fraction of small firms (less than 20 percent, compared to almost 80 percent for large firms) were able to purchase generators to alleviate a chronic lack of access to government-provided electricity. Similarly, in a study of the constraints imposed by deficiencies in public infrastructure on manufacturing industries in Nigeria, Indonesia, and Thailand, Lee, Anas, and Oh (1999) found that small firms bear a significantly greater burden than large firms.

The link between public capital in infrastructure and adjustment costs was formalized by Turnovsky (1996) and Agénor and Aizenman (2006). To illustrate the argument, suppose that a typical firm faces adjustment costs that are a convex

> 8They include therefore costs associated with the sale, purchase or productive implementation of capital goods, over and above the price of these goods. Such costs are associated with, for instance, searching for, and deciding upon, the proper type of equipment needed for a particular purpose, scrapping obsolete machines, installing the new capital stock, and reorganizing and training the workforce (see Hamermesh and Pfann (1996)). Note also that, with time-to-plan and time-to-build constraints, investment itself (rather than the stock of capital) could be subject to adjustment costs; see, for instance, Gertler and Gilchrist (2000).

> 9Other factors, such as underdeveloped or poorly functioning capital markets, may of course be equally (if not more) important in these countries.

13