rafmacalaba/datause-encoder-data huggingface dataset:
involvement allow us to develop a nuanced argument about the conditions under which the UN can help build self-sustaining peace.

We code UN mandates based on a close reading of each mission’s operational guidelines, status of forces agreements (where those were available), and a review of UN documents that indicated how much of the mandate was actually implemented.<sup>10</sup> We focus on UN operations because they are the predominant form of multilateral peace operation since 1945.<sup>11</sup> Another obvious measure of international capacities is foreign economic assistance. Measuring the amount of economic assistance available to all countries from all sources (bilateral, NGO, multilateral) was not possible.<sup>12</sup> Thus, we measured instead the amount of _net current transfers per capita_ to the balance of payments of the country.

The results presented here are robust to extensive specification tests: we controlled for _per capita size of government military_ as a measure of the government’s capacity to deter any external intervention; _Cold War conflicts_ as a measure of systemic constraints to UN intervention; region-specific effects;<sup>13</sup> and time-trends in the data, which we capture with a variable denoting the _decade during which the war started_ .

# _Short-Term Participatory Peace_

The participatory peace model two years after the war’s end is estimated using logistic regression, clustering observations from the same country. The results are presented in Models A and B, Table 1. All explanatory variables are significant and have 10 The results are robust to recoding ambiguous cases (Additional Results pages 22-24). 11 We have also researched _non-UN peace operations_ , but did not find evidence that they are effective (see “Additional Results”, section 5). Controlling for the presence of nonUN missions does not affect the results. See Shimizu and Sandler (2002) for an analysis of burden-sharing in both UN and non-UN peace operations. They show that there is a public good component to peacekeeping as evidenced by the pattern of contributions, which tend to be disproportionately shared by large countries.

12 For the period after 1970, we found data on _effective development assistance_ as a percent of GDP. Results using this measure are “Additional Results,” page 15. 13 We did not find any regional differences in peacebuilding outcomes, but we did find that the UN is less likely to intervene in Asia and more in Europe or Africa. Thus, we used geographic region as an instrumental variable in two-stage least squares model that treats UN intervention as an endogenous regressor. UN intervention was still positive and significant in those models (supplement, pp 45-49; additional results, pp. 78-93).

7
underlines a positive and significant association between preparation, supervision and post-conflict projects. Again, our analysis seems to be a lower bound.

The IEG data provide information on the outcome of the project, but also on the characteristics of the project. The characteristics of project j, _Pj_ , do not vary overtime. They include the main sectors of intervention, whether the project is an IDA or an IBRD project, whether it is an investment project or not, as well as the quality of preparation and the supervision efforts assessed by IEG.<sup>2</sup> All projects considered in the analysis have been evaluated and therefore have been closed. We deliberately used the original closing date of the project to define its duration in order to avoid potential endogeneity linked to the revised closing date _vis-`a-vis_ the success of the project. Projects started during war are dropped from our sample. Indeed, we want to distinguish post-conflict projects from projects in countries at peace.

In order to define the civil war episodes, we used the PRIO version 4-2007 database and chose the high intensity criterion defined as at least 1,000 war related deaths per year.<sup>3</sup> This, of course, inevitably leads us to different results than if we used a lower level of casualties. Indeed, wars are shorter using this definition and our database includes more post-conflict episodes and relapses to civil war than if we used a total of 1,000 deaths for the conflict as a threshold. It also implies that the country reported as being in a post-conflict period or at peace, for example in the few years preceding the collapse into war, may have been considered at war using a lower intensity criteria, thus impacting on the results. However, the high intensity criterion allows defining clear episodes when a lower threshold could have led to unclear dating and would potentially be biased towards the level of information available on casualties.

Table 1 presents some basic descriptive statistics on the database of projects we used and the history of war of the countries.

Table 2 presents the evolution of the rate of success depending on time and the history of conflict (first column). The rate of success does not vary widely depending on the environment of implementation: projects in countries at war, in post-conflict and at peace seem to have around the same chance of being successful. Interestingly, we remark that the rate of success of projects did somewhat decrease over time: only 52 percent of the projects were rated as successful or very successful in the 1990s while 87 percent of the projects were rated as successful in the 1960s, 73 percent in the 1970s and 54 percent in the 1980s. This apparent drop in the rate of success in the 1980s and 1990s

> 2Variables are presented in detail in Table 5.

> 3The list of wars is presented in Table 6.

5
24 important for growth in Uganda household income. So, connectedness to markets as captured by access to infrastructure (especially roads, but also electricity) and distance to urban centers is likely to be a major factor in determining how growth in any country transmits it benefits to the population.

- Third, the potential for economic growth and poverty reduction further depends on a household's private endowments. Households with larger private endowments-be it more and better qualified labor or land-not only tend to be less poor, they are also better placed to profit from new opportunities generated by liberalization and institutional change.

- Finally, it is vital to separate out the effect of shocks when assessing the role of policy changes. Dercon highlights rainfall and health shocks, both of which are certain to be relevant to poor households in most African countries. The importance of health is also underscored by Deininger and Okidi for the Ugandan case.

We now examine the evidence on distribution and poverty changes in other countries covered in this review, looking for echoes of the findings from the panel data of Ethiopia and Uganda.

# _Distribution, poverty and liberalization_

The changes in relative prices through exchange rate devaluations, the opening of domestic markets, and changes in the structure of production are certain to lead to shifts in income distribution, with producers of tradable goods (mostly exportables) benefiting from the economic policy reforms. The Ugandan and Ethiopian studies show that these effects were evident during the 1990s, and that they directly benefited poor households. The experience of Ghana in West Africa echoes these East African findings. Ghana experienced sharp poverty reductions among cash (export) crop producers during the 1 990s, a result of more favorable world cocoa prices and an increase in cocoa production. Table 11 compares trends in poverty among crop producers in rural Uganda and Ghana.

In both countries about two fifths of the population are food-producing farmers, of whom about two thirds were poor in the early 1990s. And in both countries, poverty fell among food producers, but the decline was not as great as that experienced by export crop producers. Most of the rural poor appear to have benefited from growth, but those producing export crops have
# **1. RICARDIAN STUDY OF SRI LANKAN FARMERS**<sup>1</sup>

Pradeep Kurukulasuriya and Mohamed Ihsan Ajwad Pradeep Kurukulasuriya, Yale FES, 210 Prospect Street, New Haven CT 06511. 718623-0835 pradeep.kurukulasuriya@yale.edu Mohammed Ihsan Ajwad, World Bank, 1818 H Street, NW Washington D.C. <u>majwad@worldbank.org</u>

> 1 This chapter has been submitted for possible publication in a Special Issue of _Climatic Change_ in February 2004.  We are indebted to Robert Mendelsohn for his guidance and recommendations during this research.  We would also like to thank the participants at the World Bank/GEF Conference on “Climate and Agriculture in Africa”, held in Cairo, November 2003 for helpful comments. In addition, we benefited from comments from David Balan, Shivanthi Gunasekera, Shamila Kurukulasuriya, David Maddison, Stephen Miller, Sheila Olmstead and Hilmy Sally.  The views expressed here are those of the authors, and they do not necessarily represent those of any institution.

9
Another factor that may have contributed is the confusion that often arises between business and labor formality, which are different but connected processes. Figure 2 shows both formality pathways by firm size. Firm formality (registration with SUNAT) grows faster with firm size and reaches almost 100 percent after the size of 20 workers. Instead, labor formality grows slower with firm size, reaching 80 percent in very large firms (more than 100 workers).

# **4. Data and Methodology**

Our primary data come from firms in the Peruvian Survey of Micro and Small Enterprises (EMYPE for its acronym in Spanish), which was carried out by the National Bureau of Statistics (INEI). The sampling frame comes from companies registered with SUNAT as of 2008. To catch micro and small firms, SUNAT’s registry is restricted to firms with annual sales less than US$3.5 million. From this total, the registry is further restricted to firms in the manufacturing industry that located in the capital province from eight regions of the country, including firms from Lima and Callao (the capital city).

A particular characteristic of EMYPE that makes it suitable for our purposes is that it contains information on firms’ business and labor formality. For business formality, the survey includes information on firms’ compliance with municipal regulations for business operation. For labor formality, the survey includes information on firms’ compliance with registration of their workers in the Electronic Payroll Registry. The survey also includes information on production and sales, total number of workers, age of the firm, specific branch of manufacturing, legal status of the firm (whether it is incorporated or not), as well as the sex and age of the manager of the firm.

EMYPE was carried out annually between 2010 and 2013 and recorded information from the previous fiscal year of business operations. Nevertheless, the information on business and labor formality was recorded only in the surveys for 2012 and 2013, corresponding to fiscal years 2011 and 2012, respectively.

We complement these data with public information from INEI, the Ministry of Labor, the Supervisory Agency for Investment in Energy and Mining (OSINERGMIN), and SUNAT. From these sources, we obtained additional data at the district or regional level. From INEI, we gathered data on district-level population. From OSINERGMIN, we retrieved data on districtlevel electricity consumption, a variable we use to proxy for local or district-level economic 11
corruption ratings, but in most months very few ratings are changed. September to October 2001 was typical, with a single 1⁄2 point change for Switzerland. From October to November, however, 47 ratings were reduced and 10 increased. Such a dramatic reassessment had not occurred in the ICRG either before (dating back to 1984) or since. As shown in Figure 1, the month-to-month correlations in ICRG always exceed .99, but fell to .88 in November 2001. Although the ICRG has not responded to repeated requests for an explanation for this break in the data, there is some evidence that ratings were readjusted to conform much more closely to the TI rankings. As shown in Figure 1, the ICRG ratings were correlated with the TI 2001 ratings (released at end of June 2001) at only .72. However, the correlation with TI rose to .91 with the massive re-calibration by ICRG in November.<sup>24</sup> This evidence of interdependence between TI and ICRG does not directly present a problem for the TI index, which does not include ICRG as a source. It does imply a circularity problem for WBI, however, which uses ICRG and most of the TI sources.<sup>25</sup> It also indirectly suggests a problem for TI (and WBI), to the extent that ICRG may not be unique among sources in sometimes free riding on the assessments of other sources - including the TI and WBI indexes themselves - rather than basing assessments on their own independent information.<sup>26</sup> Fortunately, correlation with other sources is not the only proxy for accuracy that could be used in assigning differential weights in index construction. Some more

> 24 Over the last four years, this correlation has gradually declined and recently was at .79.

> 25 This problem is relatively minor, as the WBI index uses many other sources in addition to the ICRG and those used by TI.

> 26 Surowiecki (2004) makes an analogous argument that more information of the wrong kind can contribute to stock market bubbles and crashes.  The advent of cable television and the Internet, including particularly CNBC, “magnified the dependent nature of the stock market because it bombarded investors with news about what other investors were thinking.”  A “herd mentality becomes endemic” as investors cease to make independent judgments about asset values, and the efficiency gains from aggregating information from large numbers of investors is lost.

26
# **Preferential Trade Agreements and Global Value Chains:**

# **Theory, Evidence, and Open Questions**<sup>**1**</sup>

Michele Ruta<sup>2</sup> World Bank Keywords: Global value chains; Deep and Shallow Trade Agreements JEL codes: F13; F15 1 This paper was prepared for the Global Value Chain Development Report which is a joint project by the World Trade Organization, World Bank, Organization for Economic Co-operation and Development, Institute of Developing Economies – Japan External Trade Organization, and the Research Center of Global Value Chains, University of International Business and Economics. I would like to thank my coauthors on the papers on which this paper is in part based, Claudia Hofmann, Alen Mulabdic, Alberto Osnago, Nadia Rocha. I am also grateful to Zhi Wang for sharing his data and to Andrew Crosby, Michael Ferrantino, Aaditya Mattoo, Alen Mulabdic, Zhiguo Xiao and seminar participants at the two preparatory conferences in Beijing and Washington DC for comments. Remaining errors are my responsibility only. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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.

2 Trade & Competitiveness Global Practice, World Bank. Email: mruta@worldbank.org
# **Figure 6. Differences in Equity Issuance Behavior: Shanghai and Shenzhen Events**

This figure shows differences in equity issuances between connected and unconnected firms listed in Shanghai and Shenzhen. The figure plots the difference-in-differences (DiD) coefficients (and their 90% confidence intervals) obtained by estimating Equation (1), using the cumulative amount of equity raised over 2012 assets as the dependent variable. The DiD coefficients show, for each year, the average differences in equity issuances between connected and unconnected firms (relative to the 2012 difference). The 2012 coefficients show the differences between connected and unconnected firms in 2012. Panel A uses the propensity score matched (PSM) sample of firms listed in Shanghai. Panel B uses the PSM sample of firms listed in Shenzhen. The shaded areas capture the formal announcement and implementation of the Shanghai – Hong Kong Stock Connect and Shenzhen – Hong Kong Stock Connect. Table 8 reports the coefficients shown in this figure.

## **A. Shanghai Listed Firms**

<!-- Start of picture text --> 0.30<br>Shanghai – Hong Kong<br>Stock Connect<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>-0.05<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>B. Shenzhen Listed Firms<br>0.65<br>Shenzhen – Hong<br>Kong Stock Connect<br>0.55<br>0.45<br>0.35<br>0.25<br>0.15<br>0.05<br>-0.05<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>DiD Connected versus Unconnected Confidence Interval (90% CI)<br>DiD Coefficient<br>DiD Coefficient<br><!-- End of picture text -->
1960s through a combination of variables representing some push factor in country i, size in country i; openness and size in country j and distance between i and j. The basic measure is IVij � ln(popi � immstj=distij) � confli where popi is the population size in the 1960s of country i, immstj is the immigration stock of country j in the 1960s, distij is the distance between i and j and confli is a dummy variable capturing the occurrence of armed con‡icts in country i during the 1960s.

Our instrument should be correlated with the size of the diaspora observed in 1990. The variable popi is used as a proxy for the size of potential migrants in sending country i while immstj is a proxy of the openness and the size of the receiving country j in the 1960s. The product of the two is divided by the distance between the two countries captures the size of migration costs. This variable is multiplied by the con‡ict variable speci...c to the sending country to capture push factors causing people to leave country i. If this last variable is not correlated too much over time, this should impact the stock of migrants in the 1960s but not the ‡ows of migrants coming from country i in subsequent periods such as the 1990-2000 period. In other terms, the low degree of serial correlation in the confl variable ensures that our IVij variable is uncorrelated with our dependent variable, as the usual over identi...cation test supports the exclusion restriction.

We only consider con‡icts observed between 1946 and 1960 in order to capture push-factors leading to emigration in the 1950s and 1960s. We distinguish minor con‡icts (number of battle-related deaths between 25 and 999) denoted CONFL1 and wars (at least 1,000 battle-related deaths in a given year) denoted CONFL2. We ...rst use CONFL1; then we use CONFL2 and ...nally we add up the two variables. F-stat statistics of ...rst stage regressions show that the correlation between this instrument set and the diaspora is relatively high. The results of the Hansen over-identi...cation test suggest furthermore that the second condition of no correlation between the instrument set and the error term is supported by the data.

# 5 Estimation Results

## 5.1 Impact on Flows

Table 2 presents the estimation results regarding the determinants of migration ‡ows and especially the role of diasporas. Columns (1) through (4) report the results on aggregate ‡ows while columns (5) through (8) give the results for low-skilled and highskilled migration ‡ows. The OLS estimates of equations (10) and (11) are presented in columns (1) and (2) where a signi...cant number of observations with zero migration ‡ows (and the size of the diaspora in 1990) are dropped. Columns (3) through (8) report the results from the two-step Heckman approach where the regressions without additional instruments are used as the benchmark.

21
# **Table 7. The Effect of Characteristics of Land Title on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable and two land title characteristics, whether the previous tenure has expired and whether the land’s user rights is in contention. The independent variable is the averaged investment (capital expenditure divided by PP&E) between 2010 and 2014 net of investment between 2010 and 2012. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. _Tenure Expired_ is a dummy variable that is 1 if the tenure of land controlled by the firm has expired and 0 if otherwise. _Rights contended_ is a dummy variable that is 1 if the user rights of the land were fragmented in terms of land was being leased to other users or subject to rights contention from multiple claims and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

|||∆(Investm<br>|ent/PP&E)<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Titling Firm|0.046|0.013|0.100|0.081|
||(0.305)|(0.091)|(0.707)|(0.601)|
|Tenure expired * Titling Firm|0.435**|0.423**|||
||(2.379)|(2.377)|||
|Rights contested * Titling Firm|||0.430**|0.393**|
||||(2.453)|(2.267)|
|ROA|0.002|0.002|0.002|0.002|
||(1.285)|(1.364)|(1.285)|(1.353)|
|Tobin's Q|0.007*|0.007|0.007*|0.007|
||(1.658)|(1.640)|(1.658)|(1.640)|
|Constant|-0.034*|0.035|-0.034*|0.038|
||(-1.910)|(0.789)|(-1.911)|(0.859)|
|Industry FE|No|Yes|No|Yes|
|Exchange FE|No|Yes|No|Yes|
|Adj. R<sup>2</sup>|0.039|0.05|0.040|0.051|
|# of Obs.|1250|1250|1250|1250|

53
across ownership categories in more detail in subsection C below. The results on the state share are consistent with rising productivity for privatizing enterprises. We also find that the coefficients on the final goods tariff measures are generally negative and statistically significant; our expanded discussion on the role of trade policy is in subsection D below.

Our results differ significantly from Javorcik (2004) and other studies of vertical linkages through foreign investment; all previous studies find significant and positive coefficients for ― _Backward_ ‖ but not for ― _Forward_ ‖, and they explain that the vertical spillovers occurred through contracts between multinational consumers and domestic suppliers. In our case, an additional linkage occurs—vertical spillovers take place through contracts between domestic firms who source inputs from multinational suppliers as well.

One possible explanation is that the foreign participation in the upstream sectors may increase the variety of inputs and provide more sources of inputs to the downstream firms and thus lead to a higher productivity in downstream firms. Ethier (1982) provides theoretical support for this argument, showing that access to a greater variety of inputs results in a higher productivity of downstream industries. Arnold, Javorcik and Mattoo (2008) also show that FDI can improve the performance of downstream firms by increasing the range of intermediate inputs available. Since costs of intermediate inputs account for a much larger share of output than is typically the case in other countries, it is not surprising that access to lower cost or higher quality inputs has such a significant impact on domestic firm productivity.

To the extent that foreign investors induced additional competition among supplying enterprises, we would expect that foreign firms would have led to downward pressure on prices in those sectors where backward linkages are greatest. Without proper deflators, this would have appeared as falling productivity in those sectors, with falling prices being misinterpreted as falling output instead. One way to test if this possibility is correct is to examine whether sector-level prices during the sample period were systematically related to foreign activity. Appendix Table 18