rafmacalaba/datause-encoder-data huggingface dataset:
transparency includes issues such as the participation of stakeholders in the agency’s rule-making process, the publication by the agency of its decisions, the organization by the agency of public consultations, the existence of advisory committees in the agency’s structure, the existence of a website, and the registration of users’ claims.

**Figure 14: Social Transparency Index**

<!-- Start of picture text --> Social Transparency Index<br>Source: LAC Electricity Regulatory Governance Database, The World Bank, 2007.<br>1<br>.8<br>.6<br>Index (0-1)<br>.4<br>.2<br>0<br>T&T COL T1 R D PER ESV BOL ARG URU PAN GUA NIC MEX ECU BRA HON C R BAR T2 JAM CHI<br><!-- End of picture text --> Agencies’ positions in social transparency are represented in the Social Transparency Index (Figure 14). The index is headed by Trinidad and Tobago and followed by Colombia, the Dominican Republic, Peru, El Salvador, and Bolivia. Countries at the bottom of the index are Jamaica and Chile. Among countries at the top of the index, only Trinidad and Tobago and Colombia meet the conditions to develop “desirable” levels of social transparency. On the other hand, Nicaragua, Panama, and Ecuador do not meet the minimum conditions that allow users an appropriate access to the agency’s information and their involvement in their regulatory-making process.

Leading countries in the Transparency Index keep relatively similar positions in social transparency, with the exception of the Dominican Republic, which significantly improves its score in social transparency. By contrast, countries at the bottom of the Transparency Index—namely Ecuador and Honduras—show significantly improved positions in the Social Transparency Index.

Differences between countries at the top and bottom of the Social Transparency Index center on three main aspects. The first aspect is the participation of the stakeholders in the agency’s rulemaking process. While public consultations or public hearings are aimed at allowing the involvement of stakeholders in the agency’s main decisions, the rule-making process is the mechanism through which the regulatees are invited to contribute with their opinions in the elaboration of the agency’s regulations. Contrary to countries at the top, countries at the bottom of the Social Transparency Index either lack provisions to involve stakeholders in the rule-making process or, even though these provisions exist, stakeholders do not actually get involved in that process.

The second aspect is the existence of advisory committees integrated by different stakeholders in the structures of best performing agencies. These committees are supposed to play an important role in the agency’s decision-making by representing and promoting different group interests (mainly consumers).

- 27 -
Policy ReseaRch WoRking PaPeR 4572

# **Abstract**

Previous research proposes that peace is more likely to become durable if all rebel groups are included in the settlement reached. The argument implies that if actors are excluded and continue to pursue the military course, this could have a destabilizing effect on the actors that have signed an agreement. This article argues that all-inclusive peace deals – signed by the government and all rebel groups – are not the panacea for peace that many seem to believe. Given that the parties are strategic actors who are forward-looking when making their decisions, the signatories should anticipate that the excluded parties may continue to fight. Therefore, the risk of violent challenges from outside actors is likely to already be factored into the decision-making calculus when the signatories decide to reach a deal, and so does not affect their commitment to peace. Implications from this theoretical argument are tested using unique data on the conflict behavior of the government and each of the rebel groups in internal armed conflicts during the post-Cold War period. The results are well in line with the theoretical expectations and show that whether an agreement leaves out some actor does not affect whether the signatories stick to peace. The results demonstrate that even when excluded rebel groups engage in conflict, this does not affect the signatories’ commitment to peace. Hence, the findings suggest that partial peace is possible.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department of a research project entitled Post-Conflict Transitions focusing on development issues particularly relevant to countries recovering from conflict. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted atdesiree.nilsson@pcr.uu.se@worldbank.org.

_The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at http://econ.worldbank.org/programs/ conflict ). An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._ Produced by the Research Support Team
**Table 5. Percent Underemployed by Quarter and Gender**

||**|**<br>**Ist**|**2nd**|**3rd**|**4th**|**Total**|
|---|---|---|---|---|---|
|Male|12.4|11.6|11.7|11.3|11.7|
|Female|12.5|12.9|13.3|13.2|13.0|
|**Total**|12.4|12.1|12.4|12.1|12.2|

There are two possible reasons why the data would not show seasonality if, in fact, such variation exists.7 First, while the data are broken up by quarter, the seasons may not actually follow this pattern, and peaks or troughs in labor demand may straddle quarters, confounding this distinction. Second, seasons may vary across regions in Indonesia, and so a peak demand in Sumatra, for example, might be counterbalanced by weak labor demand elsewhere.

Although the first problem cannot be corrected given the SAKERNAS data, it is possible to disaggregate to the regional and provincia **l** evel to see if seasonality emerges at these levels. Table 6 shows both the province and regional rates of underemployment. Although the data obviously show variation across provinces and regions, this variation shows no obvious seasonal pattern. In fact, the variation observed in levels of underemployment in adjacent provinces on any given island, where relevant conditions should be similar, seems more likely to be a statistical artifact than the result of any seasonal phenomenon.

The data in Table 6 identify several provinces with higher than average rates of underemployment. Underemployment in West Nusa Tenggara (NTB) averages almost 33 percent, fifty percent higher than the second highest rate recorded by Bali with 21.1 percent. While NTB's rate is not unexpected, the figure for Bali is certainly surprising and difficult to interpret; at the very least, the high underemployment rate reported for Bali reinforces some of the doubts expressed earlier in this paper about the assumed connection between underemployment and income insecurity. With its productive agricultural sector, thriving traditional industrial sector and booming tourist sector, Bali residents might be expected to have among the most diversified and stable income streams. The evidence from the SAKERNAS data raises fewer doubts about popular preconceptions of the Balinese economy than about the reliability of the data and the underlying connection between underemployment and income insecurity.

7 Of course, another possibility is that labor demand does not experience significant changes across the seasons. This alternative explanation certainly would be consistent with the data in Table 5 and with agricultural wage data published by BPS, which show no seasonal fluctuations; Kenneth Chomitz, among others, has suggested that widespread irrigation on Java **may** explain the absence of seasonality in labor demand there. Even so, the widespread perception of seasonality in labor demand is based on evidence provided by rural workers who describe seasonality in their work patterns.

9
# **2 A New Dataset for Capital Flows Research**

## **2.1 Data Construction**

What is commonly called “gross flows” in the literature is actually more accurately described as “net inflows” and “net outflows”. Net inflows are gross liability flows, net of repayments. Net outflows are gross asset flows, net of disinvestment. Capital flows data found in the BOP, which is based on residency principle, conform to this definition. Thus, although these measures are often called “gross”, they can be positive or negative. The separation of flows into asset and liability flows allows interpreting liability flows as net inflows from foreign agents, and asset flows as net outflows by domestic agents. This is the primary working definition of capital flows, which we use across all data sources for consistency.

The focus of this paper is on the differentiation of capital flows by source or destination sector in the domestic The term “sector” is used here to refer to institutional economy.<sup>9</sup> sectors: general government, central banks, depository corporations except the central bank (“banks”), and other sectors (“corporates”).<sup>10</sup>

### To build our dataset, we combine and harmonize several publicly available sources: Bal-

### ance of Payments (BOP) and International Investment Position (IIP) statistics of the Interna-

9Galstyan, Lane, Mehigan, and Mercado (2016) use data after 2013 from IMF’s CPIS to examine portfolio debt and portfolio equity stocks by the sectoral identity of the issuer and holder of the security. We focus on all the components of debt, that is the flow of portfolio debt and other investment debt by sector over a much longer time horizon in quarterly data. Cerutti and Hong (2018) use internal vintages of the BOP dataset in order to split portfolio equity flows by sector. They follow our work for decomposition of debt flows by sector, replicating some of our results, but use only data from BOP. Hence their sample is limited to 2003 onwards and 43 countries. Arslanalp and Tsuda (2014b) and Arslanalp and Tsuda (2014a) decompose sovereign/government loan and bond debt by creditor, both foreign and domestic. They employ QEDS data to split by foreign and domestic and BIS data to identify external bank lenders, similar to our approach but only for the sovereign starting 2005, where we consider all three sectors since 1996: banks, corporates and the sovereigns.

10It should be noted that the BOP category “other sectors” is broader than what is captured by the term “corporates”. Nevertheless, in most cases, there is fairly broad overlap between the two categories. That is why, in the rest of this paper, we use the two terms interchangeably for presentational convenience.

10
**Chart 1: Global Tariffs and Trade, 1985-2006**

<!-- Start of picture text --> %<br>70.0 80.0<br>Bound tariffs<br>60.0<br>75.0<br>trade/GDP,<br>right axis<br>50.0<br>70.0<br>40.0<br>65.0<br>30.0<br>60.0<br>20.0<br>Applied tariffs 55.0<br>10.0<br>0.0 50.0<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> Source: World Bank’s World Development Indicators, and WITS database 22
year and the previous year. We also examine a series of variables that adjust financial performance by netting fiscal transfers.

We take advantage of the fact that the database compiles operating and capital subsidies that SOEs receive from the government to adjust financial performance by netting such transfers.<sup>4</sup> We first create a measure of adjusted Total Comprehensive Income (Adj. Income), which subtracts total subsidies from Total Comprehensive Profit (Loss) for the year. We then use that measure of adjusted income to create a ratio that measures the adjusted return on average assets (AdjROAA).

# **4 Descriptive Analysis of Infrastructure State-Owned Enterprises**

# **_4.1 Size_**

Figure 1 depicts three proxies of SOE size for each country in the sample, averaged over the period 2009–18 (the period for which the panel is mostly balanced). It shows that infrastructure SOEs are indeed large. On average, total assets represent almost 18 percent of GDP—an extremely large share given that countries typically have only a handful of infrastructure SOEs, and our study did not cover all of them. This number masks a great deal of heterogeneity. Total assets account for as much as 82 percent of GDP in Bhutan and a mere 1.7 percent in Argentina. This variation likely reflects the sectoral distribution of SOEs in each country. Assets exceed 10 percent of GDP in most countries in the sample.

Panel b shows that the total liabilities of SOEs are also high. Average liabilities are about 8 percent of output and as much as 30 percent (Bhutan). Countries with large infrastructure SOE assets as a percentage of GDP also have large liabilities from infrastructure SOEs as a percentage of GDP. The high level of liabilities may just be an indication that infrastructure SOEs are large, but it can also be a red flag, a symptom of an underperforming SOE sector that, if unattended and persistent, could be a major source of fiscal risk. (We characterize SOE profitability below and discuss the sustainability of these companies.) Panel c shows that operating expenses of infrastructure SOEs as a share of GDP are high. This finding reflects the fact that most infrastructure projects are financed with public rather than private money and provides further evidence that infrastructure SOEs are large. The distribution of countries is similar in all three measures of size, suggesting some robustness. It also suggests that our panel is well balanced for large firms in the sample. These numbers are extremely large 4 Operations subsidies adds up all the subsidies the SOE received for operational expenses. It includes both extraordinary and recurrent subsidies to cover overall losses or quasi-fiscal operations, as disclosed by the SOE, including subsidies reported as revenues by the SOE. Capital Subsidies is the amortized amount of deferred income from capital assets granted to the SOE by the government. If not reported as a separate item in the income statement, the figures were inferred from the deferred income notes.

6
**<u>Table 1: Descriptive Statistics at Household Level</u>**

|||**By land**|**market partic**|**ipation**|
|---|---|---|---|---|
||**Total**|**Lease-out**|**Autarkic**|**Lease-in**|
|**Household characteristics**|||||
|Household size|5.81|4.55|6.47|6.38|
|Number of dependents|2.51|2.05|2.62|2.82|
|Number of male adults|1.61|1.06|1.91|1.86|
|Number of female adults|1.62|1.37|1.86|1.64|
|Female head dummy|0.22|0.47|0.17|0.03|
|Head literate|0.33|0.25|0.30|0.43|
|Farming is head's primary activity|0.76|0.46|0.87|0.95|
|Dwelling has corrugated iron roof|0.74|0.69|0.68|0.83|
|<br>Number of rooms in dwellings|2.14|1.91|2.20|2.29|
|**Agricultural endowments**|||||
|<br>Total value of livestock owned (birr)|5712.10|2442.70|6081.68|8337.60|
|Value of bulls and oxen (birr)|2495.61|771.34|2661.17|3905.30|
|Value of other livestock (birr)|3216.49|1671.36|3420.52|4432.30|
|Number of bulls and oxen owned|1.29|0.42|1.40|1.98|
|Value of farm assets (birr)|160.35|78.79|152.68|240.13|
|<br>Total owned area (ha)|1.27|1.35|1.31|1.18|
|Share of good quality land|0.64|0.63|0.76|0.54|
|**Agricultural production**|||||
|Cultivates any land|0.88|0.63|1.00|1.00|
|Owned cultivated area (ha)|0.98|0.45|1.30|1.18|
|<br>Area of sharecropped out land (ha)|0.28|0.85|0.00|0.00|
|Area of fixed rented out land (ha)|0.02|0.04|0.00|0.00|
|Area of sharecropped in land (ha)|0.29|0.00|0.00|0.78|
|<br>Area of fixed rented in land (ha)|0.03|0.00|0.00|0.07|
|Area of leased out land (ha)|0.29|0.89|0.00|0.00|
|<br>Area of leased in land (ha)|0.31|0.00|0.00|0.85|
|**Assets and social capital**|||||
|Value of household assets (birr)|300.32|264.51|274.26|354.72|
|House value (birr)|7382.48|6560.25|8901.85|6827.77|
|No. of male adult children living in the village|0.41|0.61|0.41|0.22|
|<br>No. of female adult children living in the village|0.33|0.48|0.32|0.20|
|<br>No. of head's brothers living in the village|1.03|0.80|1.02|1.24|
|<br>No. of spouse‟s brothers living in the village|0.65|0.54|0.69|0.72|
|<br>No. head's sisters living in the village|0.69|0.56|0.68|0.81|
|No. spouse's sisters living in the village|0.51|0.41|0.54|0.57|
|<br>No. of adult children living in the village|0.74|1.09|0.73|0.42|
|No. of brothers and sisters livingin the village|2.88|2.31|2.94|3.33|
|Number observations|2487|813|770|904|

_Source:_ Own computation from AU/WB/UG land tenure survey.

17
cost difference is further exacerbated by the quality of product as reflected in reject rates of >3% shows Kenyan firm’s performance. The example discussed here clearly illustrates that geography alone is not driving Kenyan apparel industry’s lower competitiveness in US markets; nor is lower labor costs compensating for other overriding constraints.

# **b. Lesotho: Apparel**

The signing of AGOA has resulted in rapid growth of the textile and apparel industry in Lesotho. The growth rate has been helped by the entry of Asian firms, to the point where the sector now contributes a huge share of overall output. What we are witnessing now is a process of growth led infrastructure as Chinese and Taiwanese investors demand improved infrastructure in transport, energy and water, all necessary for profitable operations in the sector. As a result, the government is beginning to address these issues with the help of donors.<sup>31</sup> The textile and garments sector in Lesotho faces a double challenge: overcoming increased competition after the phasing out of the MFA in 2005, and dealing with the scheduled 2007 expiration of the special dispensation provision in AGOA. Right now, Lesotho is able to sell duty-free in the American market while at the same time buying cheap fabric from Asia. It is not clear if the sector will be able to survive past the 2007 expiration.<sup>32</sup> It is also not sure what will happen to the Taiwanese investors when they are no longer able to reap the benefits of the current arrangement.

<!-- Start of picture text --> Figure 14: Value Added of African Textile Exports<br>to the U.S. under AGOA<br>5.41<br>6<br>5 4.08<br>3.79 3.64 3.64 3.47<br>4<br>3<br>2<br>1<br>0<br>Mauritius Madagascar Lesotho South Africa Kenya Malawi<br>US$ Value per m2<br><!-- End of picture text --> <u>Source:</u> _Value Chain Analysis of Selected Sectors in Lesotho_ , Consultant Report prepared for the World Bank, 2004

> 31 Stephen Gelb, “South-South Investment,” p. 204

> 32 FIAS, “Competitiveness and Corporate Social Responsibility in Lesotho’s Apparel Industry” _World Bank Group_ , June 2006

20
Using World Bank survey data including about 10,000 households in five countries -- Burkina Faso, Kenya, Nigeria, Senegal and Uganda – we find that receiving international remittances increases the probability that the household opens a bank account in all the countries in our study. This result is robust to controlling for the potential endogeneity of remittances, using as instruments indicators of the migrants’ economic conditions in the destination countries.

Our paper is related to an emerging literature on remittances and financial inclusion. Using household level data from El Salvador, Anzoategui et al. (2014) show that remittances have a positive effect on the likelihood that households have a bank account. Relatedly, Ashraf et al. (forthcoming) find that migrants from El Salvador save more in the home country when offered accounts providing greater ability to monitor and control savings. On the other hand, Brown et al. (forthcoming) provide household level evidence that remittances have a negative, and at best no, effect on the propensity of having a bank account in Azerbaijan, while finding a positive and significant, but very small size, effect in Kyrgyzstan.

The rest of the paper is organized as follows. Section II discusses the data used and the empirical methodology implemented. Section III presents the results, while section IV concludes.

# **II. Data and Methodology**

Data used in the paper come from household surveys conducted by the World Bank in 2010 in six Sub-Saharan African countries: Burkina Faso, Kenya, Nigeria, Senegal, South Africa, and Uganda. We use survey data for the first five countries since South Africa is regarded as a remittances sending country.

4
<!-- Start of picture text --> Percentile KY<br>mam 60-70 aos t<br>mm 70-80 ~“GSi.- jf } }<br>90-100 “wf <Nova J”<br>Bethe 2,<br><!-- End of picture text --> <!-- Start of picture text --> ~y EGO<br><!-- End of picture text --> <!-- Start of picture text --> \<br><!-- End of picture text -->
**B. HOUSEHOLD INFORMATION**

# **ENUMERATOR READS: FOR THE PURPOSE OF RESEARCH ONLY, I WOULD LIKE TO ASK YOU SOME BASIC INFORMATION ABOUT YOUR HOUSEHOLD.**

B1_housetype Type of dwelling of the household [Enumerator to record]

1. Building – Flat up to 4 stories

2. Building- Flat multiple storied ( more than 4 storied) 3. Buiding- one storied single house 4. Building- multi-storied single house 5. Tin-shed single unit house 6. Tin-shed Terrace house 7. Katcha one unit house 8. Katcha terrace house <u>9. Other (specify)</u> B2_houseown Considering the unit where you live, do you:

1. Own it

2. Live in it without paying rent 3. Pay rent to live in it 4. Pay a subsidized amount of rent to live in it 5. Other (specify) 888. Don’t know <u>999. Refused</u> B3_land Other than your homestead, how much land does your household own

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

B4_mainincome What is the main source of the household’s income?

1. Self-employed / family business (agricultural work)

2. Self-employed / family business (non-agricultural work) 3. Paid employment 4. Pension, dividends, interest, house, rent, property income 5. Other (specify) 888. Don’t know <u>999. Refused</u> B5_avgincome_range In what range does the household’s monthly average income fall?

1. Less than 5,000 Taka 2. 5,000-10,000 Taka 3. 10,000-20,000 Taka 4. 20,000-30,000 Taka 5. 30,000 -40000 Taka

6. 40000-50000 Taka 7. 50000-60000 Taka 8. 60000- 70000 Taka 9. 70000- 80000 Taka 10. Above 80000 Taka 888. Don’t know <u>999. Refused</u>