rafmacalaba/datause-encoder-data huggingface dataset:
sustainability. In 2017, the median EMBI spread stood at 310 basis points over US bonds, down from a decade‐high in 2016 of 414 basis points. It is currently around 340 basis points.

In this context, the Global Economic Prospects Report (June 2018) highlighted the need for EMDE policymakers to rebuild monetary and fiscal policy buffers and be prepared for rising global interest rates and possible episodes of financial market turbulence. The report projects a significant deterioration in the fiscal sustainability gap driven by a 1‐standard deviation interest rate increase, particularly for Latin America and the Caribbean (LAC), AFR, and ECA (about ‐1.5%).

# 3.2. Growth of the banking systems, elevated non‐performing loans, and strengthening of capital buffers

Banking system assets and credit across EMDEs have been growing relative to GDP in the last decade ‐‐ particularly larger systems have expanded continuously and reached all‐time highs (Figure 3, A‐D). The median bank assets to GDP ratio stood at 32.5% in 2007 and has increased to 36.5% in 2017 although it has fallen from a peak of 41.6% in 2014. However, importantly, the largest systems (75<sup>th</sup> percentile) have seen continued growth in bank assets which currently stands at an all‐time high of 64.7%, up from 57.5% in 2007. Bank credit to the private sector relative to GDP has exhibit a similar growth pattern. At 34% in 2017, median private credit to GDP is near its highest since 2000. For the larger systems private credit stands at 54.4% of GDP up from 48.9% in 2007.

At the same time, bank capital buffers have continued to strengthen in the last decade (Figure 3, E and F). Non‐performing loans increased since the crisis, remained stable, and only improved in 2017. However, poor performers seem to face debt overhang issues (Figure 4, A and B). Median non‐performing loans (NPLs) as a fraction of total gross loans increased to 5.5% during the crisis and only came down in 2017 to 4.2%. Countries with larger NPL problems (75<sup>th</sup> percentile) have seen a slight improvement since the crisis, but remain relatively high at 10%, suggesting debt overhang problems. Notwithstanding important caveats regarding NPL data integrity and cross‐ country comparability, median regulatory capital to risk‐weighted assets has steadily increased 19
# **1 Introduction**

In an effort to promote urbanization, the Chinese government began to upgrade counties to become county-level cities between the 1980s and up until 1997. Counties were eligible to graduate into county-cities if they met certain minimum requirements (Ministry of Civil Affairs, 1993). An English version of this policy document can be found in Zhang and Zhao (1998). To become cities, counties needed to show that (1) their levels of industrialization were above certain thresholds, (2) the share of population engaged in non-agricultural activities was above a certain threshold, and (3) its fiscal status (as measured by total fiscal revenues and per-capita fiscal revenue) were in good shape – see Table 1 for the full list (taken from Fan, Li, & Zhang, 2012).

**Table 1: County to county-level-city minimum upgrading requirements**

|**Population Density (p**|**erson/km**<sup>**2**</sup>**)**|**>400**|**100-400**|**<100**|
|---|---|---|---|---|
|**Percentage of**<br>**counties in this**<br>**category**||25%|45%|30%|
|**Industrialization**<br>**level**|Industrial output (_yuan_)<br>Share of industrial output value<br>in gross value of industrial and<br>agricultural output|1.5 billon<br>80%|1.2 billion<br>70%|0.8 billion<br>60%|
|**Population engages**<br>**in non-agricultural**<br>**activities**|Size of non-agricultural<br>population|150 K|120 K|100 K|
||Share of non-agricultural<br>population in totalpopulation|30%|25%|20%|
|**Fiscal Strength**|Fiscal revenue (_yuan_)<br>Per capita fiscal revenue(_yuan_)|60 million<br>100|50 million<br>80|40 million<br>60|

In reality, these three requirements were not strictly enforced, partly because of large regional disparities across the country, wherein even after accounting for factors such as population density, counties in western and inland regions would still have trouble meeting these requirements. Instead, it seems that the decision to upgrade a county to a county-level city was based on rates of economic growth as well as the central government’s discretion (Li, 2011). Thus, this led to a situation where counties that did not meet any of the three requirements were upgraded, while some counties that met all three requirements were not upgraded – see Table 2 (taken from Fan, Li, & Zhang, 2012).

2
Table 1: Household Expenditure Surveys for Core Sample

|**Country**|**Code**|**Survey**|**Year**|**GDPper capita**|**Sample size**|**Nb items/HH**|
|---|---|---|---|---|---|---|
|**Benin**|BJ|EMICOV|2015|828|19871|32|
|**Bolivia**|BO|ECH|2004|1658|9149|49|
|**Brazil**|BR|POF|2009|10595|56025|41|
|**Burkina Faso**|BF|EICVM|2009|563|8404|152|
|**Burundi**|BI|ECVM|2014|245|6681|90|
|**Cameroon**|CM|ECAM|2014|1400|10303|81|
|**Chad**|TD|ECOSIT|2003|572|6697|94|
|**Chile**|CL|EPF|2017|14749|15239|129|
|**Colombia**|CO|ENIG|2007|5999|42373|60|
|**Comoros**|KM|EDMC|2013|1373|3131|82|
|**Congo DRC**|CD|E123|2005|301|12098|107|
|<br>**Congo Rep**|CG|ECOM|2005|2569|5002|85|
|<br>**Costa Rica**|CR|ENIGH|2014|8994|5705|68|
|**Dominican Rep**|DO|ENIGH|2007|5121|8363|88|
|**Ecuador**|EC|ENIGHUR|2012|5122|39617|89|
|**Eswatini**|SZ|HIES|2010|4169|3167|44|
|**Mexico**|MX|ENIGH|2014|9839|19479|61|
|**Montenegro**|ME|HBS|2009|6516|1223|149|
|**Morocco**|MA|ENCDM|2001|2095|14243|90|
|**Mozambique**|MZ|IOF|2009|416|10832|221|
|**Niger**|NE|ENCBM|2007|330|4000|192|
|**Papua NG**|PG|HIES|2010|1949|3810|111|
|**Peru**|PE|ENAHO|2017|6315|43545|78|
|**Rwanda**|RW|EICV|2014|690|14416|54|
|**Sao Tome**|ST|IOF|2010|1095|3545|100|
|**Senegal**|SN|EDMC|2008|1278|2503|299|
|**Serbia**|RS|HBS|2015|6155|6531|105|
|**South Africa**|ZA|IES|2011|7455|25328|44|
|**Tanzania**|TZ|HBS|2012|788|10186|318|
|**Tunisia**|TN|ENBCNV|2010|4142|11281|139|
|**Uruguay**|UY|ENIGH|2005|9079|7043|77|

This table lists alphabetically the 31 countries in the core sample, the survey names and years. GDP per capita is in PPP USD in the year of the survey, obtained from the World Bank Development Indicators. Code refers to the country acronym which we use in the figures. The sample size refers to the number of households in the survey, and the number of items reported is the number of expenditure items reported on average across all households in the survey.

46
may also allow firms that were paying lower wages to increase the wages they offer once they start exporting. In this case, rising tradeable goods prices may reduce inequality.

<mark>Our theoretical framework combines the Melitz (2003) heterogeneous firms with the Salter-Swan model.</mark><sup>5</sup> <mark>The Salter-Swan model has been applied to developing countries in which non-traded (or semitraded) goods play a significant role in the economy. Thierfelder and Robinson (2003), for example,</mark> <mark>illustrate how the presence of semi-traded goods can reverse the Stolper-Samuelson predictions. Our goal is to identify how the relationship between the traded and non-traded sector can affect inequality, which makes the Salter-Swan model appropriate. The demand and production structure outlined below are followed by the solution of the model. The solution generates predictions about the relationship between inequality and the relative price of tradeable goods.</mark>

## A. <mark>Demand</mark>

<mark>Consider an economy with three goods. The first (</mark> _<mark>a</mark>_ <mark>) is produced by heterogeneous firms and can be exported or sold domestically. The second is a non-traded good (</mark> _<mark>b</mark>_ <mark>) that is produced domestically by homogeneous firms. The third (</mark> _<mark>m</mark>_ <mark>) is imported. Consumers consume all but exports, so that utility is a function of the remaining goods. All goods are imperfect substitutes in consumption. Consumers maximize utility subject to their budget constraint, which is a function of the prices of each kind of consumed good.</mark>

# (1) max _U_ ( _a_ , _b_ , _m_ ) s.t. _B_ ( _y_ , _pa_ , _pb_ , _pm_ )

Workers each have one unit of labor that they inelastically supply to the market. The variable _y_ represents the income of a representative worker.

The price of the non-traded good _pb_ is taken to be numerairre. <mark>As in many trade models, the SalterSwan model assumes that the economy is small, which implies that import and export prices (</mark> _<mark>P</mark>_ m <mark>and</mark> _<mark>P</mark> a_ <mark>) are exogenous and therefore can be represented with a composite traded goods price</mark> _<mark>P</mark> t_ _<mark>= (P</mark> a_ _<mark>/P</mark>_ m <mark>). This price</mark>

> 5 Salter (1959) and Swan (1960).

7
|Table 6. Effective|residenti|al tariffs b|ylevel of|consumpti<br>Level|on(cents)<br>of consum|ption(kW|h/month)||||
|---|---|---|---|---|---|---|---|---|---|---|
||**50**|**75**|**100**|**150**|**200**|**300**|**400**|**450**|**500**|**900**|
|Benin|12.6|13.3|13.6|14.0|14.1|19.7|22.5|23.5|24.2|27.2|
|Botswana|9.1|8.0|7.5|6.9|6.7|6.4|6.3|6.2|6.2|6.0|
|Burkina Faso|20.6|20.2|20.0|19.9|19.8|20.1|20.3|20.4|20.4|20.6|
|Cameroon|8.6|10.9|10.9|10.9|10.9|12.0|12.0|12.0|12.0|12.0|
|Cape Verde|23.6|25.1|25.8|26.5|26.9|27.3|27.4|27.5|27.5|27.7|
|<br>Chad|22.9|27.3|30.0|32.7|34.1|35.4|36.1|36.3|36.5|37.2|
|Congo, Dem.|4.0|4.0|4.0|4.0|4.0|3.9|3.9|3.9|3.9|5.5|
|Congo, Rep. of|21.1|17.7|16.0|14.3|13.5|12.6|12.2|12.1|12.0|11.5|
|Côte d’Ivoire|9.6|11.1|11.9|12.6|13.0|13.4|13.6|13.6|13.7|13.9|
|Ethiopia|3.9|4.1|4.1|5.3|5.6|6.1|6.2|6.4|6.6|7.2|
|Ghana|8.7|8.4|8.2|8.0|7.9|7.8|9.1|9.6|9.9|11.8|
|Kenya|8.4|12.7|14.8|16.9|18.0|19.1|19.9|20.1|20.4|21.2|
|Lesotho|7.2|7.2|7.2|7.2|7.2|7.2|7.2|7.2|7.2|7.2|
|Madagascar|6.0|4.0|3.0|2.0|1.5|1.0|0.7|0.7|0.6|0.3|
|<br>Malawi|4.8|4.3|4.0|3.8|3.7|3.6|3.5|3.5|3.5|3.4|
|Mali|26.6|26.6|26.6|26.6|26.6|28.1|28.8|29.1|29.3|30.0|
|Mozambique|9.6|7.7|6.8|7.4|7.7|9.0|9.6|9.8|10.0|10.9|
|<br>Namibia|11.7|11.7|11.7|11.7|11.7|11.7|11.7|11.7|11.7|11.7|
|Niger|14.5|14.2|14.1|13.9|13.9|13.8|13.7|13.7|13.7|13.7|
|Nigeria|2.5|3.8|3.4|3.8|4.2|4.9|5.3|5.4|5.6|6.0|
|Rwanda|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|14.6|
|Senegal|23.8|23.8|23.8|23.8|24.2|24.8|25.1|25.2|25.3|25.7|
|South Africa|—|2.4|3.6|4.8|5.4|6.0|6.3|6.4|6.5|6.8|
|Sudan|—|—|—|—|—|—|—|—|—|—|
|Tanzania|3.2|5.5|6.7|7.9|8.5|9.0|8.8|8.8|8.8|8.6|
|Uganda|19.5|20.7|21.4|22.0|22.3|22.6|22.8|22.8|22.9|23.1|
|Zambia|4.2|3.3|2.9|2.4|2.2|2.0|2.1|2.1|2.1|2.5|

# _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

_Notes:_ Average residential consumption level is highlighted in bold — Not available.

15
# **2. EPZs in Africa**

Although several African countries launched EPZ or free zone programs in the early 1970s – Liberia in 1970, Mauritius in 1971, and Senegal in 1974 – most African countries did not operationalize programs until the 1990s or 2000s<sup>4</sup> . Table 1 provides a broad overview of the African zone programs initiated in each decade since the 1970s. It indicates nearly 30 countries in the region have programs (60% of all countries), with more than 80% of them starting only within the last two decades.

So, most African countries are relative latecomers to economic zones. This has several important implications when considering their success to date. First, few zones see rapid growth in their early years. Even the most successful zones generally grew slowly in their initial five to ten years, later shifting to an exponential growth curve before eventually reaching maturity and experiencing slowing growth. Thus for many African zones programs it may be too early to pronounce on their success or failure or to anticipate it from their initial growth patterns. Second, the macro environment in which these zone programs have been developed differs substantially from that experienced by zones setting up in Asia and Latin America during the 1970s and 1980s. Specifically, most African zones were established during and after the rise of Asia as a manufacturing superpower, and the subsequent structural shift in trade and FDI patterns. As such the level and nature of competition for traditional manufacturing “export platform” FDI is a significant factor that may hinder the speed and scale of growth potential for African zones.

**Table 1: Overview of African zone programs by decade of launch**

|**1970s**|**1980s**|**1990s**|**2000s**|
|---|---|---|---|
|Liberia<br>Senegal<br>Mauritius|Djibouti<br>Togo|Burundi<br>Cameroon<br>Cape Verde<br>Equatorial Guinea<br>Ghana<br>Kenya<br>Madagascar<br>Malawi<br>Mozambique<br>Namibia<br>Nigeria<br>Rwanda<br>Seychelles<br>Sudan<br>Uganda<br>Zimbabwe|Gabon<br>Gambia<br>Mali<br>South Africa<br>Zambia<br>Eritrea<br>Mauritania<br>Tanzania|

Source: FIAS (2008) with author’s additions / amendments According to FIAS (2008), a total of 114 zones exist in Sub‐Saharan Africa – this represents somewhere between 3% (based in ILO data) and 4.5% (based on FIAS data) of the total number of global zones. This

> 4 Both the Liberia and Senegal programs became dormant and are in the process, as of 2010, of being overhauled and relaunched.

4
it took him between one to three months to get the information he needed to form SHGs. “I remember going to a village once where for several months no one would speak to us. The environment was extremely tense, but no one would tell us what was going on. It took six months of probing in and around the village to figure it out: We had happened to enter the village at a moment when a twenty-five-year-long feud between two dominant caste groups over a piece of land had reach its culmination and both groups were waging war against each other. The entire village was split up on the issue. We immediately took note of this and knew that village-wide meetings were impossible, as were inter-caste SHGs, at least in the initial stages. We had to find an alternative strategy.”<sup>14</sup> There were insights gained in this stage that were specific to each village, but some that were universal as well. All ACs and CCs, for instance, mentioned three things that they learned early on. First, they understood that they were about to operate in a context with an extremely high density of failed projects that had microcredit components similar to JEEViKA. As a result, there was a strong mistrust towards such projects and villagers were skeptical of putting their savings into them. Second, the facilitators learned that a long history of identity politics had created a culture of ‘caste-based handouts’: People expected to be treated as beneficiaries, and expected more if they belonged to particular castes. Finally, they learned that the villagers’ closest point of reference for outsiders coming to collect demographic data was the census officers who were derided for gathering all their information by talking only to key stakeholders and only stopping by the first few houses in the village. As we will see later, the project’s messaging during the initial mobilization stages rested heavily on disassociating itself from these three things.

Once information was gathered, an ongoing conversation between facilitators and key stakeholders began in Phase I villages, in particular between local leaders from traditional caste panchayats and elected Panchayati Raj Institution (PRI) members ( _Mukhiya_ , _Sarpanch_ , _Panch_ , and ward members.)<sup>15</sup> One of the DTOs gives an example, “...In a few villages in Purnea district, for instance, we were not offered a room to stay in

> 14 DTO and former AC, Cycle 3, Ramganj.

> 15 Mukhiya is the elected leader/president and the Sarpanch is the judicial head of the Gram Panchayat. The ward members and Panchs report to the Mukhiya and Sarpanch respectively.

17
**Table 2: Urban access, Cash crop potential and Non-farm Employment**

|Dependent variable: Type of|Ma|rginal effects||
|---|---|---|---|
|Employment in Non-Farm|Multinomi|al Logit regres|sions|
|sector|(1)|(2)|(3)|
|**_Wage employment-Low Return_**||||
|Cash Crop Suitability Index|-0.012||-0.014|
||(1.84)||(2.02)*|
|Distance to Major cities (log)||0.003|0.007|
|||(0.40)|(0.78)|
|Distance to towns (KM)|-0.0002|-0.0001|-0.0002|
||(0.84)|(0.78)|(0.91)|
|**_Wage employment-High Return_**||||
|Cash Crop Suitability Index|-0.044||-0.032|
||(3.59)**||(2.56)*|
|Distance to Major cities (log)||-0.056|-0.048|
|||(4.76)**|(4.10)**|
|Distance to towns (KM)|-0.001|-0.001|-0.001|
||(1.79)|(1.59)|(1.66)|
|**_Self-Employment_**||||
|Cash Crop Suitability Index|-0.040||-0.034|
||(2.80)**||(2.32)*|
|Distance to Major cities (log)||-0.031<br>(2.79)**|-0.023<br>(2.04)*|
|Distance to towns (KM)|-0.0004|-0.0003|-0.0004|
||(1.27)|(1.05)|(1.20)|
|Loglikelihood<br>|-7249|-7247|-7215|
|Pseudo R<sup>2</sup>|0.09|0.09|0.1|

All regressions include individual characteristics (gender, education, age), household characteristics (size, land ownership, asset ownership, electricity connection), village characteristics (if village has electricity, NGO programs, credit programs, market) and an intercept term as regressors. Robust z statistics in parentheses Significant levels: * = 5%, ** = 1%
# B. Classification of “GVC products” in product‐level trade data

The GVC product codes in disaggregated trade data<sup>6</sup> are compiled from lists generated by Athukorala (2010)<sup>7</sup> and Sturgeon and Memedov (2011). The first list by Athukorala contains the parts and components in the UN Broad Economic Classification (BEC) Registry and the product list of the WTO Information Technology Agreement. The author then used firm‐level surveys from Thailand and Malaysia to refine the list. The registry has been used a number of times to measure GVC trade, as in a recent paper by Wignaraga, Kruger and Tuazon (2013). The second list by Sturgeon and Memedov (2011) has broader sectoral coverage. Using the BEC registry, the authors combined capital and consumption goods into a single “final goods” category and then isolated “differentiated, customized, product‐specific” intermediates.

To get the broadest measure of GVC products, we combine the two lists of GVC codes.<sup>8</sup> In total, the Athukorala’s list provides 500 product codes and the Sturgeon and Memedov’s list provides 860 product codes. Duplicate codes were removed, resulting in 1,236 product codes. Table 2 shows the breakdown of the 1,236 GVC products by sector. Our list spans 12 key manufacturing product categories: chemicals, plastic/rubber, hides/skins (which includes leather goods), wood, textiles, clothing, footwear, stone/glass, metals, machinery/electronics, transport (which includes rail, road vehicles, aircraft and ships), and miscellaneous manufactures (which includes furniture). Two GVC sectors – wood and chemicals – comprise only one product. As shown in Annex Table A3, global trade in electronics, transport, clothing and footwear largely takes place mainly within the context of GVCs and seldom outside that context (93 percent of footwear and 88 percent of clothing exports are traded as part of GVCs).

**Table 2: Number of GVC and Non‐GVC Product Codes by Sector**

|**HS Category**|**GVC**|**Non‐GVC**|**Total**|
|---|---|---|---|
|01‐05 Animal|0|193|193|
|06‐15 Vegetable|0|312|312|
|16‐24 Foodstuffs|0|178|178|
|25‐27 Minerals|0|147|147|
|28‐38 Chemicals|1|735|736|
|39‐40 Plastic/Rubber|12|177|189|
|41‐43 Hides,Skins|5|58|63|
|44‐49 Wood|1|218|219|
|50‐60 Textiles|320|194|514|
|61‐63 Clothing|233|58|291|
|64‐67 Footwear|42|13|55|
|68‐71 Stone/Glass|6|181|187|

> 6 We use importer records tabulated from UN Comtrade considered to be more accurate than the corresponding exporter records.

> 7 Athukorala, Prema‐Chandra (2010). “Production Networks and Trade Patterns in East Asia: Regionalization or Globalization” ADB Working Paper Series on Regional Economic Integration, No. 56.  This was compared to another set of products coded by Sturgeon and Memedovic (2011). Athukorala coded a greater number of network products, although mainly focused on parts and components. There was only minor overlap between the two coding systems.

> 8 The original lists by Athukorala in HS 1996 nomenclature and Sturgeon and Memedov in SITC Revision 3 nomenclature were converted to a common HS1988/92 nomenclature using a concordance table.

8
# **Table 5: Is it always conservative to ignore the method of randomization?** **_Proportion of replications where controlling for stratum or pair dummies lowers the p-value on a test of difference in means between treatment and control groups_**

||Stratified<br>on 2<br>variables|Stratified<br>on 4<br>variables|Pairwise<br>Greedy<br>Matching|Big<br>Stick<br>Rule|Draw with<br>minmax<br>t-stat|
|---|---|---|---|---|---|
|**Panel A: Sample Size 30**||||||
|Microenterprise profits (Sri Lanka)|0.690|.|1.000|0.493|0.555|
|Child schooling (Indonesia)|0.373|.|0.699|0.567|0.854|
|Household expenditure (Indonesia)|0.622|.|1.000|0.523|0.657|
|Labor income (Mexico)|0.820|.|1.000|0.546|0.773|
|Height z-score (Pakistan)|0.579|.|1.000|0.537|0.825|
|Math test score (Pakistan)|0.684|.|1.000|0.522|0.740|
|**Panel B: Sample Size 300**||||||
|Microenterprise profits (Sri Lanka)|0.668|0.731|1.000|0.526|0.689|
|Child schooling (Indonesia)|0.705|0.634|1.000|0.506|0.674|
|Household expenditure (Indonesia)|0.869|0.733|1.000|0.522|0.738|
|Labor income (Mexico)|0.874|0.712|1.000|0.525|0.725|
|Height z-score (Pakistan)|0.860|0.655|1.000|0.522|0.754|
|Math test score(Pakistan)|0.882|0.735|1.000|0.533|0.776|

Notes: Statistics are based on 10,000 simulations of each method. Details on methods and variables are in Table A2.