rafmacalaba/datause-encoder-data huggingface dataset:
We define episodes of debt distress as periods in which any one or more of the three following conditions hold: (a) the sum of interest and principal arrears is large relative to the stock of debt outstanding, (b) a country receives debt relief in the form of rescheduling and/or debt reduction from the Paris Club of bilateral creditors, or (c) the country receives substantial balance of payments support from the IMF under its nonconcessional Standby Arrangements or Extended Fund Facilities (SBA/EFF). The first condition is the most basic measure of debt distress: the inability to service external obligations resulting in an accumulation of arrears. But countries that are unable to service their external debt need not necessarily fall into arrears; they can also obtain balance of payments support from the IMF and, in addition, seek debt rescheduling or debt reduction from the Paris Club. The Paris Club assesses the “extraordinary external financing needs” of countries based on balance of payments projections set within a macroeconomic framework agreed as part of an IMF program, and takes into account all other sources of external financing available to the country. This paper does not define debt reductions under the HIPC Initiative as a separate indicator of debt distress, because all debt relief under the Initiative requires parallel debt reduction by the Paris Club on Naples terms (which involves a reduction in the debt of Paris Club members by 67 percent in present value terms).

Data on arrears and debt are taken from the World Bank’s Global Development Finance (GDF) publication. The arrears data consist of arrears to all creditors (official and private), and refers only to arrears on long-term debt outstanding. We obtain data on commitments under SBA/EFF programs from the electronic version of the IMF’s International Financial Statistics. Finally, we have compiled a comprehensive list of Paris Club relief episodes from data provided to us by the Paris Club. Our sample of countries consists of all low and midde-income countries reporting debt data in the GDF. As will be clearer below, we will select our explanatory variables for debt distress episodes to ensure that as many low-income countries as possible remain in our sample.

To implement our rule for identifying debt distress eposides, we need to identify thresholds for “large” values of arrears and “substantial” levels of IMF support. Our 8
We combine data on built-up area with information on agro-ecological zones to capture the wide variety of landscapes and agricultural potential that characterize Ghana.<sup>5</sup> We divide this information into two variables, which do not vary over time. The first of these variables characterizes the humidity of a particular region, i.e., whether moist/dry, semi-humid, or humid. In Ghana as in the rest of West Africa, the rains follows a monsoon pattern and rainfall generally decreases along a North-South gradient, with the North being drier and the South being more humid. There are exceptions to this general pattern, however – the immediate coastal area, for instance, tends to be drier than inland. In the data, humid soils are associated with the entire valley of the Volta river and with the forested region North of Takoradi.

The second agro-ecological variable we construct captures soil quality – going from poor to moderately good to good. There are two additional categories: hydromorphic soil, which is characterized by excess moisture which tends to suppress aerobic factors in soil-building; and soils unsuitable for buildings or agriculture, such as steep soils or soils covered by standing water at least part of the year – these soils require special investments (e.g., terassing, drainage) in order to become usable. Given that Ghana is relatively flat with no mountains and a relatively homogenous physical landscape, we abstract from altitude: whatever variation in altitude there may be, it translates into variation in rainfall and soil quality and is already captured. Information on agro-ecological zones comes from Global Agro-Ecological Zones (GAEZ) 2012 data from FAO. The GAEZ2012 dataset has a resolution of 10km which was overlaid on our square pattern in order to derive relevant values for each cell.

To this data we add longitude and latitude information expressed in decimal degrees, which we normalize to have mean zero and unit variance. A higher value of longitude means more to the West; a higher value of latitude means more to the North. Population density in Ghana is in general higher in the humid South than in the drier North, largely due to the higher agricultural potential and better soils in the South. But there is a lot of local variation, something we aim to capture with our set of agro-ecological and geographical variables.

We also have information about the presence on a cell of a land border, sea border, or large body of water. We drop from the _yit_ database of all the cells outside Ghana, as well as those that are entirely covered by water, since they cannot be built on. This includes lake Volta, a large

> 5The agro-ecological information is missing for some of the cells located at the edges of the map.

7
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

# **APPENDIX E IMPACT OF REFORMS ON INDIVIDUALS**

Considerations on pension benefit adequacy, distributional and equity issues are as important in the assessment of pension reform options as considerations on financial sustainability, affordability and fiscal impact. Individuals with four different profiles representing the four different groups of employees were modeled to compare how they would fare under each of the reform options presented in the paper: (1) a pure DC reform (referred to in the paper as Reform I), and (2) a hybrid reform (referred to as Reform II). Since in funded DC schemes benefits are very sensitive to the interest rate earned on individual accounts, variants with 5% and 3% real rate of return on assets were considered for each reform option.

The study targeted individuals of different gender and – within each gender group – with different job entry ages, income level, earning profile, and mortality rates. The specific individual profiles were selected in consultations with and based on the data provided by our Ugandan counterparts from the MPS. Four types of individuals specified by entry age, income level, and life expectancy were considered for each gender.

- 1) Lifetime low income worker (approximating the average career of a **Teacher** or **Policeman** ):

- a) Starts working at age 18 and retires at 60;

- b) Wage starts at about 50% of the age/gender cohort average and sustains that ratio throughout the person’s career;

- c) Individual’s life expectancy is assumed to be lower than for the respective age and gender group of the general population by applying 110% adjustment coefficient to mortality rates.

- 2) Lifetime average income worker (approximating the average career of a **Head Teacher** ):

- a) Starts working at age 20 and retires at 60;

- b) Earns age/gender cohort average wage throughout his/her career;

- c) Individual life expectancy is assumed to be equal to the respective age and gender group’s average.

- 3) Lifetime high income worker (approximating the average career of a **Commissioner** ):

- a) Starts working at age 25 (after getting higher education) and retires at 60;

- b) Wage starts at about 150% and increases to about 250%  of the age/gender cohort average by the end of the person’s career;

- c) Individual life expectancy is assumed to be higher than for the respective age and gender group of the general population by applying 90% adjustment coefficient to mortality rates.

- 4) High income worker with a steep earning profile (approximating the average career of a **Permanent Secretary** ):

- a) Starts working at age 20 and retires at 60;

- 100 -
**Figure 7: Total labor content per $100 of gross exports across sectors, South Africa 2001 vs. 2011**

<!-- Start of picture text --> PubAdmin/Defence/Health/Educat<br>Wearing Apparel<br>Metal Products<br>Wood Products<br>Construction<br>Minerals nec<br>Mineral Products nec<br>Textiles<br>Paper Products, Publishing<br>Leather Products<br>Trade and Transport Services<br>Other Private Services<br>Ferrous Metals<br>Machinery and Equipment nec<br>Processed Foods<br>Beverages and Tobacco Products<br>Chemical, Rubber, Plastic Products<br>Agr, Forestry, Fisheries<br>Manufactures nec<br>Electricity, Gas, Water<br>Transport Equipment<br>Metals nec<br>Energy Extraction<br>Dwellings<br>0 20 40 60 80<br>Total LVAX share (%)<br>2001 2011<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

The services sector absorbs a much larger share of labor value added from exports than it generates directly via its exports. Figure 8 shows how much the labor of each sector is used by other sectors for their exports (i.e., the forward linkages). The picture again is very different, highlighting an asymmetry that is typical of many economies. The services sectors, while being relatively small contributors to the direct labor content of exports, are the most important contributor of labor to exports when accounting also for the forward linkages. Trade and transport services as well as other private services have large wage bills servicing export sectors, paying for over $8 billion and $7 billion in wages, mostly indirectly by supplying services to other export sectors rather than exporting directly.

Consistent with other literature (Farole, 2015), domestic value chains in South Africa appear to be relatively short. While the goods sector shows strong backward linkages in the domestic economy, particularly through services, it also has very weak forward linkages. Labor in the minerals and metals sectors are hardly employed to produce inputs for other exporting sectors, indicating very limited domestic downstream connectivity. Perhaps even more surprising, while the machinery and equipment sector has a large labor content in exports, labor in machinery and equipment is also not involved in the production to supply other exporting sectors. The majority of its wages are paid to employees producing equipment to be directly exported (even though doing so requires inputs from other domestic sectors of the economy, as illustrated in Figure 6). This asymmetry suggests a mismatch between the domestic supply of machinery and the demand for machinery from the domestic minerals, metal and manufacturing sectors.

15
|Table 9<br>Expenditure on Special Central Pr<br>(Bn. dongs)|ogram in Pr<br>|ovinces<br>|
|---|---|---|
||1993<br>(Actuals)|1994<br>(BudgetedEstimates)|
|I.Educadon|216|430|
|ti) Popularising primary education and spread of literacy|30|40|
|<br>(ii) Education for mountain regions|<br>60|s0|
|(iii) Separation or primary schools fromsecondaryschools|50|50|
|(iv) Improvement in class rooms||100|
|(v)<br>Elimination or 3rd shift in schools|70|100|
|(vi) Books and Newspapers for mountain pupils|8|15|
|(vii)Impruvemcnt in srcondary schools|6|25|
|<br>(viii) Computer education for schools||20|
|2. Training Programs|10|105|
|ti) lmprtvement in teaching material and courses<br>|10|15<br>|
|(ii) Computerfiorschools||20|
|<br>(iii)Upgradingbasicmaterials||20|
|3. Medicine and health programs|132|305|
|<br>(i)UNICEF water supply programs for mountain region||1 5|
|<br>(ii)Anti-Malariaprogram|52|70|
|<br>(iii)Anti-GoiLre program|30|59|
|(iv) Immunization program|15|25|
|(v) Anti-HIV program|10|40|
|<br>(vi)Upgrading material bases|25|70|
|<br>(vii) Establishing health care centers in non-existent areas||26|
|4. Population and family planning program|93|225|
|_5._Cultural and information programs||30|
|<br>ti)<br>Information and cultural activities forthe communes||10|
|(ii)Filmmaking. renovation and developing||10|
|(iii)Cultural and historical heritage reproduction||10|
|6. Child care|2|30|
|7. Job programs.|300|250|
|8. Support for Khmer, Cham and other minorities|10|30|
|9. Administration of.boundarymapping|30|42|
|10. Pricc suppon policy for specified commodities|170|250|
|11. Project 327: Reforostation|584|610|
|12. Anti-social vice programs|60|70)|
|<br>(i) Anti-opium plantation|_S0_|_S0_|
|fii) Abolition of prostitution|10|20|
|Total|1615|2377|

19
<!-- Start of picture text --> I antipoverty [J poverty alleviation [J redistribution of wealth {J redistribution of income {J income redistribution<br>0.0000700%<br>0.0000600%<br>0.0000550%<br>0.0000500%0.0000450%0.0000400% (\\ /<br>0.0000300%0.0000250% ifri AKS<br>0.0000200% i —<br>0.0000150% |<br>0.0000100% /». SS<br>0.00000007a5 Te20 Te40 TB60 iea0008 7940 7360 ~—‘1980 2000<br><!-- End of picture text -->
|**Table A27: World Business Enterprise Survey (80 developed an**|2005<br>2004<br>**d developing**|2003<br>**coun**|2002<br>**tries)**|2000|1998|1996|
|---|---|---|---|---|---|---|
|**Voice and Accountability**|||||||
|<br>Business have voice to express|..<br>..|..|..|X|X|..|
|Business are informed|..<br>..|..|..|X|X|..|
|**Political Stability**|||||||
|Political instability|..<br>..|..|..|X|..|..|
|Likelihood of uncostitutional|..<br>..|..|..|..|X|..|
|Threat of terrorism|..<br>..|..|..|..|X|..|
|**Government Effectiveness**<br>|||||||
|Quality of customs<br>|..<br>..|..|..|X|X|..|
|Quality of public works (roads,...)|..<br>..|..|..|X|X|..|
|Quality of power company|..<br>..|..|..|X|..|..|
|Quality of Water|..<br>..|..|..|X|..|..|
|Quality of public health|..<br>..|..|..|X|X|..|
|Quality of public education|..<br>..|..|..|X|..|..|
|Quality of central government|..<br>..|..|..|X|..|..|
|Quality of central bank|..<br>..|..|..|X|..|..|
|<br>Efficiency of government in delivering services|..<br>..|..|..|X|X|..|
|Likelihood that when a government official acts against the rules, one can go|||||X||
|to another official or a superior and get correct treatment|..<br>..|..|..|..||..|
|Management time spent with bureaucrats|..<br>..|..|..|..|X|..|
|The efficiency of mail delivery|..<br>..|..|..|..|X|..|
|Predictability of changes in rules and laws|..<br>..|..|..|..|X|..|
|Credibility of government's commitment to policies|..<br>..|..|..|..|X|..|
|**Regulatory Quality**|||||||
|<br>Regulations on starting new businesses|..<br>..|..|..|X|X|..|
|Price controls|..<br>..|..|..|X|X|..|
|Regulations on foreign trade|..<br>..|..|..|X|X|..|
|Foreign currency regulations|..<br>..|..|..|X|X|..|
|General uncertainty about regulations|..<br>..|..|..|X|X|..|
|**Rule of Law**|||||||
|Corruption of bankers|..<br>..|..|..|X|..|..|
|Quality of the Police|..<br>..|..|..|X|..|..|
|Organized crime<br>|..<br>..|..|..|X|..|..|
|Street crime|..<br>..|..|..|X|X|..|
|Courts-- fair & impartial|..<br>..|..|..|X|..|..|
|Courts-affordable|..<br>..|..|..|X|..|..|
|Courts-consistent/predictable|..<br>..|..|..|X|X|..|
|Court's enforceability|..<br>..|..|..|X|..|..|
|Confidence in judicial system today in insuring property rights|..<br>..|..|..|X|X|..|
|General constraint—functioning of the judiciary|..<br>..|..|..|X|..|..|
|Obstacles to competition-violation of patents|..<br>..|..|..|X|..|..|
|<br>Quality of courts|..<br>..|..|..|X|..|..|
|**Control of Corruption**|||||||
|Frequency of additional payments|..<br>..|..|..|X|X|..|
|Dishonest courts|..<br>..|..|..|X|..|..|
|Corruption as obstacle to business|..<br>..|..|..|X|X|..|
|Bribery (% of Gross revenues)|..<br>..|..|..|X|..|..|
|_Country coverage_|..<br>..|..|..|_80_|_74_|..|

79
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Table 4: Employment adjustments

|||Extensiv|e margin|||Intensi|ve margin||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Conditional|(2)<br>Cont. initial<br>productivity|(3)<br>Conditional|(4)<br>Cont. initial<br>productivity|(5)<br>Conditional|(6)<br>Cont. initial<br>productivity|(7)<br>Conditional|(8)<br>Cont. initial<br>productivity|
||||**Panel A.**|**Baseline estim**|**ations**||||
|Informality|-0.0805**|-0.115**|||-0.136**|-0.0700**|||
||(0.0244)|(0.0318)|||(0.0279)|(0.0342)|||
||||**Panel B. B**|**y type of infor**|**mality**||||
|Fully formal|||_Baseline_|_Baseline_|||_Baseline_|_Baseline_|
|Necessity|||-0.0172|-0.0331*|||-0.0413**|-0.0498**|
||||(0.0161)|(0.0182)|||(0.0191)|(0.0219)|
|De Soto|||-0.0237*|-0.0681**|||-0.0416**|-0.0653**|
||||(0.0139)|(0.0154)|||(0.0173)|(0.0193)|
|Parasite|||-0.0715**|-0.0964**|||-0.158**|-0.127**|
||||(0.0140)|(0.0213)|||(0.0176)|(0.0272)|
||||**Panel C. By**|**margin of inf**|**ormality**||||
|_PCA 1_|-0.00889|-0.0183|||0.0166|0.0500*|||
||(0.0211)|(0.0270)|||(0.0246)|(0.0300)|||
|_PCA 2_|-0.0783**|-0.106**|||-0.176**|-0.141**|||
||(0.0196)|(0.0241)|||(0.0239)|(0.0289)|||
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|8886|6809|9119|6902|8888|6810|9121|6902|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

-Parasites exhibit a significantly lower probability of adjustments at the 1% level in Panel B, cols. 3, 7, and 8. Both the Soto and parasite firms adjust less at the 5% level of significance in Panel B, col. 4.

-The coefficients of PCA 1 and PCA 2 are statistically different at the 1% level in Panel C, cols. 1, 2, 5, and 6. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
# **Table 1. Tracking Institutional Changes on the Path to Development Impact**

|**Indi**<br>**Are**|**cators /**<br>**as for Measurement**|**Data Sources /**<br>**Methods**|**Supporting Conditions**|**Reference**|
|---|---|---|---|---|
||Establishment of<br>federal legislation to<br>ensure public access to<br>budget information|Legislative records|Presence of an<br>enforcement agency or<br>mechanismwithin the<br>public sector|Robinson (2006)|
||Movement according<br>to a “transparency<br>index” (defined by<br>Islam 2003)|Constructed using 11<br>representative<br>variables from 4<br>sectors (real, fiscal,<br>financial, and<br>external) for 169<br>|Passage of a freedom of<br>information act|Islam (2003)|
|||countries|||
|**C Y**|Level of discrepancies<br>between commitments<br>and actual<br>disbursements|Independent budget<br>analysis<br>or<br>Public Expenditure<br>TrackingSurvey|Presence of a proven<br>accountability mechanism<br>to address discrepancies|Joshi (2010)<br>Robinson (2006)|
|**T R A N S P A R E N**|Breadth of information<br>available about<br>international aid, as<br>reflected by:<br>*degree to which<br>projected future<br>disbursements and<br>targets are published<br>*timeliness and<br>consistency of<br>information from<br>various sources<br>*compliance with<br>national and<br>international aid<br>effectivenesstargets|Expert review|Presence of demand-side<br>(citizen voice) factors to<br>ensure that information<br>can be used to make<br>changes as needed—such<br>as opportunities for<br>citizens and/or CSOs to<br>participate in setting<br>budget priorities|McGee and<br>Gaventa (2010)|
||Country-level<br>comparisons using<br>independent,<br>comparative, and<br>regular measure of<br>budget transparency<br>and accountability<br>around the world.|Overall Open Budget<br>Index—rankings on<br>budget transparency<br>questions by non-<br>government country<br>experts|Index shows relationship<br>with development<br>outcomes even when<br>controlling for other<br>factors. Note: demand-<br>side and supply-side<br>factors play a role in<br>helping to change OBI<br>rankings over time.|Fukuda-Parr,<br>Guyer, and<br>Lawson-Remer<br>(2011)|

23
Henderson, and Tsui 2008). Raw Herfindahl index scores for industries for Beijing and Tianjin fell between 1993 and 2001, while normalized Herfindahl index scores increased, suggesting that industry structure in the PRC has become more specialized, and differentiated from the national average.

However, even with the increasing concentration of economic activities in large cities, the PRC may still be underconcentrated and underurbanized. Evidence from the 2000 Census suggests that the PRC was underurbanized. The PRC ranked low in a worldwide comparison of developing and large countries with respect to the share of its population living in large cities. Its population Gini coefficient, which in this context means the area between the 45 degree line and the Lorenz curve as a measure of the aggregated population share of its cities, is remarkably low for a large country. Formal restrictions of the hukou system combined with limited access to housing, education, and health care for migrant labor has discouraged migration, while rural industrialization through the promotion of TVEs has retained labor in rural areas. Au and Henderson (2006) estimate the efficient sizes of cities in the PRC as a function of each city’s manufacturing over service ratio, as well as a variety of control factors. They find that the populations of 85% of prefecture-level cities in the PRC are smaller than their efficient sizes, and 43% of prefecture-level cities lie below the 95% confidence intervals generated by their models. Both results suggest that cities in the PRC are significantly undersized.

# **B. Rural-Urban Integration**

The initial chasm between the rural and the urban in the PRC was deep and wide. After the 1949 Revolution in the PRC, the country’s leaders promoted the development of heavy industry and implemented a planned economic system that fixed prices and allocation of all inputs. Labor allocation was conducted by the Bureau of Labor and Personnel (Cai, Park, and Zhao 2008). Agriculture was collectivized in self-sufficient communes by 1957, and any excess grains the commune produced were prohibited from being traded. In line with the Soviet economic policy, rapid industrialization was the PRC’s focus and urban areas were favored (Chan, Henderson, and Tsui 2008) by biasing the terms of trade against farmers.

The root of the long existing rural–urban division is the _hukou_ (household registration) system, which has assigned agricultural or nonagricultural status to every person, mostly based on place of 6
# **Wage Growth, Landholding, and Mechanization in Agriculture**

**Evidence from Indonesia**<sup>**1**</sup>

**Futoshi Yamauchi**<sup>**2**</sup>

**The World Bank**

Key words: Wage growth, farm size, mechanization, Indonesia JEL Classifications: J31, Q12, Q15

> 1 I would like to thank Fabrizio Bresciani, Jikun Huang, Yanyan Liu, Will Martin, Manabu Nose, Keijiro Otsuka, and Xiaobing Wang for their comments. The current work is financially supported by the Gates Foundation. The opinions and conclusions expressed in this paper are those of its author and do not necessarily reflect positions of the World Bank and its member governments. Any remaining errors are mine.

> 2 Correspondence: Futoshi Yamauchi, The World Bank, 1818 HJ Street, NW, Washington D.C. 20433; Email: fyamauchi@worldbank.org, Phone: 202 458 4262