rafmacalaba/datause-encoder-data huggingface dataset:
17 effective management of subnational default risks goes in tandem with broader development of capital markets.

# **References**

- Alam, Asad, Stepan Titov, and John Petersen. 2004. “Russian Federation.” In _Subnational Capital Markets in Developing Countries: From Theory to Practice_ , ed. Mila Freire and John Petersen, 571–92. Washington, DC: World Bank.

- Bailey, Robert W. 1984. _The Crisis Regime: The MAC, the EFCB, and the Political Impact of the New York City Financial Crisis_ . Albany: State University of New York Press.

- Barrientos, Laura. 2002. “Subsovereign Defaults in Mexico.”Moody’s Investors Service, New York.

- Canuto, Otaviano, and Lili Liu. 2010a. “Subnational Debt Financing and the Global Financial Crisis.” Premise Note, Poverty Reduction and Economic Management Network. Washington, D.C: World Bank.

- Canuto, Otaviano, and Lili Liu. 2010b. “Subnational Debt Finance: Make it Sustainable” in _The Day After Tomorrow: A Handbook on the Future of Economic Policy in the Developing World,_ ed. Otaviano Canuto and Marcelo Giugale _._ Washington, DC: World Bank.

- Dillinger,William. 2002. “Brazil: Issues in Fiscal Federalism.” Report 22523-BR, Brazil Country Management Unit, PREM Sector Management Unit, Latin America and the Caribbean Region. Washington, DC: World Bank.

- English, William B. 1996. “Understanding the Costs of Sovereign Default: American State Debts in the 1840's.” _American Economic Review_ , Vol. 86, No. 1.

- FitchRatings, 2009. European Local and Regional Government Outlook 2010. December 16.

- Gaillard, Norbert. 2009. “The Determinants of Moody's Sub-Sovereign Ratings.” _International Research Journal of Finance and Economics_ , Issue 31.

- Glasser, Matthew. 2005. “Legal Framework for Local Government Insolvency.” Paper presented at a World Bank seminar, Washington, DC, March 8.

- Hochman, Steve. 2002. “Subsovereign Defaults in Argentina.”Moody’s Investors Service, New York.

- Ianchovichina, Elena, Lili Liu, and Mohan Nagarajan. 2007. “Subnational Fiscal Sustainability Analysis: What Can We Learn from Tamil Nadu?” _Economic and Political Weekly_ 42 (52): 111–19.

- Jókay, Charles, Gábor Szepesi, and György Szmetana. 2004. “Municipal Bankruptcy Framework and Debt Management Experiences, 1990–2000.” In _Intergovernmental Finances in Hungary: A Decade of Experience_ , ed. Mihály, Kopányi, Deborah L.Wetzel, and Samir El Daher.

- Khemani, Studi. 2002. “Federal politics and budget deficits: evidence from the states of India.” _World Bank Policy Research Working Paper_ , WPS #2915.  October.  Washington, DC: World Bank.

- Kupetz, David S. 1995. “Municipal Debt Adjustment under the Bankruptcy Code.” _Urban Lawyer_ 27 (3): 531–605.

- Liu, Lili. 2008. “Creating a Regulatory Framework for Managing Subnational Borrowing.” In _Public Finance in China: Reform and Growth for a Harmonious Society_ , ed. Jiwei Lou and Shuilin Wang. 171– 190. Washington, DC: World Bank.
years with an average duration equal to 0.7 years, which implies _πLH_ = 0 _._ 3 and _πHL_ = 0 _._ 8.<sup>29</sup> The parameter values that govern the endowment process are chosen to mimic the behavior of logged and linearly detrended GDP. The estimation of the stochastic process for the cyclical component of GDP yields _ρ_ = 0 _._ 66 and _η_ = 0 _._ 034. I set the reentry probability to match an average duration in financial exclusion of 9 years, which corresponds to the time period in which Mexico was excluded from financial markets in its last default episode (1982-190). This yields _θ_ = 0 _._ 11. Finally, the central bank’s discount factor is disciplined by the average money market interest rate observed in Mexico during the period that the central bank has been independent. This is a real interest rate of 3 _._ 5%, which implies _β_<sup>_M_</sup> = 0 _._ 966.

Table 2: Parameters Calibrated from the Data

|Parameter|Description|Value|Source / Target|
|---|---|---|---|
|_σ_|Risk aversion|2|Alfaro and Kanczuk|
|_r_<sup>_∗_</sup>|International risk-free interest rate|0_._011|US Treasury bills rate = 1_._1%|
|_πLH_|Probability of transiting to high risk premium|0_._30|Global EMBI +|
|_πHL_|Probability of transiting to low risk premium|0_._80|Global EMBI +|
|_ρ_|Auto-correlation of_y_|0_._66|Mexico’s GDP|
|_η_|Variance of_y_|0_._034|Mexico’s GDP|
|_θ_|Reentry probability|0_._11|9 years in default (1982-1990)|
|_β _<sup>_M_</sup>|Central Bank’s discount factor|0_._966|MX money market rate = 3_._5%|

Table 3: Parameters Calibrated by Simulation

|Parameter|Description|Value|Target|
|---|---|---|---|
|_β _<sup>_F_</sup>|Government’s discount factor|0_._939|Debt to GDP = 44_._4%|
|_d_0|Default cost parameter|_−_0_._809|Average spread = 267 bp|
|_d_1|Default cost parameter|0_._902|Increase in spread = 296 bp|
|_κH_|Pricing kernel parameter|0_._27|Default probability = 2%|

> 29On average, the global EMBI+ was 2 percentage point higher in those episodes than in normal periods.

25
(score of +1) or pro-employer (-1) or neutral (0) over the period 1958 to 1992. Given the limited time-series variation within states, Hasan, Mitra, and Ramaswamy (2007) build on the BesleyBurgess dataset to cumulate the coded scores in net years into a time-invariant index of the general direction in labor regulations, classifying states with anti-employee amendments as those with flexible labor markets and the others as inflexible labor markets. They argue that the scores on cumulative amendments between 1980 and 1997 do not vary much over time within states, with eight of the states showing no amendment activity since 1980. Dougherty (2009) further reports that only 8 amendments (in 3 states) have been recorded since 1990, and only one amendment passed in 2004 appears to be of material importance to labor market outcomes. Gupta et al. (2008) build a composite index based on a simple majority rule across the indicators proposed in Besley and Burgess (2004), Bhattacharjea (2006), and Dougherty (2009), and classify five states to have flexible labor regulations, (Andhra Pradesh, Karnataka, Rajasthan, Tamil Nadu, and Uttar Pradesh), three states to have inflexible labor market regulations (Maharashtra, Orissa, and West Bengal and the remaining seven to be neutral (Assam, Bihar, Gujarat, Haryana, Kerala, Madhya Pradesh, and Punjab).<sup>17</sup> Following Gupta et al. (2008)’s composite classification, we create a **Flexible State** dummy that takes the value 1 for states with flexible labor regulation and 0 for states with rigid or neutral labor regulations as shown in panel B of Table 1. We make one change to the Gupta et al. classification of using the pre-2000 state boundaries in classifying states. So our flexible states classification includes Uttaranchal (which split from Uttar Pradesh in 2000) and inflexible states classification includes Jharkhand and Chattisgarh that were formerly part of Bihar and Madhya Pradesh respectively).

> 17 The labor market regulation index is not available for the following states and union territories: Jammu & Kashmir, Chandigarh, Nagaland, Manipur, Tripura, Meghalaya, Daman & Diu, Dadra & Nagar Haveli, Pondicherry, Lakshadweep, and Andaman & Nicobar Islands.

15
above 70% and _energy extraction_ with the share close to only 10%. By and large these shares confirm the importance of labor as a key factor in the direct production of services and agriculture and fisheries. The overall labor intensity of manufacturing exports tends to be slightly lower but the importance of labor value added from other sectors is generally higher.

The labor content of exports shows substantial heterogeneity across developing countries’ region as shown in Figure 10, which reports the export-weighted LVAX share in middle and low income economies by region in 2011. The share of total labor value added in exports was on average the lowest in MENA countries and the highest in EAP, on par with the world average though still below high-income economies. The result for EAP is entirely explained by the large share of indirect labor, which comprises around two third of the labor value in EAP exports, while the direct LVAX share in EAP exports is the lowest across all regions. Export sectors in the EAP region are relatively well connected with the rest of the economy, a result almost entirely explained by China. As the next section will clarify part of this effect is compositional: for example the specialization of MENA countries and of Europe and Central Asian (ECA) countries in capital intensive extractive sectors is less conducive to developing backward linkages than that of EAP countries in manufacturing exports. The labor intensity of exports – both through its direct and indirect components – is low in Sub-Saharan Africa (SSA) relative to most other regions. Coupled with the relatively low value of SSA exports, this result confirms that exports still support only a limited amount of domestic wages in the region.

**Figure 10. LVAX share in exports in middle and low income countries, by region, in 2011**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>indirect unskilled<br>0.15<br>indirect skilled<br>0.10<br>direct unskilled<br>0.05<br>direct skilled<br>0.00<br><!-- End of picture text --> Note: Income group is defined according to the World Bank country group 2011 classification.

# **3.3. Patterns of labor value added across countries**

While these general patterns are interesting, they mask a large heterogeneity among countries. Table 1 starts unveiling that heterogeneity by presenting measures of labor value added at the country level for the 15 countries in the sample with the highest and the 15 countries with the lowest total labor value added in exports (in nominal US$, shown in the first column). Columns two and three show the amount of labor 19
deeper for the poorest children and that in Indonesia, after achieving almost universal coverage, an income measure (per capita expenditures) became important again after the cnsis.

The finding of relative improvements of enrolment among older children is corroborated by evidence from other countries. Schady (2002) uses cross sectional data from Peru to show that during crisis children do not decrease their enrolment because of lower opportunity costs (poor job market retributions). The Peruvian evidence shows, on the other hand, that the fraction of children studying and working decreased during crisis. Neri and Thomas (2001) show that household head movements from formal-to-informal labor have more deleterious effects than movements into unemployment in Brazil. Such movements raise the probability of a child entering the labor market, but only during growth periods. For children staying at school, such movements also increase the probability of repeating the school year, but only during recessions.

# _What happened in Moldova?_

Since enrolment and most expenditure decisions are made at the beginning of the school year, this paper separates the sample in periods before and after the crisis. Periods before the crisis are quarters in the academic year 1997/98. The quarters corresponding to the year 1998/99 are difficult to characterize since the effects may have been observed once the school year had started. The year 1999/2000 is clearly a post-crisis years, despite the continued increased in poverty incidence.

As other FSU countries, Moldova evidences an almost universal enrolment rate in its basic education level (grades 1 through 9). In the academic year 1999/2000 more than 95 percent of the children between 7 and 15 attended school (see Table 4.9). Enrolment is lower (about 55 percent) for children between 16 and 18, corresponding to Upper Secondary or Technical School. During the periods of analysis, net enrolment rates remained high or even increased for Basic Education. Preschool enrolment for children aged 6 years, however, declined partly due to the lower funding to this education level. Even enrolment for those directly facing labor market opportunities (aged between 19 and 20 years) increased during the worsening of economic conditions. The overall picture suggests that households were able to protect educational investments of their children (or that at the same time job opportunities were limited).

Table 4.9:Moldova: Enrolment rates 1997-2000 (percent of children attending school)

|School level(ages)|97/98|98/99|99/00|
|---|---|---|---|
|Preschool(6)|11.5|8.5|7.5|
|Primary(7-10)|91.6|93.7|94.9|
|LowerSec.(11-15)|96.5|96.0|96.9|
|UpperSec. (16-18)|54.1|55.9|54.8|
|College(19-20)|18.7|25.1|22.2|

Source: Moldova Household Budget Survey (1997-2000).

21
Finally, freight costs lie roughly in the $1,000-3,000 range for distances between 3,000 and 5,000 km, suggesting average rates of less than $500 per thousand kilometers. Although the shortest route is the Afghan one to Karachi via Quetta, the Bandar Abbas rail route appears the most attractive. Taking this as a basis for our calculations, maintaining the freight rate below 10% of shipment value (a high rate given that it covers only overland transport to the port of destination) implies a cargo worth at least $15,000, a very high value for a 20’ container −and one that would be risky to ship on a hazardous route.

# **_Transit infrastructure_**

Landlocked countries are completely dependent on their transit neighbors’ infrastructure to transport their goods to the nearest port. There are many causes of weak infrastructure, including lack of resources, poor governance, conflict and natural disasters. Regardless of the cause, weak infrastructure impose direct costs on trade passing through transit countries and thus limit the ability of products from landlocked countries to compete in global markets. The relative impact of the weak infrastructure of its neighbors has a particularly negative impact on those landlocked countries which mainly export primary commodities with low value-to-cost ratios rather than high-value products or services. A weak infrastructure of a transit country also limits the return to investment in landlocked countries’ internal infrastructure due to the restrictions it places on market opportunities. The data yielded by CIF/FOB ratios allowed Limão and Venables (2001) to estimate that improving the infrastructure of transit countries, all other things being equal, would reduce the transport cost differential between the median landlocked economy and the median coastal country from 46% to 43%, and thus lead to a 2% increase in trade volume.

Transport infrastructure in the Central Asia sub-region has been heavily influenced by the needs of the former Soviet Union, with road and rail networks designed to facilitate traffic flows towards the European part of Russia, particularly Moscow and Ukraine. Road and rail connections are less well developed both within Central Asia itself, and between Central Asia and its neighbours to the east and south. Connections through China, Iran, Afghanistan and Pakistan are limited and poorly developed. There is one major paved road corridor through the sub-region, running east to west and linking Tashkent to Almaty, with connecting roads to China and Turkmenistan. There is also a single rail corridor to China, passing through the high mountains of the Kazakh-China border. For more details on the major trade corridor for Central Asian Trade, see Ojala (2005).

10
|PIP|1 639,63|68,44|
|---|---|---|
|RLW|1 639,63|68,44|
|TPP|12,86||
|TRK|1 639,63|68,44|
|TRO|1 639,63|68,44|

_Source: Authors’ estimations_ Table A22. Regional emissions intensity index of non-CO2 emissions, Urals and Near East region, WTO model's sectoral classification (thousands tons of emission per billion 2001 RUB of output)

|**"Urals and**<br>**near east"**|**Hydrocarbons**|**Carbon**<br>**monoxide**|**Nitrogen**<br>**oxide**|**Particulate**<br>**matter**|**Sulphur**<br>**dioxide**|**Volatile organic**<br>**components**|
|---|---|---|---|---|---|---|
|**Row Labels**|**CnHm**|**CO**|**NOx**|**PM**|**SO2**|**VOC**|
|MAR|1 675,72|339,81|191,19|253,84||83,73|
|AGR|48,95|110,05|50,40|80,04||15,77|
|AIR|1 675,72|339,81|191,19|253,84||83,73|
|CHM|137,51|602,75|224,89|464,13||221,70|
|CLI|32,52|302,78|155,22|354,48||30,07|
|CNM|37,62|659,28|400,05|1 487,57||53,83|

129
2 devoted to foreign aid by the same industrial country governments (less than $80 billion per year). Politically, this focus is potentially appealing to developing countries because it also implies large-scale farm policy changes are needed only in developed countries.

Why, then, have economists and organizations such as the World Bank argued that even more important than direct producer subsidies are barriers to agricultural imports and therefore that, from the viewpoint of economic welfare and trade, it is import barriers that need to be targeted in the DDA – and in developing countries as much as in high-income countries? This question has been asked by numerous trade negotiators and their advisors seeking an intuitive understanding of the modeling result that suggests 93 percent of the global cost of agricultural tariff protection and subsidies is due to tariffs and only 2 percent to export subsidies.<sup>1</sup> The purpose of the present paper is to provide such an explanation.

At the outset it is helpful to recognize two things. The first is that the OECD estimates refer to the extent of support to farmers not only directly via producer subsidies from the treasury but also indirectly by propping up domestic prices via export subsidies and, far more importantly, import barriers. This is something the OECD has gone to great lengths to explain in its documentation (OECD 2005 and earlier issues), but some misconceptions remain. Secondly, what matters is how those various elements of support affect production, consumption and trade and thereby national economic welfare. It is the net impact on economic welfare globally and in developing countries that the World Bank uses as its main criterion for determining the relative importance of the various measures. To estimate that requires a multi-country model of the global economy that takes into account the size of the sectors being distorted, the price responsiveness of supply and demand in different regions, the extent to which distortions vary across commodities and countries, and the fact that production subsidies distort only production responses while border measures distort both production and consumption.

> 1 See, for example, Anderson and Martin (2005), drawing on the chapter in their subsequent edited volume by Hertel and Keeney (2006), as well as the earlier partial equilibrium study by Hoekman, Ng and Olarreaga (2004). Hertel and Keeney’s finding that 93 percent of the global welfare cost of agricultural support programs is due to import tariff barriers to market access (using a 2001 protection database) is very close also to the 89 percent finding of Diao, Somwaru and Roe (2001) who used a 1995 protection database.
**Table S1.2.** Attrition

||**One week**|**Six months**||**One week**|**Six months**||**One week**|**Six months**||**One week**|**Six months**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**post inter-**|**post inter-**|**Condom**|**post inter-**|**post inter-**|**Condom**|**post inter-**|**post inter-**|**Condom**|**post inter-**|**post inter-**|**Condom**|
|**Dep. Var.: Attrited= 1 **|**vention**|**vention**|**voucher**|**vention**|**vention**|**Voucher**|**vention**|**vention**|**Voucher**|**vention**|**vention**|**voucher**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|
|Treatment students|0.009|0.012|−0.008||||||||||
||(0.020)|(0.019)|(0.040)||||||||||
|Spillover students|0.013|0.024|0.045||||||||||
||(0.024)|(0.018)|(0.046)||||||||||
|Treatment students *||||0.004|0.005|−0.001|||||||
|socioeconomic level||||(0.007)|(0.008)|(0.016)|||||||
|Spillover students *||||0.008|0.008|0.015|||||||
|socioeconomic level||||(0.010)|(0.008)|(0.019)|||||||
|Treatment students * father’s|||||||0.001|0.001|−0.000||||
|education|||||||(0.002)|(0.001)|(0.003)||||
|Spillover students * father’s|||||||0.001|0.001|0.003||||
|education|||||||(0.002)|(0.001)|(0.003)||||
|Treatment students *||||||||||0.001|0.001|−0.001|
|mother’s education||||||||||(0.001)|(0.001)|(0.003)|
|Spillover students * mother’s||||||||||<br>0.001|<br>0.001|<br>0.003|
|education||||||||||(0.002)|(0.001)|(0.003)|
|Constant|0.126***|0.100***|0.313***|0.115***|0.098***|0.304***|0.117***|0.150***|0.333***|0.134***|0.143***|0.393***|
||(0.016)|(0.014)|(0.030)|(0.017)|(0.019)|(0.035)|(0.019)|(0.029)|(0.040)|(0.023)|(0.024)|(0.047)|

_Source_ : Authors’ analysis from student surveys.

_Note_ : *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Attrition = 1 for students observed at baseline but not at first follow-up (column 1), second follow-up (column 2), or without working cellphone or email for voucher offer 6 months after intervention (column 3). Columns 4–6 include socioeconomic status variable, columns 7–9 include father’s education variable, and columns 10–12 include mother’s education variable as controls. Standard errors clustered at the school level in parentheses.
- de Melo, Jaime. and David G. Tarr (1992), _A General Equilibrium Analysis of US Foreign Trade Policy_ , MIT Press, Cambridge, MA.

- Dixit, A. and J. Stiglitz (1977), ―Monopolistic Competition and Optimum Product Diversity,‖ _American Economic Review_ , 76(1):297-308.

- Dixon, Peter B. and Maureen T. Rimmer (2002), ―Dynamic General Equilibrium Modeling for Forecasting and Policy: A Practical Guide and Documentation on MONASH,‖ _Contributions to Economic Analysis_ 256, North Holland Publishing Company.

- Eschenbach, Felix and Bernard Hoekman (2006), ―Services Policy Reform and Economic Growth in Transition Economies,‖ _Review of World Economics_ , Vol. 142(4), 746-762.

- Elbers, C., Lanjouw, J.O., Lanjouw, P., 2003. Micro-level Estimation of Poverty and Inequality. _Econometrica_ 71, 355-64

- Ethier, W.J. (1982), ―National and International Returns to Scale in the Modern Theory of International Trade,‖ _American Economic Review_ , 72(2):389-405.

- Ethier, W., 1984, ―Higher Dimensional Issues in Trade Theory,‖ in Jones, R., Kenen. P. (Eds.), _Handbook of International Economics_ , Volume I: International Trade, North-Holland, Amsterdam.

- Falvey, R., N. Foster, and D. Greenaway, (2002)  ̳ ̳Imports, Exports, Knowledge Spillovers and Growth.‘‘ _Economic Letters_ , 85, 209–13.

- Feenstra, R., D. Madani, T.H. Yang and C. Y. Liang (1999), ―Testing Endogenous Growth in South Korea and Taiwan,‖ _Journal of Development Economics_ Vol. 60, 317-341.

- Feenstra, R and H. Kee (2004), ―On the Measurement of Product Variety in Trade,‖ _American Economic Review_ Vol. 94, 145-149.

- Fernandes, Ana M and Caroline  Paunov (2012), ―Foreign direct investment in services and manufacturing productivity: evidence for Chile,‖ _Journal of Development Economics_ , Volume 97, Issue 2, March, 305-321

- Fernandes, Ana M. (2009), ―Structure and performance of the services sector in transition economies,‖ _Economies of Transition,_ Vol. 17(3), 467-501.

- Findlay, C. and Warren, T., 2000. Impediments to Trade in Services: Measurement and Policy Implications. Routledge, London.

- Fink, Carsten, Aaditya Mattoo and Ileana Cristina Neagu (2005), ―Assessing the Impact of Communications Costs on International Trade,‖ _Journal of International Economics_ , Vol. 67(2), 428-425.

85