rafmacalaba/datause-encoder-data huggingface dataset:
that outpaced the supply possibly due to an increase of international trade promoting technological change (Acemoglu 2003).

**Figure 1. Returns to schooling and average years of schooling for transition and nontransition countries**

<!-- Start of picture text --> Returns to schooling and average years of schooling<br>10<br>2010<br>9 2005 2010 2005<br>8 1975 2000<br>7 1985 1990 1995 2000<br>1980 1995<br>6<br>1990<br>1975<br>5<br>1985<br>4<br>3 1980<br>2<br>6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12<br>Average years of schooling<br>Transition countries Non-transition countries<br>Returns to schooling<br><!-- End of picture text --> The returns to education in the non-transition countries considered in Table 1 remained stagnant although their average gross domestic product (GDP) per capita increased almost fivefold, changing from $7,301 before 1992 to $36,116 in the after-transition period. The GDP per capita also grew in the post-socialist countries listed in Table 1 for which data on this economic indicator is available before 1992.<sup>2</sup> In this case, the GDP per capita changed from $2,519 before the transition to $8,427 after the transition. This shows that the growth of GDP per capita was visibly smaller in transition countries than in non-transition countries.

# **_Analytical Results_**

The returns to education of post-socialist transition countries and non-transition countries followed similar trends before the fall of the Soviet Union in December 1991 (see Figure 2). Before the dissolution of the Soviet Union, the returns to schooling were markedly lower in (what became) transition countries. Post-Soviet Union, there is convergence, and the gap disappears once the postsocialist countries transitioned to a market economy. Although the returns to schooling fluctuated pre-transition in Eastern and Central Europe, the gap remained, until the dissolution of the Soviet Union. This suggests that the parallel trend assumption is likely valid.

> 2 These countries are Bulgaria, Czechia, Hungary, Poland, Romania, the Russian Federation, the Slovak Republic, and Ukraine.

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# **IV. Conclusions**

To date, there has been relatively little discussion, at least within academic circles, and almost no empirical analysis of the effects that prudential supervision is likely to have on MFI profitability and outreach. To address these issues, we combine high-quality balance sheet and income statement data for 245 leading MFIs with a newly constructed database of the type of supervision that each faces.

Strong patterns emerge from the supervisory data indicating that its assignment is non-random. Specifically, supervision tends to be more stringent for commerciallyoriented MFIs, non-NGOs that collect deposits from the public, lend to individuals (rather than groups), make larger loans, have proportionately fewer female customers, and have a higher share of staff concentrated in the head office (and thus fewer staff with contact with clients in the field).

Searching for appropriate instrumental variables is of course difficult because many of the characteristics that define supervised MFIs are likely to be endogenous in profitability and outreach regressions. We therefore offer three instruments for use in treatment effects and IV regressions that have bearing on whether an MFI is regulated but do not directly affect MFI performance. The first is the general propensity to supervise formal financial institutions in a country as reflected in a dummy variable indicating whether large banks face onsite supervision at regular intervals. The second is a dummy indicating that an MFI was not chartered as an NGO or NBFI, since charter status was determined at the outset of the creation of each MFI, prior to and without substantial consideration for whether the MFI would face supervision. Similarly, our third instrument is a dummy indicating whether an MFI takes deposits, because that decision was also presumably taken at the inception of the MFI in most cases.

The selection stage of our treatment effects regressions confirms that nonNGO/NBFI’s that take deposits and are located in countries that supervise large banks at regular intervals are most likely to face onsite supervision at regular intervals. Controlling for the non-random assignment via those instruments, we find that regular onsite supervision is positively associated with average loan size and negatively 26
Table **II.** Balance of Payments: Current Account Surplus and Deficit (millions of dollars)

|||1970|||1986||
|---|---|---|---|---|---|---|
||S|D|NS|S|D|NS|
|Industrial Market Economies|10.8|3.2|7.5|147.8|170.6|-22.7|
|High Income Oil Exporters|1.6|-|1.6|14.3|10.3|3.9|
|_Subtotal_|12.4|3.2|9.2|162.1|181.0|-18.9|
|Other Countrbs (as in Statement<br>1)|0.5|3.5|-3.0|31.0|1.0|30.0|
|_Total_|12.9|6.7|6.2|193.2|182.0|11.2|

_Source:_ Same as in Statement I Note: S **=** Surplus D = Deficit NS = Net Surplus
# **6. References**

Abras, Anna., Cuesta, Joce and Sailesh, Tiwari. (forthcoming). How equal are opportunities among children in Tajikistan. World Bank.

Attanasio, Orazio and Szekely, Miguel eds. 2001. Portrait of the Poor: An Assets-based Approach, Inter-American Development Bank. Washington, DC.

- Azevedo, Joao Pedro, Minh Cong Nguyen & Viviane Sanfelice, (2012). "ADECOMP: Stata module to estimate Shapley Decomposition by Components of a Welfare Measure," Statistical Software Components S457562, Boston College Department of Economics.

- Basu, Kaushik. 2013. Shared Prosperity and the Mitigation of Poverty in Practice and in Precept. Policy Research Working Paper 6700. World Bank. Washington, DC.

Development Coordination Council (2012). Tajikistan: 2012 Development Forum.

FINDEX. Global Financial Inclusion Database. Available from: http://datatopics.worldbank.org/financialinclusion/

- Kaufmann, Daniel and Kraay, Aart and Mastruzzi, Massimo, The Worldwide Governance Indicators: Methodology and Analytical Issues (2010). World Bank Policy Research Working Paper No. 5430.

- Heleniuk, Е., Sulla, V. and Rajabov, A. (2012). Sustainable Poverty Reduction and Emerging Middle Class in Tajikistan.

- Lanjouw, Peter, Jill Louto, and David McKenzie. 2011. “Using Repeated Cross-Sections to Explore Movements in and out of Poverty.” World Bank Policy Research Working Paper 5550.

Newman, John, L., Joao Pedro Azevedo (2013) “Setting reasonable performance targets for public service delivery”, Policy research Working Paper Series 6385.

National Statistical Committee of the Republic of Tajikistan. www.stat.tj Sattar, S., Bierbaum, M., Barfieva, S., Flanaga, J., Lakhani, S. and Swinkels, R. (2013). Tajikistan Gender Diagnostics Note. Report No: ACS5015.

World Bank (2009). Republic of Tajikistan poverty assessment. Report No. 51341-TJ.

World Bank (2013a). When Prosperity is not Shared: On the Weak Links between Growth and Equity in Dominican Republic. Washington, DC: World Bank.

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the coefficient and significance is unchanged when adding month fixed effects and remains similar (.70 standard deviations at the 10% level) when also adding province fixed-effects.

Overall, theses validation exercises establish a promising basis for the use of CDR data as a proxy measure of firm activity. Firm location, firm size and aggregate tax revenues are all correlated with CDR based measures, with the validation of firm location measures proving particularly compelling. Limitations notwithstanding, this suggests the potential of this methodological approach, particularly in settings like Afghanistan where reliable data on the temporal or spatial distribution of firm activity is scarce.

# **4 Insecurity and Industrial Organization**

How does insecurity affect the spatial and temporal organization of firms in Afghanistan? We take a two-pronged approach to this question using our CDR measures. First, we provide an empirical case study of a shock to local security in Kunduz, Afghanistan’s fifth largest city; second, we quantify the effects of violence across the country using a district-month panel fixed effects model. These two complementary approaches allow us to first exploit the uniquely fine-grained features of the CDR data in the context of a high-profile event, and then demonstrate generalizability and examine persistence and heterogeneity of effects.

## **4.1 Kunduz Case Study**

To develop our theory of how business responds to violence, and our research design for testing that relationship, we begin with a brief case study. On 28 September 2015, Taliban fighters overran Kunduz city, following a battle that had ebbed and flowed since the previous April in neighboring districts. This marked the first time since 2001 that the Taliban had captured a major city and signaled the continuing strength of the insurgency. Kunduz was retaken by the Afghan National Army (ANA) on 13 October, with support from U.S. ground and air forces. Since then, sporadic violence has continued in and around the city, and the 15
- 35 -

**Tablo 12: CAUSALIY** TESTS ON **NWIINAL CXONDl1Y** PRICE INDICES, **CONSMER** PRICES, AND NOEY StUPPLY

||||Commodity<br>Pric|e Indices|||
|---|---|---|---|---|---|---|
||LnICP33<br>LnlCPAM|LnICPMM|LnICPAGF|<br>LnICPAGNF|<br>LnICPAGOF 1/|<br>LnICPAG1 2/|
||A.l Coimxo|dity<br>Price Fluct|uations<br>"Cause|" CPIG? Fluctu|ations||
|x2(<br>5<sup>)</sup>|22.235<br>2X.186|0.476|4.770|28.019|26.246|10.098|
||A.2 Consumer Price F|luctuations<br>(C|PIG7)<br>"Cause" C|ommodity<br>Pric|e Fluctuations||
|**_X2(_**|3.484<br>4.869|6.675|5.487|7.542|6.682|30.253|
||8.1 Commodity<br>|<br>Price Fluctuat|ions<br>"Cause" Mo|ney Supply Flu|ctuations||
|2ff|^4.370<br>0.362|7.220|2,913|6.763|5.242|0.263|
||8.2 Money Supp|ly Fluctuatio|ns<br>"Cause" Commo|dity<br>Price Flu|ctuations||
|**_x2()_**|13.725<br>21.150|11.136|37.879|0.9ti|9.293|6.597|

Note: Critical values for X(5) are: 15.086 at 99.9S, 12.832 at 97.5%, 11.071 at 95% and 9.236 at **90%** level of significance.

1/ LnICPAGOF Is the price Index for foods other than beverages.

2/ LnICPAGB Is the beverage price index.
22

# _7.1 Global motor vehiclefleet growth_

Table 6 shows the share of population, gross national product GNP, and motor vehicle fleet held in 1995 by 23 high-income countries and by 125 low- and middleincome countries; it also shows motor vehicle fleet projections for the same sets of countries.<sup>3 4</sup> In 1995, the low- and middle-income countries had a modest share of global GNP and motor vehicles, but a high share of population. The simple illustrative motor vehicle fleet projection in table 6 is based on the assumption that GNP grows at three percent in high-income countries (close to actual experience in 1980-1990) and five percent in low- and middle-income countries (an optimistic rate based on the highest rates experienced in each region over the past 15 years).

**Table 6. Share of Global Population, GNP, and Motor Vehicle Fleet in High-Income and Low- and Middle-Income Countries, and Motor Vehicle** Fleet **Projections**

|||Low and middle<br>income countries|High income<br>countries|All<br>countries|
|---|---|---|---|---|
|**_Initial global_**|**_shares - 1995_**||||
|Populat|ion|84%|16%|100%|
|**GNP**||19%|81%|100%|
|Motor v|ehicles|25%|75%|100%|
|**_Projected mot_**|**_or vehicles_**||||
|**_by year (in mi_**|**_llions)_**||||
||1995|164|487|651|
||2000|209|_565_|774|
||2010|340|759|1,099|
||2020|555|1,020|1,575|
||2030|905|1,370|2,275|
||2040|1,470|1,840|3,310|
||2050|2,400|2,475|4,875|

_Data sources:_ World Bank, _1997 World Development Indicators_ (Washington: World Bank, 1997); American Automobile Manufacturers Association, _World Motor Vehicle Data_ (Washington: American Automobile Manufacturers Association, 1996).

Based on these growth rates, the projections indicate that more than half of the world's annual increase in motor vehicles will occur in high-income countries until 2025. The motor vehicle fleet in low- and middle-income countries is not projected to exceed that in high-income countries until after 2050. Because the GNP growth rates used for lowand middle-income countries are optimistic, the time taken to achieve equivalence of fleet increments is likely to be longer than suggested by these estimates. The growth rates of incomes and fleets in low- and middle-income countries are high, but are operating on a small base.

_34_ High income countries are defined by the World Bank as those having a GNP per capita of $9,386 or more in 1995. See World Bank, _1997 World Development Indicators_ (Washington, D.C.: World Bank, 1997), p. xxii.
demand for the products or services the SOE sells (Herrera Dappe et al. 2022); shocks to the prices of inputs, such as commodity price fluctuations; and financial, exchange rate, and climate risks. Some of the climate risk can be insured against, through explicit insurance policies for physical infrastructure or national disaster funds (De Janvry, Del Valle, and Sadoulet 2016). Because of increased variability in weather patterns and the severity of extreme events, however, some insurance mechanisms may be insufficient to cover unexpected costs, especially in countries with no disaster relief endowments. For this reason, a major source of implicit contingent liabilities associated with climate change will be the increase in frequency of large, severe weather shocks that can impair infrastructure assets.

# **_2.2 Bailout Risk_**

Bailout risk refers to the implicit contingent liability risk associated with having to disburse large fiscal transfers to recapitalize an SOE or help it avoid default or bankruptcy after it faces large, mostly unexpected shocks for which the SOE has insufficient capital buffers. Bailout risk also arises when cash flow shortfalls happen in a way that erodes the equity of the firm with uneven, continuous write-off of losses, eventually generating the need for a major recapitalization.

Bailout risk also includes the contingent risk that governments face because SOEs may accumulate large liabilities for which the government is an implicit guarantor and that can turn into a responsibility of the government when large shocks occur. SOE debt is usually not added to the total debt of a country, especially if the SOE has been partially privatized or operates under corporate rather than under some form of administrative law. Even if SOE debt is explicitly not guaranteed by the government, however, if an SOE is too big or important to fail—the case of most infrastructure SOEs—governments cannot avoid having to bail them out in the event of a negative shock or threatened financial default (Lewis and Mody 1998; Wagner, Musacchio, and Jara-Bertin 2016). As budgets are computed on a cash basis, they rarely contain an estimate of the net present value of those contingent liabilities or the government’s risk exposure to such liabilities.

# **3 Data**

The World Bank Infrastructure SOEs Database was compiled as part of a broader research project to analyze the fiscal costs and risks of infrastructure. The data come from financial statements from SOE websites, government websites that include SOE financial statements, annual reports, and other sources, such as the EMIS Intelligence database and stock exchange websites. The database covers all SOEs operating infrastructure assets in the power (generation, transmission, and distribution) and transportation (roads, railroads, and airlines and airports) sectors for 19 countries between 2000 and 2018. The countries were selected based on data availability and to maximize sectoral coverage. They include the following: 8
33 As hirings in the Polish administrative data only include unemployed persons flowing into regular, non-subsidised jobs, this variable is orthogonal to ALMPs like public works or wage subsidies. With participation in a public works or wage subsidy scheme an unemployed is automatically de-registered, i.e. flowing out of unemployment. Regressing these ALMP measures on hirings one might pick up some spill-over effects which occurr from ALMPs to regular hirings. One cannot, however, establish whether these ALMPs have improved the participants' ability to search for a regular job. The situation is different with the Polish Further Training and Re-training scheme. Participants of a Polish training measure receive an allowance which is slightly higher than benefit, they do stay on the unemployment reaister, though. Choosing the stock of training participants, lagged appropriately, as a regressor the question can be evaluated whether an increase in the stock of training participants has a positive net effect on the hirings of the unemployed. A positive net effect would mean that some of the unemployed through their participation in a training scheme have become more effective in their search for a regular job and that substitution of non-participants by participants in the hiring process is not a 100%.

To test whether the search effectiveness of some of the unemployed has been enhanced by being on a training scheme, the following augmented matching function is derived. Let c be an index of the search effectiveness of the unemployed in the absence of search enhancing labour market schemes which takes a value between 0 and 1. Let M is here the weighted sum of search enhancing employment measures Ei (i=1 ....,n), while t is the search effectiveness index which is impacted by such labour market measures. In the case of Polish training measures a. is assumed to be > 0. Enhancing the search effectiveness of some of the unemployed and having a substitution effect which is less than 100% would imply a positive a(. Let H be the number of unemployed being hired in regular, non-subsidised jobs, measured during a certain period, U and V the stock of registered unemployed and of notified vacancies respectively, both measured at the beginning of the period as are the search enhancing employment measures Ei Then (WU) is the search effective stock
side intervention involved training local masons<sup>14</sup> to build the kind of latrines that the local populations desired. Some districts also instituted schemes to make latrines more affordable (by providing access to credit, instituting rotating saving schemes with savings to be used for latrine construction ( _arisan-jamban_ ), and mobilizing community labor to build latrines).

The implementation of the supply side of TSSM, however, lagged behind that of the demand side and was implemented on a much smaller scale. Table 13 shows how much households estimated it would cost to construct the cheapest possible latrine. On average, households estimated it costs approximately Rp 1.2 million to build a latrine (US$135). This is about onethird of the annual per capita income for the average household in our sample. TSSM estimates of the actual cost to install a latrine with a slab (exclusive of the superstructure) are in the range of US$50 to US$90 (WSP, 2012). There is no statistically significant difference in the costs reported in treatment communities and control communities. The majority of households (64 percent) reported that these costs rose in the last 12 months, in both treatment and control communities. There are also no differences between treatment and control communities with respect to access to materials and tradesmen. Slightly more people in control communities reported that they do not know who to contact to obtain materials and tradesmen (4.3 versus 3.1 percent, _p<_ 0.05). Slightly more people in treatment communities reported that the costs are affordable, but this difference is not significant.

**Figure 6. Main reason for not building a toilet**

<!-- Start of picture text --> Tenancy issues 0.11<br>Permit issues 0.22<br>Too complex 0.34<br>Water not available 0.45<br>No one to build 0.67<br>Soil condition 0.90<br>No material 1.35<br>Satisfied with current facility 1.91<br>No savings 3.03<br>Others 5.38<br>Limited space 9.87<br>Other competing priorities 28.92<br>High cost 46.86<br>0 5 10 15 20 25 30 35 40 45 50<br><!-- End of picture text -->

> 14Later, this was replaced by capacity building of more full-fledged sanitation entrepreneurs who employ masons.

21
# **VI. Some Concluding Remarks**

This study attempts to investigate the underlying causes of the mismatch in poverty and inequality levels and trends between the NPS and HBS surveys. The analysis has focused on the key candidates for the divergence between the two surveys. These include the differences in methods of consumption data collection, methodological differences in the construction of the consumption aggregates and estimation of the poverty lines, adjustments for temporal and spatial price variations, and the consistency of within‐household spending and asset ownership trends with poverty trends.

The main findings can be summarized as follows:

- I. Despite noticeable differences in the methods of household consumption data collection, both HBS and NPS show close consumption levels when using comparable inter‐year price deflators. The comparison of the levels and distribution of consumption between the two surveys, when adjusted by the CPI, shows that total consumption and food consumption are significantly higher in NPS 2008/09 than in HBS 2007 for all population groups, particularly the poorest ones for whom aggregate and food consumption levels are, respectively, 35 percent and 70 percent higher in NPS 2008/09 than in HBS 2007. However, the difference vanishes in the last rounds with mean household consumption levels in HBS 2011/12 and NPS 2010/11 being very close, and the mean consumption level in NPS 2012/13 being around 15 percent higher. The difference is driven by higher levels of aggregate and food consumption by the better‐off groups. The consumption levels of the poorest groups are slightly higher in HBS 2011/12 than in the last two rounds of NPS, but the aggregate and food consumption levels of the richest group are, respectively, 25 percent and 70 percent higher in NPS than in HBS. These differences could be explained by underestimation of consumption by the poor in HBS 2007 and sample weighting or overestimation of consumption by the non‐ poor in NPS 2012/13.

- II. The difference between HBS and NPS poverty levels is essentially explained by the differences in the methods of estimation of the poverty line. When comparable methods for estimating the poverty line are applied to both surveys, the poverty line and headcount estimates are relatively close in magnitude.

- III. The discrepancy in poverty trends can be mainly attributed to the difference in temporal price deflators and, to a lesser extent, spatial price deflators. The use of the CPI for adjusting

46
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