rafmacalaba/datause-encoder-data huggingface dataset:
their standard deviation, i.e., _σc_ and _σp_ are standard deviations of children’s and parent’s schooling respectively. The focus in most of the literature is on estimating the parameter _ρ_ 1 which provides an estimate of Pearson correlation between educational attainment of two generations. It is called intergenerational correlation (IGC) in the literature.

An increasingly popular measure of relative mobility is rank correlation, used originally by Dahl and DeLeire (2008), and made salient by the recent work of Chetty et al. (2014). The rank correlation is estimated from the following OLS regression: where _Ri_ is the percentile rank of child _i_ in the distribution of children’s schooling, and _Pi_ is the percentile rank of the parent of child _i_ in the distribution of parental schooling. The parameter of interest is _δ_ 1 which provides an estimate of Spearman rank correlation in schooling across generations.

We compare the bias in the estimates of the slope and intercept terms in the rankrank regression in equation (3) with the bias estimates for the slope and intercept from equations (1) and (2). As noted before, Emran et al. (forthcoming) provide estimates of coresidency bias in IGC and IGRC using the same data sets on India and Bangladesh used here. However, the regression specification used in that paper includes quadratic age controls for both children and parents. Thus their estimates are not comparable to the rank-rank estimates without any controls in this paper; we report estimates of _β_<sup>ˆ</sup> 1 and _ρ_ ˆ1 without any controls for comparability.<sup>9</sup> The different measures of relative mobility discussed above have been the preferred ones in the economics literature for analyzing intergenerational mobility in both developed and developing countries. However, as emphasized by Chetty et al. (2014), relative mobility measures have an important limitation: an improvement in relative mobility may be driven

> 9These estimates provide additional robustness checks for their central conclusion that IGC is preferable to IGRC as a measure of relative mobility in coresident samples. We also note that the main conclusions regarding the bias in rank-based relative and absolute mobility estimates in this paper remain valid if we include age of parents and children in the regressions. However, with quadratic age controls for both children and parents, the standard error of the estimate of the intercept becomes large in many cases.

7
A composite groundwater risk map (Figure 3) in red, green and blue (RGB) colors represents a combination of three hydrological indicators such groundwater arsenic concentration (Green), salinity or groundwater EC (blue) and depth to dry-season groundwater levels (Red) in Bangladesh. The composite map clearly shows that the northwestern and northcentral Bangladesh is featured by deep dry-season groundwater levels whereas arsenic problem in shallow groundwater is mainly concentrated in southcentral region, and the southern, coastal region of Bangladesh has the highest level of groundwater salinity. The composite map clearly highlights the areas where more than one hydrological indicator or risk variable (i.e. high arsenic and high salinity; or high arsenic and deep groundwater levels; or a combination of all three elements) exists to a varying degree.

# **3.2 Groundwater risks and population exposure**

Two groundwater risk maps for Bangladesh using two sets of combination criteria (model 1: groundwater arsenic, salinity and storage; model 2: groundwater arsenic and salinity) are shown in Figure 4. Each groundwater risk map has four classes: extremely high risk, high risk, medium risk and low risk that are based on a set of criteria (models) described in the method section. In addition, groundwater risks posed by arsenic and salinity alone are mapped considering various concentrations of arsenic and electrical conductivity in water (see risk maps in supplementary Figures S7 and S8).

In the groundwater risk map under model 1 (Figure 4a), the mapped area (208 km<sup>2</sup> ; Table 1) within the zone of extremely high risk is small and located mainly in central Comilla district of the southeastern part of Bangladesh (Figure 4a). There are some 219,193 (0.15% of total population) people exposed to the extremely high risk of groundwater (Table 2). The high risk zone covers a large area of 7,146 km<sup>2</sup> (5.3% of total land area) from southwestern to southeastern Bangladesh encompassing a number of districts such as Satkhira, Jessore, Narail, Fardipur, Dhaka, Munshiganj, Comilla, Feni and Chittagong where some 6.4 million (4.4%) people are exposed to groundwater risks. The medium risk zone covers a larger area of 10,350 km<sup>2</sup> (7%) over most part of the Ganges-Brahmaputra-Meghna delta except for the exposed coastal region and about 9.5 million (6.6%) people are exposed to medium groundwater risks. Combined population of approximately 16 million (11%) are currently at variable risk (extremely high to medium risk) of elevated arsenic, salinity and depth to dry-season groundwater levels. Lastly, the mapped zone under low risk in model 1 covers an area of 28,123 9
Do Consumers Bene...t from Supply Chain Intermediaries? Evidence from a Policy Experiment in the Edible Oils Market in Bangladesh M. Shahe Emran<sup>1</sup> IPD, Columbia University Dilip Mookherjee Boston University Forhad Shilpi DECRG, World Bank M. Helal Uddin University of Dhaka Keywords: Marketing Intermediary, Trader Margin, Commodity Prices, Market Power, Double Marginalization, Supplier Credit, Credit Rationing, International Prices, Passthrough, Policy Experiment, Edible Oils, Bangladesh JEL Codes: O12, L13, Q13

> 1We would like to thank seminar participants at the AEA Annual Conference 2016 at San Francisco, NEUDC 2014 at Boston University, Georgetown University, World Bank, and IGC conference at Dhaka for comments on earlier drafts and to acknowledge Wally Mullin, Chris Woodru¤, Raymond Guiteras, Andrew Foster, Sabyasachi Das, Russell Hillberry, Hanan Jacoby, Marcel Fafchamps, Garance Genicot, Sebastian Bustos, Nidhiya Menon, Wahiduddin Mahmud, Fahad Khalil and Will Martin for helpful comments on earlier drafts and/or discussions at various stages of this project. Thanks to Rubaiya Emran for help with the graphs. The project received funds from KCP Trust Fund (World Bank) and IGC Bangladesh country program. An earlier version of the paper was circulated under the title “Food Prices and Marketing Intermediaries: Evidence from a Policy Experiment in Edible Oils Market in Bangladesh”. This paper supersedes the earlier version.
# **3.1 Surplus Hydropower Potential in Afghanistan, Bhutan and Nepal**

One of the key barriers to develop hydropower in South Asia, particularly Afghanistan and Nepal, is the lack of access to neighboring markets. These countries’ domestic demands in the next several decades are likely to be much smaller than the hydropower potential they have. Since these countries may not be able to absorb the available hydropower potential domestically, export markets and transmission access to the export market are needed. Therefore, regional or subregional electricity trade is necessary to exploit the surplus hydropower potential in these three countries.

Figures 7(a), (b) and (c) show that technical and economic potentials of hydropower in these countries are higher than domestic loads projected for several decades to come. Several load forecasts and associated generation capacity needs are available for Nepal. These projections include WECS (2017), NEA (2016) and Timilsina and Toman (2016). WECS (2017) considers five scenarios. WECS’s Business as Usual (BAU) scenario that assumes the continuation of the status quo with 4.5% average annual GDP growth over the next two decades estimates that Nepal would need 3,384 MW, 5,787MW, 8,937 MW, 13,242 MW and 19,151 MW in years 2020, 2025, 2030, 2035 and 2040, respectively. If the average annual GDP growth increases to 7.2%, the corresponding capacity requirements would be, respectively, 7%, 14%, 24%, 37% and 54% higher than the BAU projections. If Nepal achieves high economic growth (9.2% on average annually) due to the change in state administrative structure and political stability that has been achieved after decades of political uncertainties, generation capacities required in 2020, 2025, 2030, 2035 and 2040 would be respectively, 3,794MW, 7,366MW, 13,296MW, 23,588MW and 42,228MW. If targeted policies are implemented to substitute, by 2020, 75% of the water heating in urban 13
- **Burbidge, John B, Lonnie Magee, and A Leslie Robb.** 1988. “Alternative transformations to handle extreme values of the dependent variable.” _Journal of the American statistical Association_ , 83(401): 123–127.

- **Cai, Shu.** 2020. “Migration under liquidity constraints: Evidence from randomized credit access in China.” _Journal of Development Economics_ , 142: 102247.

- **Callaway, Brantly, and Pedro HC Sant’Anna.** 2021. “Difference-in-differences with multiple time periods.” _Journal of econometrics_ , 225(2): 200–230.

- **Chilton, Adam, and Bartosz Woda.** 2022. “The expanding universe of bilateral labor agreements.” _Theoretical Inquiries in Law_ , 23(2): 1–64.

- **Chilton, Adam S, and Eric A Posner.** 2018. “Why countries sign bilateral labor agreements.” _The Journal of Legal Studies_ , 47(S1): S45–S88.

- **Clemens, Michael A.** 2019. “Measuring the Spatial Misallocation of Labor: The Returns to India-Gulf Guest Work in a Natural Experiment.” _CGD Working Paper 501 (January), Center for Global Development, Washington, DC_ .

- **Clemens, Michael A.** 2020. “The emigration life cycle: How development shapes emigration from poor countries.” IZA Institute of Labor Economics 13614.

- **Clemens, Michael A.** 2024. “The effect of lawful crossing on unlawful crossing at the US southwest border.” Peterson Institute for International Economics Working Paper 24-10.

- **Clemens, Michael A, and Erwin R Tiongson.** 2017. “Split decisions: Household finance when a policy discontinuity allocates overseas work.” _Review of Economics and Statistics_ , 99(3): 531–543.

- **Clemens, Michael A., Claudio E. Montenegro, and Lant Pritchett.** 2019. “The Place Premium: Bounding the Price Equivalent of Migration Barriers.” _The Review of Economics and Statistics_ , 101(2): 201–213.

- **Clemens, Michael A, Ethan G Lewis, and Hannah M Postel.** 2018. “Immigration restrictions as active labor market policy: Evidence from the mexican bracero exclusion.” _American Economic Review_ , 108(6): 1468–1487.

26
_n_ In equation 4,   _s_<sup>_R_</sup> _t_  _s_ is included as an index of retrospective events, such as labor _s_  1 shock, marriage, economic distress, and so on, which an individual experienced in a particular year from 1989 to 2006, while _Eijkt_ is a measure of current emotional state. Finally, _Z_ includes a set of demographic, human capital, social capital and other variables measuring values and attitudes at an individual level.

We estimate equation 4 using an ordered probit model, which is standard in the literature<sup>6</sup> . There are some caveats with life-satisfaction estimation<sup>7</sup> . Here we explain how we address these identification problems and measurement errors in our estimation. We start with the identification of reference group. Life in transition survey does not ask for respondent’s opinion for a suitable reference group (Melenberg, 1992; Knight and Song, 2006). We define reference group at various geographic levels. In addition we use information about ranking in the ladder of life as a proxy for relative position in the country.

We use a number of past events which are documented only once a year. One year window for an event seems too imprecise to judge adaptation in the long period, especially compared to laboratory experiments. Despite the fact that precise observation of the timing of any event is not feasible to do in a laboratory for a sufficiently longer period of time, a window lower than a year would certainly provide a better estimate of life satisfaction. As a robustness check we examine various time frames since transition separately in the regression, and group similar events (e.g., closely related events to labor shock, like losing job, getting unemployment benefit, etc) to trace certain type of events more consistently.

> 6 Log linear specification has received some criticisms which could be overcome with panel estimation (Frey and Stutzer, 2002). LiTS is available only for 2006, so we do not have much choice on this.

> 7 In a recent study Clark et al (2007) nicely summarizes potential problems associated with life-satisfaction estimation

7
Table 23. The determinants of innovation

||Ne<br>|pal<br>|Bangl<br>|adesh<br>|In<br>|dia<br>|Paki<br>|stan<br>|
|---|---|---|---|---|---|---|---|---|
||Technol.<br>innovation|innovation<br>sales|Technol.<br>innovation|innovation<br>sales|Technol.<br>innovation|innovation<br>sales|Technol.<br>innovation|innovation<br>sales|
|Firms Size - in log|-0.5037***|-0.0190|0.2318|0.0055|0.2800***|-0.0504***|0.3496***|0.0124|
||(0.118)|(0.035)|(0.228)|(0.054)|(0.028)|(0.016)|(0.048)|(0.010)|
|Invests in R&D|0.2946|0.1817***|-1.1062***|0.4595***|-1.8674***|0.1978**|0.1914|0.0924***|
||(0.375)|(0.025)|(0.339)|(0.033)|(0.066)|(0.096)|(0.451)|(0.035)|
|ICT Index|1.9345***|0.0497|-0.5478|-0.1465|0.2354|0.1908***|-1.5988***|-0.0468|
||(0.197)|(0.096)|(0.911)|(0.203)|(0.239)|(0.062)|(0.122)|(0.043)|
|Log Firm Age|-0.1699*|-0.0126|0.0246|-0.0085|-0.0697|-0.0088|0.2090**|0.0152|
||(0.089)|(0.010)|(0.063)|(0.015)|(0.043)|(0.006)|(0.085)|(0.011)|
|Education as Obstacle|-0.0404|-0.0280*|0.2207*|-0.0045|-0.0412|-0.0218*|-0.1052|-0.0310**|
||(0.117)|(0.016)|(0.124)|(0.022)|(0.041)|(0.013)|(0.109)|(0.015)|
|Firm Exports|-0.4398***|-0.0475*|0.3432|-0.0309|0.1827***|0.0290*|0.4155***|-0.0076|
||(0.151)|(0.027)|(0.288)|(0.076)|(0.053)|(0.016)|(0.159)|(0.021)|
|Demand Pull Effect|-0.0611|-0.0173|0.1592*|-0.0526***|0.0376|0.0220**|0.0413|0.0264**|
||(0.098)|(0.015)|(0.095)|(0.019)|(0.037)|(0.010)|(0.061)|(0.013)|
|workcap|-0.0005|-0.0001|-0.0017|0.0008**|-0.0005|-0.0001|-0.0059*|-0.0001|
||(0.002)|(0.000)|(0.001)|(0.000)|(0.001)|(0.000)|(0.003)|(0.000)|
|duo_mono|-0.1417|0.0355|-0.2531|0.0467|0.0796|0.0133|-0.0351|0.0061|
||(0.226)|(0.030)|(0.350)|(0.069)|(0.065)|(0.015)|(0.140)|(0.017)|
|business|-0.2419**|-0.0294*|-0.0306|0.0253|0.0177|0.0020|0.1533|0.0204|
||(0.118)|(0.017)|(0.097)|(0.020)|(0.023)|(0.010)|(0.154)|(0.022)|
|spillover|-2.8679|-0.1570|2.4450*|-0.7407***|8.4422|3.1811**|1.5389|0.3170|
||(1.877)|(0.274)|(1.482)|(0.284)|(7.763)|(1.536)|(1.258)|(0.205)|
|Constant|2.2236***|0.1590|-0.3917|-0.0933|-0.8571***|0.2784***|-1.7833***|-0.0998|
||(0.433)|(0.189)|(1.426)|(0.296)|(0.241)|(0.063)|(0.569)|(0.087)|
|Observations|470|470|990|990|3,481|3,480|499|502<br>ISIC-|
|Sector dummies|ISIC-1digit|ISIC-1digit|ISIC-2digit|ISIC-2digit|ISIC-2digit|ISIC-2digit|ISIC-1digit|1digit|
|||Standard error|s in parenthese|s. *** p<0.01,|** p<0.05, * p|<0.1|||

32
appreciated by nearly 20 percent in the two-year period.<sup>12</sup> By mid-19819 the government replaced the annual foreign exchange allocation system with a weekly system and tightened exchange contrels in an attempt to protect reserves. These moves led to development of a substantial black market in foreign exchange (Jeanneney, 1986, p.184).

- The following subsections (3.1 3.3) analyze more carefully some of the structural problems that tend to give rise to fiscal unsustainability in SSA.

# 3.1 Budgetary Fragility: **The** Tan3i Effect

Tanzi (1977) pointed out that if taxes are levied in nominal **terms** today and collected in nominal terms after some time lag, the real value of the taxes will have been eroded in the interim by **any** domestic inflation. Failure to index the tax system for collection lags of this sort can reduce the sustainable steady state level of expenditures. This is illustrated by the - - curves DD' and zz in Figure 2, which treat the real primary deficit, d (g t), as an inkreasing function of the inflation rate: d - d(Cf), d'>0. The diagram shows a situation that is unsustainable precisely because of this effect. In the stable lagged adjustment case (curve zz), reliance on the inflation tax (by the early 1980i, reserves were exhausted in many SSA countries and access to international borrowing was zero on the margin) will lead to steadily rising inflation (e.g., the path starting at B).<sup>13</sup> 12 Narrow money is currency plus demand deposits of the banking system; the inflation rate Ai the change in the GDP deflator. All data are from the IMP International Financial Statistics Yearbook, 1987.

13 As has been done in Brazil, indexation of the tax system to domestic prices will protect the real deficit from collection lags (while simultaneously removing, the fiscal dividend provided by *bracket creep", which 9
evidence that the reliability of such information also eroded, so that trends in indicators, from 1987 to the present, may represent genuine economic changes or effects of incomplete reports with new biases. Hence, the starting point for this study was 1987 and trends since then were analyzed with an eye on likely statistical distortions.

# a. Basic Data Issues

It is generally recognized that available sources and methods do not, for the FSU, conform with the UN System of National Accounts (SNA). The problem is more complicated however, since the new nations themselves have had little time to compile and analyze the available data, particularly information regarding so-called Union-wide enterprises and activities, which encompasses the bulk of foreign trade and defense-related activities. This may explain why data they submit to the Bank and other international agencies differ from data available to IECSE from the old FSU central records, sometimes by analytically significant amounts.

As republics distanced themselves from the Union, as penalties for noncompliance lost force, economic agents seem to have altered their reporting behavior in ways that color seemingly objective indicators for 1990-91; the picture is even cloudier when preliminary reports and projections for 1992 are considered. Nor is certain that such problems will ever be resolved for 1992--and 1993 will be problematic unless supporting actions occur soon.

The nations emerging from the FSU did not, as republics within the Union, have full access to the information used by Union-level planners; even today, it is not clear that the authorities for new nations have received and had time to digest the relevant information. To an unusual extent, staffs of international agencies have had to help national authorities assemble and analyze basic information. This study benefited from essentially all such international and national efforts, and relied heavily on data collected by Bank missions to the 15 economies.

National accounts brought back by missions tend to be based on the Material Product System (MPS) traditionally used by CMEA economies. Adjustment to international standards of the SNA have tended to be limited to 'bridge tables" showing major adjustment items from an expenditure approach, to move from the Net Material Product (NMP) of the MPS to the Gross Domestic Product (GDP) measure of the SNA. Major adjustments are addition of depreciation (since NMP is a "net" and GDP is a "gross' concept) and the bulk of services (which MPS ignores because they are not "material"). This explains the paucity of details, for most FSU economies, under GDP measures reported in the Bank's new _Statisdcal Handbook: States of the former USSR._ MPS concepts may have been the same throughout CMEA but there were noticeable differences in practice.<sup>18</sup> Such expenditure-side bridge tables are not well designed to identify

> t8 See, for example, the country practice notes in Marer et al.

16
||**India, AP I **|**ndia, Odisha **|**India, Raj **|**Pakistan**|**International**<br>**Benchmark**|
|---|---|---|---|---|---|
|**Regulatory Substance**|**76%**|**76%**|**79%**|**83%**|**76%**|
|**Tariff Regulation**|**75%**|**75%**|**75%**|**83%**|**77%**|
|_Regulatory Frameworkfor Tariffs_|_100%_|_100%_|_100%_|_100%_|90%|
|_Determination of Tariffs_|_50%_|_50%_|_50%_|_67%_|64%|
|**Quality Regulation**|**83%**|**83%**|**92%**|**100%**|**75%**|
|_Quality of Service Standards_|_100%_|_100%_|_100%_|_100%_|82%|
|_Quality of Service Enforcement_|_67%_|_67%_|_83%_|_100%_|68%|
|**Market Entry Regulation**|**71%**|**71%**|**71%**|**67%**|**77%**|
|_Permitting New Entrants_|_75%_|_75%_|_75%_|_100%_|90%|
|_PPA Approvals_|_67%_|_67%_|_67%_|_33%_|57%|

_Source: Data collected for the Rethinking Power Sector Reform Project._

# _3.4.2.4 Quality regulation_

When it comes to quality of service regulation, at least on paper, Pakistan’s regulatory framework is comparatively well structured, particularly with regard to the creating of enforcement measures (Table 17).

For **quality regulation** , there are specific written and publicly available quality of service standards for quality of product, supply, and commercial service. Although utilities are legally required to meet them, and there are fines for non-compliance, utilities struggle to do so due to technical challenges. Fines for non-compliance are enforced in practice and whenever a utility fails to meet the standards, this is disclosed to the public.

For **quality of service enforcement** regulated entities are required to report various indicators, such as SAIDI, SAIFI, voltage indicators, and distribution losses. These are independently validated and reviewed by NEPRA. There are “Guaranteed Standards of Performance” for individual consumers and “Overall Standards of Performance”. The latter provide the underlying indicators of assessment of supply reliability, quality of supply, and safety of electric power supply that a distribution company provides to its consumers. Distribution companies are required to maintain information on all power supply interruptions and on all consumer complaints. More importantly, the rules establish that each distribution company shall supply to NEPRA an Annual Performance Report on specified lines.

Summing up, there is a wide, comprehensive quality regulatory framework, with specific and publicly available requirements of information, including formats, processes, and consequences of non-compliance.

Although the performance de jure of the quality regulation is high, some qualifications must be made. It seems that, in practice, even if there are fines for failing to meet quality of service standards, these are seldom applied and there is no reliable sanction if the distribution company fails to meet standards.

53