rafmacalaba/datause-encoder-data huggingface dataset:
tute a tax base and governments have an incentive to track import data. Whenever import data was missing we used mirrored export data if it was available (this represented only half percent of the observations). Trade data is deflated using the reporter country’s GDP deflator. Income and population are taken from the World Development Indicators database. Geographic data, together with dummies for same language and colonial links, are taken from Clair et al (2004).<sup>3</sup> The distance data are calculated following the great circle formula, which uses latitudes and longitudes of the relevant capital cities.

We are ultimately interested in the dual role of institutions and infrastructure. Our data include indexes produced by the World Bank on infrastructure, and by the Fraser Institute for institutions. The institution indexes are from the ”Economic Freedom of the World” (EFW) database.<sup>4</sup> The EFW indexes are supplemented with robustness checks (with shorter panels) using data from Transparency International and Kaufmann, Kraay and Mastruzzi (2005). The EFW indexes are themselves based on several sub-indexes designed to measure the degree of ’economic freedom’ in five areas: (1) size of government: expenditures, taxes, and enterprises; (2) legal structure and protection of property rights; (3) access to sound money: inflation rate, possibility to own foreign currency bank accounts; (4) freedom to trade internationally: taxes on international trade, regulatory trade barriers, capital marke controls, difference between official exchange rate and black market rate, etc.; and (5) regulation of credit, labor, and business. Each index ranges from 0 to 10 reflecting the distribution of the underlying data. Notionally, a low value is bad, and a higher value is good. We work with indexes for 1985, 1990, 1995, 2000, 2001 and 2002, with in interpolated values for years without values.

> 3http://www.cepii.fr/anglaisgraph/bdd/distances.htm

> 4http://www.freetheworld.com/download.html#efw

8
Martins, P. M. G. 2019. “Structural Change Rediscovered: The Role of Human and Physical Capital.” World Bank Research and Policy Briefs, July 2019, Washington, DC.

- Micco, A., and C. Pagés. 2006 “The Economic Effects of Employment Protection: Evidence from International Industry-Level Data.” Discussion Paper No. 2433, IZA, Bonn.

- Nicoletti, G., and S. Scarpetta. 2005. “Product Market Reforms and Employment in OECD Countries.” Economics Department Working Paper No. 472, OECD Publishing, Paris.

- Penn World Table, version 9.0:  Feenstra, R. C., R. Inklaar, and M.P. Timmer 2015. “The Next Generation of the Penn World Table.” American Economic Review 105 (10): 3150‒82.

- Pesaran, M. H. 2004. “General Diagnostic Tests for Cross Section Dependence in Panels.” <u>https://doi.org/10.17863/CAM.5113.</u>

- Psacharopoulos, G. 1994. "Returns to investment in education: A global update," World Development, Elsevier, vol. 22(9): 1325-1343.

- Rodriguez, F., and D. Rodrik. 2001. “Trade Policy and Economic Growth: A Skeptic's Guide to the Crossnational Evidence.” NBER Macroeconomics Annual 2000 15: 261‒338. Cambridge, MA: MIT Press.

- Rodriguez-Pose, A. and T. Ketterer. 2019. “Institutional Change and The Development of Lagging Regions in Europe”. Regional Studies: 1-13.

- Rodrik, D., A. Subramanian, and F. Trebbi. 2004. “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development.” Journal of Economic Growth 9: 131-165.

- Summers, L. H. 2014. “U.S. Economic Prospects: Secular Stagnation, Hysteresis, and the Zero Lower Bound _.”_ Business Economics 49(2): 65‒73.

- ———. 2018. “Secular Stagnation and Macroeconomic Policy.” IMF Economic Review 66(2):226‒50.

- Sunde, U., and T. Vischer. 2015. “Human Capital and Growth: Specification Matters.” Economica 82 (326): 368-390.

- Teulings, C., and R. Baldwin. 2014. “Secular Stagnation: Facts, Causes and Cures.” Vox eBook.

- Vandenbussche, J., P. Aghion, and C. Meghir. 2006. “Growth, Distance to Frontier and Composition of Human Capital.” Journal of Economic Growth 11(2): 97‒127.

- WEF (World Economic Forum). The Global Competitiveness Report 2016‒17 database.

World Bank Group. World Development Indicators database. World Bank, Washington, DC.

- ———. 2017a. “Lessons from Poland, Insights for Poland: A Sustainable and Inclusive Transition to High Income Status.” World Bank, Washington, DC.

- ———. 2017b. “The Western Balkans: Revving up the Engines of Growth and Prosperity.” World Bank, Washington, DC.

- ———. 2018. “The Western Balkans: Higher but Fragile Growth.” World Bank, Washington, DC.

- ———. 2019a. “Financial Inclusion. ECA Economic Update.” Spring. World Bank, Washington, DC.

- ———. 2019b. “Western Balkans Regular Economic Report. Rising Uncertainties.” No. 16. Fall 2019.

- ———. Forthcoming. “Global Economic Prospect Report. 2020”

- Yanikkaya, H. 2003. “Trade Openness and Economic Growth: A Cross-country Empirical Investigation.” Journal of Development Economics 72(1): 57‒89.

30
# _4.4.2 Alternative Measures of Classroom Age Dispersion_

The measure of classroom age heterogeneity used so far has been the standard deviation of student age. Table 10 shows that the adverse effect of classroom age heterogeneity is robust to alternative measures of dispersion. Columns (1) and (3) present estimates based on the difference between the 75<sup>th</sup> and 25<sup>th</sup> percentile of the classroom age distribution and columns (2) and (4) report estimates based on age range (i.e., maximum – minimum). Since the standard deviation of the 75<sup>th</sup> -25<sup>th</sup> percentile age difference is 0.256 (Table 1), the effect size is roughly 0.07 standard deviations for both mathematics and science. Similarly, as the standard deviation of age range is 1.092, the effect size is also roughly 0.06 standard deviations. These estimated effect sizes are similar to the 0.08 using the standard deviation of age as the measure of classroom age dispersion.

# _4.4.3 Estimating Using TIMSS 2003 and TIMSS 2007 Separately_

The estimates presented above are all based on pooled TIMSS data. For pooled data to be sensible, the point estimates should be similar across both waves of TIMSS. Table 11 shows that the estimated effects of classroom age heterogeneity remain significantly negative despite the reduction in sample size. Furthermore, even though only three countries overlap in TIMSS 2003 and TIMSS 2007, the estimated effects of classroom age heterogeneity are not too different between the two waves of TIMSS. Thus, the estimates are not sensitive to pooling both waves of TIMSS.

19
declines in overall racial segregation, the exposure of Blacks to Whites declined between the 2000 and the 2010 censuses.

The paper’s first metro-level analysis correlates changes in exposure with changes in credit standards: where _∆Creditj,2010−2000_ is a measure of the change in lending standards for metropolitan area j, xj is a set of controls, including controls for demographic changes, and Metroj is a metro-area fixed effect. Two key measures of lending standards, the change in approval rate and the change in the loan-to-income ratio, experience significant changes between 2000 and 2006: the loan-toincome ratio of mortgage originations increases by 0.4, and the approval rate for mortgage applications increases by 13 percentage points. As changes in credit conditions typically reflect both shifts in the demand for credit as well as shifts in the supply of credit, identification of the causal impact β requires an exogenous source of credit supply shifts. Banks' liquidity level in the early 1990s is a significant predictor of the rise in leverage and approval rates between 2000 and 2006. Specifically, banks that were liquidity constrained or that had a less securitizable portfolio of assets in the early 1990s benefited more from the securitization boom of the early 2000s. Such boom was arguably triggered by the Federal Housing Enterprises Financial Safety and Soundness Act of 1992, by preferential treatment to banks holding mortgage-backed securities backed by the Government Sponsored Enterprises.

While early 1990s bank liquidity and asset securitizability is a significant predictor of 2000-2006 changes in lending standards, there is little empirical evidence of its correlation with observable demand shifters or with observable drivers of racial segregation. Results suggest an economically significant impact of credit supply shifts on the exposure of Blacks to Whites: without the credit supply shock, Black households would have had between 2.3 and 5.2 percentage points more White neighbors in 2010.

22
observe realizations of the infection process and therefore does not know her HIV status. Assuming she knows the mortality process associated with HIV infection, however, survival during each additional period gives her information about her status. Specically, she reduces her subjective probabilities of having become infected in each of the past periods based on the fact that she is still alive. According to these probabilities and given the mortality process, she updates her survival expectations. HIV infection also increases child mortality probabilities through mother-to-child transmission. In the model, the woman also updates expectations about the survival of each of her children depending on the probability assigned to her having been infected at the time of birth.

The dynamic fertility model is estimated using the Malawi Diusion and Ideational Change Project (MDICP) dataset, a rich longitudinal dataset collected in rural areas of three dierent districts of the country. The data contain extensive information on more than 4,000 individuals at the individual and household level. The three sampled regions vary signicantly in several aspects that are potentially relevant for the analysis, such as HIV prevalence rates, polygamy rates, and schooling levels. A unique feature of the MDICP data is that they include measures of subjective expectations regarding a range of outcomes, including the likelihood respondents assigned to being HIV-infected at the time of the interview. The expectations data were collected using a novel bean-counting method, developed by Delavande and Kohler (2009), which is appropriate for populations with low levels of numeracy. Delavande and Kohler (2009) nd that the reported subjective expectations follow basic properties of probabilities and that the assessments of HIV-infection vary meaningfully with observable characteristics associated with dierent levels of HIV prevalence. I use a subsample of 1006 married women who were interviewed at least once during the 2006 and 2008 rounds, when family rosters and subjective expectations were collected.

Since 2004, each round of data collection included HIV testing of respondents and the prevalence rate was found to be about seven percent.<sup>7</sup> Although individuals who received

> 7HIV prevalence is lower in rural areas than in urban areas. The prevalence in the MDICP sample is lower than the rural 2004 DHS prevalence because the sample does not include peri-urban areas such as

4
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

commercialisation of publicly owned utility, and the least common step undertaken is privatisation.

It should be noted that not all the above reform elements will be appropriate in all countries. For example, a particular issue that arises is whether smaller systems require vertical separation and third-party-access. Studies such as those of Bacon do not attempt to evaluate reform effects but they do provide potentially important information on the extent of reforms which facilitates testing of the empirical impact of different stages of reform on performance.

# **2.1.3 Sector endowments**

Specific reform measures and some features of market structure and institutional framework can generally be regarded as controllable. However, these, and ultimately the sector performance, are also influenced by such factors as size of the system, available resources for generation (in particular hydroelectric resources and availability of domestic sources of fuel,) interconnections, and geographical factors. While the sector-level endowments are given, they can exert considerable influence on the capabilities and options of individual electricity sectors.

# **2.1.4 Market structure**

In our conceptual model of reform, the market structure component refers to the wider framework within which the interaction of supply of and demand for electricity takes place. At the time of reform this structure is the result of the sector’s history, of resource endowment and of past policies. The initial structure defines the starting point of the reform and is a given factor. Important features of market structure are the composition of demand for electricity, degree of vertical integration, market concentration of generation capacity, production technologies and degree of market opening.

Reform measures change the market structure, and this has a direct impact on the behaviour of the market actors and consequently on the performance of the sector. Structural changes take time to implement and often generate opposition from vested interests. It is, therefore, important that the appropriate structure is envisaged from the start of the reform and where this does not happen, it can take some years to sort things out. For example: horizontal market power in the England and Wales electricity generation market resulting from the initial creation of a duopoly (Newbery and Pollitt, 1997); vertical market power arising from the joint ownership of the largest generation incumbent and the high voltage transmission monopoly in Chile (Basañes, Saavedra, and Soto, 1999). At the same time, shortcomings in the inadequate restructuring increases the need for future intervention and imposition of rules and regulation that can increase uncertainty and tend to be imperfect substitutes to proper structure and that increase uncertainty.

8
20 years, it is expected that further accumulation of benefits will be found with longer exposure. A likely possibility, though, is that the previously-mentioned challenges that have arisen since 1985, i.e. the time of the von Braun et el.’s study, have reduced the profitability of snowpeas over time. As already postulated, we also consider that adoption in the early years may have been different from adoption in later years, affected by global prices for snowpeas and their inputs, as well as more local factors such as community experience, crowding, and land depletion.

For descriptive purposes, we identify three types of households based on their adoption histories: 1) _early adopters_ , who adopted by 1985, 2) _late adopters_ , who adopted after 1985 but before 2005, and 3) _non-adopters_ . A basic comparison of per capita consumption in 2005 of these three groups can be seen in Table 4. The results seem to suggest that, overall, adopters in 2005 had consumption levels 14 percent higher than non-adopters, though the difference was not significant. In terms of timing of adoption, we see that early adopters in 2005 were better off than late adopters, who were in turn had higher consumption than those who had never adopted, though only the difference between the early and non-adopters was significant at the 95 percent level.

**Table 4: Per Capita Consumption in 2005**

||Adopters||||
|---|---|---|---|---|
|Early|Late|Total|Non-Adopters|Total|
|5357*|5,022|5,215|4,582|5,105|

*significantly different from non-adopters at the 95% level

This simple contrast, although informative, may be misleading. In fact, along with the complexity of considering different adoption spells over different periods, analyzing the impact of snowpeas adoption on welfare is complicated by the fact that households that adopt have chosen to do so. It may be the case that households that adopt are fundamentally different from non-adopters and the differences in per capita . consumption noted above reflect these differences and not the impact of adoption _per se_ Provided that adopting and non-adopting households have a similar range of 31
**Figure 1: Social Service Use as a Function of (Mean) Income with Threshold Effects**

<!-- Start of picture text --> hx Q *<br>hb<br>ha<br>a b t x a b Mean(x)<br><!-- End of picture text --> Source: Author’s compilation.

This simple example illustrates two important points. First, it highlights the importance of the distribution of determinants at both the household and the local(ity) level for social service use. Second, it implies that the level of public service use follows a pattern similar to the cumulative distribution function of any bell-shaped distribution function—if the respective determinant follows such a distribution. This means that progress towards social service utilization would be slow initially, gain momentum at middle stages, and eventually slow down—an s-shaped transition path.

The example uses the case of income, which may indeed represent an important demand-side constraint in a number of social sectors. However, such factors are not limited to income. On the demand side, another important factor is education, and an argument similar to the one made for income can be made for educational levels. Another example is the distance to the next urban area, for example, with a health facility. Here, a process of urbanization that typically accompanies economic development would not only decrease the mean but would also compress the distribution of these distances. This mechanism may thus also cause an s- shaped transition.

> In these examples, we have made our argument _ceteris paribus_ , that is, assuming that other factors that influence service use remain constant. In reality, of course, this is not the case and all these determinants, for example, income, education, and the degree of urbanization, change simultaneously. As suggested above, intertemporal complementarities, for example, the role of future mothers’ education, would also have to be considered if one examined

6
participating institutions in the program. Among the criteria important to the Bank were percentage of portfolio current and not overdue, extent of lease contracts to MSBs, and access to long-term funds other than those from the Bank.

45. The results of OLP's participation in the Project are summarized in the analysis tables on MSB sub-projects financed by OLP under the Microenterprise Project which are shown in Tables 2 and 3 below 21.

46. Of the total amount of Rs 178.5 million for 669 projects onlent by OLP [as of September 1994], 75% or Rs 133.5 million were taken up by 400 lease contracts for small scale businesses, and 25% or Rs 45.0 million were for 269 lease contracts for microenterprises. The average size of lease contracts was Rs. 167,300 (- US$ 7,600) for microenterprises, roughly half of the average of Rs. 333,800 (- US$ 15,300) for small scale businesses. For microenterprises, 83% of the number of leases were for contracts Rs 250,000 (a US$ 11,300) and below, and these leases took up 57 % of the total amount of leases financed by OLP for microenterprises. It is interesting to point out that within this category of lease contracts below Rs 250,000 lease contracts for Rs 100,000 (_ US$ 4,500) and below comprised 41.6 % of the total number.

**Table 2. Size Classification of Leases Financed by OLP under the Project**

||**Percen**|**tage Distributi**|**on, by Numbe**|**r and Amount**|||
|---|---|---|---|---|---|---|
|**Size of Leases**<br>**in Rs**|**Microe**<br>**Number**|**nterprise**<br>**Amount**|**Small Sc**<br>**Number**|**ale Business**<br>**Amount**|**Number**|**Total**<br> <br>**Amount**|
|**Up to 100,000**|**41.64 %**|**14.28 %**|**19.5%**|**4.1 %**|**28.40 %**|**6.70 %**|
|**100,001-250,000**|**41.65 %**|**43.04 %**|**32.0 %**|**16.29 %**|**35.87 %**|**23.03 %**|
|**250,001-500,000**|**13.03 %**|**26.87 %**|**30.25 %**|**33.65 %**|**23.32 %**|**31.96 %**|
|**500,001-1,800,000**|**3.72 %**|**15.81 %**|**18.25 %**|**45.90 %**|**12.41 %**|**38.32 %**|
|**Total, %**|**100.00 %**|**100.00 %**|**100.00**%|100.00 %|100.00 %|<br>100.00 %|
|Total, in # & Rs.|**269**|**45,000,972**|<br>**400**|**133,508,787**|<br>**669**|**178,509,759**|
|**Ave. Lease, Rs.**||**167,290**||**333,777**||**266,830**|

47. For both microenterprises and small-scale businesses, about two-thirds of leases were for machinery and equipment, as shown in Table 3. For lease contracts to microenterprises, office equipment accounted for 20%, slightly higher than the similar category of leased assets for small-scale businesses. Lease contracts for commercial vehicles comprised 15% of the total of leased assets for both microenterprises and smallscale businesses. The microenterprises that were clients of OLP belonged predominantly to the services sector, followed by the steel and engineering, and garments, jute and allied activities sectors. In contrast, of the small-scale businesses accessing OLP's lease financing program, 21% were from the services sectors, 15% in steel and engineering and 6.6% in chemical and phannaceutical sectors.

**21de** MemoiTe. Back-To-Office Report, Bank Review Mission, September 1994.

21
# **<mark>Table S17. Estimated population concerned about health and finances</mark>**

||_Panel _<br>|_A: Concerned abo_<br>|_ut household he_<br>|_alth_<br>||
|---|---|---|---|---|---|
||AllCountries|Ethiopia|Malawi|Nigeria|Uganda|
|Total|257,414,816|71,449,632|17,517,492|137,937,232|30,510,464|
||(3,083,491)|(1,858,111)|(189,537)|(2,295,302)|(623,196)|
|Mean|0.779|0.707|0.933|0.821|0.716|
||(0.009)|(0.018)|(0.010)|(0.014)|(0.015)|
|Observations|8,849|3,058|1,646|1,948|2,197|

||_Panel _|_B: Concerned abo_|_ut household fina_|_nces_||
|---|---|---|---|---|---|
||All Countries|Ethiopia|Malawi|Nigeria|Uganda|
|Total|292,328,256|81,848,456|17,919,304|157,232,592|35,327,904|
||(2,431,731)|(1,526,779)|(164,040)|(1,785,593)|(510,836)|
|Mean|0.884|0.810|0.954|0.935|0.829|
||(0.007)|(0.015)|(0.009)|(0.011)|(0.012)|
|Observations|8,850|3,058|1,646|1,949|2,197|

_Note_ : Each column reports the estimated total number of households using a coping strategy in response to COVID-19-relaated income loss and average household-level incidence of using a coping strategy. Adoption of a strategy is condition on experiencing a COVID-19 related loss of income per Table S8. Data come from the most round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda.

44
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