rafmacalaba/datause-encoder-data huggingface dataset:
These conclusions hold in each of the follow-up rounds, suggesting that this is not a case of training taking time to show impact.

In contrast, the combination of training and the cash grant does have positive and significant impacts on capital stock, which hold across survey rounds. Truncated capital stock increases by an ITT of 10,000 Rs, with the TOT for the pooled rounds of 15,357. Thus capital stock appears to have increased by the same amount as the grant for those who completed the training and received the 15,000 Rs. There is a significant increase in profits when using the pooled sample with truncated or log profits. This effect comes from the round 2 and 3 data. However, it appears that this increase in profits is only temporary; the point estimate on profits is much smaller by the rounds 4 and 5 surveys.<sup>16</sup> This is seen also in Figure 4B, where the CDF of profits in round 5 almost on top of that of the other two groups. The sales data are noisier than the profits data, and we generally see a positive, but not significant, impact on sales. Hours worked are higher in the round 2 survey, but not significantly different from hours worked in the control group in subsequent rounds.

The combination of training and a grant therefore have immediate effects which appear to dissipate a year later. Since we wanted to be able to compare the impact of training and cash to that of training alone, cash grants are only given to those who completed training. Ex ante it was not possible to tell who would complete training, precluding the inclusion of a treatment group receiving only cash conditional on being the sort of person who would complete training if offered it. To learn whether the short-term impact on profits is due just to the cash grants, or to the combination of the cash and training, we therefore re-estimate the impact on profits, controlling directly for capital stock. The control for capital stock nets out both the liquidity effect of the grant and the effect of training on the optimal capital stock that might arise, for example, through learning about how to access credit, or through re-investing profits. Under a sequential ignorability assumption (Imai et al, 2010), controlling for capital therefore gives us a lower bound on the impact of training among the sample of individuals receiving the grant.

Controlling for capital stock reduces the coefficient for the impact of the joint treatment on truncated profits from 1207 in the pooled sample to 425 (s.e. 544), suggesting that as much as two-thirds of the effect is due to the grant. Looking just at the round 2 profits, the control for

> 16 However, a test of equality of the impact on truncated profits over rounds 2 and 3 and the impact over rounds 4 and 5 has p-value 0.16 (p=0.0998 for log profits), so we can at best marginally reject no decline in profits.

19
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Accounting and auditing. rules as well as<sup>information disclosureandtransparency</sup> requirements are of paramount importance.

Mutual funds also require well-developed securities<sup>marketswith a highlevel of</sup> market integrity and liquidity. Market integrity<sup>impliesthatinsidersarebarredfrom</sup> taking advantage of privileged information,<sup>whilelargeshareholdersandmarket</sup> intermediaries are prevented from engaging<sup>in market manipulation. Market integrity also</sup> requires that officers of listed corporations<sup>observehigh standardsofcorporate</sup> governance and honesty and do not engage<sup>in extensivefraud and theft. Marketliquidity</sup> ensures that transaction costs are low and investors<sup>do not suffer from large adverseprice</sup> movements when they initiate transactions in individual<sup>securities.</sup> The recent corporate, accounting and securities<sup>marketscandals intheUS have</sup> undermined confidence in the integrity of<sup>USmarketsand mayhavecontributedtothe</sup> increased volatility of markets. Their implications<sup>forthe futureevolutionof mutual</sup> funds are difficult to assess at this juncture,<sup>although effortstostrengthencorporate</sup> governance, ensure auditor independence,<sup>andenhancethecredibilityofpublished</sup> corporate information would help in averting<sup>anyfurther erosion of investorconfidence</sup> in market integrity.

**Statistical problems.** This paper uses aggregate<sup>datafroma crosssectionof 40</sup> developed, developing and transition countries<sup>tostudythestructureand growthpattern</sup> of mutual funds in different countries and<sup>analyzethedeterminantsof mutualfund</sup> growth. The data cover the period 1992-98 and<sup>werecollectedfrom a varietyof sources.</sup> Some were primary, such as mutual fund<sup>industry associationsandcapital market</sup> regulatory authorities. Others were secondary,<sup>such astheEuropean Federationof</sup> Investment Funds and Companies (FEFSI),<sup>the InvestmentCompany Institute(ICI) of the</sup> United States, the Organization of Economic<sup>Co-operationandDevelopment(OECD),</sup> and Goldman Sachs Investment Research.

The collected data suffer from a number of<sup>importantdeficiencies.Mutualfunds</sup> have been created to serve the financial needs<sup>ofhouseholds.Indeed,undertheright</sup>

> 4 Beneficial regulation has been attributed<sup>asa keyfactor behind thestrong growth of the US mutualfund</sup> industry (Reid 2000).

4
**<u>Table 1.  Survey Experiment Consumption Modules</u>**

|Module|Description|Details|Number of<br>Households|
|---|---|---|---|
|1|Long list (58 food items)<br>14 day|Quantity from purchases, own-production, and<br>gifts/other sources;<br>Tshilling value of consumption from purchases|503|
|2|Long list (58 food items)<br>7 day|Quantity from purchases, own-production, and<br>gifts/other sources;<br>Tshilling value of consumption from purchases|504|
|3|Subset list (17 food items; subset of 58<br>foods), scaled by 1/0.77<sup>a</sup>|Quantity from purchases, own-production, and<br>gifts/other sources;|504|
||7 day|Tshilling value of consumption from purchases||
|4|Collapsed list (11 food items covering<br>universe of food categories)<br>7 day|Tshilling value of consumption|504|
|5|Long list (58 food items)<br>Usual 12 month|Consumption from purchases: number of<br>months consumed, quantity per month,<br>Tshilling value per month<br>Consumption from own-production: number of<br>months consumed, quantity per month,<br>Tshilling value per month<br>Consumption from gifts/other sources: total<br>estimated value for last 12 months|504|
|6|Household diary, frequent visits<br>14 day diary||502|
|7|Household diary, infrequent visits<br>14 day diary||501|
|8|Individually-kept diary, frequent visits<br>14 day diary||503<br>4,025|

_Notes_ : Frequent visits entailed daily visits by the local assistant and visits every other day by the survey enumerator for the duration of the 2-week diary. Infrequent visits entail 3 visits: to deliver the diary (day 1), to pick up week 1 diary and drop off week 2 diary (day 8), and to pick up week 2 diary (day 15). Households assigned to the infrequent diary but who had no literate members (about 18 percent of the sub-sample) were visited every other day by the local assistant and the enumerator.

Non-food items are divided into two groups based on frequency of purchase. Frequently purchased items (charcoal, firewood, kerosene/paraffin, matches, candles, lighters, laundry soap, toilet soap, cigarettes, tobacco, cell phone and internet, transport) were collected by 14-day recall for modules 1-5 and in the 14-day diary for modules 6-8. Non-frequent non-food items (utilities, durables, clothing, health, education, contributions, and other; housing is excluded) are collected by recall identically across all modules at the end of the interview (and at the end of the 2-week period for the diaries) and over the identical one or 12-month reference period, depending on the item in question.

a The 17 foods account for 77 percent of the food budget, so the measured value of food consumption is scaled up by 1/0.77.

7
of subsidies’ distributional and targeting performance are arithmetic, nonbehavioral, and express a partial equilibrium.<sup>4</sup>

# **2.1 Data sources**

As no single data source provides all the information needed to compute the efficient economic cost of service, which is needed to estimate efficient subsidies at the household level, we use several data sources combined with a series of assumptions to construct the variables for our analysis.

## **Household surveys**

We estimate piped water access, connections, and expenditure using information self-reported during national household-level surveys (see the appendix for details). These surveys also provide, with varying degrees of quality across countries, the information necessary to construct total expenditure (and/or income) variables for each household. The quantities of water consumed, and the prices or tariffs paid by each household, are usually not reported in household surveys. We therefore estimate these by complementing the household-level surveys with administrative data from service providers and government programs detailing tariff structures.

## **Administrative data on tariff structures**

We gathered information about tariff structures from the International Benchmarking Network for Water and Sanitation Utilities (IBNET) database. Some countries list most of their water providers in IBNET, while others list only a few. For example, IBNET includes data on the tariff structures of all water providers in Mali and Niger, each of which has only one piped water provider. Meanwhile, for countries such as Ethiopia, Nigeria, Uganda, and Bangladesh—where the provision of services is decentralized and a multitude of utilities provide piped water—IBNET covers only a sample of providers.

## **Data on cost-reflective tariffs**

Cost-reflective tariffs cover the costs of providing service, including not only the efficient economic cost but also the costs arising from the inefficiencies of the service provider. The _efficient economic cost_ , meanwhile, covers all the economic resources required for efficient service delivery. These include operation and maintenance expenses and also all capital costs such as depreciation and return on capital. Estimating cost-reflective tariffs is a data-intensive endeavor.

For our purposes, cost-reflective tariffs are taken from a novel data set provided by Andres et al. (2019). The alternative approach followed by Andres et al., despite its assumptions and simplifications, improves on the existing estimates. In a nutshell, this approach entails answering the following question: “What is the long-run incremental cost of providing water and sanitation services for a given company?” It is based on a simple, utility-wide, bottom-up model, drawing

> 4 See, for instance, Abramovsky and Phillips (2015) for a description of different microsimulation models for taxes and benefits using household survey data.

4
# References

- Atje, R., and B. Jovanovic. 1993. “Stock Markets and Development.” European Economic Review, April.

- Balassa, B. 1985. ‘Exports, Policy Choices and Economic Growth in Developing Countries After the 1973 Oil Shock.” Journal ofDevelopment Economics, May/June.

- Baldwin, R. 1989. ‘Measuring Nontariff Trade Policies.” National Bureau of Economic Research (NBER) Working Paper No. 2978. National Bureau of Economic Research, Cambridge, Massachusetts.

- Barrera, F., and E. Lora. 1997, ‘Una Década de Reformas:El crecimiento, la productividad y la inversién ya no son como antes.” Inter-American Development Bank, Washington, D.C. Mimeograph. . oo

- Barro, R. 199]. ‘Economic Growth in a Cross Section of Countries.” Quarterly Journal of Economics, May. . ,

- Barro, R., and J.W. Lee. 1994. ‘Sources of Economic Growth.” Carnegie-Rochester Conference Series on Public Policy. (40) 1-57.

- Barro, R., and X. Sala-i-Martin. 1995. Economic Growth. New York: McGraw-Hill, Inc. Bruno, M., and W. Easterly. 1996. ‘Inflation’s Children: Tales of Crises that Beget Reforms.” AEA Papers and Proceedings, May.

- Burki, S.J., and S. Edwards. 1996. Dismantling the Populist State: The Unfinished Revolution in Latin America and the Caribbean. Latin American and Caribbean Studies Viewpoints, World Bank, Washington, D.C.

- Caselli, F., G. Esquivel, and F. Lefort. 1996. ‘Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics.” Journal ofEconomic Growth, September.

Chenery, H., and M. Syrquin. 1975. Patterns ofDevelopment. New York: Oxford University Press.

. 1989. “Three Decades of Industrialization.” World Bank Economic Review, May.

- Demetriades, P., and K. Luintel. 1996. ‘Financial Development, Economic Growth and Banking Sector Controls: Evidence from India.” The Economic Journal, March.

- Demirguc-Kunt, A., and R. Levine. 1996. ‘Stock Market Development and Financial Intermediaries: Stylized Facts.” WorldBank Economic Review, May.

- Dollar, D. 1992. ‘Outward-Oriented Developing Countries Do Grow More Rapidly: Evidence from 95 LDCs, 1976-85.” Economic Development and Cultural Change, April.

30
<u>Table 3 Educational attainment of teachers and other wage workers (proportion of workers)</u>

|<br>||<br>Teachers<br>|<br> <br>|<br>Othe|<br>r wage wor<br>|<br>kers<br>|<br>Pri|maryteac<br>|hers<br>|Sec|ondarytea<br>|chers<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Country|P|S|P-S|P|S|P-S|P|S|P-S|P|S|P-S|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Burkina Faso|6.1|73.1|20.8|59.3|31.8|8.9|7.1|82.7|10.2|0.0|12.8|87.2|
|Côte d'Ivoire|13.5|2.5|84.0|73.5|1.7|24.8|18.7|2.4|78.9|5.2|2.6|92.2|
|DR Congo|2.5|82.7|14.8|17.0|56.4|26.6|2.6|86.6|10.8|1.8|62.0|36.3|
|The Gambia|5.8|36.7|57.5|41.5|47.2|11.4|9.8|42.7|47.6|0.0|27.8|72.2|
|Ghana|0.4|26.0|73.5|22.5|54.5|23.0|0.5|35.8|63.7|0.3|13.0|86.7|
|Liberia|1.9|76.9|21.2|39.7|49.8|10.5|2.3|85.2|12.5|0.0|39.3|60.7|
|Malawi|5.1|65.1|29.8|52.9|36.4|10.7||N.A.|||N.A.||
|Namibia|1.1|30.1|68.8|18.4|68.0|13.6|0.5|39.0|60.5|2.2|15.4|82.4|
|Niger|4.2|67.2|28.6|61.4|27.8|10.8|5.4|79.6|15.1|0.0|23.1|76.9|
|Nigeria|1.7|10.8|87.5|18.5|40.1|41.4|1.9|15.5|82.6|1.3|1.3|97.5|
|Senegal|2.2|48.5|49.4|57.3|33.6|9.2|3.4|75.3|21.4|0.7|15.8|83.6|
|Sierra Leone|1.8|22.5|75.7|16.6|50.9|32.5|2.3|26.2|71.5|1.1|17.1|81.8|
|Tanzania|0.3|34.9|64.9|39.6|36.8|23.6|0.4|44.2|55.4|0.0|12.7|87.3|
|Uganda|0.6|7.2|92.2|54.4|26.3|19.3|0.7|7.9|91.4|0.0|3.9|96.2|
|Zambia|15.8|64.8|19.4|17.4|68.9|13.6|0.9|30.2|68.9|0.0|23.1|76.9|
|**Average**|**4.2**|**43.3**|**52.5**|**39.3**|**42.0**|**18.7**|**4.0**|**46.7**|**49.3**|**0.9**|**19.3**|**79.8**|

Notes: P stands for primary school education. S stands for secondary school education. P-S stands for post-secondary school education. The number in each cell represents the proportion of workers with each level of education. Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. Other wage workers include respondents who self-identified as a wage worker in other occupations; university faculty, special education teachers and other teachers were excluded from the group of other wage workers. Primary teachers include respondents who self-identified as a primary school teacher and a wage worker. Secondary teachers include respondents who self-identified as a secondary school teacher and a wage worker. The survey in Malawi did not differentiate the levels of teachers. Source: Surveys listed in Table 1.

35
**Sales:** Firms’ total turnover. Sales are in real 2005 national currency units using the consumer price index reported by the World Bank World Development Indicators. Firm-level sales for Georgia are calculated from the GEOSTAT Statistics Survey of Enterprises for 2006-2017.

**Employment:** Number of persons employed. Firm-level employment for Georgia is collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, measured as the average number of persons employed (employees, employed shareholders and employed family members in case of family owned enterprise) in the enterprise during the year.

**Female employment:** Number of female persons employed. Firm-level female employment for Georgia is collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, measured as the average number of female persons employed (employees, employed shareholders and employed family members in case of family owned enterprise) in the enterprise during the year.

**Wages:** Total annual cost of labor or remuneration (including wage, salary, premium, bonus, social payments, etc. of both permanent and temporary employees) which was accrued or paid in kind to employees (including income tax) during the year divided by employment. Wages are in real 2005 national currency units using the consumer price index reported by the World Bank World Development Indicators. Firm-level average wages for Georgia are calculated from the GEOSTAT Statistics Survey of Enterprises for 2006-2017.

**Female wages:** Remuneration (including wage, salary, premium, bonus, social payments, etc.) which was accrued or paid in kind to female employees during the year divided by female employment. Female wages are in real 2005 national currency units using the consumer price index reported by the World Bank World Development Indicators. Firm-level average female wages for Georgia are calculated from the GEOSTAT Statistics Survey of Enterprises for 2006-2017.

**Nominal effective exchange rate:** A measure of the value of a currency against a trade-weighted average of several foreign currencies. The nominal effective exchange rate for Georgia for each destination market was calculated from the World Bank World Development Indicators using trade weights calculated from UNCOMTRADE for 2006-2012.<sup>8</sup>

> 8 We experiment with an alternative instrument that is also interacted with the country’s deposit dollarization rate, but the results do not change.

6
**Figure 2: Sector-level concentration of jobs, by gender**

<!-- Start of picture text --> Sector composition (%) by sex<br>Agriculture 38.5<br>61.5<br>Mining 6.4<br>93.6<br>Manufacturing 46.3<br>53.7<br>Public 18.1<br>utilities 81.9<br>Construction 3.5<br>96.5<br>Commerce 30.6<br>69.4<br>Transport & 12.0<br>communications 88.0<br>Finance & 33.2<br>Business 66.8<br>Public 37.3<br>administration 62.7<br>Other 48.7<br>services 51.3<br>0 20 40 60 80 100<br>Percent<br>Female Male<br><!-- End of picture text --> Source: Staff estimates based on Sri Lanka HIES 2016; confidence intervals of estimated workers can be requested from the authors.

**Manufacturing jobs are concentrated in some western and northern provinces** . Manufacturing jobs are concentrated in the Colombo, Gampaha and Kalutara in the Western Province; Kurunegala and Puttalam in the North Western province and Kandy in Central province (Figure 3). The jobs in the textiles and wearing apparel sectors are similarly distributed as the manufacturing sector, which tend to demand similar infrastructure. The level of urbanization is higher in these districts compared to the other districts or provinces in Sri Lanka. Trade and transportation related jobs are rather evenly spread out by districts, except for Colombo and Gampaha districts that employ a disproportionately higher share of jobs in this sector. A similar profile with the social services sector (education, health care and domestic personnel) is observable with jobs evenly distributed across Sri Lanka. However, most jobs in the agriculture sector are found in districts outside the Western, North Western and Central provinces (Figure A32-Figure A35 in Annex).

10
trade reforms that has swept the developing countries. These reforms have affected all regions, and all of the major types of policy distortions. As discussed _in Global Economic Prospects 2001_ (World Bank 2001, chapter 2) and presented in Figure 4, average tariff rates in developing countries have halved, from around 30 percent in the early 1980s, to around 15 percent in the late 1990s. The absolute reductions in tariff rates in developing countries have been much higher than in industrial countries and, of course, decreases from a higher level are likely to have a much greater welfare benefit than corresponding decreases from a lower base (see Martin 1997). In addition, the dispersion of tariff rates, which typically increases the welfare cost of any given average tariff rate (Anderson 1995), was substantially reduced.

**Figure 4-. Changes in tariff rates since the early 1980s**

<!-- Start of picture text --> 70<br>60 1980-85 1986-90 1991-95 1996-98<br>50<br>40<br>30<br>20<br>10<br>0<br>South Asia Latin America East Asia Sub-Saharan Middle East Europe and Industrialized<br>Africa and North Central Asia Economies<br>Africa<br><!-- End of picture text --> Source: World Bank (2001) One must be careful when examining changes in tariff rates, because a decline in tariffs may reflect substitution of nontariff barriers for tariffs. However, during this period, the coverage of nontariff barriers, including state trading monopolies, in developing countries also appears to have fallen considerably, as is evident in Table 2.

21