rafmacalaba/datause-encoder-data huggingface dataset:
activities, while some people in rural towns may continue to obtain the bulk of their income from agriculture. Only minor inaccuracy is introduced in assuming that those in mega-cities derive their income almost exclusively from non-agricultural sources.<sup>19</sup> Furthermore, by using the one million or more as cut-off to define a metropolis, measurement challenges in consistently defining rural and urban areas across countries (Ravallion, Chen, Sangraula, 2007) are also circumvented.

Nonetheless, noise in the data cannot be denied, also when categorizing the population in agricultural and nonagricultural categories, instead of rural and urban (Headey, Bezemer, and Hazell). This would pose a problem, especially if such measurement error is systematic in the sample and correlated with the change in poverty. No such trends were uncovered.<sup>20</sup> Further robustness tests are undertaken by dropping observations for which there is a large difference between the nonagricultural employment share reported in FAO and the share calculated from the corresponding Demographic Health Survey.

The sample is limited to low and middle-income countries according to the World Bank’s classification in 2008 and spans about a quarter of a century, from 1980-2004. The complete list of available poverty spell observations in _Povcal_ consists of 52 countries and 219 country-spell observations. As poverty measures fluctuate substantially in some countries, country-spell observations, for which the change rate of the poverty headcount ratio at $1 a day is in the top 1 or bottom 1 percent of the sample, are dropped. Missing observations on agricultural employment further reduce the sample, resulting in a sample of 206 poverty spells covering 51 countries from across the world (Table 1).

> 19 For example, according to Dorosh and Thurlow (2012), less than 0.1 percent of agricultural GDP was generated in their category of Ethiopian and Ugandan cities (which included large urban centers beyond Addis Ababa and Kampala), while 17.9 and 5.3 percent of agricultural GDP was generated in Ethiopia’s and Uganda’s rural towns respectively.

> 20 Following Headey, Bezemer and Hazell (2010), who explore this in the context of nonagricultural employment shares in Asia and Africa,<sup>20</sup> the nonagricultural employment shares reported by FAO are compared with the nonagricultural employment shares of men reported in the Demographic Health Surveys (DHS) for each country/year in the sample where there was a match. Even though the differences were at times substantial—FAO reporting 32.8 and 20.4 percentage points more people employed in non-agriculture than DHS in Kazakhstan and Nigeria respectively and 25.4 and 19.7 percentage points less in Kenya and Senegal respectively—there were about as many positive as negative deviations and the correlation coefficients between both series was 0.67. Furthermore, when regressing the change in headcount poverty on the observed difference in nonagricultural employment share between FAO and DHS estimates, there was no correlation.

13
# **2. Data**

Our database consists of: (i) country weights or weights, , which are the country portfolio allocations of international mutual funds (those investing in several countries); (ii) benchmark weights, , which are the country allocations in the relevant benchmarks; (iii) mutual fund-specific information, such as its assets, returns, and relevant benchmarks; (iv) country-specific information, such as stock and bond market index returns.<sup>6</sup> Throughout the paper, the sub-index _i_ refers to funds, _c_ to countries, and the supra-index _B_ to benchmarks. For the final database, we cleaned the raw data and merged data from several sources, some of which had not been previously used or matched in the literature. The final data cover the period from January 1996 to July 2012 and constitute an unbalanced panel.

Our two main sources for country portfolio allocations of international mutual funds are EPFR (Emerging Portfolio Fund Research) and Morningstar Direct (MS). Both sources include dead and live mutual funds. The data from EPFR are at a monthly frequency, and include open-end equity and bond funds classified according to their geographical investment scope. Global funds invest anywhere in the world, global emerging funds only in emerging countries, and regional funds in groups of countries within a specific geographical region (e.g., developed Asia).<sup>7</sup> Frontier market funds are usually classified as regional funds. The data also comprise portfolios of ETFs. We use only funds that have information for at least one year. For each fund _i_ and each month _t,_ the data contain information on the share of the fund’s assets invested in each of 124 countries and cash, as well as its total net assets (TNAs, ). We also have information on each fund’s static characteristics, such as the asset class, domicile, currency, whether it is an ETF, its strategy (passive or active), and, crucially, its declared benchmark. We complement these data with information on each fund’s net asset value (NAV), obtained from Datastream and MS. We match the funds from these different databases.

We use similar data from MS to complement the EPFR data. That is, we use data on country weights, TNAs, NAVs, and static fund characteristics for additional international mutual funds not included in EPFR with at least one year of monthly data.<sup>8</sup>

> 6 Benchmark weights are fund-specific because each fund chooses its benchmark. We thus denote it with sub-index _i._ The same applies to other benchmark characteristics such as benchmark returns. 7 While global funds theoretically can invest anywhere in the world, a large proportion of them track the MSCI World Index, which only has developed countries as constituents. A minor proportion of these funds gauge their performance relative to the MSCI All Country World Index that contains both developed and emerging countries.

> 8 Although MS includes funds that report quarterly, almost 90 percent of the original MS sample reports allocations on a monthly frequency.

6
(FUGs), which are groups of community‐members that are involved in forest management and land use decisions. Respondents reported that they did not because new stoves were unavailable (64.2%), they did not know how to use one (20.1%) or because they were expensive (11.2%).

Figure 2 – Primary Fuel Sources (%) <!-- Start of picture text --> 0.66%<br>7.18% 1.10%<br>firewood<br>dung<br>small<br>91.16%<br>branches<br>other<br><!-- End of picture text -->

# **5. Results and Discussion**

## _5.1 Choice experiment results_

The results of the main effects specification using a conditional logit, a mixed multinomial logit with fixed price and a mixed multinomial logit with a random price are presented in Table 2. The coefficients for all the attributes are significant and positive but respondents most preferred choices were characterized by high levels of durability, cooking time reduction, fuel use reduction and smoke reduction.

The MMNL model also provides information about the heterogeneity of preferences. In the main effects model, the standard deviations for durability, time reduction, smoke reduction and fuel reduction and cost are significant. This implies that there is heterogeneity within the sample and significant variation among responses regarding these attributes and that a MMNL model should be used for the analysis.

13
cial networks still fits within a two-hour survey. Although the speed with which respondents can go through the photo books alleviates concerns about respondent fatigue, the network module came near the end of the survey. Randomly ordering photos and using multiple photo books with pictures placed in a different random order in each location allows us to precisely quantify and correct for respondent fatigue. We estimate the likelihood that a given woman is identified based on the page where her picture appears and the position of her picture on the page in the relevant photo book.<sup>17</sup> We find that women appearing later in the photo book may be identified up to 73% fewer times than those appearing on the first page, and that women appearing lower down on each page may be identified up to 18% fewer times than women appearing at the top (see Table A11.) Given that these differences are distributed randomly, they do not bias our estimates; however, we use estimates of respondent fatigue to re-weight the network data for ease of interpretation.

We complement sales and profit data from our three in-person surveys with high-frequency data collected through SMS surveys. Starting the week after baseline surveys were completed, all women in our sample received a weekly text message on a randomly selected day asking them to report total sales revenue from the previous day. Those who did not have a business were told to reply with zero. We incentivized responses by offering participants UGS 1,000 (USD 0.30) in airtime. Each month, an enumerator supplemented the SMS surveys by calling each woman who had not responded to any SMS survey in the past month. We find that average daily revenues reported in SMS surveys in the month that baseline, midline, or endline surveys took place in-person have a correlation of 0.31 with in-person reports, with 37% of reports being lower in the SMS survey than the in-person survey, 50% being higher in the SMS reports than the in-person reports, and 13% matching precisely. Given high variability in daily revenues, we view the SMS reports as credible.

> 17See Appendix C for more information on the weights.

17
Policy ReseaRch WoRking PaPeR 4746

# **Abstract**

This paper analyzes data from a randomized experiment on mean returns to capital in Sri Lankan microenterprises. The findings show greater returns among men than among women; indeed, returns were not different from zero for women. The authors explore different explanations for the lower returns among female owners, and find no evidence that the gender gap is explained by differences in ability, risk aversion, or entrepreneurial attitudes. Differential access to unpaid family labor and social constraints limiting sales to local areas are not important. However, there is evidence that women invested grants differently from men. A smaller share of the smaller grants remained in the female-owned enterprises, and men were more likely to spend the grant on working capital and women on equipment. The gender gap is largest when maledominated sectors are compared with female-dominated sectors, although female returns are lower than male returns even for females working in the same industries as men. The authors examine the heterogeneity of returns to determine whether any group of businesses owned by women benefit from easing capital constraints. The results suggest there is a large group of high-return male owners and a smaller group of poor, high-ability, female owners who might benefit from more access to capital.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to study microenterprise dynamics. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- Moody, 1998. “Subnational Governments: A Rating Agency Perpectives.” Global Credit Research, Moody’s Investors Service. July.

- ———. 2002. “Moody’s US Municipal Bond Rating Scale.” November.

- ———. 2004. “Moody’s State Rating Methodology.” Global Credit Research, Moody’s Investors Service. November.

- ———. 2005. “Revised Policy With Respect to Country Ceilings.” Global Credit Research, Moody’s Investors Service. November.

- ———. 2008a. “Regional and Local Governments Outside the US: Updated Rating Methodology.” Global Credit Research, Moody’s Investors Service.  May.

- ———. 2008b. “Rating Migration and Default Rates of Non-U.S. Sub-Sovereign Debt Issuers, 1983-2007.” Global Credit Research, Moody’s Investors Service. September.

- ———. 2008c. “Sovereign Default and Recovery Rates, 1983-2007.” Global Credit Research, Moody’s Investors Service.  March.

- Reinhart, C. 2002. “Default, Currency Crises, and Sovereign Credit Ratings”, _World Bank Economic Review_ , Vol 16, No.2, pp. 151-170.

- Reisen H. and Maltzan J. 1999. “Boom and Bust and Sovereign Ratings.” OECD, Paris.

Rowland, P. (2005) “Determinants of Spread, Credit Ratings and Creditworthiness for Emerging Market Sovereign Debt: A Follow up Study Using Pooled Data Analysis.” Standard & Poor’s. 2003. “Annual Review of AAA Rated US Municipalities.” September 3.

- ———. 2004. _Local and Regional Governments._ McGraw-Hil.

- ———. 2007a. “Sovereign Defaults and Rating Transition Data: 2006 Update.” February 1.

- ———. 2007b. “Rating International Local and Regional Governments.” March _7._

- ———. 2008.  “Sovereign Credit Ratings: A Primer.” May 29 _._

- ———. 2009.  “Rating International Local and Regional Governments.” January 5 _._

- Sy, A. 2002. “Emerging Market Bond Spreads and Sovereign Credit Ratings: Reconciling Market Views with Economic Fundamentals.” January 31. International Monetary Fund, Washington, DC.

- Tan, Kim Song. 2005. “Financial Performance of Local Governments: A Roadmap for Municipal, Creditworthiness.” Paper prepared for The World Bank, Washington D.C. June.

37
# Table 1: Summary Data and Estimated Welfare Changes for Rural Poor, Bangladesh, 1978-79

|Monthly<br>household<br>income<br>(Taka)|Cumulative<br> <br>percent<br>households|<br>Expen<br>(% inc<br> <br>Food<br>(total)|diture<br>ome)<br>Food-<br>grain|In<br>Vages|come by sou<br> <br>Business<br>income|rce_(W)_<br> <br>Imputed<br>income|*<br> <br>n<br>(Z)|Welfare ef<br>10% price<br> <br>Short-run|fects of a<br>increase<br> <br>Long-run|
|---|---|---|---|---|---|---|---|---|---|
|<300|8.7|70|46|62|11|24|35|-.84|.71|
|300-399|19|72|48|57|16|24|35|-1.1|.28|
|400-499|31|72|47|48|21|27|42|-.94|.26|
|500-749|55|73|47|38|26|30|45|-.86|.09|

Sources: Expenditure data are from BBS (1984, Table 15.25) and are given as a proportion of income, uising the ratios of mean expenditure (BBS, 1984, Table 15.25) to mean income (BBS, 1984, Table 15.28). Income data are from BBS (1984, Table 15.28). Welfare<sup>changes are estimated using the fact that,</sup> from equation (7), the monetary value the welfare effect (expressed as a % of income) of a I0O foodgrain price increase is given by

- Iincome share income share on wage rate elasticity income share

- 10 x from foodgrain - foodgrain + w.r.t. foodgrain x from wage production consumption price labour
Table 1. McCrary Test for Election Manipulation by Incumbent Mayors in the 2007 Elections

||Dependent v|OLS Estimates<br>ariable: Fraction of mu|nicipalities within bin|
|---|---|---|---|
||Full Sample<br>(1)|Poor Municipalities<br>(2)|Rich Municipalities<br>(3)|
|Incumbent victory|0.016**|0.025*|-0.0061|
||(0.0079)|(0.013)|(0.013)|
|Margin|-0.17|-0.22|0.46|
||(0.27)|(0.46)|(0.48)|
|Margin_×_ incumbent victory|-0.24|-0.56|-0.62|
||(0.39)|(0.65)|(0.61)|
|Constant|0.028***|0.027***|0.044***|
||(0.0056)|(0.0094)|(0.010)|
|Municipalities|153|75|78|
|Observations (bins)|30|30|30|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ Results from a probability density test for manipulation of the running variable (McCrary, 2008) are reported. The running variable is the incumbent margin of victory in the 2007 mayoral elections. All regressions are weighted by a triangular kernel with a bandwidth of 0.05. Observations are 30 bins of equal width. The dependent variable is the fraction of municipalities with an incumbent margin of victory that falls within the bin. Standard errors are in parentheses.<sup>_∗_</sup> ,<sup>_∗∗_</sup> and<sup>_∗∗∗_</sup> denote statistical significance at the 10%, 5% and 1% levels, respectively.

13
current backhaul composition is 4% fixed fiber, 6% fixed copper, 84% wireless and the remaining 6% using satellite. A least-cost design is also used to connect areas via a backhaul link into the main fiber network. Using a minimum spanning tree, the cheapest network structure to connect all regional nodes and sites is estimated, which can either be linked using fiber or a wireless technology (as illustrated in Figure S4 of the Supplementary Materials).

Once the additional sites required and the distance of the backhaul links are known, costs can be developed using estimates from a literature survey (5G NORMA, 2016; Frias and Pérez, 2012; Johansson et al., 2004; Markendahl and Mäkitalo, 2010; Paolini and Fili, 2012; Smail and Weijia, 2017), combined with validation by local operators. Mean per site capex costs include $40k for all active equipment, $30k to build a full 30m tower and $30k for installation. Mean per site opex costs include operation and maintenance of $7.4k, power of $3k, along with site rental of $10k (urban), $5k (suburban) and $3k (rural). For the backhaul, mean fiber costs per meter are $25, $15 and $10 for urban, suburban and rural respectively. Mean wireless backhaul costs are based on $15k, $20k and $45k for each small (<10km), medium (<20km) and large (<40km) backhaul unit (of which two are required to form a wireless connection). Connections over 40km require multiple hops (Kusuma and Boch, 2021; Oughton et al., 2022a). Core and backhaul links use an annual opex of 10% of the initial capex required for all active equipment to cover network maintenance and operation (Vannieuwenborg et al., 2018). An administration cost of 10% of the RAN cost is added to cover all necessary operations activities (subscriber acquisition, marketing, R&D etc.), which is below highincome countries (Rendon Schneir et al., 2019), although labor costs are substantially lower in the countries assessed here. The cost calculations estimate the PV over the assessment period (20202030) using a discount rate of 5% to account for inflation (International Monetary Fund, 2021). A market-set Weighted Average Cost of Capital of 15% is used broadly reflecting the risk of capital lending for the countries included (WACC Expert, 2020). Three input cost scenarios are applied, with the baseline being the costs described here, derived from the literature. The low and high scenarios then represent a ±20% change in the cost inputs from the baseline.

17
|P045052|2000|2007|12/29/2006|16 Closed|Nepal|
|---|---|---|---|---|---|
|P045052|2000|2007|6/22/2007|17 Closed|Nepal|
|P045053|1999|1999||1 Closed|Nepal|
|P045053|1999|2000|6/30/2000|4 Closed|Nepal|
|P045053|1999|2001|6/14/2001|6 Closed|Nepal|
|P045053|1999|2002|1/9/2002|9 Closed|Nepal|
|P045053|1999|2003|1/15/2003|11 Closed|Nepal|
|P045053|1999|2004|7/14/2003|12 Closed|Nepal|
|P045073|1997|1997||0 Closed|Turkey|
|P045073|1997|1998||3 Closed|Turkey|
|P045073|1997|1999||4 Closed|Turkey|
|P045091|2000|2000||0 Closed|Rwanda|
|P045091|2000|2001|6/26/2001|2 Closed|Rwanda|
|P045091|2000|2002|5/6/2002|4 Closed|Rwanda|
|P045091|2000|2003|6/25/2003|6 Closed|Rwanda|
|P045091|2000|2004|6/28/2004|8 Closed|Rwanda|
|P045091|2000|2005|6/1/2005|10 Closed|Rwanda|
|P045091|2000|2006|1/6/2006|11 Closed|Rwanda|
|P045091|2000|2007|9/11/2006|12 Closed|Rwanda|
|P045091|2000|2007|6/29/2007|13 Closed|Rwanda|
|P045091|2000|2008|12/28/2007|14 Closed|Rwanda|
|P045091|2000|2008|6/28/2008|15 Closed|Rwanda|
|P045091|2000|2009|8/31/2008|16 Closed|Rwanda|
|P045091|2000|2009|4/22/2009|17 Closed|Rwanda|
|P045091|2000|2009|6/29/2009|18 Closed|Rwanda|
|P045091|2000|2010|12/28/2009|19 Closed|Rwanda|
|P045134|1998|1998||1 Closed|Bosnia and Herzegovin|
|P045134|1998|1999||3 Closed|Bosnia and Herzegovin|
|P045134|1998|2000|6/5/2000|6 Closed|Bosnia and Herzegovin|
|P045134|1998|2001|6/25/2001|9 Closed|Bosnia and Herzegovin|
|P045134|1998|2002|6/27/2002|11 Closed|Bosnia and Herzegovin|
|P045134|1998|2003|2/21/2003|13 Closed|Bosnia and Herzegovin|
|P045134|1998|2020|2/9/2020|14 Closed|Bosnia and Herzegovin|
|P045174|2000|2000|6/26/2000|1 Closed|Lebanon|
|P045174|2000|2001|6/21/2001|4 Closed|Lebanon|
|P045174|2000|2002|6/27/2002|7 Closed|Lebanon|
|P045174|2000|2003|6/27/2003|11 Closed|Lebanon|
|P045174|2000|2004|6/8/2004|13 Closed|Lebanon|
|P045174|2000|2005|4/29/2005|15 Closed|Lebanon|
|P045174|2000|2005|6/28/2005|16 Closed|Lebanon|
|P045174|2000|2006|12/1/2005|17 Closed|Lebanon|
|P045174|2000|2006|6/19/2006|18 Closed|Lebanon|
|P045174|2000|2007|12/22/2006|19 Closed|Lebanon|
|P045174|2000|2007|6/26/2007|20 Closed|Lebanon|
|P045174|2000|2008|12/25/2007|21 Closed|Lebanon|
|P045174|2000|2008|6/28/2008|22 Closed|Lebanon|
|P045174|2000|2009|12/30/2008|23 Closed|Lebanon|