rafmacalaba/datause-encoder-data huggingface dataset:
As a second piece of evidence to highlight the role of underlying investors, we compare the maturity structure of Chilean mutual funds that invest in Chile (domestic mutual funds) to that of Chilean mutual funds that invest in emerging economies. The latter are targeted to different underlying Chilean investors. Those investing in emerging economies are probably targeted to investors that are wealthier, more sophisticated, and more willing to take on risk and seek higher returns, in comparison to investors that prefer to invest only domestically. Figure 12 shows that, mutual funds investing in emerging markets are slightly more long term that those investing in Chile. For example, domestic mutual funds invest 38% of their portfolio up to one year, 59% up to three years, and 88% up to ten years, compared to 20%, 52%, and 99% in the case of mutual funds that only invest in emerging economies. While the portfolios of these two groups of investors have a similar average maturity and the hypothesis that they have the same maturity structure can only be rejected at the 10% significance level, the share invested in assets with maturities of less than one year and more than ten years are significantly different at conventional levels.

A second factor that might affect incentives is the structure of liabilities. To test its importance, we analyze insurance companies. Unlike mutual and pension funds, insurance companies have long-term liabilities as they mostly provide annuities for pensioners and life insurance; moreover, they are not evaluated on a short-term return basis by investors that can pull out their funds.

Figure 13 shows the maturity structure of Chilean insurance companies. The figure shows that insurance providers are much more heavily invested in long-term instruments than mutual and pension funds are. The differences are quite startling. For 30
increased efforts in this direction become particularly importan'. given the projected increases in population lending. We_recommend that the Bank make increased efforts at both the country,and the international levels to improve coordinat,ion and,,cooperation with other international donor and imnem n,ti g agencies.

10. THE MARKETING OF LENDING FOR POPULATION: The Bank by its charter must work primarily through lending. Although the businesslike manner of ti.e Bank is appreciated by some, the restriction of the Bank to lending puts it at a disadvantage in the eyes of most potential borrowers. The issues involved in different modes of finance are not widely understood by managers in the population sector or even, in many cases, by government officials in finance ministries. There is. for, example, often confusion over the differences between IDA and IBRD credits. There is also still some feeling that loans should not be used for "soft" sectors. We recommend that the Bank undertake special efforts to educate governiment leaders c(fncerMtlilg the issues involved in the decision to use loans to finance population projects.

There are several areas of concern which cut across the recommendations presented above.

LEARNING: The Bank's population lending activities will be greatly enhanced if more efforts can be made to learn from the experience of previous projects in the same country and from similar efforts in other countries. The most effective evaluations of Bank projects are circulated years after the projects are completed and the new generation of projects have been initiated. Of course, less formal efforts do take place as a part of the process of project supervision, but they are not enough by themselves. Efforts must be made to shorten this cycle so that systematic learning can take place more rapidly.

INSTITUTION BUILDING: The development of strong institutions capable of defining and implementing effective programs is at the heart of the Bank's population program, yet the record in this area is not strong, and we feel that the Bank should be seeking ways to strengthen its ability to help develop institutions.

THE SKILLS OF BANK STAFF: We have not done a systematic survey of the current skills of Bank staff, but it seems clear that the implementation of recommendations concerning sector work, research and projtst design will involve a higher demand for skills that are currentLy poorly represented among PHN staff. Thus there may be a need for either some retraining of current staff or for the selective recruitment of new professionals with the required backgrounds.

The recommendations and observations presented above are based on the detailed population lending review. By the nature of our assignment, with the focus on projects and sector work, the two documents cannot provide a complete view of all aspects of PHN's work, but we hope that this review will be one element In the continuing effort to improve the quality and the quantity of the Bank's involvement in population.

75
**Table 4:** Impact of changes in DBI components on GDP per capita growth

|Model|(17)|(18)|(19)|(20)|(21)|(22)|(23)|
|---|---|---|---|---|---|---|---|
|Dep. Variable|Four-year|logGDPpe|r capitagro|wth(2006-2|009)|||
||||-|||||
|initial log|-0.116|-0.104|0.618***|-0.198|-0.090|-0.178|-0.354*|
|GDP/cap|(0.139)|(0.131)|(0.215)|(0.132)|(0.149)|(0.152)|(0.208)|
|initial deviation|0.127***|0.150***|0.107**|0.134***|0.145***|0.149***|0.133***|
|fr. GDP deflator|(0.043)|(0.042)|(0.0419)|(0.043)|(0.0428)|(0.0448)|(0.0427)|
|Civil war|-0.641<br>|-0.197<br>|-0.907<br>|-0.639<br>|-1.090<br>|-0.576<br>|-0.792<br>|
||(1.864)|(1.717)|(1.688)|(1.765)|(1.621)|(1.740)|(1.645)|
|**Starting a business**||||||||
|Number of payments<br>(improvement)|-1.790<br>(1.350)|||||||
|Number of payments<br>(initial value)|-1.517<br>(0.948)|||||||
|Time<br>(improvement)||6.996*<br>(3.733)||||||
|Time<br>(initial value)||-0.384<br>(0.844)||||||
|Cost<br>(improvement)|||9.330***<br>(0.754)|||||
|Cost<br>(initial value)|||2.326*<br>(1.213)|||||
|Min. capital<br>(improvement)||||2.557<br>(4.043)||||
|Min. capital||||0.306||||
|(initial value)||||(0.722)||||
|**Enforcing contracts**||||||||
|Procedures|||||0.788|||
|(improvement)|||||(0.626)|||
|Procedures<br>(initial value)|||||-1.357*<br>(0.808)|||
|Time<br>(improvement)||||||2.150<br>(2.640)||
|Time<br>(initial value)||||||-0.034<br>(1.006)||
|Cost<br>(improvement)|||||||6.205***<br>(0.565)|
|Cost<br>(initial value)|||||||1.556<br>(1.157)|
|Constant|4.188***|3.084***|6.043***|3.691***|3.786***|3.764***|4.302***|
||(1.155)|(1.138)|(1.301)|(1.132)|(1.109)|(1.105)|(1.326)|
|Observations|151|151|151|151|150|150|150|
|R-squared<br>|0.119<br>|0.192<br>|0.165<br>|0.105<br>|0.122<br>|0.106|0.146|
|Robust standard error|s in parenth|eses; *** p|<0.01, ** p<|0.05, * p<0|.1.|||
|Changes in DBI sub-co|mponents f|rom 2005 t|o 2006. See|table 1 for m|ore details.|||

15
et al. 2002). The informal settings and business climates in which rural enterprises operate make for much different entry barriers.

According to the RIC survey data, start-up capital is the most important obstacle to starting rural farm and nonfarm business as perceived by existing rural entrepreneurs (figure 4.1). The second most important obstacle is access to inputs or raw materials for nonfarm enterprises, and the availability of water for farm business. The third most important obstacle, which affects both farm and nonfarm businesses, is market information and knowledge. Market knowledge can be hindered because of poor infrastructure such as limited roads and transportation. Availability of electricity comes at the fourth for nonfarm business, whereas the availability of skilled labor and roads and transportation are the fourth for farm business.

In terms of motives to start a business, identifying market opportunities (44.4 percent) and obtaining skills (32.6 percent) were the top two motives reported by nonfarm entrepreneurs. For farm entrepreneurs, identifying market opportunities was less prevalent, and reported by just 18.2 percent of respondents. 34.3 percent reported obtaining skills as their primary motive for beginning operations (figure 4.2). These self-reports on the part of farm and nonfarm entrepreneurs should be useful in informing policies that promote incentives for business entry. Policies that effectively enable people to obtain skills and identify existing market opportunities clearly fall within this category.

**Figure 4.1: Obstacles to Start Business Figure 4.2: Motives to Start Business**

<!-- Start of picture text --> farm nonfarm farm nonfarm<br>50.5 18.2<br>start-up capital/funds 71.1 market opportunities 44.4<br>34.3<br>access to inputs/raw 9.1 obtain skills 32.6<br>materials 19.5<br>28.3<br>19.2 household lost wage earning 26.9<br>knowledge about market<br>17.3<br>39.4<br>no other income<br>1 18.3<br>availability of electricity<br>12.3 18.2<br>other<br>18<br>10.1<br>roads & transportation 11.1 low agriculture income 4 14.6<br>10.1<br>availability of skilled labor 0<br>7.7 no accesss to agric. Land 10.6<br>29.3 18.2<br>availability of water 2.2 social/economic independence 7.2<br>0 20 40 60 80 0 10 20 30 40 50<br><!-- End of picture text --> _Source:_ Yemen RIC Survey 2010 weighted _Note:_ Each entrepreneur chose up to two obstacles.

_Source:_ Yemen RIC Survey 2010 weighted _Note:_ Each entrepreneur chose up to two motives.

10
the X it are therefore informed only by within-country variation over time. In all specifications we include year dummies (ηt) to account for common shocks to aid volumes in any given year.

The vector Z it represents a set of other plausible time-varying control variables, most of which have been included in one or more studies in the literature; these variables have typically not been shown to be robustly significant, but we use them in our robustness tests.<sup>4</sup> The economic control variables from the literature include fiscal budget surplus (Round and Odedokun 2004; Faini 2006; Boschini and Olofsgard 2007; Frot 2009), unemployment (Boschini and Olofsgard 2007; Frot 2009; Mendoza, Jones, and Vergara 2009), inflation (Mendoza, Jones, and Vergara 2009), and trade integration (Boschini and Olofsgard 2007); we add to this list government expenditures and real exchange rates. The political variables include inequality (Boschini and Olofsgard 2007; Chong and Gradstein 2008; Mendoza, Jones, and Vergara 2009), corruption (Chong and Gradstein 2008), and political orientation of the governing party (Round and Odedokun 2004; Boschini and Olofsgard 2007; Chong and Gradstein 2008; Mendoza, Jones, and Vergara 2009).

In all regressions, we report results using standard errors clustered at the country level, to correct for heteroskedasticity and within-panel serial correlation. Using conventional standard errors yields much higher levels of significance, and could be equally valid given the small number of countries (Wooldridge 2002, 275-76). However, we use the corrected standard errors to be more conservative.

# _Aid data_

Data for our main test are taken from the OECD-DAC database, which is the standard source used in the literature, and cover the period from 1977 through 2007. In the empirical work reported below, we follow most other studies in using data on net aid disbursements rather than aid commitments. There is typically a wide gap between commitments and disbursements, and ultimately it is the disbursements of aid – and not the higher committed amounts – that matter to the recipient countries.

> 4 Summary statistics are shown in Appendix Table 3 for our main sample.

6
in rural areas both differences in characteristics and returns to those characteristics matter equally. While the factors identified as driving the gender gap hold across both urban and rural areas, we also find that female businesses’ lower likelihood of maintaining accounting records helps increase the gap in rural areas, while women’s concentration in the clothing sector increases the gender gap in urban areas. As shown in Table 1 Panel B, 8.2% of male-run businesses in the country keep business records, as opposed to only 3% of female-run businesses. Female-run businesses are also slightly more likely to be in the clothing sector (28.1% for women versus 25.4% for men).

In sum, we find large gender gaps in business revenues—and that three-quarters of the gender gap in business revenues can be explained through women’s lower use of male household labor in their businesses. This result echoes what we found for wage earnings, were women were less likely to work as managers. We also find that a lack of access to utilities, such as electricity, and access to finance matter.

# **3.4 Agricultural Production**

Lastly, we show the results for agricultural production. Column (5) of Table 1 Panel A shows a 61% gender gap in the imputed value of agricultural harvests, while Column (6) shows a 62% gender gap in the value of crop sales.

Table 2 shows that the gender gap in harvest value has increased by a small but significant 5 percentage points between 2014 and 2019, while the gender gap in crop sales is statistically unchanged. The increase in the agricultural production gender gap holds across both age groups but is proportionately larger for younger adults, with the increase in the gap being most pronounced in the West of the country. For crop sales, we instead notice a significant decline in the gender gap among younger adults, as well as in the North of the country.

We next decompose the factors underlying gender gaps in both harvest value and crop sales in Table 6. Both gender gaps can be explained by a mix of gender differences in endowments and differences in returns, though differences in endowments are more important for crop sales. The largest driver of the gender gap in agricultural harvest value is female farmers’ concentration in lower-value crops, as opposed to cash crops like cotton or cashews. This variable alone explains 1 percent of the overall gender gap. The next largest factor is the fact that men in the household tend not to work on female-managed plots: this explains an additional 10 percent of the gender gap. This is similarly echoed by the fact that women face much lower returns to their agricultural production from a higher number of adults in the household. Unlike for business revenues, we also find that women’s lower use of male hired (non-household) labor widens the gender gap for both agricultural production and crop sales.

14
Cerra, Valerie, and Sweta Chaman Saxena. 2008.“Growth Dynamics: The Myth of Economic Recovery.” American Economic Review, 98(1): 439–57.

Chainey Collier, P. (1999), “On the Economic Consequences of Civil War”, Oxford Economic Papers 51(1): 168-183.

Collier, Paul, and Anke Hoeffler. "Aid, policy and growth in post-conflict societies." European economic review 48.5 (2004): 1125-1145.

Collier, Paul, and Anke Hoeffler, ‘‘On Economic Causes of Civil War,’’ Oxford Economic Papers, 50 (1998), 563–573.

Collier, P., Elliot, L., Hegre, H., Hoeffler, A., Reynal-Querol, M. and Sambanis, N. (2003), Breaking the Conflict Trap: Civil War and Development Policy, World Bank, Oxford University Press.

Conley, Timothy G., Christian B. Hansen, and Peter E. Rossi (2012) “Plausibly Exogenous”, The Review of Economics and Statistics, 94(1): 260-272 Congo Free State, 1885-1908 (http://www.yale.edu/gsp/colonial/belgian_congo/) Datta, Saugato (2012), “The Impact of Improved Highways on Indian Firms”, Journal of Development Economics, 99(1): 46-57.

Deininger, K. (2003), “Causes and Consequences of Civil Strife: Micro-Level Evidence from Uganda”, Oxford Economic Papers 55: 579-606.

Deininger, Klaus W., and Derek Byerlee. Rising global interest in farmland: can it yield sustainable and equitable benefits?. World Bank Publications, 2011.

Do, Quy-Toan, and Lakshmi Iyer. 2007. “Poverty, Social Divisions and Conflict in Nepal.” Harvard Business School Working Paper 07-065.

Dorosh, Paul, et al. "Road connectivity, population, and crop production in Sub‐Saharan Africa." Agricultural Economics 43.1 (2012): 89-103.

Dave Donaldson, 2010. ”Railroads of the Raj: Estimating the Impact of Transportation Infrastructure", NBER Working Papers,16487, National Bureau of Economic Research, Inc.

Deininger, Klaus W., and Derek Byerlee. Rising global interest in farmland: can it yield sustainable and equitable benefits?. World Bank Publications, 2011.

Donaldson, D. (2013). Railroads of the raj: Estimating the impact of transportation infrastructure. American Economic Review, forthcoming.

Dube, O. and Vargas, J. (2013) Commodity price shocks and civil conflict: evidence from Colombia, Review of Economic Studies, 80, 1384–421.

25
obtained. Using the available data, initial investment and operation and maintenance costs per unit of KtCO2e abatement are calculated. Average per unit capital costs and operation and maintenance cost of abatement across the CDM projects categorized by project types are calculated and then used as proxies for the projects for which such data were not available.

The present value of emissions abatement costs for each project are calculated as described in equation (6). The operation and maintenance costs are discounted using real interest rates for the year of fixed capital investment (i.e., the prior year of credit start period). Real interest rates in the host countries are used for unilateral projects, while the rates in the partner countries are used for bi- and multi-lateral projects. Real interest rates for the host and partner countries are obtained from the World Bank (WDI 2010). For the electricity generating CDM projects, the net present value of emissions abatement costs are calculated by subtracting the sum of the discounted flow of electricity sales revenue from the present value of total costs. Wholesale electricity tariffs in different host countries obtained from the PDDs are used to calculate the flow of revenues from electricity sales. Real interest rates are used to discount those revenues. As implied by duality, the abatement cost function includes the sum of the discounted-weighted flows of CERs and electricity outputs. Real interest rates as mentioned above are used to discount those streams of outputs. Table 3 presents the categorical means and standard deviations of the (discounted) total amount of emissions abatement and electricity outputs over the life of the projects and net present value of total abatement costs of the projects, for which all information are available. Wholesale electricity tariffs in some of the host countries were not available, leaving 6,326 observations for use in the empirical analyses.

# **4 Estimation Results and Discussions**

The log-transformed net present values of the total cost of mitigation by each individual CDM project are plotted against corresponding total abatement and presented in figure 2.<sup>11</sup> We estimate the mitigation cost first employing the log-log model in equation (7), and then examine the more flexible log-quadratic

> 11 Plots of costs against abatement levels for specific project types show a similar pattern.

13
learning approach, we offer two potential interpretations for this difference. First, individuals may have stronger prior beliefs about the exchange rate than about the inflation rate. This interpretation is consistent with the evidence documented in Section 3.1 showing substantially more interest in learning about the exchange rate than the rate of inflation, presumably because it is more consequential for everyday economic decisions. An alternative interpretation is that individuals do not trust the precision of the signal. That is, they are less likely to trust expert forecasts about inflation than about the exchange rate. However, as we do not provide specific information about the sources of inflation and exchange rate forecasts used in our experiment, this interpretation seems less likely.

# **6.4 Effect of Information on Spending**

Having shown that our information treatments are effective at shifting beliefs, we turn to their impacts on consumption. The main goal of our experiment is to test whether changes in macroeconomic expectations affect actual spending, as measured in administrative data covering the universe of credit card transactions for bank customers in our sample. To examine this question, we estimate the following regression equation: Note that the right-hand-side of equation (6) is identical to the learning equations in (4) and (5). The only difference is that the dependent variable is now a generic outcome _Yi,t_ +1. For example, this dependent variable may be the average monthly spending in the 3 months post-treatment. Recall that in the set of control variables ( _Xi,t_ ), we include the pre-treatment spending, which exploits the persistence in spending patterns to help reduce the variance of the error term and improve statistical power (see McKenzie, 2012).

Table 4 reports the results. In columns (1) and (2), we estimate the relationship between the information shock and the resulting change in self-reported macroeconomic expectations. The results confirm that participants update their macroeconomic expectations in response to the information provided to them through our experiment. In Table 4, columns (3) through (6), we use the same empirical specification to examine whether the changes in macroeconomic expectations documented in columns (1) and (2) translate into changes in consumption behavior. To do so, we estimate equation (6), with spending on durables, tradable durables, credit card debt, and total spending as the respective outcomes. Each outcome is measured in the administrative data obtained from our partner bank. We observe credit card spending for 3 months after the intervention and average the monthly spending over the entire period to mitigate concerns 23
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