rafmacalaba/datause-encoder-data huggingface dataset:
# _Savings_

_Poor and Very Poor Women._ The evidence on savings is less rich than that on loans and grants but more consistently shows positive economic outcomes for women. It also shows that women’s demand for saving accounts is high. A review of nine randomized field experiments in countries covering different regions (with studies in Kenya, Philippines, Nepal and Guatemala reporting data disaggregated by sex) shows that savings are a promising way to improve rural women’s productivity (Knowles 2013). In both Ghana and Rwanda, differences between men and women in the existence of savings accounts explained the largest proportion of the gender wage gap for entrepreneurs (Gamberoni, Heath, and Nix 2013). For Ghana, where panel data were available, savings accounts were especially important to increase the profits of the smallest, subsistencelevel women entrepreneurs, shifting business performance from losses to profits.

The recent experimental evidence sheds light on reasons why savings may work especially well for women. In Western Kenya, women with access to savings accounts invested 45 percent more in their businesses and were less prone to sell business assets to address health emergencies (Dupas and Robinson 2009). These effects were sustained in a follow-up survey conducted three years later, showing that savings interventions can have lasting effects (Dupas and Robinson 2013).

In rural Mindanao, Philippines, the decision-making ability and control over assets of less empowered women with below-median reported household decision-making power increased as result of commitment savings, where deposits could only be withdrawn after a savings goal was reached; there was no similar effect for men (Ashraf, Karlan, and Yin 2006, 2010). These savings likely provided women with greater legal and psychological control over funds. Commitment savings were particularly attractive to women who reported a present bias. The 10
2 (1983) or LNIDO (1982). A second related line of analyses, often associated with Lary's (1968) work for the U.S. National Bureau of Economic Research (NBER), is based directly on a standard Heckscher-Ohlin model and has been applied by Tuong and Yeats (1981), Eizan and Yeats (1989) or Yeats (1989) for tests of trade theories and country export performance.' This approach attempts to determine the relative labor and capital inputs of specific goods, normally defined at the three or four-digit SITC level, and then assumes that d3veloping countries would have a comparative advantage in the production and trade of relatively labor intensive items (Yeats, 1989 provides an empirical verification of this point). However, in view of the number of empirical studies that have been undertaken on comparative advantage, it is surprising that no previous efforts were made to examine the direct link between the Balassa RCA and NBER (Heckscher-Ohlin) measures of developing countries' comparative advantage at the product and industry level.<sup>2</sup> This study conducts several empirical tests relating to this I For example, Yeats (1989) examined the export performance of developing countries for a group of three, four and five-digit SITC products that Lary (1968) determined were manufactured by labor-intensive production processes in the mid-1960s. The results showed that developing countries achieved a major increase in their market shares (from 8.7 to 21.2 per cent) for labor-intensive goods exported to developed market economy countries while the shares for all other nonfuel goods declined from 21.7 to 14.1 per cent. In the United States, developing countries increased their share of labor-intensive good imports from 17.9 to 40.5 per cent while their share of other nonfuel goods fell from 33.0 to 19.3 percent. See Appendix Table 2 for a comparision of developing countries' export performance for labor intensive and other goods in major OECD markets. These, and various other empirical tests lead to the conclusion that the NBER labor intensity indices provide a useful and accurate guide to the future composition of developing country exports.

> 2 Balassa (1979) calculated RCA and capital intensity indices for some 184 four-digit SIC products and analyzed this information after it had been aggregated to the national level for some 36 developed and developing countries. This lead to a "stages" theory of national comparative advantage. Tyers and Phillips (1S89) examined RCA indices and measures of labor intensity, technology, and human capital for very broad categories of goods (agriculture, minerals, etc.) at the national level for selected Asian countries.
Policy Research Working Paper 7250

# **Abstract**

Ghana is experiencing its third gold rush, and this paper sheds light on the socioeconomic impacts of this rapid expansion in industrial production. Using a rich dataset consisting of geocoded household data combined with detailed information on gold mining activities, the authors conduct two types of difference-in-differences estimations that provide complementary evidence. The first is a local-level analysis that identifies an economic footprint area very close to a mine, and the second is a district-level analysis that captures the fiscal channel. The results indicate that men are more likely to benefit from direct employment as miners compared to men further away, and that women in mining communities may more likely gain from indirect employment opportunities and earn cash for work. Authors also find that infant mortality rates decrease significantly in mining communities, compared to the evolution in communities further away.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at andreas.kotsadam@econ.uio.no, anja.tolonen@economics.gu.se, and pchuhan@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
2022 to early 2023, 1,444 participants were reached for a phone survey. In total, the three rounds of data cover a wide range of outcome families including those listed out above and several others; these outcomes are described in further detail in the appendix and associated pre-analysis plan.

3. _Neighbor surveys:_ A round of in-person data collection of the Jordanian neighbors of the study participants was gathered in 2022 roughly one year after the program had ended. In total, 1,455 Jordanian neighbors of refugee participants were surveyed.

4. _Forecast short surveys:_ Two rounds of brief 10 minute forecasts were collected to gather the predictions of both academic and non-academic experts on the likely impacts of the program on the primary outcomes. Each round of predictions collected information for approximately 60 individuals, with the first round focused on impacts on study participants, and the second on impacts among neighbors.

Figure 2 depicts the data collection timeline and the response rate of each of the surveys. Overall, 86% of the main refugee study sample was surveyed at least once: 80.3% were surveyed at midline, and at least 75% were surveyed in each of the endline and follow-up rounds. Among the treatment group, survey completion was 6 to 8.6 percentage points higher in each survey round, as shown in Table 3, and this differential attrition is statistically significant. As such, beyond the pre-specified TOT results presented, the analysis was expanded to include Lee bound estimates of the treatment effects (following Tauchmann 2014).

# **3.4 Descriptive statistics**

We present characteristics of the sample using baseline vulnerability assessment in Table 1.<sup>10</sup> Several patterns are noteworthy. First, a quarter of respondents likely have a disability, calculated

> 10The table only includes data for 1,619 out of the 2,017 households sampled for the study since the implementing partner only facilitated data on this assessment for the individuals found in the midline sample. The table also presents statistics for gender and age, which are taken from the midline survey.

15
<mark>increasing new demand. During this period, public investment in telecommunications only represented an average of 0.70 to 1% of the</mark> gross fixed capital <mark>in Algeria against a standard industry ratio of 3% (see Fig.2).</mark> <!-- Start of picture text --> 250 1.20%<br>200 Invest. Tel (M$) 1.00%<br>Invest.Tel/GDP (%)<br>0.80%<br>150<br>0.60%<br>100<br>0.40%<br>50<br>0.20%<br>0 0.00%<br>19751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997<br>Investment in M$<br>% Telecom Invest/GDP<br><!-- End of picture text --> Fig. 1: Public investment in telecom. Source: ITU, _World Telecommunication Indicators,_ 2002 <!-- Start of picture text --> 2.50%<br>Telec.Invest/GFCF<br>2.00%<br>1.50%<br>1.00%<br>0.50%<br>0.00%<br>197519761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998<br>Telecom Invest as % of GFCF<br><!-- End of picture text --> Fig. 2: Telecom investment as % of Gross Fixed Capital Formation Source: ITU data.

<mark>As a result of public investment reduction, the waiting time for telephone connections lengthened and reached seven years at the beginning of the 1990s. The unmet demand was estimated in 2000 at 800,000 for fixed lines, and 600,000 for mobile phones (see Fig. 3). The quality of service deteriorated while faults became more frequent. Service quality and staff productivity were low</mark> . In 1998 only 66% of faulty lines were back in service within 48 hours, while in 2000 there were 98 main lines per employee.

<!-- Start of picture text --> 2,500<br>2,000 Waiting List<br>Capacity to connect<br>Connected Subscribers<br>1,500<br>1,000<br>500<br>0<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999<br>(x 000)<br><!-- End of picture text -->

**Source: ITU data.**

<mark>Fig. 3: Telecommunications supply inefficiencies</mark> 4
386 not breach 1.7 °C in the long-run, in compliance with the Paris Agreement targets.<sup>15</sup> The planner 387 applies a discount rate of 2%, in line with empirical evidence on long-run real interest rates (Bauer and 388 Rudebusch, 2023) and the median estimate from a recent expert elicitation (Drupp et al., 2018). See 389 Appendix D for full details on our calibration of each model parameter. The results of our calibration 390 is summarized in Table II.

391 In our simulations of policy suites 2 and 3, we assume that two sectors may be politically chal392 lenged: energy and industry. Our motivation is that energy is the sector with the highest abatement 393 potential (and therefore annual emissions rate), implying that delaying this sector would require the 394 largest reallocation of emissions allocations in the ETS. Industry, on the other hand, has moderate 395 abatement potential (about half that of energy), but is over six times as expensive in terms of marginal 396 investment costs compared to energy. Therefore, these two sectors provide useful guideposts for our 397 discussion, where energy signifies delaying a cheap sector with substantive annual emissions, whereas 398 industry represents delaying a sector with moderate emissions but with expensive marginal investment 399 costs. Moreover, modeling delay in the energy sector could represent a situation in which a strong 400 fossil fuel lobby delays climate action, while delaying heavy industry could represent a nation that is 401 concerned about the competitiveness of domestic industries and therefore delays the decarbonization 402 of its industrial sector. We will explore the impact of delaying decarbonization for each sector listed in 403 Table II, and relate this to their sectoral characteristics, in Section VI. 404 To summarize, our experiments solve for the optimal abatement investment schedule in each policy 405 scenario described in Table I with either energy or industry serving as the politically challenged sector 406 (i.e., each set of simulations are carried out independently, with one politically challenged sector). This 407 means we solve either (II.1) or (II.3) with a specified _δT_ delay to the decarbonization date in our 408 politically challenged sector. We repeat this process for a number of _δT ∈_ (0 _,_ 10] with a discretization 409 of 0 _._ 1 years. Each model is solved in continuous time over an infinite horizon.

19
# **Institutions and Firms’ Return to Innovation:**

# **Evidence from the World Bank Enterprise Survey**

<mark>Ha Nguyen</mark><sup>∗</sup> <mark>Patricio A. Jaramillo</mark> <mark>World Bank George Washington University</mark>

**Key words** : Innovation, Institutions, Rule of Law, Property Right Protection

**JEL Classification** : O31, O57.

> ∗ The paper is a part of the World Bank Latin America Economic Policy Sector (LCSPE)’s  project on Latin America’s convergence. We thank the support from LCSPE. We thank Luis Serven, Ufuk Akcigit, Jorge Araujo, Francesco Caselli, Maya Eden, Matteo Iacoviello, Bill Maloney, Ekaterina Vostroknutova, Pluvia Zuniga for very helpful comments and feedback. We thank Maria Ivanova Reyes for her able research assistance.
number cf weeks of trading.<sup>4</sup> Eighteen funds are fronm EMs **ard** 14 from DMs. Table 2 provides the initial size, number of shares, price and value at the end of 1990. It should be noted that the initial size represents total capital raised by the fund tnrough one or more offerings up to the end of 1990. About 39% of the global market valuL :n 1990 corresponds to EM funds. Figure 1 documents the dramatic increase since 1981 througih 1990.

The data base contains wcekly data for each fund since its inceotion. It comprises: Friday closing prices as reported in the NYSE records; net asset value (NAV) as obtained from fund managers; dividends and distributions of capital gains; local stock market inde; pro"ided by local exchanges; representative indices calculated by the International Finance Corporation for fifteen funds -eleven countries- (series start only in January i989); exchange rates (from IMF) between local currencies and U.S. dollar; and the Standard & Poors Composite Stock Price Index of 500 Stocks. Tables 3 to 6 report returns on the various funds. Table 7 reports returns on the coiresponding local stock market indices.

# **II** - GAINS FROM DIVERSIFICATION INTO EMERGING MARKETS

The gains from international portfolio diversification documented in traditional analyse. do not take account of barriers to capital flows and the associated costs of accessing capital markets across national boundaries.j It is apparent that portfolio capital does not flow freely 4Twelve closed-end multi-country funds that traded in New York are not included in the study. We also do not consider non-diversified country funds, warrant funds or debt-conversion funds.

> 'See Levy and Sarnat (1970) and Errunza (1977), among others.

5
# - <u>3. Global inequality over the long run: From proletarians to migrants</u>

I will now look at global inequality over the long sweep of history. It is here that we can establish an important finding, which goes, I think, into some core issues of political philosophy and economics.

Let us try to do for the entire period since the Industrial Revolution the same type of global inequality calculations which we have just shown for the last 20 years. We ask, “what was global inequality then—say, around the mid-19<sup>th</sup> century?” It is a question impossible to answer with any precision, because we do not have household surveys or any other reliable sources of income data for these times. Nonetheless, some important attempts to estimate it have been made before, notably by François Bourguignon and Christian Morrisson (2002), who were the first to take such a long-run view. They used income levels (GDP per capita) from Angus Maddison’s database (2004; 2007), and some of their own (necessarily often shaky) estimates of income distributions for different parts of the world, to create global income distributions for eleven benchmark years spanning the period 1820-1992. They did the best one could do with the available data —and their results have been corroborated, to the extent that it is possible to corroborate something as tentative, by several later authors (Van Zanden, Baten, Foldvari and van Leeuwen 2010; Milanovic, 2011). The basic story that emerges from these calculations of income inequality in far-away times is that since the Industrial Revolution, which launched a score of European countries and their overseas off-shoots onto a path of faster growth, global inequality kept on rising until the mid-20<sup>th</sup> century. There was a period of more than a century of steady increase in global inequality, followed by perhaps fifty years (between the end of the Second World War and the turn of the 21<sup>st</sup> century) when global inequality remained on a high plateau, changing very little. We saw this in Figure 2 where the six dots are all within several Gini points of each other, that is, within onestandard error of the calculated Gini coefficients. It is only in the early 21<sup>st</sup> century that global inequality might have commenced its downward course. If indeed this happens to pass, global 17
using logit and tobit respectively.<sup>12</sup> The predicted values are used as instruments in the regression. Again, all of the instrumented variables except openness remain significant. In this case, there is less of a jump in the coefficient on the interactive term.

Using the same instruments, and instrumenting for rule of law as well as the other endogeneous variables leads to insignificant results for all the endogeneous variables. There are too many highly correlated endogenous variables to be identified by the same set of instruments. While all signs remain robust, none of the endogenous variables are significant (not reported). In the final column, the results are reported including rule of law, but assuming the regulation dummy variable is exogenous.<sup>13</sup> Column 5 reports results of the same regression, excluding rule of law for comparison purposes. When rule of law is included, the regulation index is not significant. The trade interactive term remains highly significant.

While the IV results are supportive of the notion that trade does not support growth in highly regulated countries, it is very difficult to find good instruments that uniquely identify the endogenous variables. The lower half of Table 3 reports the Shea partial R<sup>2</sup> and the P-value of the Hansen J-statistic for each regression. The Shea R<sup>2</sup> takes into account the collinearity between the endogenous variables and also the extent to which the same instruments are being relied upon for identification. The Shea R<sup>2</sup> is low for trade, indicating that part of the reason we may not get significant results for trade is that it is not well identified. Still, the highly significant results for the interactive term imply that any positive effect of trade on poorly regulated countries is highly suspect. The Hansen J-statistic tests the null hypothesis that the

> 12 We use only a selection of the exogenous variables because it could not converge when all variables were included.

> 13 In this case, in order to help identify the interactive term, we also add two instruments, an interaction between landlock and the dummy and between fraction of the population speaking European languages and the dummy.

17
was launched roughly a decade after displacement had begun from the Syrian Civil War, allowing ample time for construction of additional housing units and social integration.

The results also speak to the active ongoing policy debate on how best to support refugees and host states at the same time (Ash and Huang 2018; Baseler et al. 2023). When study participants were asked directly about what form of assistance they preferred (during the 1.5 year follow-up survey), respondents overwhelmingly stated that they preferred direct cash transfers to landlord subsidies (at 70%). Cash transfers to refugees in other contexts have been shown to provide only short-run benefits rather sustained gains (Hidrobo et al. 2014; Özler et al. 2021; Quattrochi et al. 2022; Moussa et al. 2022; Aygün et al. 2024), but the findings of this study suggest that delivering benefits more discreetly (such as via cash or mobile money) may at a minimum reduce the risk of host community backlash. An alternative approach, supported by Baseler et al. 2023, would explicitly pair refugee assistance with enhanced host community assistance, which in this case might have led to some housing investments among host community neighbors as well. There is promising evidence from Colombia that labor market reforms may be an effective way to improve refugee living standards without leading to crowd-out of host community jobs (Bahar et al. 2021; Rozo et al. 2023; Ibáñez et al. 2024). These remain critical areas for future research and policy innovation, and present important opportunities to improve program design and ultimately refugees’ lives.

# **References**

Agness, D. (2023). Housing and human capital: Condominiums in ethiopia. _3ie Registry_ .

- Anderson, M. L. (2012). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the abecedarian, perry preschool, and early training projects. _Journal of American Statistical Association_ .

- Ash, N. and C. Huang (2018). Using the compact model to support host states and refugee selfreliance. _World Refugee Council Research Paper Series_ .

Aygün, A. H., M. G. Kırdar, M. Koyuncu, and Q. Stoeffler (2024). Keeping refugee children in 34