rafmacalaba/datause-encoder-data huggingface dataset:
Superintendency of Pensions ( _Superintendencia de Pensiones, SP_ ). The other data used, described throughout the paper, come from the Central Bank of Chile ( _Banco Central de Chile_ ) and other sources (Appendix Table 1).

The data on Chilean mutual funds contain detailed portfolios of all existing medium- and long-term funds at a monthly frequency during the period January 2002 to December 2008. The database comprises 965,209 observations. It includes information on the type of security, currency denomination, price, units held, and maturity date. In addition to these medium- and long-term funds, there are several short-term mutual funds providing money market services. We exclude those from the analysis to focus solely on funds established to invest long term.<sup>8</sup> For pension funds, we use a panel of their portfolio investments in fixed-term assets for each of the existing funds during the period 1996-2008 at monthly and daily frequencies. We perform more detailed analysis for the period 2002-2008, when the investment options expanded to more funds. We use panel data with the amount of deposits (including cash as deposits with a one-day maturity), corporate bonds, and government bonds held by fund per unit of time.<sup>9</sup> There are a total of 6,659,681 observations on a monthly frequency, representing the portfolio holdings of the funds. The data set contains information on the holdings of 76,498 different securities for 45

> 8 Chilean mutual funds are classified according to the type and investment horizon of their assets. Fixedincome funds include money management funds (with horizons of less than 90 days or less than 365 days) and medium- and long-term funds. We only use the latter two, since the first ones would be tilted toward the short term by construction. In 2008, approximately 60% of the existing funds were categorized as medium- or long-term funds, 12% as money management funds (less than 365 days), and 28% as money management funds (less than 90 days).

> 9 Since September 2002, each pension fund administrator (PFA) offers by law five funds with different risk profiles and investments in equity, subject to different portfolio regulations. The PFAs organize their trading desks in different forms that vary over time. For example, some pension fund companies have specialists for each asset class across fund types while others have dedicated managers for each fund, selecting the portfolio in each asset category.

12
reduce these risks. Current social policies—unemployment insurance, severance pay (FGTS), and the pension system—cover employees in formal employment for loss of income or job. Formal sector workers are well covered, perhaps excessively so. The group at risk is the two-thirds of the labor force working in the informal sector that does not have access to any social insurance programs, which gives access to low-paid workfare programs, constituting a form of basic risk coverage. The _Programa Nacional de Geração de Emprego e Renda_ (National Program for Job and Income Generation, PRONAGER) helps the unemployed to increase their job opportunities. During 19952001, PRONAGER benefited 6,086 people in seven municipalities in Pará. Hence, 145 municipalities still do not participate in the program.

The group age 65 and older faces the risk of low income. The role of social protection is to supply pensions to this age group. The contributors to the social insurance system (contributory pensions) face little risk because they are insured. The role of social assistance is to provide income transfer to the group that is not insured. The risk for this age group is currently fairly adequately covered.

Many of the programs find it difficult to reach the very poorest. A recent study of Brazil found that of the total spent on social programs, 14 percent accrued to the first quintile of the income distribution (World Bank 2001). Many programs lack broad reach amongst the poor, good targeting, or both. Some guidelines for the design of good social programs are provided in Box 6.1.

The federal-financed _Bolsa Escola_ and _Programa de Erradicação do Trabalho Infantil_ (Child-Labor Eradication Program, PETI) programs are designed to increase school attendance and attainment and to reduce child labor by providing financial transfers (grants) to families who abide by certain rules. The target group is children aged 7 to 14 from poor households. The programs are widely seen as successful, both in terms of improving educational outcomes and improving family welfare of the recipient families. In Pará, 439,339 children participated in the _Bolsa Escola_ program in 2000. In 2003, 318,056 children participated in the PETI program.

One welcome side effect of these two programs stems from the way that they are administered. The programs specifically give the money to mothers of the participating students. The thinking is that this will increase the likelihood that the funds will go to the improvement of the welfare of the family as a whole and of the children in particular. This is the first time many of these women have been entrusted with this kind of financial responsibility, or have opened a bank account. This “official recognition” has also led to a greater recognition and valuation of women’s role in the family – including by their partners – and in many cases has increased the women’s self-confidence. In field visits by the World Bank, the money received by the mothers through the Bolsa Escola and PETI programs is repeatedly mentioned by both men and women as one way in which gender relations and the roles of men and women are slowly changing.

42
# **1 Introduction**

The world population has been growing rapidly. This is particularly true in Africa where the population has multiplied manyfold since the beginning of the 20th century, and is predicted to reach four billion by the end of the 20th (UN 2013). Increases in the African population have spurred urbanization in general, and the emergence of mega-cities of several million inhabitants in many -- if not most -- African countries (Lall, Henderson and Venables 2017). How this rapid expansion of human settlements unfolds over geographical space is of paramount importance for development (e.g., Duranton 2015) and has likely deep consequences on inequality and poverty (e.g., UNDP 2017). While these structural changes create formidable challenges for policy makers, relatively little is known -- beyond the above general observations -- on the process by which urbanization is unfolding in the continent.

The purpose of this paper is to document how the spatial distribution of buildings has evolved over four decades in Ghana. To do this, we take advantage of the recently available Global Human Settlement Layer (GHSL) data from the European Commission, Joint Research Centre, which used machine learning tools to predict the presence of buildings from satellite imagery. At the heart of this study is a panel dataset of 3.8 million rectangle-shaped cells covering the entire territory of Ghana and observed in 1975, 1990, 2000, and 2014. For each of these cells, the GHSL dataset has an ML prediction of the proportion of that cell’s area that is covered by buildings. We regard the existence of buildings as indicating the presence of people and economic activity. Given that we have precise geocoordinates for each cell, we can combine this large dataset with information on roads, humidity, soil types, and proximity to borders and bodies of water.

We start by documenting the evolution of the distribution of land by intensity of construction. Over the period of our study, the average proportion of the country with buildings went from 0.4% in 1975 to 0.8% in 1990, 1.1% in 2000 and 1.8% in 2014. The distribution of built-up area proportions has changed over time. The proportion of cells without buildings has dropped from 98.5% to 93.8% while the number of built-up cells has gone up. When we limit our attention to cells with at least one building, we note an increase in the proportion of built-up cells that are fully built (i.e., with a buildup density of 90% or more): this proportion rises from 7% to 12% of the built-up cells. The rest of the distribution of built-up cells is largely constant over time. To put 1
<u>Table 8: Predicted Choice Transition Probabilites</u>

|||**P**|**ost-dri**|**p**|||
|---|---|---|---|---|---|---|
|**Pre-drip**|_U_|_A_|_L_|_P_|_C_|All|
|_U_|0.622|0.126|0.000|0.003|0.249|0.462|
|_A_|0.000|0.011|0.004|0.234|0.751|0.153|
|_L_|0.000|0.000|1.000|0.000|0.000|0.023|
|_P_|0.000|0.000|0.031|0.968|0.000|0.146|
|_C_|0.000|0.000|0.014|0.166|0.820|0.216|
|All|0.287|0.060|0.031|0.215|0.407|1.000|

_Notes:_ Computed using estimation sample.

selling water would rise from 36% to 62% upon adoption of drip irrigation.<sup>36</sup> Given the evident importance of groundwater markets in farmers’ exploitation of drip irrigation, we next explore how land fragmentation, the driving force behind these markets, affects the benefits of drip adoption. To isolate the role of groundwater market frictions, we use the analog of equation (17) to compute ∆ _VU_ , the gross return to drip under the first-best (unconstrained) counterfactual. The ratio _ρ_ = ∆ _V_<sup>_∗_</sup> _/_ ∆ _VU_ thus represents the surplus gain from drip adoption given frictions relative to the surplus gain absent frictions. The mean of _ρ_ in the estimation sample is 0.94; so, the distortions uncovered in this paper reduce the gross return to drip adoption by an average of 6% relative to first-best.

Looking beyond averages, Figure 8 shows that _ρ_ increases (toward unity) with borewell plot size. In other words, consistent with what we have already seen in Figure 7, groundwater market frictions are more salient on smaller plots. However, _ρ decreases_ with average _adjacency_ plot size. Recall that adjacencies with larger plots have a higher borewell density (cf. Figure 3) and, in our model, have a higher fixed cost of groundwater transactions (because _β >_ 0). As a result, farmers surrounded by larger plots (i.e., more borewells) would profit less, ceteris paribus, by switching to drip irrigation, and this is especially true when they themselves have a small plot. Paradoxically, fragmentation of neighboring land is conducive to drip adoption even as fragmentation of own land is detrimental to it.

> 36One caveat is that, as the effective supply of groundwater expands, the fixed cost of arranging a sale (difficulty of finding a buyer) may increase. Since this type of general equilibrium consideration lies outside of our model, we should think of the results in Table 8 as an upper bound on extensification via the market.

34
Policy Research Working Paper 11074 Community Health Workers as Key Providers of Easy-to-Use Contraceptive Injectables Experimental Evidence from Rural Burundi _Michele Andreottola Olivier Basenya Victor Orozco-Olvera Arndt Reichert Paula Spinola_ Development Economics Development Impact Group February 2025 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
# **1 Introduction**

Curbing fossil fuel consumption is essential to keep global warming within levels that will still allow societies to function (Intergovernmental Panel on Climate Change, 2018). Yet, governments are reluctant to take significant measures to reduce emissions, including decisive taxes on carbon. This resistance is partly due to the concern that an increase in the price of energy could harm domestic producers by increasing the cost of a key production input (Rentschler et al., 2017). The argument is particularly salient in industrializing countries where fuel consumption is often subsidized.<sup>1</sup> Mexico and Indonesia are cases in point. In 2013-14 both countries set out to reverse years of sizable fossil fuel subsidies. The Mexican government proposed an ambitious carbon tax on fossil fuel consumption in 2013. However, during the approval process, industrial and social groups successfully lobbied the Mexican Congress to reduce the level of the proposed tax on the ground that it would harm the competitiveness of domestic industries and raise inflation (Arlinghaus and Van Dender, 2017).<sup>2</sup> Similarly, the Indonesian government removed much of the long-standing fuel subsidy from the national budget in 2015 so that by 2016 the subsidy had declined to 2.2% of central government’s expenditures (from 13.5% in 2014).<sup>3</sup> However concerns similar to those of the Mexican case eventually led the government to reinstate part of the subsidy, which by 2018 accounted for 4.4% of central government’s expenditures.<sup>4</sup> This paper uses data from these two countries to provide novel evidence that increases in energy prices do not necessarily worsen the performance of manufacturing firms. The key innovation of the analysis is testing for the impact of energy price variations separately for electricity and fuels, the two main types of energy

> 1Coady et al. (2015) estimate that energy subsidies would account for between 13% and 18% of GDP in Developing and Emerging Asia, the Middle East, North Africa, and Pakistan (MENAP), and the Commonwealth of Independent States (CIS).

> 2Eventually a considerably less ambitious carbon tax was approved at the end of 2013, including the rule of capping the tax to a level which would not increase the fuels retail price by more than 3%.

> 3These figures are based on the audited Indonesian government expenditure reports.

> 4These types of concerns are illustrated for instance in a recent interview of a top bureaucrat in the Indonesian Ministry of Industry, who stated that it is important to maintain cheap fuel prices to ensure industrial development including downstream industries (Sulmaihati, 2019).

1
9 where rpxy-P,(l+xs)/P. The *quation shows a very strong supply response to relative prices (inclusive of export subsidies). No good results were obtained with inclusion of the black market premium; the smuggled<sup>part of total exports</sup> is apparently too small for this variable to show up significantly although it should be part of is. Note hat x is total Turkish exports measured from foreign trade statistics where available (see the data appendix<sup>for details).</sup> This equation is estimated on a subset of 9 countries because of limited availability of counterpart trade data. In particular the Middle East had<sup>to</sup> )he oxcluded, but the nine largest trading partners outside the Middle East were in the sample (see data appendix for details). Note incidentally that X,/x can exceid 1.

(12) log(xo/x) - 0.05 + 0.30*log((l+xs)/(l1+)) (3.11) (3.88) R2 _ 0.44, DW - 1.79, OLS, SMPL - 67-87 Not surprisingly there is a substantial residue of unexplained variance, but one thing is clear. The share of total exports that is actually<sup>reported and</sup> thus shows up in the Turkish trade statistics is highly sensitive to the relative attractiveness of reporting versus not reporting as deteramined by subsidies and the black market exchange rate premium.

As to export demand, we looked separately at exports to oil exporting countries (x,) and exports to other countries (x,). Consider the latter first. Analysis of the error structure of the equation showed that frt differencing was necessary for stationarity (auto correlation coefficients below one). Estimation of equation (9) yields the following results: (13a) dlog(x,,) - 0.03 + 0.83*dlog(Px/(EPm*)) + 0.<sup>33</sup> *dlog(P1<sup>(-l)/(EPw(-l)))</sup> (0.42) (1.61) (0.68) + 0.27*dlog(Ph./Pm,) + 1.32*dlog(A) (0.26) (0.66) R2 - 0.31, TSLS, SMPL - 71-87 The equation performs rather poorly as specified; in particular the terms involving substitution on the first level (home versus foreign goods in the country exported to) come up with very insignificant parameter estimates. We therefore reestimated the equation on the second level only, focusing exclusively on competition between Turkish exports and other exports to Turkey's export markets. This yields better results: (13b) dlog(xnO) - - 0.01 + 1.l5*dlog(P/(EP".)) (0.18) (2.48) + 0.26*dlog(P,(-l)/(E(-l)P,.(-l))) + 1.50*dlog(C) (0.64) (2.45) R<sup>2</sup> - 0.43, TSLS, SMPL - 69-87
<u>reports/digital-technologies-and-learning-outcomes-students-low-socio-economic-</u> - <u>background analysis</u>

- Rogers, Halsey and Shwetlena Sabarwal. 2020. The COVID-19 Pandemic: Shocks to Education and Policy Reponses. Washington, DC: World Bank.

- Sacerdote, Bruce. 2012. When the Saints Go Marching Out: Long-Term Outcomes for Student Evacuees from Hurricanes Katrina and Rita. American Economic Journal: Applied Economics. Vol 4, No. 1, January 2012, 109-135. Available online at: =

- <u>https://www.aeaweb.org/articles?id 10.1257/app.4.1.109</u>

- Shores, Kenneth and Matthew Steinberg. 2017. The Impact of the Great Recession on Student Achievement: Evidence from Population Data. Available online at <u>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3026151</u>

- Singh, A. 2019. Learning More with Every Year: School Year Productivity and International Learning Divergence. Journal of the European Economic Association. 10.1093/jeea/jvz033.

- <mark>Thamtanajit, K., 2020. The Impacts Of Natural Disaster On Student Achievement: Evidence From Severe Floods in Thailand.</mark> _The Journal of Developing Areas_ , _54_ (4). Available online at =

- <u>https://muse.jhu.edu/article/738666/pdf?casa_token 4v6n1GYw3g0AAAAA:DEmn7PE2uk</u> -

- <u>ZshqfHb6O vjXYQ2blC6q7_8v4cO88vwrJumd5sfSZtZ4Q2npFtZEjgMAymhaY_N4</u>

- UNESCO. 2019a.  Minimum proficiency levels (MPLs): outcomes of the consensus building meeting. GAML5/REF/4.1.1-29

- UNESCO. 2019b. Global Education Monitoring: Migration, displacement and education: building bridges, not walls.

- Yousafzai, Arshad. Low-cost private schools may not be able to survive COVID-19 crisis. Available online at <u>https://www.thenews.com.pk/print/637486-low-cost-private-schoolsmay-not-be-able-to-survive-covid-19-crisis.</u>

- Villasenor, J., and B. C. Arnold. 1989. Elliptical Lorenz curves. Journal of Econometrics 40 (2): 327–338.

- Wilkinson T. et al. 2014. Bill and Melinda Gates Foundation Methods for Economic Evaluation Project (MEEP): Final Report. London: National Institute for Health and Clinical Excellence.

- Wills, G. 2014. The Effects of Teacher Strike Activity on Student Learning in South African Primary Schools, Working Papers 402, Economic Research Southern Africa. Available online at: <u>https://www.econrsa.org/publications/working-papers/effects-teacher-strike-activitystudent-learning-south-african-primary</u>

- Woessmann, L. 2016. The Importance of School Systems: Evidence from International Differences in Student Achievement, Journal of Economic Perspectives, Vol. 30/3, pp. 3-32, http://dx.doi.org/10.1257/jep.30.3.3.

- World Bank. 1998. Education in Indonesia: From Crisis to Recovery. Available online at -

- <u>http://documents.worldbank.org/curated/en/558971468752104023/pdf/multi page.pdf.</u>

- World Bank. 2019. Ending Learning Poverty : What Will It Take?. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/32553

39
emissions. The extent of forest loss in the past 30 years has been unprecedented: on net, the world lost approximately 178 million hectares of forest area between 1990 and 2020 (FAO, 2020). In addition to an important environmental outcome in its own right, satellite-derived deforestation measures are also spatially explicit, local measures of environmental harm that circumvent many limitations associated with administrative data on environmental damage.

We test whether the inclusion of provisions in RTAs aimed at protecting forests and/or preserving biodiversity mitigate the ecological impacts of trade liberalization. We use a novel dataset with detailed information on the content of all RTAs in force and notified to the WTO between 1958 and 2018 (Monteiro and Trachtman, 2020) and combine these data with satellite-derived estimates of annual forest loss for 193 countries. From the set of RTAs that enter into force from 2004 and 2014, we estimate the predicted probability that an RTA includes a forest and/or biodiversity provision via the use of machine-learning techniques and a variety of RTA and group-level characteristics. We then use these predicted probabilities to match RTAs that include these provisions to similar RTAs without them to create an appropriate set of counterfactuals. We construct a panel dataset on total forest loss for all countries that join a given RTA and, using a triple-difference model, test whether aggregate increases in forest loss associated with RTA enactment are lower for RTAs with environmental provisions than those without. We find large, significant net increases in annual forest loss following RTAs without provisions (23%) and that the inclusion of these provisions entirely offsets the rise in forest loss observed in the counterfactual RTA groups after enactment. This effect is largely driven by changes to forest loss in tropical, developing countries with high levels of biodiversity–the locations where deforestation is of greatest concern.

We then investigate the mechanisms through which forestry and biodiversity provisions in RTAs mitigate environmental damage. Analysis using the same matched triple-difference approach indicates that environmental provisions limit agricultural land expansion that otherwise occurs following the entry into force of trade agreements. RTAs without these environmental provisions lead to a 5% increase in the annual land area harvested on average while there is no increase in agricultural extensification following RTAs that include these provisions. Trade liberalization also leads to increases in agricultural output (as measured in tonnes harvested) that is partially, but not completely, 2
# **Figures**

**Figure 1. Concentration Curve for Visits to Hospital (4-week recall)**

<!-- Start of picture text --> Hosp. visits (cons.) Hosp. visits (asset indx)<br> 45 degree line<br>1<br>0<br>0 1<br>Population, poorest to richest<br>Figure 2. Concentration Curve for Visits to Health Center (4 week recall)<br> Hlth. center visits (cons.) Hlth. center visits (asset indx<br> 45 degree line<br>1<br>0<br>0 1<br>Population, poorest to richest<br>Cumulative proportion of hospital visits<br>Cumulative proportion of health centre visits<br><!-- End of picture text --> 19
OECD (2009), Innovation in Firms: A Microeconomic Perspective, Paris, France: Organization for Economic Cooperation and Development.

Parisi, Maria Laura, Fabio Schiantarelli, Alessandro Sembenelli (2006), “Productivity, Innovation, and R&D: Micro evidence for Italy,” European Economic Review, 50:2037-2061.

Restuccia, Diego and Richard Rogerson (2008), “Policy Distortions and Aggregate Productivity with Heterogeneous Establishments,” Review of Economic Dynamics, 11:707-720.

Sinha, Rajeeva (1993), "Foreign Participation and Technical Efficiency in Indian Industry," Applied Economics, 25: 583-88 Seker, Murat (2009), “A Structural Model of Establishment and Industry Evolution: Evidence from Chile,” World Bank Policy Research Working Paper No. 4947.

Seker, Murat (2011) “Importing, Exporting, and Innovation in Developing Countries,” forthcoming in Review of International Economics.

Seker, Murat and J. Daniel Rodriguez (2011), “Imported Intermediate Goods and Product Innovation: Evidence from India,” Working Paper, Enterprise Analysis Unit, World Bank.

Topalova, Petia and Amit Khandelwal (2011), “Trade Liberalization and Firm Productivity: The case of India,” The Review of Economics and Statistics, 93(3): 995-1009.

Vishwasrao, Sharmila and William Bosshardt (2001), “Foreign Ownership and Technology Adoption: Evidence from Indian Firms,” Journal of Development Economics, 65:367-387.

20