rafmacalaba/datause-encoder-data huggingface dataset:
of maximum (80-100%) clearing 2.8-fold, from 19,820 to 55,660 hectares. Significant change also occurs in the Southwest Amazon moist forests (central, western and northern areas), where the maximum cleared area increases from 164,790 hectares to 177,500 hectares. Figure 9 provides another perspective by identifying impacts as changes in percent cleared. Here the most striking changes are observable in the Bolivian Yungas and Southern Andean Yungas.

# **Table 6:  Road improvement impact by moist forest ecoregion**

|||Area<br>(th|Cleared 80-<br>ousandhecta<br>|100%<br>res)||
|---|---|---|---|---|---|
||||After||Impact|
|Country|MoistForestEcoregion|2014|Upgrading|Change|Ratio|
||Southern Andean Yungas|1.50|10.39|8.90|6.9|
||Bolivian Yungas|19.82|55.66|35.84|2.8|
|Blii|Madeira-Tapajós moist forests|30.36|33.93|3.57|1.1|
|ova|Southwest Amazon moist forests|164.79|177.50|12.71|1.1|
||Iquitos varzeá|0.15|0.16|0.01|1.1|
||Monte Alegre varzeá|0.01|0.01|0.00|1.0|
||Cameroonian Highlands forests|4.17|392.00|387.83|94.0|
||Northwestern Congolian lowland forests|10.19|118.02|107.83|11.6|
|Cameroon|Cross-Sanaga-Bioko coastal forests|24.14|68.90|44.76|2.9|
||Atlantic Equatorial coastal forests|11.65|24.09|12.44|2.1|
||Mount Cameroon and Bioko montane forests|1.56|2.50|0.95|1.6|
||Irrawaddyfreshwater swampforests|1.04|2.98|1.95|2.9|
||Irrawaddymoist deciduous forests|55.13|91.78|36.65|1.7|
||Myanmar coastal rain forests|30.16|48.64|18.48|1.6|
||Chin Hills-Arakan Yoma montane forests|15.28|23.92|8.63|1.6|
|Manmar|Northern Indochina subtropical forests|108.61|166.42|57.81|1.5|
|y|Tenasserim-South Thailand semi-evergreen rain forests|22.43|31.51|9.08|1.4|
||Mizoram-Manipur-Kachin rain forests|30.85|41.40|10.55|1.3|
||Northern Triangle subtropical forests|26.08|34.81|8.73|1.3|
||Kayah-Karen montane rain forests|38.97|49.90|10.94|1.3|
||Lower Gangetic Plains moist deciduous forests|0.00|0.00|0.00|.|

27
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others in their community as opposed to save for themselves, and preferences for the village to develop together versus separately).

**_Sociodemographics._** Most sociodemographic measures including Proxy Means Targeting (PMT) poverty score (Premand & Schnitzer, 2021), age, relationship to household head, and nomad status were collected in a census survey prior to the randomization.

# _Empirical strategy_

The analysis plans were pre-specified (https://www.socialscienceregistry.org/trials/3570). Deviations from this plan are also uploaded and primarily reflect changes to align analyses with the policy experiment, as published in Bossuroy et al. (2022).

To assess treatment effects at endline, our primary model compares each psychosocial intervention to a control condition that included no additional psychosocial intervention.<sup>18</sup> We control for a vector of stratification variables used in randomization, including timing of the program implementation (early versus late), policy experiment treatment arm (Psychosocial versus Full), and whether or not participants were randomly selected in the policy experiment sample for data collection (presented in Bossuroy et al., 2022). This model is represented as follows: 0 1 P P P 2 P P 0 ii ii ii ii ii YY = ββ + ββ TT. P _p-_ values and cluster robust + ββ TT. IIPPIIP ppP _p-_ PP + γγ XX + εε We present both robust _p- p-_ values. We present clustered standard errors to account for the fact that women were organized into groups as part of the program and thus their outcomes are likely to be correlated and because a group-level randomization was conducted on top of the individual-level randomization. However, given recent evidence (Abadie et al., 2022), these cluster robust _p_ -values may be overly conservative. For this reason, we present both clustered and non-clustered robust _p_ values and prioritize the latter for interpretation.

A second implication of this design is the potential for contamination of intervention content in case women were to discuss intervention with others in their program group. In this case, the differences across intervention conditions would likely be attenuated and thus can be considered conservative estimates of true intervention effects.

> 18 We did not pre-register analyses of differences between the two treatment arms (Personal Initiative and Interpersonal Initiative) because we believed that we could be underpowered to detect such a difference. Nevertheless, we compute the p value for comparisons between the two treatments in the supplementary appendix and find, as expected, that _p_ s>.05 across all economic and psychosocial outcomes.

42
most cases, be reduced. The externalities related to miles driven, in contrast, will increase, since the sign in the content of the second brackets will always be positive (there is always a rebound in demand for miles, since mileage related private marginal costs are reduced). It follows from this that if damages related to miles driven are significant compared to damages related to fuel consumption, tightened fuel efficiency standards will likely increase _total_ negative externalities.

The table below summarizes some studies of externalities from transport that distinguish externalities related to fuel use from those related to miles traveled. It appears from these that two important categories of externalities related to miles driven, accidents and congestion, easily can be far greater than the other externalities. This represents an a priori warning against programs that raise fuel efficiency standards without simultaneously raising fuel taxes (see below).

|Comparison of Externalities Associated|with Fuel c<br> <br>Fuel|onsumptio<br>Extern|n and with mile<br>ality Due to|s driven|
|---|---|---|---|---|
||Consum|ption***|VKM***|Ratio|
|Parry, Fischer, and Harrington (2004)<sup>*</sup><br>|11||2.5|4|
|ECON(2003)<sup>**</sup>Cars|0.03||0.31|10|
|ECON(2003)** Goods Transport|0.04||0.20|5|
|Schreyeret al. (2004)*|26.5||50|2|
|Parry and Small(2005)*|5||17|3|

*Measured in Cents per mile.  **Measured in Øre per kilometre.  ***  See text for categorization of externalities.  Across the studies reported here, there are important differences in terms of the externalities included.

21
WPS4002

# **Latin American Universities and the Third Mission**

## **Trends, Challenges and Policy Options**<sup>1</sup>

Kristian Thorn Maarja Soo<sup>2</sup>

### World Bank Policy Research Working Paper 4002, August 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 The authors wish to thank the following individuals for their invaluable comments: Enric Banda (Director, Catalan Research and Innovation Foundation); Andreas Blom (Education Economist, World Bank); Eduardo Velez Bustillo (Sector Manager, World Bank); Kurt Larsen (Sr. Program Officer, World Bank); William F. Maloney (Lead Economist, World Bank); Jamil Salmi (Lead Education Specialist,World Bank); and Simon Schwartzman (President, Brazilian Institute for Studies of Labor and Society).

2 Kristian Thorn is Education Specialist in the Department of Human Development in the Latin America and Caribbean region of the World Bank. Maarja Soo is a doctoral student of public policy at the University of North Carolina at Chapel Hill and research associate for the Public Policy for Academic Quality research program.

1
consumption data in the two surveys and concerns around data quality in the 2009/10 survey, it is not possible to simply compare them to construct a trend and understand the country’s poverty dynamics.<sup>3</sup> This paper aims to address this issue by estimating a poverty trend in Nigeria for the period 2009-2019 through two separate approaches, which leverage different data sources. First, the paper presents the results of a “backcasting” exercise that uses the latest survey estimates of household consumption from the 2018/19 NLSS and sectoral GDP growth rates for the previous decade. Second, the paper uses surveyto-survey imputation methods: a simple model linking monetary and non-monetary variables is estimated with the 2018/19 NLSS and is used to impute into another household survey – the General Household Survey (GHS) – available in 2010/11, 2012/13, 2015/16, and 2018/19. One key contribution of the paper is that the results from these two alternative and very distinct approaches are robust and similar in magnitude. While both approaches carry certain caveats (see section 7.2 for an extensive discussion), the survey-to-survey imputation methods can offset some of the limitations of the backcasting exercise and vice versa.

The backcasting exercise maps macroeconomic data on sectoral real GDP growth rates to micro-data from the 2018/19 NLSS – via the household head’s sector of employment – then constructs estimates of the full consumption distribution for each year in the decade prior to 2018/19. This household consumption vector can then be used to calculate the share of the population living in poverty, defined using different poverty lines, for all years between 2009 and 2019. Using this backcasting approach, we estimate a poverty rate of between 42.2 and 46.3 percent in 2009, depending on the assumptions made about the pass-through rate from national accounts growth data to household consumption. The backcasted series suggests a small decrease in the poverty rate at the beginning of the decade and then stagnation, or even reversal, in poverty reduction following the 2016 recession. Qualitatively, these results remain unchanged even after running sensitivity analysis to test different assumptions about the pass-through rate and about the mapping between micro- and macro-data.

The survey-to-survey imputation approach constructs a consumption model using a set of comparable non-monetary variables, which are available in both the 2018/19 NLSS and the GHS, to impute consumption into the 2010/11, 2012/13, 2015/16, and 2018/19 waves of the GHS. This approach follows a wide literature on survey-to-survey imputation techniques (Christiaensen et al. 2012; Dang et al. 2017; Douidich et al. 2016; Newhouse et al. 2014; Stifel and Christiaensen 2007; Yoshida et al. 2015; Yoshida et

> 3 For a discussion on data quality concerns around the 2009/10 HNLSS surveys, see Nigeria Poverty Assessment 2016.

3
be a lot of heterogeneity in effects, our sample is not large enough to allow us to look at all the firm level interactions that might be of interest, such as the competitive nature of the industry and sector; the age and gender of the owner; and the owner’s ambition level, risk taking ability, or general skill levels. We believe this is a critical area for further research.

# **References**

Banerjee, Abhijit, Esther Duflo, Rachel Glennerster, and Cynthia Kinnan. 2009. “The Miracle of Microfinance? Evidence from a Randomized Evaluation”. Working paper.

Bennedsen, Morten, Kasper Meisner Nielsen, Francisco Perez-Gonzalez, and Daniel Wolfenzon. 2007.

- “Inside the Family Firm: The Role of Families in Succession Decisions and Performance.” _Quarterly Journal of Economics_ 122 (2): 647–691.

- Bertrand, Marianne, and Antoinette Schoar. 2003. “Managing with Style: The Effect of Managers on Firm Policies.” _Quarterly Journal of Economics_ 118 (4): 1169–1208.

- Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts. 2011. “Does

- Management Matter? Evidence From India.” _World Bank Policy Research Working Paper No. 5573._

Bloom, Nicholas, and John Van Reenen. 2007. “Measuring and Explaining Management Practices Across Firms and Countries.” _Quarterly Journal of Economics_ 122 (4): 1341–1408.

- ———. 2010. “Why Do Management Practices Differ Across Firms and Countries?” _Journal of Economic Perspectives_ 24 (1): 203–224.

- Bruhn, Miriam, Dean Karlan, and Antoinette Schoar. 2010. “What Capital Is Missing in Developing Countries?” _American Economic Review_ 100 (2) (May): 629–633. doi:10.1257/aer.100.2.629.

- Bruhn, Miriam, and David McKenzie. 2009. “In Pursuit of Balance: Randomization in Practice in Development Field Experiments.” _American Economic Journal: Applied Economics_ 1 (4): 200– 232.

- Bruhn, Miriam, and Bilal Zia. 2011. “Stimulating Managerial Capital in Emerging Markets: The Impact of Business and Financial Literacy for Young Entrepreneurs.” _World Bank Policy Research Working Paper No. 5642._

Caselli, Francesco, and Nicola Gennaioli. 2005. “Dynastic Management.” _Economic Inquiry, Forthcoming_ .

- Cole, Shawn, Thomas Sampson, and Bilal Zia. 2011. “Prices or Knowledge? What Drives Demand for Financial Services in Emerging Markets?” _Journal of Finance_ 66 (6): 1933–1967.

- Gine, Xavier, and Ghazala Mansuri. 2011. “Money or Ideas? A Field Experiment on Constraints to Entrepreneurship in Rural Pakistan.”

- Gompers, Paul, Josh Lerner, and David Scharfstein. 2005. “Entrepreneurial Spawning: Public Corporations and the Genesis of New Ventures, 1986 to 1999.” _Journal of Finance_ 60 (2): 577– 614.

Karlan, Dean, Isaac Osei-Akoto, Robert Darko Osei, and Christopher R. Udry. 2012. “Agricultural Decisions After Relaxing Credit and Risk Constraints.” _Yale University Economic Growth Center Working Paper_ . doi:10.2139/ssrn.2169548. http://www.ssrn.com/abstract=2169548.

- Karlan, Dean, and Martin Valdivia. 2011. “Teaching Entrepreneurship: Impact of Business Training on Microfinance Clients and Institutions.” _Review of Economics and Statistics_ 93 (2): 510–527.

19
of Madagascar, that isolation (defined as travel time during dry season from the commune center to the nearest urban center) implies lower agricultural productivity, increased transport and transaction costs and increased insecurity. The authors found a major jump of per capita consumption from the least remote quintile to the second quintile and therefore a negative relationship between isolation and poverty.<sup>5</sup> So far, most development partners and governments in SSA have mainly relied on the assumption that most households in rural areas in Africa are not connected to markets by paved or passable roads for motorized transport and therefore need a road passable for a truck/bus. Many investments in rural roads seem to be built on the assumption that they will lead to market provision of transport and thus, poverty reduction and income generation. Estache (2010) points out a lack of rigorous evidence on these assumptions; namely there is a lack of randomized impact evaluations in the field of investments in roads, which call into question these assumptions and limit the ability to quantify the economic and even social benefits of the provision of rural roads.

In fact, there is some empirical evidence that calls into question current transportation strategies in Sub-Saharan Africa. Using the second Cameroonian national household survey (Enquête Camerounaise Auprès des Ménages II, 2001) and the Cameroon case study, Gachassin et al. (2010) demonstrate that investing in tarmac roads is likely to have a low impact on poverty. For example, isolation from a tarred road is found to have no direct impact on consumption expenditures in Cameroon. The paper reasserts the fact that access to roads is only one factor contributing to poverty reduction (and not necessarily the most important). Considering that increase in non-farming activities is the main driver for poverty reduction in rural Africa, the authors suggest that emphasis on roads investments should be given to locations where nonfarming activities could be developed, which does mean that the last mile in rural areas should not be probably a road with a high road level of service (except in peculiar cases of high agricultural potential areas).

> 5 Quoted in Raballand et al. (2010).

3
# **Table 7 Descriptive statistics on stock selection**

||Mean<br>||||<br>|
|---|---|---|---|---|---|
||(%)|Std. Dev|Min|Ma|x<br>N|
|3-month window<br>||||||
|Individual stock selection<br>||||||
|ft+3<br>|0.56|5.67|0|1|97,838|
|Peer stock selection<br><br>||||||
|Nt−3<br>|1.14|7.47|0|1|97,838|
|Nt+3|0.94|5.74|0|1|97,838|
|12-month window<br>||||||
|Individual stock selection<br>||||||
|ft+12<br>|0.92|5.96|0|1|97,838|
|Peer stock selection<br><br>||||||
|Nt−12<br><br>|1.47<br>|7.74<br>|0<br>|1<br>|97,838<br>|
|Nt+12<br><br>|0.92<br><br>|4.72<br><br>|0<br><br>|1<br><br>|97,838<br><br>|
|The table presents descriptive statistics of<br>|individual a<br>|nd peers stoc<br>|k selec<br>|tion.<br>|fi,c,t+∆,sis the fraction<br>|
|invested in stock_s_by investor_i_in∆months<br>the average fraction invested in stock_s_by ex|(3 and 12)<br>perienced s|after the start<br>tudents in cou|of cour<br>rse_c_b|se_c_.<br>efore|Nc,t−∆,sandNc,t+∆,sare<br>and after the training.|

47
<mark>Tanzania.”</mark> _<mark>Journal of Humanitarian Assistance</mark>_ <mark>4 (1):1–23.</mark> <mark>Whitaker, Elise B. 2002. “R</mark> efugees in Western Tanzania: The distribution of burdens and benefits among Local Hosts. <mark>”</mark> _<mark>Journal of Refugee Studies</mark>_ <mark>15 (4):339-358</mark> Zhibo Tan, Xiaobo Zhang and Chih Ming Tan.2015. "Sins of the Fathers: The Intergenerational Legacy of the 1959-1961 Great Chinese Famine on Children's Cognitive Development.” _Working Paper series 15-33, Rimini Centre for Economic Analysis_ .

27
commercial banks' balance sheet is thus used to determine _FLB_ (see Figure **4):** 25 Given (89), and given that all their profits are distributed to households, the net worth of commercial banks evolves over time according to, where the second term on the right-hand side represents again valuation effects associated with nominal exchange rate changes.

Banks set the loan interest rate as a premium over the average cost of funds-including the devaluation rate, which affects the cost of foreign borrowing-taking into account the (implicit) cost of holding reserve requirements: where 0 < _ab_ < 1 is measured by the initial share of domestic deposits in banks' total funds (that is, _Ceb = Eh DDh,o/(Eh DDh,O + FLB,oERo)),_ and _PR_ denotes the finance premium, which is assumed to be set according to: where 0 < **_p,_** _<_ 1 is the speed of adjustment, 0 < 6, < 1, and _NWp_ is the net worth of private urban firms in nominal terms, defined as _NWp = PK Kp-DLp-ER FLp._ _NWp_ changes over time according to _NWp = NWp,-1 + PK AKp - ADLp - ER AFLp - AER. FL,9.3) +APK- Kp,l._ The last two terms on the right-hand side of this expression represent capital losses associated with depreciations of the nominal exchange rate and capital gains associated with changes in the price of capital.

> 25 Note that capital losses associated with nominal exchange rate changes, _AER*FLB, 1I,_ axe accounted for in equation (70).

43
Before the crisis, and unsurprising given the sampling strategy, the main occupation for the interviewed people was commerce, while very few were farmers or shepherds. While most of the civil servants were able to keep their jobs, those working in commerce were badly affected by the crisis. As shown in the left panel of Figure 3, IDPs had the highest employment rate before the crisis, while only around half of the returnees used to work. All groups lost jobs during the displacement. Nevertheless, most returnees <!-- Start of picture text --> Figure 3: Employment rate by group<br>Baseline Follow-up<br>Displaced Refugee Returnee Displaced Refugee Returnee<br>Before crisis R1 R2 R3 R4<br>During displacement R5 R6 R7 R8<br>Once returned R9 R10 R11 R12<br>Analytic weights used<br>Source: LDPS 2014-15<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Employment rate Employment rate<br>.2 .2<br>0 0<br><!-- End of picture text --> managed to work again once back, and in the last waves (right panel or Figure 3) they reached high employment rates, similar to those for IDPs. On the other hand, employment rates among refugees remained low and actually deteriorated over time. This may be one of the main reasons behind the high willingness to return among this group.

As already pointed out in Etang-Ndip et al. (2015), average asset ownership of the people in the <!-- Start of picture text --> Figure 4: Asset index<br>Displaced Refugee Returnee<br>Before simple Before weighted<br>Now simple Now weighted<br>Source: LDPS 2014-15<br>.6<br>.4<br>Index<br>.2<br>0<br><!-- End of picture text --> sample was higher than the average inhabitant of the North. All three respondent groups reported big losses of livestock. To deepen our analysis, and using the information on asset ownership before the conflict and in August 2014 (the time of the baseline), we computed simple and weighted asset indices. These are shown in Figure 4.<sup>7</sup> It is interesting to note that before the crisis IDPs were on average better off, while the conflict deeply affected the refugees. On the other hand, returnees were least affected and their weighted asset index in August 2014 was roughly at the pre-crisis level.<sup>8</sup> In addition to this, both

> 7  A detailed description of how we computed these asset indices is available in the Appendix.

> 8  Nevertheless, it should be stressed that this index gives only a partial picture of their wealth. Indeed, we cannot say a priori if the refugees completely lost almost all their assets, or they just sold it before leaving since most of them were heavy to carry or useless in a refugee camp. If the latter case is true, the drop described above would simply indicate a shift from durable assets to liquid wealth (cash).

9