rafmacalaba/datause-encoder-data huggingface dataset:
Dawson, J. F. (2014). Interpreting Interaction Effects. Retrieved October 27, 2014, from http://www.jeremydawson.com/slopes.htm.

de Janvry, A., Sadoulet, E., & Zhu, N. (2005). The role of non-farm incomes in reducing poverty and inequality in China. CUDARE Working Paper 1001. Berkeley: University of California.

Deichmann, U., Shilpi , F. And Vakis, R. 2008. “Spatial Specialization and Farm-Nonfarm Linkages”, World Bank Policy Research Working Paper No. 4611.

Fafchamps, M. and Shilpi, F. 2003. The spatial division of labour in Nepal, _The Journal of Development Studies_ 39(6), 23-66.

Fafchamps, M. and Shilpi, F. 2005. Cities and Specialisation: Evidence from South Asia, _The Economic Journal_ 115(503), 477-504.

Economist (2000). _The Economist,_ May 13.

Elbers, C., & Lanjouw, P. (2001). intersectoral transfer, growth and inequality in rural Ecuador. World Development, 29(3), 481–496.

FAO (1998). _The State of Food and Agriculture: Rural Non-Farm Income in Developing Countries._ Rome: Food and Agriculture Organization.

Haggblade, S., Hazell, P. and Reardon, T., eds. (2007). _Transforming the Rural Nonfarm Economy_ . Johns Hopkins University Press.

IFPRI (2012). Patterns of Growth and Structural Transformation in Africa. _Thematic Research Note 02_ , April. Washington DC: IFPRI.

IMF (2012). _Regional Ecomonic Outlook. Sub-Saharan Africa. Maintaining Growth in an Uncertain World._ October, Washington DC: IMF.

Lanjouw, P. (1999). Rural nonagricultural employment and poverty in Ecuador. _Economic Development and Cultural Change_ , 48(1), 91.

Lanjouw, P., & Feder G. (2001). Rural nonfarm activities and rural development: From experience towards strategy. Rural Development Strategy Background Paper #4. Washington, DC: The World Bank.

Lanjouw, J. and Lanjouw, P. (2001). The Rural Nonfarm Sector: Issues and Evidence from Developing Countries. _Agricultural Economics_ 26(1): 1-23.

Lanjouw, P., & Shariff, A. (2002). Rural non-farm employment in India: Access, income and poverty impact. NCAER Working Paper Series No. 81. New Delhi: NCAER.

McKinsey Global Institute, 2010. _Lions on the move. The progress and potential of African Economies._ McKinsey and Company.

Reardon, T., Taylor, J. E., Stamoulis, K., Lanjouw, A., & Balisacan, P. (2000). Effects of non-farm employment on rural income inequality in developing countries: An investment perspective. Journal of Agricultural Economics, 51(2), 266–288.

Reardon, T., Berdegue, J., & Escobar, G. (2001). Rural nonfarm employment and incomes in Latin America: Overview and policy implications. World Development, 29(3), 549–560.

Rodrik, D. (2013). Africa’s Structural Transformation Challenge. Project Syndicate.

UCLA. 2014. UCLA: Statistical Consulting Group. Stata Annotated Output. Multinomial Logistic Regression. (http://www.ats.ucla.edu/stat/stata/output/stata_mlogit_output.htm . Accessed 2 April 2014).

Von Thünen, J.H. 1966. Isolated state; an English edition of Der isolierte Staat. Edited by Peter Hall, Oxford, New York, Pergamon Press.

30
costs (Euro/km) by dividing it by average speed. Their imputation suggests a per-km cost of 1.23 Euro/km in 1980, which fell to 1.02 Euro/km in 2007. Their methodology helps to decompose the drop to its productivity and infrastructure capital components. Persyn et al. (2020) apply the same methodology to all of Europe starting from a one-square km resolution. They sample centroids for each NUTS-2 European region based on the spatial population distribution.

Imputation-based transport cost calculations lend themselves to practical applications. For example, in an effort to give shippers a benchmark for freight costs, the Colombian Ministry of Transportation developed a simulation tool called _SICETAC_ . Using this web-based application, shippers can obtain trucking cost estimates for all routes within the country.<sup>5</sup> The main data used in this application comes from a survey of per-unit freight prices paid to truck owners at the origin-destinationcommodity level. The developers used an imputation-based approach to calculate the determinants of unit costs based on the characteristics (such as distance and slope) of sampled routes. Through a GIS interface that contains information about these characteristics in the entire road network of the country, the simulation tool then yields predictions on monetary costs on any hypothetical route. As in this example, data collection efforts can provide useful inputs to imputation-based calculations that generalize cost calculations to a wider geography. While imputation-based methods can predict monetary shipping costs across hypothetical routes, they cannot inform how these costs affect shipping demand and trade between locations. Doing so requires additional conceptual tools discussed in the subsequent section.

# **5 Estimation-based Methods**

Before delving into the estimation of trade costs, it is in order to note that the methodology discussed in the previous section provides some of the key variables to be used. In what follows, it is assumed that the travel distance and travel time variables are the output of a network-based optimal itinerary/route calculation along the lines described above.

## **5.1 Gravity Approach: Estimation Using Trade Flows**

Empirical studies of trade have long established that bilateral exports between countries or regions within countries are proportional to their economic size and inversely

> 5Information in Spanish available in `https://www.mintransporte.gov.co/publicaciones/4462/ sice-tac/`

11
access to credit. Consider the case in which a borrower expects to be credit constrained as the result of debt relief. Without access to credit, first period investment is constrained to _k ∈_ [0 _, k_<sup> ̃</sup> ], and the household optimizes (2) subject to _k ≤ y_ 1. In this case, investment depends on initial wealth and the impact of debt relief on investment is determined by the relationship between the unconstrained investment choice _k_<sup>_∗_</sup> and _k_<sup> ̃</sup> .

1. If _k_<sup>_∗_</sup> _≤ k_<sup> ̃</sup> , then debt relief increases investment _k_ , as shown above, until _k_<sup> ̃</sup> .

2. If _k_<sup>_∗_</sup> _> k_<sup> ̃</sup> , the household’s investment is reduced relative to the base case. The household will want to invest all available resources, i.e. _k_<sup> ̃</sup> to maximize expected revenue, but is constrained to choices _k ≤ y_ 1, so that investment depends on initial wealth.

Thus, in the case where the household plans to borrow to finance investment, debt relief may decrease investment by altering beliefs about future access to credit: without the upper bound _k_<sup> ̃</sup> introduced by the debt relief, the household may borrow to invest an amount greater than _k_<sup> ̃</sup> . Debt relief restricts investment to be no higher than _k_<sup> ̃</sup> . This generates the following empirical predictions that can be tested against the basic debt-overhang hypothesis:

**Proposition 2** (Debt relief with anticipation of future borrowing constraints) _Debt relief decreases investment if households expect to be credit constrained in the future as a result of debt relief. This reduces investment to k for all households for whom k_<sup> ̃</sup><sup>_∗_</sup> _> k._<sup> ̃</sup> _Because k_<sup> ̃</sup> _≤ y_ 1, _investment depends on the household’s initial wealth_ . _Proof: See Appendix._ ■

I take these predictions to the data, using the eligibility rules of India’s debt relief program for small and marginal farmers as a source of exogenous variation in the level of debt forgiveness. As we shall see, the effect of debt relief on investment and productivity is consistent with the predictions of the model for the case of debt relief with the anticipation of borrowing constaints. While there is no positive effect of debt relief on productivity, the balance of the evidence suggests that debt relief beneficiaries reduced investment expenditures in the expectation that it will become more difficult to obtain institutional credit in the future.

10
|P126773|2015|2019|6/28/2019|9 Active|Western Africa|
|---|---|---|---|---|---|
|P126773|2015|2020|11/27/2019|10 Active|Western Africa|
|P126773|2015|2020|1/28/2020|11 Active|Western Africa|
|P126773|2015|2021|7/21/2020|12 Active|Western Africa|
|P126776|2012|2012||0 Closed|Indonesia|
|P126776|2012|2013||0 Closed|Indonesia|
|P126776|2012|2020|3/18/2020|1 Closed|Indonesia|
|P126782|2013|2013|5/16/2013|1 Active|Armenia|
|P126782|2013|2014|12/28/2013|2 Active|Armenia|
|P126782|2013|2014|6/28/2014|3 Active|Armenia|
|P126782|2013|2015|1/6/2015|4 Active|Armenia|
|P126782|2013|2015|6/18/2015|5 Active|Armenia|
|P126782|2013|2016|12/22/2015|6 Active|Armenia|
|P126782|2013|2016|6/29/2016|7 Active|Armenia|
|P126782|2013|2017|1/24/2017|8 Active|Armenia|
|P126782|2013|2018|8/31/2017|9 Active|Armenia|
|P126782|2013|2018|12/16/2017|10 Active|Armenia|
|P126782|2013|2018|6/27/2018|11 Active|Armenia|
|P126782|2013|2019|12/21/2018|12 Active|Armenia|
|P126782|2013|2019|6/26/2019|13 Active|Armenia|
|P126782|2013|2020|12/18/2019|14 Active|Armenia|
|P126782|2013|2020|6/16/2020|15 Active|Armenia|
|P126788|2012|2012||0 Closed|Nicaragua|
|P126788|2012|2013||0 Closed|Nicaragua|
|P126791|2013|2013||0 Closed|Antigua and Barbuda|
|P126791|2013|2014|11/22/2013|1 Closed|Antigua and Barbuda|
|P126791|2013|2014|6/21/2014|2 Closed|Antigua and Barbuda|
|P126791|2013|2015|12/23/2014|3 Closed|Antigua and Barbuda|
|P126791|2013|2015|6/29/2015|4 Closed|Antigua and Barbuda|
|P126791|2013|2016|12/22/2015|5 Closed|Antigua and Barbuda|
|P126791|2013|2016|5/31/2016|6 Closed|Antigua and Barbuda|
|P126791|2013|2017|12/19/2016|7 Closed|Antigua and Barbuda|
|P126791|2013|2017|6/19/2017|8 Closed|Antigua and Barbuda|
|P126812|2011|2012||0 Closed|Nicaragua|
|P126812|2011|2013||0 Closed|Nicaragua|
|P126813|2013|2013||0 Active|China|
|P126813|2013|2014|9/17/2013|1 Active|China|
|P126813|2013|2014|3/30/2014|2 Active|China|
|P126813|2013|2015|9/9/2014|3 Active|China|
|P126813|2013|2015|3/17/2015|4 Active|China|
|P126813|2013|2016|7/29/2015|5 Active|China|
|P126813|2013|2016|4/14/2016|6 Active|China|
|P126813|2013|2017|10/3/2016|7 Active|China|
|P126813|2013|2017|3/13/2017|8 Active|China|
|P126813|2013|2018|9/28/2017|9 Active|China|
|P126813|2013|2018|2/7/2018|10 Active|China|
|P126813|2013|2019|8/25/2018|11 Active|China|
# **OA3. Robustness Check: Control for Ability**

**Table A.9 : Estimates with Ability Controls in Indonesia**

||**Yrs. Sch**|**Res. Sq. **|
|---|---|---|
|||**Full Sample**|
|Father's Edu|0.497***|-0.420***|
||(0.011)|(0.057)|
|Constant|6.437***|14.806***|
||(0.113)|(0.452)|
|R-squared|0.212|0.030|
|Observations|18356|18356|
|||**Urban Only**|
|Father's Edu|0.468***|0.468***|
||(0.016)|(0.016)|
|Constant|7.225***|7.225***|
||(0.171)|(0.171)|
|R-squared|0.236|0.236|
|Observations|5919|5919|
|||**Rural Only**|
|Father's Edu|0.467***<br>(0.012)|0.467***<br>(0.012)|
|Constant|6.331***|6.331***|
||(0.115)|(0.115)|
|R-squared|0.161|0.161|
|Observations|12437|12437|
|||**Sons Only**|
|Father's Edu|0.461***|0.461***|
||(0.013)|(0.013)|
|Constant|6.971***|6.971***|
||(0.133)|(0.133)|
|R-squared|0.194|0.194|
|Observations|8558|8558|
|||**Daughters Only**|
|Father's Edu|0.528***|0.528***|
||(0.013)|(0.013)|
|Constant|5.964***|5.964***|
||(0.123)|(0.123)|
|R-squared|0.231|0.231|
|Observations|9798|9798|

_Notes_ : (1) The data are from the IFLS 2014 round for Indonesia. The regression includes the cognitive ability index and its square. The cognitive ability index was constructed taking the first principal component of Raven test scores and two memory tests net of age and age square.

45
<!-- Start of picture text --> A. Baseline Surve B. Policy Experiment C. Mechanism Experiment _<br>y (N=4,712, N=322 villages)" (N=2,628, N=33 villages) D. Follow-up surveys<br>Baseline survey (N=4,608) Control arm Follow-up survey at six months<br>. Economic outcomes Monthly cash transfers (N=1,206) post-program (N=4,476)<br>(e.g., consumption, + Economic outcomes (e.g.,<br>women’s business Capital arm \ consumption, women’s<br>outcomes) Monthly cash transfers + economic > business outcomes)<br>* Intra-personal CENPERET SS ENG EL(C AUIS), 4 + Intra-personal psychosocial<br>psychosocial outcomes P h ial outcomes (e.g., self-efficacy)<br>(e.g., .self-efficacy) . M ‘onthlysychosoclalcash transfers + economicarm + Relational psychosocial.<br>* Relational psychosocial components+ social-psychaogical outcomes (e.g., social support)<br>outcomes (e.g., social interventions (N=1,112)<br>supportpport) Full arm Quantitativeita ti survey measures<br>Monthly cash transfers + economic of sociocultural context<br>rmative‘ wen gequentintive psychologicalcomponents +interventions cash grant +(N=1.203) social- (control arm onh»)<br>surveysocioeconomicsocioculturalmeasurescontextand of Subsetvillagestt of Personal Initiativewee ge<br>condition (N=666)<br>Initiative<br>Interpersonal _ Follow-up survey at six months<br>condition (N=666) /| post-program (N=2,493) .<br>+ Subset of measures in box<br>Control conditionwae above<br>(N=1,296)<br><!-- End of picture text -->
<!-- Start of picture text --> \ — - avg. costs in case of mutual recognition<br>- \ —— avg. costs per export market in case of<br>S regulation heterogeneity<br>8\<br>a<br>8 \<br>a5 \<br>S\<br>@<br>8\<br>>\<br>© : : :<br>g \<br>home market export market 1 export market 2 export market 3<br><!-- End of picture text -->
# **Figure 11**

<!-- Start of picture text --> Forecasted nuclear energy share in 2010 according to the year of publication Forecasted shares Forecasted share =<br>-0.81 x Publication Year + 1636.9<br>Robust Tukey Smoother<br>R 2 = 0.64<br>Linear regression line<br>45<br>40<br>35<br>30<br>x 100)<br>25<br>20<br>N uclear share (%<br>15<br>10<br>5<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->

**Forecasted nuclear energy share in 2010 according to the year of publication**

19
<!-- Start of picture text -->
* oO} ——. .<br>41" artawric ‘ sic <4 ~~ > > o<br>\, peowt § \cae" artawnieocean ocun el Oe ApMane pi pe : ny _ ‘ ‘i<br>: | hai nat ae ae Se rey Os" a al<br>ren<br>nO < honaes . 7 : 28 Bn d<br>~ ; af " —"<br><!-- End of picture text -->

<!-- Start of picture text --> Sem Leow<br>F bec al » Aechiee<br>racine ' tee<br>octan :<br>rT<br>; !<br>_ amtannic ocean — is<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>\ ATLANTIC<br>{ OCtaN :<br>! 5<br>n<br>‘ eoSEFTIMEERoKm 2015 ; .<br><!-- End of picture text -->
||(1.51)|(2.12)|(2.60)||
|---|---|---|---|---|
|Reduced portion of food for adults in favor of|27.03|51.72|24.69|6429|
|children's|(1.32)|(2.13)|(2.50)||
|borrow food or rely on help from family and|21.85|39.05|17.20|6424|
|friends|(1.10)|(2.08)|(2.36)||
|Refrain from consuming expensive and resort|42.51|75.71|33.20|6436|
|to alternatives (buying cheaper kind of food)|(1.61)|(1.81)|(2.42)||
|_Average _|30.02|55.46|25.44|6434|

Notes: Responses indicating more severe outcomes are operationalized in this module as responses of 1-7 days. Estimates are unweighted, and clustered standard errors are reported in parentheses.

|**COVID**<br>**Question**|**-19 exposur**<br>**Share of r**<br>**choosing**<br>**that indi**<br>**severe ou**<br>_PPCS_|**e**<br>**espondents**<br>**responses**<br>**cate more**<br>**tcomes (%)**<br>_Facebook_|**Difference**<br>**(Percentage**<br>**point)**|**N**<br>**(combined)**|
|---|---|---|---|---|
|Have you ever had, or do you believe that you|46.32|48.60|2.28|6804|
|have ever had Coronavirus?|(1.04)|(1.64)|(1.94)||
|In the last 14 days, do you think you have met|23.66|36.67|13.01|6806|
|(seen) anyone who you think had the<br>coronavirus when you met them?|(1.11)|(1.58)|(1.93)||
|Do you think your area has a high incidence of|28.54|23.93|-4.61|6808|
|coronavirus?|(1.35)|(1.40)|(1.94)||
|Do you personally know someone who has|55.24|75.27|20.03|6802|
|died from the coronavirus in your area?|(1.33)|(1.42)|(1.94)||
|_Average _|38.44|46.12|7.68|6805|

Notes: Responses indicating more severe outcomes are operationalized in this module as responses of “Yes”. Estimates are unweighted, and clustered standard errors are reported in parentheses.

25
Heterogeneity in Subjective Wellbeing: An Application to Occupational Allocation in Africa<sup>∗</sup> Paolo Falco<sup>†</sup> William F. Maloney<sup>‡</sup> Bob Rijkers<sup>§</sup> Mauricio Sarrias<sup>¶</sup> October 9, 2012

**Keywords:** Subjective Wellbeing, Mixed Ordered Probit, Self-employment, Informality, Developing Country Labor Markets, Africa JEL codes: C35, J2, J3, J41, L26, I32, 017 Sector board: EPOL

> ∗The views expressed here are those of the authors and do not necessarily represent the views of the World Bank, its Executive Board or member countries. We are grateful to Kathleen Beegle, Caroline Freund, Francis Teal, Carolina Sanchez Paramo, Mary Hallward-Driemeier and attendants of the 2011 CSAE and NEUDC conferences for valuable comments and to the World Bank’s RSB Committee for funding. All errors are our own.

> †Centre for the Study of African Economies, Department of Economics, Oxford University

> ‡Development Economics Research Group, The World Bank, and Universidad de los Andes, Bogota, Colombia

> §Chief Economist Office for the Middle East and North Africa, The World Bank

> ¶Universidad Catolica del Norte, Antofagasta, Chile

1
# **5.4 The role of nationalist parties**

In section 3 we have shown that the effect of a reduction in the bilateral migration costs, here approximated by the presence of visa provision in RTA, is attenuated for destinations having strong anti-immigration sentiment (i.e. high resistance term to immigration Ω _jt_ ). In this section we test such a prediction of the model by approximating the anti-immigration sentiment of countries by their share of vote in favor of extreme right wing parties. Namely, in Table 9 we interact our baseline visa dummy with the share of votes for the extreme right wing nationalist party in destination _j_ in year _t_ .<sup>30</sup> In line with the prediction of a standard gravity model for migration, the positive effect of visa provisions is attenuated for destinations having a non-zero share of vote for nationalist parties (i.e. negative coefficient on the interaction between visa dummy and the share of vote for right wing parties). While for destinations with a very small (approaching to zero) share of votes for the right wing party the visa dummy boosts bilateral migration by 9.6%, for destinations having a share of right wing votes at the median of the distribution (i.e. 9%), visa provision boosts bilateral migration by 8.3%. For the country having the maximum share of votes for the right wing party (i.e. 54%), the effect of visa provision is only 1.3%.

– Table 9 about here –

# **5.5 Placebo and robustness checks**

The presence of a specific visa related provision in RTA may be correlated with the presence of other provisions and therefore with the overall horizontal depth of RTAs. In this case, the positive effect of migration-related provision shown so far can simply be the consequence of the overall depth of RTAs on bilateral migration and not that of a reduction in bilateral migration cost. To address this concern, in columns (1) and (2) of Table 10 we control for the overall depth of the RTA (i.e. the total number of provisions covered by the RTA from World Bank Deep Trade Agreement database), and show that our variable of interest keeps its sign and magnitude even after controlling for the overall depth of the RTA. It is not the overall depth of an RTA that affects bilateral migration, but the specific inclusion of migration related provisions reducing the bilateral migration costs.

– Table 10 about here –

> 30See Appendix section B for a detailed discussion on the construction of right wing nationalist party share of votes. The share of vote for the right wing party is comprised between 0 and 1. The median share of votes for the right wing party in our sample is 9% (i.e. 0.09 in our data), and the maximum level is 54%.

14
concerns and individual protection issues rather than attempting to enhance credit information per se. Monopoly pricing or other restrictive practices that might limit competition or service provision are generally the responsibility of competition authorities that have in practice rarely intervened in this specialized sector. The second form of intervention that has taken place is the direct (and frequently forced) provision of credit information sharing services by central banks or banking supervisors known as public credit registries (PCRs).

World Bank surveys have documented that PCRs exist in about 60 countries worldwide and more nations are planning to create them in the future.<sup>4</sup> In countries with PCRs, supervised financial institutions are required to provide data on individual borrowers on a periodic basis, usually monthly. Core PCR data are information on the identity of borrowers, the size of any loans or credit lines outstanding with reporting institutions and their status. Status implies whether a loan is in good standing, past due, in default or other non-accrual status.

There are multiple motivations behind the development of PCRs. Recent research has linked the existence of a PCR with the absence of significant private credit bureaus, weak creditor protection and a French civil code legal system – see Jappelli and Pagano (2003) and World Bank (2003). Anecdotal evidence suggests some countries originally developed PCRs to monitor credit by sector in the days when banks were forced into particular lending patterns. Others were motivated by the simple desire that financial institutions should know the total debts (and the standing of the various loans) of potential borrowers in the whole financial system to make informed lending decisions. Another motivation was so that bank supervisors could identify the main debtors of the financial system and analyze more carefully loan concentration risk and monitor and enforce banks’ reserve policies against problem loans.

Whatever the primary motivation, countries that have developed these tools have tended to find them useful for a wider variety of reasons that often go beyond their original thinking. This paper reviews many of the current uses and discuses the relationship between these competing roles and the design of PCRs. There are also potential pitfalls related to PCR design and management

> 4 Not all countries replied to the World Bank survey that found PCRs in 58 countries.  The actual number is then likely to be significantly more than 58 worldwide.

3