rafmacalaba/datause-encoder-data huggingface dataset:
|6. Washing machine|YES...1<br>NO....2|
|---|---|
|7. Blender|YES...1<br>NO....2|
|8. Fan|YES...1<br>NO....2|
|9. Computer|YES...1<br>NO....2|
|10. Sewing machine|YES...1<br>NO....2|
|11. Vehicle, car, or boat (for household use)|YES...1<br>NO....2|
|12. Iron|YES...1<br>NO....2|
|13. Microwave oven|YES...1<br>NO....2|
|14. Video games (Nintendo)|YES...1<br>NO....2|
|15. Air conditioning|YES...1<br>NO....2|
|16. Well or cistern|YES...1<br>NO....2|
|17. Motorcycle|YES...1<br>NO....2|
|18. Tablet|YES...1<br>NO....2|

_Households that experienced moderate to severe food insecurity:_ According to the Food Insecurity Experience Scale (FIES),<sup>22</sup> moderate to severe food insecurity prevalence refers to a range of food security conditions experienced by the households that have difficulty accessing enough safe and nutritious food for their members’ normal growth and development and that fail to enjoy an active and healthy life due to a lack of money or other resources. This variable takes the value of 1 if the household falls within the range of moderate to severe food insecurity, and 0 otherwise.

> 22 More information on the FIES can be found at FAO (2023).

63
# **5 Concluding remarks**

This paper starts from the observation that in our study area of Kagera, Tanzania, much larger shares of the rural population tend to migrate to towns than to cities, despite greater welfare gains from moving to the latter than from moving to the former. With many of the rural migrants originally also poor, town migration contributed more to poverty reduction than city migration. To shed light on this seemingly suboptimal destination choice of ruralurban migrants, we draw on an extension of the Todaro model which explicitly considers different destinations (towns and cities) as well as spatial friction/migration costs to motivate an empirical specification to examine the drivers of this equilibrium.

In particular, using a dyadic regression structure applied to our two decade long panel of individuals from Kagera, augmented with the wealth to be expected at the different possible destinations from the census, we estimate the differential effect on destination choice of migration costs and expected wealth gain at destination controlling for individual characteristics (i.e. selection) through individual fixed effects. Migration costs were proxied by distance to the destination, following a longstanding (though recently somewhat ignored) tradition in the migration literature (Lucas, 2001). Acknowledging the well-documented importance of selection in migration, individual heterogeneity in the anticipated deterrent effect of distance and the expected pull effects of expected wealth at destination were further explored through interaction with the migrants’ poverty status, education level, and age before migrating. Simulations were finally conducted to explore the empirical strengths of the different factors in explaining the migration flows.

The findings underscore the continuing relevance of distance for destination choice (rural and urban); in our sample, expected wealth at destination would have to increase by 5.7 standard deviations on average to offset a 1 standard deviation increase in (log) distance. In this, it is consistent with Ravenstein’s first law of migration, put forward in 1885, stating that most migrants move only a short distance. When confined to urban destinations only, the deterrent effect of distance remains, but the attraction of wealth increases, with the strength of these effects in turn affected by the migrant’s poverty (reduced attraction of wealth) and educational status (reduced deterrence of distance, increased attraction of wealth). With the 16
This negative relationship between change in provision of a food program and sustainability is likely to be driven by the strategic reallocation of expenditures during the project period among the project playgroups that were sustained. Recall that in Table 3, we found that increasing the share of expenditures on teacher salaries is positively associated with sustainability. Our results suggest that among the project playgroups that were sustained, much of the reallocation of expenditures toward teacher salaries came from cutting back on weekly provision of food program.

Finally, we explore the relationship between sustainability and community support. We find that centers with higher levels of parental involvement during the project period were significantly more likely to remain open (column 1, statistically significant at p<0.1) and remain open with two trained teachers (column 2, though these results were statistically insignificant) – holding constant the change in parental involvement during the project period although the estimates are modest. Figure 6 shows that the predicted probability that a project playgroup remains open in 2016 is 91% for those with the average level of parental involvement. A one standard deviation increase in parental involvement is associated with a predicted probability of 95% that a project playgroup remains open in 2016.

<u>Table 7. Logistic regression of project playgroup sustainability on community support</u>

|<br> <br>|<br>(1)<br>Playgroup is<br>open in 2016<br>(1=Yes)<br>Playgr<br>with t|<br>(2)<br>oup is open in 2016<br>wo trained teachers<br>(1=Yes)|
|---|---|---|
|**Community support**|||
|Parental involvement (z-score) in 2010|0.04*<br>(0.02)|0.03<br>(0.04)|
|Change in parental involvement from 2010-2013|-0.03<br>(0.02)|0.03<br>(0.03)|
|Log-likelihood|-67.93|-155.87|
|Observations|245|245|
|* p<0.10, ** p<0.05, *** p<0.01|||
|Note: Robust standard errors clustered by center. Coefficien|ts are average marginal effects.||

Figure 6. Probability of sustainability and parental involvement 20
and McCarthy, 2008). Evidence from India in the 1990s indicates that the entry of foreign banks expanded access to credit for only a small subset of profitable firms, and that long-term lending by development banks might have declined (Gormley, 2007). Looking at the consequences of foreign bank entry for access to financial services more generally, Beck and Martinez Peria (2010) find that primarily rich and urban municipalities in Mexico benefited from the significant increase in foreign bank participation (from 2 to 83 percent) that occurred in that country between 1997 and 2005.

Some survey evidence leads to the opposite conclusion, however. Interviews with managers of foreign banks in Central Europe and the Baltics indicate that the intention was to incorporate lending to small firms over time and that there was no intended bias toward lending to large multinationals (De Haas and Naaborg, 2006). Competition from foreign banks could conceivably compel domestic banks to pursue new market niches, and 44% of the managers of 220 banks in 60 countries indicated that their bank began lending to small businesses because of the competition in lending to large and medium-sized businesses (Jenkins, 2000). Evidence collected via surveys of banks in Latin America (de la Torre, Martinez Peria, and Schmukler, 2010) and in 45 countries around the world (Beck, Demirguc-Kunt and Martinez Peria, 2009) shows that though foreign banks are more likely to rely on hard information to lend to SMEs, there is no difference in the extent of their involvement with these firms relative to large domestic and government-owned banks.

Empirical analysis based on large samples of firms and countries helps shed light on these issues. Analysis of a survey of over 3000 firms in 35 developing and transition countries reveals that foreign bank presence is associated with improved financing conditions for all firms, though the effects were greater for larger firms (Clarke et al., 2006). Data from 60,000 firms from fourteen countries in Eastern Europe and Central Asia from 1993 to 2002 indicates that foreign bank lending was associated with growth in firms’ sales, assets, and leverage, though again the effects were less pronounced for small firms (Giannatti and Ongena, 2009a). On the other hand, these effects were most pronounced for young firms, indicating that foreign banks helped broaden access to credit.

17
9 currency denomination is the only difference between the two debt instruments and, **so,** it captures the currency or foreign exchange rate risk (more specifically, the sum of devaluation expectations and the premium for exchange rate uncertainty). Figure 5 shows the CETES-PARAFE (TESOBONO) interest rate differential.

Regressing the interest rate differential corrected for preannounced devaluation, INTDIFP18, on the same RHS variables yields:9

- (3) INTDIFF1 ***** -0.162 + 0.076*NT + 0.429*SNT (-1.01) (0.81) (2.56)

## R2 **-** 0.77 **DW -** 1.67 Sample: 1988.3 - 1990.12

This regression indicates that the variance of net transfers, rather than the projected net transfer itself, is the main factor behind the dramatic interest rate response to Mexico's debt package.

# **III.3** Interest rates and debt relief_II: country risk

Consider finally the differential between PAGAFE and the one month US T- bill, INTDIFP2. Because both are 'denominated in US dollars, the difference between them is a pure measure of country risk. Regressing nlQTDIFl2' on the variance and expected value of the net transfer yields the following: (4) INTDIFP2 **-** -0.005 + 0.115*NT **+** 0.07*SNT (0.02) (1.99) (0.76) _* IJJITDIFFZ = +lI)_ ,) with I for CETES, **iP" for** PAGAFE, and y the premrnounced rate of devaluation. Note that the prearwounced devaluation was systewaticalLy lower than the mn of the expected devaLuation and the exchange rate uncertainty premiut Cmemsured by I - IP19) _9_ **ALl** interest rate equations **were** estimated using the Naximm Likelihood procedure in SAS **assuming** first order serial correLation, as the DU statistic in OLS regressions alays Indicated serial correLation.

_30TrTTDIFFS_<sup>_1_</sup> **=** (1+ im) _(1.i =a)_
# **_2.2. AMADEUS Database_**

For the estimation of firm productivity, the analysis uses data from the May 2006 edition of the AMADEUS database, a comprehensive, pan-European commercial database compiled by Bureau van Dijk. For each firm, AMADEUS provides accounting data in standardized financial format for 24 balance sheet items, 25 profit and loss account items, 26 financial ratios, and additional information including trade description and activity codes. The database includes up to ten years of information per firm through 2004, although coverage varies by country. AMADEUS is created by collecting standardized data received from 50 vendors across Europe, where the local source for these data is generally the office of the Registrar of Companies.<sup>5</sup> The accounts for each firm are transformed into a universal format to allow for comparison across countries. All accounting data is converted into U.S. dollars using period average exchange rates, based on monthly series from the International Monetary Fund, nearest to the end date of each respective financial account. Nominal values are deflated using countrylevel GDP deflators to express values in 2001 US dollars. In addition, all firms are categorized by industry according to NACE Rev.1., and for the analysis, industry dummy variables are coded based on the 4-digit activity code following NACE Rev.1 that AMADEUS assigns to each firm.

# **_2.3. Sample Selection_**

The econometric analysis of firm-level TFP growth and changes in the business environment uses a first-differenced equation in firm characteristics with two-year changes and requires a panel of manufacturing firms from the AMADEUS database with complete information on production function variables for the years 2001 through 2004, the period that correspond to the 2002 and 2005 rounds of the BEEPS. Specifically, output, labor, material inputs, and capital are given by the operating revenues, number of employees, material costs, and tangible fixed assets of firms in the AMADEUS database. Consequently, observations that are missing values in just one of these four production function variables must be dropped from the sample. Given these data requirements, sufficient information exists in the AMADEUS database

> http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/EXTECAREGTOPANTCOR/0,,content MDK:21303980~pagePK:34004173~piPK:34003707~theSitePK:704666,00.html.

> 5 Further details about the AMADEUS database can be found on the product page of Bureau van Dijk’s website: http://www.bvdep.com/en/AMADEUS.html.

6
8 ~~~~~~~~~~~~~~21 is growing attention to economic analysis of problems of medical ethics, especially on the question of optimal spending on heroic measures to extend the lives of the elderly. Analytic work on these issues may be even more important in the developing countries, where resources are scarcer, government's role in centralized decisions r-garding resource use is often grea-er, and the biases that arise from an unequal distribution of income may exacerbate distortions in use of health resources. One example of such work would be analysis of household behavior regarding health looking at the demand for health in the broadest sense, including the demand for relief of pain and suffering associated with adult non-communicable diseases and adult disability.

Similarly, analysis of the political economy of health spending, particularly of the political impetus behind rising costs, will not be fruitful without new work on the costs and determinants (and consequences) cf adult morbidity. The epidemiological transition is already upon us in many countries of the developing world, bringing relatively higher incidence of "adult" chronic diseases alongside the continuing high incidence of the childhood infectious diseases. Data on the incidence and distribution (across age and socioeconomic groups) of adulz morbidity, including injuries and disability, are critical to analysis of the likely effects of income growth and the aging of populations on the demand and overall costs of health care in developing countries, and to the design of prevention and health promotion programs. A striking example is available from Briscoe's analysis of Brazilian data (World Bank, 1990); he shows that if various kinds of injuries are combined, including auto crashes, homicide and suicide, the category of injuries accounts for more potential years of life lost for those aged 1 to 65
In addition, engaging farmers in sustainable farming without loss of income is necessary for the persistent adoption of regenerative agriculture. Climate finance through carbon credits that offer a potential income source for farmers can be a possible pathway. Lastly, it is an open question to test whether climate finance can be designed to mitigate some of the constraints that hinder economic development in Africa. For example, with additional income from climate finance, farmers may be more willing to invest in the adoption of productivity and resilience-enhancing technologies. Also, the payment system set up by a carbon credit market may be an opportunity to increase digital financial inclusion of farmers in the future.

# **References**

- Aker, Jenny C., and Kelsey Jack. 2023. "Harvesting the rain: The adoption of environmental technologies in the Sahel." _Review of Economics and Statistics_ Accepted.

- Annan, Francis, and Wolfram Schlenker. 2015. "Federal crop insurance and the disincentive to adapt to extreme heat." _American Economic Review_ 105 (5): 262-266.

-

- Bai, Xiongxiong, Yawen Huang, Wei Ren, Mark Coyne, Pierre Andre Jacinthe, Bo Tao, Dafeng Hui, Jian Yang, and Chris Matocha. 2019. "Responses of soil carbon sequestration to climate‐smart ‐

- agriculture practices: A meta analysis." _Global change biology_ 25 (8): 2591-2606.

- Bazrafkan, Khadijeh, Naser Valizadeh, Setareh Khannejad, Nazanin Kianmehr, Masoud Bijani, and Dariush Hayati. 2022. "What drives farmers to use conservation agriculture? Application of mediated protection motivation theory." _Frontiers in Psychology_ 13.

- Behrer, A. Patrick, and David Lobell. 2022. _Higher levels of no-till agriculture associated with lower PM2. 5 in the Corn Belt._ Vol. 17. Environmental Research Letters.

- Burke, Marshall, and Kyle Emerick. 2016. "Adaptation to climate change: Evidence from US agriculture." _American Economic Journal: Economic Policy_ 8 (3): 106-140.

- Chatterjee, S. 2023. "Market power and spatial competition in rural india." _The Quarterly Journal of Economics_ 138 (3): 1649-1711.

- Chen, Xi, Mingzhe Pu, and Yu Zhong. 2022. "Evaluating China food’s fertilizer reduction and efficiency initiative using a double stochastic meta-frontier method." _International Journal of Environmental Research and Public Health_ 19 (12): 7342.

- Elevitch, Craig R., D. Niki Mazaroli, and Diane Ragone. 2018. "Agroforestry standards for regenerative agriculture." _Sustainability_ 10 (9): 3337.

- Foster, Andrew D., and Mark R. Rosenzweig. 2022. "Are there too many farms in the world? labor market transaction costs, machine capacities, and optimal farm size." _Journal of Political Economy_ 130 (3): 636-680.

- Gao, Bing, et al. 2018. "Chinese cropping systems are a net source of greenhouse gases despite soil carbon sequestration." _Global change biology_ 24 (12): 5590-5606.

21
# Behavioral Economics and Social Exclusion Can Interventions Overcome Prejudice?<sup>*</sup>

Karla Hoff<sup>‡</sup> Human beings can live, and human liberty can exist, only within a system of norms, meanings, and roles; but in any particular form, these things can impose severe restrictions on well-being and autonomy.

Cass Sunstein (1996, 911) In many societies, broad segments of the population are barred from full human rights and full participation in society: they are “socially excluded” (World Bank 2013). Social scientists seek to understand the mechanisms by which social exclusion occurs in order to determine how it can be mitigated. Rational actor models assume that individuals see all their options objectively, reason without bias, know what is in their self-interest, and act accordingly. Thus the dismantling of exploitative structures and formal barriers that certain groups face in markets, education, and neighborhoods are “all” that is required to end social exclusion. In contrast, behavioral economics recognizes that people are not always rational. It brings in insights from psychology and sociology that institutions have a “schematizing power” (Bruner 1990, 58; DiMaggio 1997, 271). Mental models that people absorb from past institutions can distort perceptions in ways that reproduce social exclusion long after the formal barriers have been removed. Spoiled collective identities need to be repaired to overcome social exclusion.

____________

> * Forthcoming in the _Proceedings of the Roundtable on Shared Prosperity,_ International Economics Association, Jordan, edited by Kaushik Basu. I would like to thank Allison Demeritt, Paola Giuliano, Priyanka Pandey, and James Walsh for helpful comments.

> <sup>‡</sup> World Bank.  Email: khoff@worldbank.org

2
and van der Meulen Rodgers 2015), especially as researchers aimed to test the World War II “effect” in other countries (Acemoglu, Autor, and Lyle 2004). However, when comparing the raw difference-in-difference of female employment outcomes in Table 7, we see that female employment has decreased. Further, when controlling for other characteristics we find no significant effect of conflict on employment in column 1 of Table 5. In column 2, we control for the difference in male and female employment statuses and find that the impact of medium intensity is slightly more significant than in the main specification.

Conflict can also change the composition of households, whereby male members are more likely to have left the household, either because they have been killed in battle, are currently fighting, or perhaps more likely have migrated in search of economic opportunities in a more stable situation (see for example, a study of the disproportionate killing of men in the Rwandan genocide and the impact on female headed households (Brück and Schindler 2009)). According to the data, the prevalence of female headed households in IRG areas has increased from 13 percent to 17 percent (Figure V.3). This higher prevalence is largely driven by households who have moved since the start of the conflict, albeit for non-conflict related reasons.

_Figure V.3: Prevalence of female headed households_ <!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2014 2014 IRG 2021 2021: Did 2021: 2021: 2021:<br>National areas YHDS not move Moved Moved for Moved for<br>from security other<br>previous concerns reason<br>location<br><!-- End of picture text --> To investigate whether changes in household composition could be driving the results, we control for whether the household is headed by a female in column 3 of Table 5, and in column 4 we additionally interact this variable with the difference-in-difference effect. In column 3, the indicator for female headed households is positive and statistically significant, with the main result staying the same. In column 4, the estimated coefficient of medium conflict districts drops in magnitude from 0.17 to 0.11 while remaining significant. When combining this effect with the marginal impact of female headed households in medium conflict affected districts, there is a 0.72 point increase in FDI. This channel partially explains the results. As further evidence, we also control for whether the household receives remittances, as well as the proportion of men to 19