rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Summary Statistics and Number of Observations for Policy Indicators

|**Variable**<br>|**# of Obs**|**Mean**|**Std. D**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Panel A**||||||
|**OECD trade facilitation **|**indicators**|||||
|Information|128|1.53|0.41|0|2|
|Involvement|119|1.20|0.70|0|2|
|Advance Rulings|106|1.08|0.74|0|2|
|Appeal|119|1.42|0.43|0|2|
|Fees & charges|104|1.34|0.52|0|2|
|Documents|130|1.32|0.36|0.01|2|
|Automation|132|1.20|0.60|0|2|
|Procedures|112|1.10|0.36|0.30|2|
|Cooperation - internal|124|1.33|0.69|0|2|
|Cooperation - external|73|1.08|0.77|0|2|
|Consularization|95|1.45|0.90|0|2|
|Governance|103|1.58|0.40|0.33|2|
|**Panel B**||||||
|**Trade infrastructure var**|**iables**|||||
|Phys. Infra.|112|0.49|0.24|0.10|1|
|ICT|112|0.54|0.23|0.12|1|
|Bus. Environment|112|0.42|0.26|0.01|1|

Notes: Summary statistics for OECD Trade Facilitation Indicators and Trade Infrastructure measures from Portugal-Perez and Wilson (2012). Missing data imply a lack of information for individual countries in the raw data used by the OECD to compile the indicators.

11
## **Figure 4 Average MSE of Poverty Gap at 6.85 USD 2017 PPP, for LAC Countries**

<!-- Start of picture text --> 3.00<br>2.00<br>1.00<br>0.00<br>2017 2018 2019 2020<br>Old Model New - Rescaling New - No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

# **Inequality**

This section presents results for the three model variations when estimating the most standard inequality measure – the Gini coefficient.<sup>22</sup> Results indicate that, like the case of the international upper poverty line, the prediction is similar for the three model variations in the short run, and the fit is generally reasonable (Figure 5). Still, errors increase with the distance between the baseline and the estimated year. In addition, results show that the assessed variations tend to constantly overestimate inequality for some countries (i.e., Bolivia, Costa Rica, El Salvador, Honduras, Mexico, Panama, Peru, and Paraguay) and underestimate it in others (like the Dominican Republic). Thus, the bias is not always in the same direction or magnitude. For instance, the _new model with rescaling_ has the lowest gap in Bolivia in 2017 but the largest from 2018 through 2020.

Figure 6 presents the MSEs for LAC countries by year and its trend to compare all the model variations over time. In this case, errors seem to increase in a linear pattern instead of the exponential-shaped trend observed in the poverty and vulnerability indicators. Overall, results suggest that the _old model_ is the best variation of the micro-simulation model to estimate inequality since its MSE is constantly lower than the other tested variations, except for atypical years such as 2020.

In summary, results suggest that no variation of the model should be considered a best-all-countries/allmeasures fit since country-specificities might arise. Depending on the purpose of the analysis, either the _new model without rescaling_ (poverty and vulnerability) or the _old model_ (inequality) might perform better. Then, to choose a model, it is necessary to deepen the analysis to obtain a variation that works decently for both poverty and inequality measures. To this end, an additional assessment of the models is made to check their performance in estimating changes along the whole income distribution. The results obtained for this evaluation are presented in the following section.

> 22 This indicator measures how dispersed the income distribution is and takes values from 0 to 1, where 0 represents perfect equality and 1 stands for perfect inequality.

20
**_Results._** Despite national curriculum parameters and assessment and the guaranteed provision of text books, improvements in classroom teaching remain a major challenge. Results on the National Education Evaluation System (SAEB) tests have shown that children from Brazil and Paraíba are learning less than what is expected.

**Table 3.8: Average Student Achievement Scores** **<u>grades 4 and 8, 1995-99</u>**

||1995|1997|1999|
|---|---|---|---|
||Grade4 -<br>|Portuguese<br>||
|**Paraíba**|178|179|168|
|**Northeast**|178|178|157|
|**Brazil**|188|186|171|
||Grade4 -<br>|Mathematics<br>||
|**Paraíba**|179|182|175|
|**Northeast**|179|181|169|
|**Brazil**|191|191|181|
||Grade 8–|Portuguese||
|**Paraíba**|237|240|225|
|**Northeast**|230|241|225|
|**Brazil**|256|250|233|
||Grade 8–<br>|Mathematics<br>||
|**Paraíba**|238|238|236|
|**Northeast**|232|240|236|
|**Brazil**|253|250|246|
|_Source:_SAEB,MEC/I|NEP.|||

SAEB test scores are available in Mathematics and Portuguese Language for the 3<sup>rd</sup> , 4<sup>th</sup> , and 8<sup>th</sup> grades. These scores show that performance by 4<sup>th</sup> graders in Paraíba is comparable the other states of the Northeast, though substantially below the rest of Brazil (Table 3.8). The results show that scores on the whole are going down rather than improving (Table 3.8). However, there may exist a problem of comparability of the 1999 results with the results from previous years because both public and private schools obtained lower test scores in Brazil as a whole and its states including Paraíba. One reason may be large inflow of new students from the lowest income groups. This could have reduced achievement since many of these children were and maybe still are malnourished, which reduce their cognitive skills.

The good news for Paraíba is that the achievement trend (Table 3.9) increased in the 1990s. During 1995-97, the percent increase in Mathematics and Portuguese scores for the 4<sup>th</sup> grade have been improving more than the average in the Northeast and Brazil as a whole. Also, during 1997-99 the scores in both Mathematics and Portuguese deteriorated less in Paraíba than in the Northeast and Brazil for 4<sup>th</sup> and 8<sup>th</sup> grade of primary school and 3<sup>rd</sup> year of secondary school.

23
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likelihood that a household would experience poverty. Other factors, such as age, family size, race, and rural living are also important in determining the likelihood of poverty. Most of the findings on Pará, presented in this section, are very much in line with those of Ferreira, Lanjouw, and Neri.

It is important to note the limitations of this analysis at the outset. First and foremost, the analysis does not capture the dynamic impact of certain causes of poverty over time. Most notably, the impact of changes in economic growth - most certainly a key determinant of poverty – cannot be assessed using this static, cross-section model. Second, the analysis is limited by the variables available at the household level from the 2000 Census and 2001 PNAD. Other factors – such as social conditions like crime and violence, or physical conditions, such as variations in climate or access to markets – could not be included due to a lack of data at this level. Finally, though theory holds that many of the variables included in the analysis do indeed contribute to (cause) poverty (or poverty reduction), the statistical relationships should be interpreted as correlates and not as determinants since causality can run both ways for some variables.

Living in rural areas in Pará does not by itself affect the probability of being poor **.** Rural living in itself is a statistically insignificant correlate to poverty. Hence, individual and household characteristics are more important than geographical location (see Table 7.1). This is good news for policy-makers as there are no non-measurable rural variable kicking-in and affecting the likelihood of a household head in Pará falling below the poverty line.<sup>10</sup> Hence, the deep rural poverty in Pará is due to, for example, lower education achievements and skill levels and not to rural living per se. Households located in rural Pará are not more or less likely to experience poverty than in the rest of Pará, which may indicate that rural dwellers in Pará have developed some forms of coping strategies. Policy interventions that facilitate poor rural people’s access to basic services and expanding high quality rural education are central to poverty reduction in Pará (see below). The largest statistical differences in poverty reduction between rural and urban areas are found in the effect of education, sector of employment, gender, and family size.

Living in urban areas in Pará does not by itself affect the probability of being poor more than in Brazil as a whole. Statistically, findings presented in Table 7.2 show that the main difference in poverty correlates between urban Pará and urban Brazil is also related to individual or sectoral characteristics. Hence, public policies have an important role in leveling such differences, for example, high school’s impact on poverty in Pará tend to be higher than in Brazil as a whole indicating a scarcity in highschool graduates in Pará.

> 10 One explanation may be that the data are adjusted by regional prices, which affect the rural dummy variable (Ferreira, Lanjouw, and Neri 1998).

45
Table 1: Trade, FDI, and Licensing Reforms

|Year|Final Goods Tariffs|Input Tariffs|FDI Reform|Delicensing|
|---|---|---|---|---|
|1985|0.89|0.58|0.00|0.34|
|1986|0.96|0.61|0.00|0.35|
|1987|0.95|0.59|0.00|0.35|
|1988|0.95|0.60|0.00|0.35|
|1989|0.96|0.60|0.00|0.36|
|1990|0.96|0.60|0.00|0.36|
|1991|0.96|0.60|0.36|0.84|
|1992|0.64|0.40|0.36|0.84|
|1993|0.64|0.39|0.36|0.85|
|1994|0.64|0.37|0.36|0.85|
|1995|0.53|0.30|0.36|0.85|
|1996|0.42|0.23|0.36|0.85|
|1997|0.34|0.18|0.43|0.89|
|1998|0.35|0.19|0.43|0.93|
|1999|0.36|0.20|0.43|0.93|
|2000|0.35|0.21|0.93|0.93|
|2001|0.34|0.21|0.93|0.93|
|2002|0.31|0.19|0.93|0.93|
|2003|0.31|0.19|0.93|0.93|
|2004|0.31|0.19|0.93|0.93|

Mean values of policy variables from 1985 to 2004. Final goods and input tariffs variables are fractions, with 1 representing an ad valorem tariff of 100%; FDI Reform is a dummy variable equal to 1 if any products within the industry are liberalized, 0 if not; and Delicensing is a dummy variable equal to 1 if any products within the industry are delicensed, 0 if not. Source: Authors’ calculations based on various publications of the Government of India, as well as the TRAINS database.

33
Policy Research Working Paper 7202

# **Abstract**

This paper addresses the role of market remoteness in explaining maize price volatility in Burkina Faso. A model of price formation is introduced to demonstrate formally that transport costs between urban and rural markets exacerbate maize price volatility. Empirical support is provided to the proposition by exploring an unusually rich data set of monthly maize price series across 28 markets over 2004–13. The methodology relies on an autoregressive conditional heteroskedasticity model to investigate the statistical effect of road quality and distance from urban consumption centers on maize price volatility. The analysis finds that maize price volatility is greatest in remote markets. The results also show that maize-surplus markets and markets bordering Côte d’Ivoire, Ghana and Togo have experienced more volatile prices than maize-deficit and non-bordering markets. The findings suggest that enhancing road infrastructure would strengthen the links between rural markets and major consumption centers, thereby also stabilizing maize prices.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” partnership project managed by the Office of the Chief Economist, Africa Region of the World Bank, in collaboration with the Poverty and Inequality Unit, Development Economics Department of the World Bank, African Development Bank, the Alliance for a Green Revolution in Africa, the Bill and Melinda Gates Foundation, Cornell University, Food and Agriculture Organization, INRA and CIRAD, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. 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 of the paper may be contacted at moctar.ndiaye@supagro.inra.fr, the task team leader of the “Agriculture in Africa” project at lchristiaensen@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
Policy Research Working Paper 8098

# **Abstract**

Using close to 800,000 (2,000,000) transactions by 66,000 (303,000) households in the United States (in Finland), this paper shows that individual investors with longer holding periods choose to hold less liquid stocks in their portfolios, consistent with Amihud and Mendelson’s (1986) theory of liquidity clienteles. The relationship between holding periods and transaction costs is stronger among more financially sophisticated households. Households whose holding periods are positively related to transaction costs also earn higher gross returns on their investments before accounting for transaction costs, suggesting that attention to non-salient transaction costs is an indication of investing ability. The main findings are confirmed by analyzing changes in investors’ holding periods around exogenous shocks to stock liquidity.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 danginer@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
- Pascual, U., Balvanera, P., Diaz, S., Pataki, G., Roth, E., Stenseke, M., Watson, R. T., Dessane, E. B., Islar, M., Keleman, E., Maris, V., Quaas, M., Subramanian, S. M., Wittmer, G., Adlan, A., Ahn, S., Al-Hafedh, Y. S., Amankwah, E., Asah, S. T., ... Yagi, N. (2017). Valuing nature’s contributions to people: the IPBES approach. _Current Opinion in Environmental Sustainability_ , _26-27_ , 7–16.

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- R Core Team. (2024). _R: A language and environment for statistical computing_ . R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/

- Rentsch, S., & Damon, A. (2013). Prices, poaching, and protein alternatives: an analysis of bushmeat consumption around Serengeti National Park, Tanzania. _Ecological Economics_ , _91_ , 1–9.

- Reynolds, W. M. (1982). Development of reliable and valid short forms of the Marlowe-Crowne Social Desirability Scale. _Journal of Clinical Psychology_ , _38_ (1), 119–125.

- Rodr ́ıguez-R ́ıos, E., & Garc ́ıa-P ́aez, B. (2018). Valuing bushmeat for people living at a subsistence level: the case of Cuniculus paca meat in Flavio Alfaro, Manab ́ı, Ecuador. _Human Dimensions of Wildlife_ , _23_ (4), 313–328.

- Rose, A. L. (2001). Social change and social values in mitigating bushmeat commerce. In M. I. Bakarr, G. A. B. da Fonseca, R. Mittermeier, A. B. Rylands, & K. W. Painemilla (Eds.), _Hunting and bushmeat utilization in the African rain forest. Perspectives toward a blueprint for conservation action._ (pp. 59–74). Center for Applied Biodiversity Science at Conservation International, Washington, D. C., USA.

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37
# **Revising Commitments: Field Evidence on the Adjustment of Prior Choices**<sup></sup>

Xavier Giné, Jessica Goldberg, Dan Silverman, and Dean Yang May 2012

**Abstract**

The very poor in developing countries often make intertemporal choices that seem at odds with their individual self-interest. There are many possible reasons why. We investigate several of these reasons with a lab-in-the-field experiment in rural Malawi involving large stakes. We make two contributions. First, we construct a new dependent variable: revisions of prior choices regarding the allocation of future income. This allows us to directly examine intertemporal choice revision and its determinants. In particular, this dependent variable permits a novel test for the existence of self-control problems: we find that revisions of money allocations toward the present are positively associated with measures of present-bias from an earlier baseline survey, as well as the (randomly assigned) closeness in time to the first possible date of money disbursement. Second, we investigate other potential determinants of revision, aside from self-control problems. We find little evidence that revisions of money allocations toward the present are associated with spousal preferences for such revision, household shocks or the financial sophistication of respondents.

**Keywords:** commitment, hyperbolic preferences, lab experiment, consumption smoothing, Malawi

**JEL codes:** D81, D91, O10

 Giné: Development Economics Research Group, World Bank and BREAD (xgine@worldbank.org); Goldberg: Department of Economics, University of Maryland (goldberg@econ.umd.edu); Silverman: Department of Economics, University of Michigan and NBER (dansilv@umich.edu); Yang: Ford School and Department of Economics, University of Michigan, NBER, and BREAD (deanyang@umich.edu). Niall Keleher, of Innovations for Poverty Action, was instrumental to the design and implementation of this study. We thank him for his important contributions to this project. We also thank James Andreoni, Stefano DellaVigna, Pascaline Dupas, Yoram Halevy, Vivian Hoffmann, Glenn Harrison, Pam Jakiela, Damon Jones, David I. Levine, Stephan Meier, Ted Miguel, Matthew Rabin, and participants in several seminars for their many helpful comments. We thank Lasse Brune, Jason Kerwin, and Prachi Jain for excellent research assistance.
toward work. At endline, women were asked whether they intended to look for a job in the subsequent 6 months, and about 30 percent of women, regardless of treatment status, expressed this intension. Similarly, in India—a setting with very low female labor force participation—McKelway (2023) finds a temporary uptake in employment among women in response to an information intervention for women’s family members fading after one year. In Croke et al.’s (2024) study on high-quality community social services jobs for women that offered full-time work for 1-1.5 years in rural Egypt, and another setting with low female labor force participation, they do not find lasting effects on women’s employment after the programs concluded. This finding is also consistent with <mark>Gehrke and Hartwig (2018) and Bagga et al.’s (2023) reviews which find t</mark> hat public works for both men and women does not result in long-run employment effects.

# **5.3. Impact on time use**

Table 5 presents the time use data results for both the female beneficiaries and their husbands. The results are the calculated average of data from two 24-hour measurements (the day before the midline survey for weeks 1 and 3, and the day preceding the three weekly endline surveys). The time use across the 7 categories in Table 5 refers to the time spent by the female beneficiaries and their husbands on the main activity reported for each hour. We compute caregiving as the number of minutes spent caring for the youngest child if caregiving was either the primary or secondary activity of that hour, since caregiving is often performed simultaneously with other activities, such as chores.

Figure 3 also shows a subset of the results for time use—namely, women in the control group allocate 67 percent of daily time to household tasks and to caring for other household members. Working time makes up, on average, only 8 percent of women’s daily time, which is consistent with their low level of employment.

According to the midline survey results, women significantly reduce the time they spent on chores when offered public works. They do not replace the time they spent on chores completely with the time they spent doing public works, which indicates that they perform their chores during a modest “second shift.” Women who participate in public works increase the time they spend on paid work, which was approximately 3 hours (the previous day). This is consistent with the daily hours of the program (4-5 hours, with breaks) and when factoring in both the reduction in self-employment activities and the program take-up by 77 percent of women among the 92 percent households reported in Table 4. The increase in time employed is also marginally offset by a reduction in personal care, which includes sleep, and substantially offset by a reduction in time spent on chores.

The public works program has no effects on the time husbands spent on household chores (see Table 5, Panel 2), but it does produce a modest increase in the time men dedicated to work. This finding is consistent with the 6 percent take-up of public works among husbands, 20