rafmacalaba/datause-encoder-data huggingface dataset:
regards mandatory savings accounts, they do not involve any risk pooling: only a spreading of the earnings loss from unemployment over the life cycle. If workers cannot "borrow" from their old-age pension account, and they do become unemployed when they are still young, they might not have enough resources to cope with job loss (unless the mandatory savings rate is very high). On the other hand, if they can "borrow" substantially, they face an incentive to become unemployed, get as indebted as possible, and withdraw permanently from the formal sector to avoid repaying their debt.

Another potential shortcoming of unemployment insurance and mandatory savings accounts programs is that they may not reach the poorest workers. Table 1 summarizes the findings of a comparative study on income support programns for the unemployed in Latin America (World Bank, 2000a). This region has experienced with a variety of programs, and can be seen as a laboratory by other middle-income countries. Table 1 shows that very few of the workers covered by unemployment insurance in Brazil and by mandatory savings accounts in Colombia, belong to the population group with the lowest earnings or consumption. These two income support programs are well suited for workers in protected industries, who are usually enrolled with social security. But the evidence discussed in the previous section indicates that many, among those who lose their jobs, come from the informal sector. Unemployment insurance and mandatory savings accounts are not the appropriate programs to assist them.

> Tab1e ll. LImcone $snpj pot _7grosms ffr_ 9¢e IJmempnloyed

|_Programand_<br>_country_|_Workers_<br>_legally_<br>_covered_<br>_bythe_<br>_program_|_Spending_<br>_per_<br>_benefi-_<br>_ciary_<br>_(US$)_|_Costof_<br>_the_<br>_program_<br>_fallson_|_Shareof _<br>_Poorest_|_beneficia_<br>_2nd_<br> _poores,_|_riesbyearni_<br>**_0%o)_**<br> _Middle_|_ngsorcon_<br>_2nd_<br>_richest_|_sumption_<br>_Richest_|
|---|---|---|---|---|---|---|---|---|
|Publicworks in<br>Argentina|In<br>principle<br>all|3,100|Taxpayers|78.6|15.3|3.5|2.1|0.4|
|Training|Eligible|393|Taxpayers|69.9|15.5|8.1|5.0|1.5|
|inMexico|onage,<br>education||||||||
|Severancepay<br>inPeru|Salaried,<br>with<br>given<br>seniority|760|Workers<br>and<br>employers|4.7|9.5|28.6|33.3|23.8|
|Unemployment<br>insurancein|Salaried<br>insocial|664|Workers<br>and|10.6|24.6|19.1|25.1|13.6|
|Brazil|security||employers||||||
|Individual<br>accounts in<br>Colombia|Salaried<br>insocial<br>security|Not<br>available|Workers|0.0|4.3|Not<br>applicable|19.1|76.6|

_Note:_ Constructed using data from World Bank (2000a).

> At the other end of the spectrum, there are income support programs for the

> unemployed that do reach poor workers. Public works, like the Maharastra employment guarantee scheme in Iadia and the _Trabajar_ program in Argentina, are among them (Ravallion, Datt and Chaudhuri, 1993; Datt and Ravallion, 1994; Jalan and Ravallion,

22
There are several reasons why the study of the causal influence of institutional and cultural factors on preferences is difficult. One obvious reason is that societies and groups typically differ in a large number of dimensions but the researcher cannot control for all of them. Another reason is that due to the existence of multiple equilibria, different societies may end up with different institutions, cultural practices, and preferences despite the fact that they started with similar factor endowments and preferences. In this case, the co-variation between institutions, cultural practices, and preferences need not indicate a causal impact of institutions on preferences. The co-variation may merely reflect the fact that other background variables (which may be idiosyncratic historical events such as a war or a natural disaster) led to different developmental trajectories.

Any investigation of causal influences of institutional and cultural factors on preferences requires settings and techniques in which these potential confounds are addressed. The papers in this symposium use a caste status divide in India, a language border within Switzerland, and a manipulation of the decision format in a blood drive to plausibly identify causal influences on preferences. In this introduction, we put these papers in the perspective of the emerging literature on endogenous preferences.<sup>2</sup> This paper is divided into three parts. We first address the well-known objection that explaining behavioral changes by preference changes introduces _too many free variables_ . We argue that today such explanations should be considered on an equal footing with constraintbased explanations. Recent progress in game theory has provided many free, unobservable, variables that can explain outcomes, whereas recent progress in modeling and measuring preferences has put more constraints on preference-based explanations.

In the second part, we discuss the progress in psychology in pinpointing the determinants of preferences. Psychological research has shown that preferences can be affected by the way they are elicited, by the framing of situations, by anchoring devices, and by the priming of individuals‘ identities. _Thus, to the extent to which social institutions prime individuals’ identities and act as elicitation, framing, and anchoring devices, to this extent the institutions also shape preferences._

> 2 Earlier surveys of the literature on endogenous preferences are Bowles (1998); Stern, Dethier, and Rogers (2005, chapter 9); and Lichtenstein and Slovic (2006, chapter 1).

3
Policy Research Working Paper 7290

# **Abstract**

This paper disentangles different aspects of land fragmentation and its impact on the efficiency of resource use. The paper uses information on the incidence of crop shocks to assess whether fragmentation provides benefits in reducing risk and parcel coordinates and terrain-adjusted travel times between parcels to more precisely account for the associated costs in 2010/11 data from Rwanda. While fragmentation increases the time required to move between a household’s parcels, this does not appear to affect overall technical efficiency on the farm. Fragmentation rather reduces the incidence of crop shocks and increases yields and productive efficiency. In Rwanda’s setting, interventions to reduce fragmentation may, therefore, be ineffective or counterproductive.

This paper is a product of the Agriculture and Rural 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 dali1@worldbank.org, kdeininger@worldbank.org, and lronchi@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
- Riley, T and Huiyan, C, _Unmasking Chinese Business Enterprises: Using Information Disclosure Laws to Enhance Public Participation in Corporate Environmental Decision Making_ , Harvard Law Review, Volume 33 Number 1 177-224, 2009

- Russell, C.S., and W.J. Vaughan. 2003. The choice of pollution control policy instruments in developing countries: Arguments, evidence and suggestions. _International Yearbook of Environmental and Resource Economics_ , Vol. VII, Cheltenham, U.K. Edward Elgar.

- Sands, P.H.  2002.  The right to know: environmental information disclosure by government and industry. _In:_ Zealke, D., D. Kaniaru and E. Kruzikova (eds). 2005. _Making the Law Work: Environmental Compliance and Sustainable Development, Vol. 2_ , pp 17-48. London, Cameron May Ltd.

- Stephan, M. 2002. Environmental information disclosure programs: They work, but why? _Social Science Quarterly_ , 83, 1, 190-205.

- Tietenberg, T.  1998.  Disclosure strategies for pollution control. _Environmental and Resource Economics_ 11(3-4): 587-602.

- Tietenberg, T. and D. Wheeler.  1998.  Empowering the community: information strategies for pollution control.  Frontiers of Environmental Economics Conference. Airlie House, Virginia. October 23-25, 1998.

- US-EPA.  2000.  Assessment of the incentives created by public disclosure of off-site consequence analysis information for reduction in the risk of accidental releases. United States Environmental Protection Agency.  Washington, D.C.  April 18, 2000.

Wang, H., Bi, J., Wheeler, D., Wang, J., Cao, D., Lu, G., and Y. Wang. 2004. Environmental performance rating and disclosure: China's GreenWatch program. _Journal of Environmental Management_ . 71, 2, 123-133.

World Bank, _Greening Industry: New Roles for Communities, Markets, and Governments_ , World Bank and Oxford University Press, 2000.

33
floors if so desired. The Central region now houses the busiest tobacco auction floor with the most registered tobacco clubs, although thousands of clubs exist in each of Malawi’s three geographic regions (Jaffee, 2003; Tsonga, 2004). All producers must sell at least one bale of tobacco to be eligible for the auctions, thus farmers on small plots may resort to selling to intermediary buyers at below market prices (Takane, 2008). Malawi’s auction sales have plateaued somewhat over the last decade, with reports of illegal exporting to neighboring countries, lower auction prices due to plastic contamination, and possible collusion amongst tobacco purchasers (Kadzandira et al., 2004).

The available empirical studies that explore the welfare effects of household tobacco adoption in Malawi mostly rely on limited data from the 1990s, when the Malawian government restricted smallholder production (Kees van Donge, 2002). Kennedy includes Malawi in cross-country reports depicting the effects of commercialization on child nutrition, and finds insignificant differences in nutritional outcomes between tobacco- and non-tobacco-producing households during times of food price stability (Kennedy, 1994; Kennedy et al., 1992). Masanjala (2006) examines the effect of smallholder Malawian tobacco market liberalization on poverty alleviation. He concludes that tobacco adoption in the mid-1990s increased total household income, with a negative effect on non-farm household income. His study determines that tobacco farming significantly decreased caloric-intake food security, with the majority of the children in his sample being stunted.

This paper constitutes a new contribution to the longstanding international and Malawi-specific debate on the links between agricultural commercialization and nutrition. We bring together the cash crop adoption literature with the work on covariate shocks in developing economies for the purpose of examining the effects of smallholder tobacco adoption on child stunting during an exogenous staple food price shock in Malawi. Our research aims to confirm whether households producing tobacco during the food price spike have experienced nutrition distress due to inadequate food stocks and limited market availability of staple goods, resulting in worse child height-for-age outcomes.

# **3 Data**

Our primary source of data is the Malawi Second Integrated Household Survey (IHS2) which was implemented from March 2004 to February 2005 by the National Statistical Office (NSO). The 7
that this paper seeks to rectify.1 In this paper I use the regional structure of the economy, proxied by the shares of services and industry in regional gross domestic product (GDP), as an indicator of the demand for educated workers. By examining whether the level of schooling as a function of shares of services and industry differs for men and women, we can detect gende bias in the demand for schooling.

Based on the theoretical framework developed in Gill and Khandker (1990), I estimate schooling demand functions for males and females using data for Peru in the 1980s. A separate estimate covers households and provinces (called "departments' in Peru). Household data are from the Peruvian Living Standards Survey (PLSS); information on the provinces is based on census data. Findings confirm the results obtained in Gill and Khandker using country-level data for about 100 countries in 1965 and 1987. The primary findings are:

- As services and industry increase their shares of GDP, relative to the share of agriculture, the demand for schooling of both males and females increases.

- **O** As the share of services in GDP increases compared to agriculture (holding the share of industry constant), the demand for schooling by women increases more than the demand for schooling by men.

IIt should be mentioned here that the absence of explicit consideration of demand-side factors in the market for labor is a weakness of much of neoclassical labor economics.

2
# **_The impact of roads investment on agricultural production_**

Based on our household surveys in our three selected districts, the main findings are the following: (i) the 2 km distance from a road is not an economic threshold (beyond or above 2 km from a road does not necessarily have a positive impact on household income), (ii) bike ownership also does not have the expected positive impact (probably due to the low value of time<sup>11</sup> ), and (iii) road passability also does not have a major impact when it is minimal (because when walking, road passability should be minimal). What seems to matter for increased income is yield, crop type, which means growing high-value crops, selling directly products to markets and increased rural roads density.

There are indications in the literature that a ‘one size fits all approach’ is not effective in addressing the problems of African countries. “In some countries large sums of money have been wasted in building roads to high geometric standards with excessive carriageway widths for these low volumes of traffic (Ellis and Hine, 1998).” Instead, countries may need to adapt an approach that supplies the appropriate road for a rural area, realizing that a large main road may not be required. Fan et al. (2004) first found a significant impact of roads on rural poverty reduction; the study then drilled down further to differentiate the effects of different types of roads. The authors found that low-grade feeder roads had a greater impact on poverty than murram or tarmac roads. In this case, feeder roads led to increased agricultural productivity, which works to reduce poverty in rural Uganda. There is other evidence from Uganda that supports the importance of smaller roads. Growth regressions demonstrate that distance to feeder roads, and not main roads, has a positive effect on per capita consumption in rural areas (Schipper et al. 2007).

> 11 Mainly because the median distance is less than 5 kilometers and the bicycle, when loaded, could be pushed.

18
TABLE 19 Channels: Impulse Responses of TFP, Investment, Employment and Actual Growth Rates to Recessions

|**Definition of otential**|**Ba**|**Recession**<br>**seline defin**|**s:**<br>**ition**|**Rece**|**ssions: Alte**<br>**definition**|**rnative**<br>|
|---|---|---|---|---|---|---|
|**p**<br>**output**<br>**h**|**World**|**AEs**|**EMDEs**|**World**|**AEs**|**EMDEs**|
|0|-0.066**|-0.019|-0.108**<br>|-0.064**|-0.041**|-0.087*|
|1|-0.359***|-0.228***|-0.471***|-0.353***|-0.251***|-0.443***|
|**TFP**<br>2|-0.626***|-0.476***|-0.743***<br>|-0.577***|-0.495***|-0.64***|
|<br>3|-0.676***|-0.495***|-0.819***|-0.635***|-0.527***|-0.723***|
|4|-0.759***|-0.497***|-0.985***<br>|-0.69***|-0.519***|-0.842***|
|5|-0.686***|-0.418***|-0.919***|-0.619***|-0.425***|-0.793***|
|0|-1.842**|-2.913***|-1.151<br>|-2.469***|-3.515***|-1.7*|
|1|-15.501***|<br>-12.809***|<br>-17.097***|<br>-15.483***|<br>-12.99***|-17.006***|
|**Itt**<br>2|-7.689***|-10.231***|-6.265**<br>|-7.37***|-9.332***|-6.151**|
|**nvesmen**<br>3|-3.348**|-4.079**|-2.936|-2.963*|-3.696***|-2.484|
|4|-2.947*|-2.897|-2.976<br>|-1.814|-2.478*|-1.414|
|5|-3.017**|-2.838*|-3.13|-3.601***|-2.588**|-4.216**|
|0|-0.432***|-0.309|-0.497**<br>|-0.446***|-0.435***|-0.444**|
|1|-1.691***|-2.898***|-1.247***|-1.723***|-2.845***|-1.248***|
|**Elt**<br>2|-1.29***|-3.4***|-0.471<br>|-1.331***|-3.13***|-0.549*|
|**mpoymen**<br>3|-1.038***|-1.592***|-0.819**|-1.025***|-1.509***|-0.817**|
|4|-0.717***|-1.046***|-0.586*<br>|-0.631***|-0.964***|-0.482|
|5|-0.398|-0.975***|-0.16|-0.393|-0.86***|-0.179|
|0|-0.039|-0.077|-0.017<br>|-0.048|-0.055|-0.044|
|1|1.326***|1.555***|1.21***|1.281***|1.588***|1.126***|
|**l**<br>2|1.88***|3.424***|1.15***<br>|1.78***|3.417***|1.048***|
|**Unempoyment**<br>3|1.786***|3.457***|1.002***|1.698***|3.515***|0.897***|
|4|1.689***|3.257***|0.902***<br>|1.577***|3.234***|0.803**|
|5|1.656***|3.34***|0.811**|1.464***|3.112***|0.695**|
|0|0.019|-0.887***|0.419<br>|-0.02|-0.986***|0.446|
|1|-8.809***|-7.157***|-9.597***|-8.474***|-6.843***|-9.305***|
|**Atl th**<br>2|-4.992***|-4.506***|-5.197***<br>|-4.649***|-3.94***|-4.979***|
|**cua grow**<br>3|-1.399**|-2.503**|-0.957|-1.337**|-2.112**|-0.988|
|4|-2.349***|-2.539***|-2.28**<br>|-2.095***|-2.012***|-2.144**|
|5<br>Source: World Bank.<br>Nt *** idit ii|-1.124**<br>i t|-1.609**<br>h 1|-0.903<br>t ll *|-0.886*<br>* t th 5|-1.209**<br>t|-0.719<br>l d *|

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. “Recessions: Baseline definition” are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years of output growth that is both negative and one standard deviation below the long-term average (as in Huidrom, Kose, and Ohnsorge 2016). “Mild Recessions: Alternative definition” are defined as years of negative output growth only, regardless of the depth of the output decline. Sample includes unbalanced panel of 32 advanced economies and 79 EMDEs for 1981-2020.

65
Table B5: Robustness check using the sample of the aged 60 and below—Entry salary

|||Log (entr|y salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.304<sup>_∗∗∗_</sup>|-0.071<sup>_∗_</sup>|-0.067<sup>_∗∗_</sup>|-0.081<sup>_∗∗_</sup>|
||(0.049)|(0.038)|(0.033)|(0.036)|
|Schooing in years||0.059<sup>_∗∗∗_</sup>|0.062<sup>_∗∗∗_</sup>|0.038<sup>_∗∗∗_</sup>|
|||(0.005)|(0.008)|(0.006)|
|Age at hire||0.018<sup>_∗∗∗_</sup>|0.016<sup>_∗∗∗_</sup>|0.011<sup>_∗∗∗_</sup>|
|||(0.003)|(0.003)|(0.003)|
|Father’s pri.school||0.062<sup>_∗_</sup>|0.062<sup>_∗∗_</sup>|0.020|
|||(0.033)|(0.029)|(0.030)|
|Mother’s pri.school||0.064<sup>_∗_</sup>|0.069<sup>_∗∗_</sup>|0.041|
|||(0.033)|(0.032)|(0.038)|
|Math z-score||0.065|0.086<sup>_∗_</sup>|0.094<sup>_∗_</sup>|
|||(0.040)|(0.048)|(0.051)|
|Language z-score||-0.105<sup>_∗∗∗_</sup>|-0.093<sup>_∗∗_</sup>|-0.086<sup>_∗_</sup>|
|||(0.036)|(0.046)|(0.050)|
|Big 5 index z-score||0.005|-0.030|-0.093<sup>_∗∗_</sup>|
|||(0.045)|(0.036)|(0.042)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.207|0.410|0.417|0.299|
|N|3763|3757|3757|3757|

_Note_ . The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

49
Because women in low socioeconomic neighborhoods consume FAFH more frequently, figure 6 shows the share of overweight and obese women (ages 15–49) in the four socioeconomic groups. The higher the socioeconomic status of the neighborhood, the smaller the share of overweight women, though the standard deviations in the high socioeconomic status group are quite high. While 42 percent of women in neighborhoods with low socioeconomic status are overweight, the proportion decreases monotonically to 32 percent in neighborhoods with high socioeconomic status.

**Figure 6: Percentage of overweight and obese women (ages 15–49), by socioeconomic neighborhoods**

<!-- Start of picture text --> Overweight Obesity<br>Socioeconomic groups Socioeconomic groups<br>low mid-low<br>mid high<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>Percentage of obese individuals<br>Percentage of overweight individuals<br>10 10<br>0 0<br><!-- End of picture text --> _Source:_ Estimates based on data of the continuous demographic and health survey 2013. _Note:_ Overweight is defined as 25 ≤ BMI < 30, and obesity as BMI ≥ 30. Data are weighted.

There is a similar pattern in the prevalence of obesity. The prevalence rate is 25 percent in in the low socioeconomic status neighborhoods and falls to 17 percent in the high socioeconomic status neighborhoods. Thus, there is descriptive evidence of a socioeconomic gradient in overweight and obesity. Especially women in neighborhoods of low socioeconomic status that more frequently consume FAFH (see figure 5) exhibit the highest prevalence of overweight and obesity, in line with the low nutritional quality of diets in the restaurants in these neighborhoods (see figure 2). However, it may be that this pattern is merely an outcome of other underlying factors. The following section examines if this is the case and estimate the strength of these relationships once important underlying factors are accounted for, such as the lifestyle and health characteristics of the respondents and other food environment characteristics that may partly explain the associations.

# **5.2 Empirical results**

Following equations 4.2, 4.3 and 4.4 of the empirical approach, the ordered logit model is applied with the dependent variables overweight and obesity.<sup>16</sup> Table 1

> 16 The category normal weight serves as a reference category.

15