rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 16**

# **Evolution of Contemporaneous Herding Statistic**

This figure presents the evolution of the average Lakonishok et al. (1992) herding statistic during the entire sample period (July 1996 to December 2005). The herding statistic is calculated using the asset-specific probability of buying an asset at a moment in time. Panel A and B present the evolution of the herding statistic for Foreign Investment and Mutual Funds and Domestic Government Paper, respectively. The averages that are significant at the ten-percent level, according to the two-tailed t-test, are marked on the figure. Significant events that occurred during this period of time are also highlighted: the Asian crisis (July 1997), the Russian crisis (August 1998), the introduction of Fund E and the widening of the minimum return band (October 1999), and the establishment of the multi-fund regime (September 2002). Numbers represent percentages (results are multiplied by 100).

Panel A. Evolution of Contemporaneous Herding Statistic - Foreign Investment and Mutual Funds (1996 - 2005) <!-- Start of picture text --> 5<br>Mean<br>4 Significant at ten percent<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>Asian Crisis Russian Crisis Fund E Multi-Fund<br>-4<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br> Herding Statistic<br><!-- End of picture text --> Panel B. Evolution of Contemporaneous Herding Statistic - Domestic Government Paper (1996 - 2005) <!-- Start of picture text --> 8<br>Mean<br>7<br>Significant at ten percent<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>Asian Crisis Russian Crisis Fund E Multi-Fund<br>-3<br>Jul-96 Mar-97 Nov-97 Jul-98 Mar-99 Nov-99 Jul-00 Mar-01 Nov-01 Jul-02 Mar-03 Nov-03 Jul-04 Mar-05 Nov-05<br> Herding Statistic<br><!-- End of picture text -->
# **4 Results**

Table 3 presents results from the discrete choice experiment estimated using conditional logistic regression. Policymakers preferred programs with larger estimated treatment effects or programs that came recommended by a local expert, as well as those that had an impact evaluation from their country (Column 2). Policy practitioners preferred programs with larger, more precisely estimated impact evaluation results as well as results from the same country as the target program and results from RCTs (Column 3). They also preferred programs that came recommended by a local expert.<sup>10</sup> Table 4 translates these results into estimates of how much policymakers and policy practitioners would be willing to pay, in terms of estimated impact, for programs with these different attributes. Policymakers would prefer a program recommended by a local expert even if it had an approximately 5.0 percentage point lower estimated impact on enrollment rates (Column 2), which is very large relative to the range of effects that programs typically have on enrollment rates.

Table A1 further breaks down responses across the World Bank and IDB preworkshop samples and tests whether policymakers and policy practitioners put statistically significantly different weights on different attributes. Results appear largely comparable across these sub-samples, though policymakers put relatively less weight on large or precise estimates and do not emphasize programs with impact evaluations done in the same country as much as policy practitioners do.<sup>11</sup> To further explore the importance placed on precise estimates, we construct an indicator for whether the result shown was significant. While this variable was not explicitly shown to participants, it could be discerned from the provided estimated impact and confidence interval. When we include this variable in the regressions, the

> 10Note that in order to avoid framing a program as _not_ recommended, whether the program was recommended was displayed outside the table that contained study attributes; this also effectively clustered the study attributes together and kept the non-study attribute separate, to make the distinction between study-related information and non-study-related information easier for participants to parse. While we cannot rule out that something about the format affects the weight policy professionals place on advice from a local expert, we do not expect it to drive the observed preference for local evidence overall given that policymakers also place so much emphasis on impact evaluation results coming from their country, and location was displayed within the table of impact evaluation characteristics.

> 11This could partially be explained by their generally putting less weight on impact evaluation results compared to the recommendation of a local expert, although this is speculative.

10
# **1. Introduction**

This paper addresses a central question in the recent literature on the microeconomics of growth: What is the impact of changes in the business environment on firm productivity? Institutions and policies determine the business environment within which individuals accumulate skills and firms accumulate capital and produce output. Regulations and laws exist to protect against diversion, but are often instruments of predation in an economy. A good business environment reduces rent seeking activities, supports productive activities, and encourages skill acquisition, capital accumulation, and innovation.

This paper builds upon the recent research by Dollar, Hallward-Driemeier, and Mengistae (2003), Bastos and Nasir (2004), and Escribano and Guasch (2005) in using data from recent World Bank Enterprise Surveys to link indicators of the business environment to firm-level productivity. These studies have done much to overcome the many shortcomings of the macroeconomic literature on this topic (Knack and Keefer, 1995; Hall and Jones, 1999; Acemoglu, Johnson, and Robinson, 2001).<sup>1</sup> However, these earlier papers that were the first to use data from the World Bank Enterprise Surveys suffer from two major estimation problems. First, the countries covered were surveyed just once and only a single year of business environment indicators is available. Given the cross-sectional nature of the data, regressions potentially suffer from a problem of reverse causality—some business environment indicators whose effect on firm productivity is estimated may themselves be affected by firm productivity. For example, financing from foreign banks may have a positive effect on firm productivity, but concurrently, financing from foreign banks may be influenced by firm productivity (i.e., foreign banks are more willing to lend to only the most productive domestic firms). Second, while multiple years of data are collected for production function variables in some of these countries, measurement error and non-response plague the recall data collected by these surveys.

The paper contributes to the literature in two important respects. First, a unique dataset is constructed by merging information from two large databases of European firms—the Business

> 1 As Dollar, Hallward-Driemeier, and Mengistae (2003) note, the literature that examines the links between the business environment and productivity at the macroeconomic level suffers from three major shortcomings: (i) few countries have good data on the business environment that are necessary to derive robust statistical results (Levine and Renelt, 1992; Rodriguez and Rodrik, 2000); (ii) the proxies used as explanatory variables provide minimal guidance about what governments need to do to improve their business environment; and (iii) using national-level data assumes that the business environment is the same across locations within a country, but interesting variation may exist based on heterogeneous local governments and institutions.

1
# **Food Insecurity Erodes Trust**<sup>*****</sup>

Woubet Kassa<sup>†</sup> Michael D. Smith<sup>‡</sup> Dennis Wesselbaum<sup>§</sup> _World Bank National Oceanic and Atmospheric University of Otago Administration_

**Keywords** : Food Insecurity, Experiential Measures of Food Insecurity, Social Contract, Trust, Governance JEL: D73, O12, Q18, Z13

* The findings and conclusions in the paper are those of the author(s) and do not necessarily represent the views of the World Bank Group or the National Marine Fisheries Service, NOAA.

> † Corresponding author. The World Bank, Washington, DC, USA. Email: wkassa1@worldbank.org.

> ‡ National Oceanic and Atmospheric Administration, Economics and Social Sciences Research, Alaska Fisheries Science Center, 7600 Sand Point Way NE, Seattle, WA 98115-6349, USA. Email: michael.d.smith@noaa.gov.

> § Department of Economics, University of Otago, Dunedin, New Zealand. Email: dennis.wesselbaum@otago.ac.nz.
# I. **Introduction**

Property rights of state owned enterprises (SOEs) display large cross-sectional and time series variations. A case in point is Chinese SOEs in the 1 980s. As will be shown later, Chinese SOE managers faced firm-specific profit retention rates, had different production decision rights and discretion in employee wage determination, and financed investments from different sources---some relied more on govermment funds, some on bank loans, and the others on their own retained profits. What determines how the involved parties partition property rights? What are the behavior patterns of the government and SOEs in this process? These questions are the central concerns of this paper. A positive study of the political economy of property rights will help us better understand SOEs' behavior, including the objectives and constraints of the government; it may, therefore, enable us to offer better prescriptions for reform of SOEs. As yet, these questions have not been addressed systematically, and this research attempts to fill that void.

The theoretical approach used here highlights the asymmetry of information between the government and managers to examine how the principal (the government) and agents (managers) partition control rights and incentives. To curb the information advantage of SOEs, the government designed incentives and control rights based on the finns' characteristics---such as the risks they faced, their sizes, capital intensities, and past performance. For instance, for firms facing a "nosier" environment, the government often designed lower profit sharing and retained more centralized production decisions to mitigate risk; for firms with higher capital intensity, the govermment's goal of maintaining the value of equipment was likely to be in conflict with the employees' short-run bonus motives when profitability-based pay was imposed. Thus monitoring was more likely to be used than pay sensitivity in inducing internal labor incentives in capitalintensive firms.

The empirical implementation and tests were carried out using _A Survey of State Enferprises: 1980-1989,_ a panel data set consists of 769 firms over 1980-1989. Firms in 1
# **_C. Financial Attitudes and Perceptions_**

The third dimension of financial knowledge that we consider is attitudes towards financial decisions. From Wave 3 onwards, we included questions in the follow-up survey wherein respondents are presented with hypothetical situations, and are asked about the financial products or financial advice they would suggest in the given scenario. Tables 7 and 8 present results for these attitudinal questions.

Note that the difference between Tables 7 and 8 is how we code our response measures. Some of the attitudinal questions (presented in Appendix II) do not have a singular correct answer, but rather have an ascending range of correct answers. For example, the first question in this series asks respondents to suggest an action to a friend who works in an injury-prone environment. The options available to the respondent are “quit job,” “purchase health/life/accident insurance,” or “increase savings.” The most financially savvy advice is to purchase an insurance product, however increasing savings can also contribute to a financial buffer in case of injury. Table 7 considers only the best answer as being correct and creates a dummy variable as the dependent variable. In Table 8, we create a continuous variable with a range from 0 to 1, coded with greater weight placed on the insurance option, and an intermediary non-zero weight on the savings option.

The results from both methods are similar. We find that those who received financial literacy training are significantly more likely to offer good financial advice for insurance, budgeting, and productive loans. Moreover, the aggregate financial attitudes and perception score is significantly higher for those who received financial literacy training.

As with the other dimensions of financial knowledge, we do not find any additional benefit of the pay for performance treatment. One explanation is that the participants did not find our offer credible and hence did not apply additional effort focusing on the videos. This is unlikely, however, since we do find significant pay for performance effects for individuals who were assigned to health videos and were paid for answers to health related questions.

15
# <u>Table A9. Schooling, skills, migration, and earnings for men in Pakistan—Mean regression estimates</u>

||(1)<br>Monthlyearnings(TC)|(2)<br>Monthlyearnings(TC)|(3)<br>Monthlyearnings(TC)|(4)<br>Monthlyearnings(TC)|
|---|---|---|---|---|
|Years of schooling (a1)|5.22***|4.55***|2.88**|0.69|
||(0.93)|(0.86)|(1.15)|(1.06)|
|Cognitive skills (a2)|||6.28|2.79|
||||(4.74)|(5.00)|
|SEM skills (a3)|||14.6***|13.0***|
||||(3.28)|(3.14)|
|Out Village (a4)||||8.35<br>(22.8)|
|Interaction YrsSchooling and Out Village (b1)||||9.36***<br>(2.51)|
|Interaction Cog and Out Village (b2)||||-4.71<br>(11.5)|
|Interaction SEM and Out Village (b3)||||-10.8<br>(7.68)|
|Constant|27.1***|33.7***|56.3***|46.1***|
||(8.44)|(7.99)|(9.71)|(10.0)|
|Observations|2340|1978|1978|1978|
|R-squared|0.060|0.040|0.050|0.13|
|Mean dependent|154.70|134.71|134.71|134.71|
|Sample|All|Has skills measures|Has skills measures|Has skills measures|
|District FE|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|
|a1 + b1=0||||10.05***|
|||||(2.49)|
|a2 + b2=0||||-1.92<br>(10.22)|
|a3 + b3=0||||2.27|
|||||(7.19)|

* p<.10 ** p<.05 *** p<.01

_Notes_ . This table reports estimates of the relationships between schooling, skills, migration, and earnings for men in Pakistan. All the columns report median regressions estimates. The dependent variable is top-coded at 100,000 PKR per month (961.5 USD). The cognitive skills index is the mean of the Urdu, Mathematics, and English scores computed using Item Response Theory with a two-parameter logistic (2PL) model. The socioemotional (SEM) skills index is computed using principal-component factor analysis. The Out of village variable is a dummy variable taking the value 1 if the respondent lives outside the village where we originally surveyed their household. The sample for column (1) is all men in the sample who are 88
distances. Figure 3a shows a histogram with the number of industries trading at each distance over the period. For example, both in 1985 and 2000 the mode is 4,000-5,000 km, with about 25-30 percent of products trading at this distance. Figure 3b is similar, but weights industry-distances by their share of trade. If industry distances remained constant but the share of trade in a short-distance industry increased from 1985 to 2000, it will be reflected as an increase in the share of trade at that distance. To see changes more clearly, Figures 3c and 3d report the change in the fraction of industries or trade from 3a and 3b, respectively. They show that more than 40 percent of both products and trade record a change in distance that is less than 10 percent, and about 80 percent of products record a change in average distance of less than 20 percent. The percentage point change in average distance is centered between zero and -10, implying that somewhat more products show a decrease in average distance than an increase. The industry data also highlight large cross-sectional differences in average distances, which are far more extreme than time-series variation. While a few products trade on average at only 2,000 km, others trade on average at more than 10,000 km.

Examining averages is useful for summarizing the data, but there are three problems with these measures. One, averages are highly driven by the large trade flows between industrial countries and thus conceal important changes in trade flows that have occurred between small countries because their weight in trade is so small.<sup>7</sup> Two, averages do not account for changes in income and other factors that affect the distribution of trade. Three, averages obscure important variation in trade by distance within industries that might offset each other.

> 6 The minimum sample coverage is 83 percent and the maximum is 87 percent.

9
Figure A.5 Regional Level Improved Sanitation Deprivation a) b) <!-- Start of picture text --> Urban Improved Sanitation Deprivation - Rural Improved Sanitation Deprivation - Geopolitical<br>Geopolitical Zone - GHS Zone - GHS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE<br>Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS<br>Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 Note: Individuals with missing age were dropped from the analysis. Estimations are based in children aged 0-17.

<!-- Start of picture text --> Figure A.6: State Level Improved Sanitation Deprivation by Region<br>a) b) c)<br>Improved Sanitation Deprivation - Improved Sanitation Deprivation - Improved Sanitation Deprivation -<br>by State in North Central - MICS by State in North East - MICS by State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Improved Sanitation Deprivation - Improved Sanitation Deprivation -<br>Improved Sanitation Deprivation -<br>by State in South South MICS by State in South West- MICS<br>by State in South East - MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Ekiti Lagos Ogun Ondo Osun Oyo<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age. . Estimations are based in children aged 0-17.

66
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19 and scored lower on the math and Telugu test by 0.17 and 0.15 standard deviations.<sup>9</sup> Similar nonlinear relationships were found between the income aspirations gap and real estate investment measured by expenditures on household construction materials and land in rural Myanmar (Bloem 2021), and between women’s aspirations for personal income and for their children’s education and their savings and expenditures on their children’s education in rural Nepal (Janzen et al. 2017).

# **Collective frustration spurring aspirations along other dimensions**

There is some evidence consistent with Ray’s (2006) theory that unfulfilled aspirations —say for jobs or social status, initially high enough to motivate but unfulfilled by a lack of opportunities— could lead individuals to seek aspirations in other realms of life, such as religion. For example, one of the possible factors for the religious revival seen in Egypt is that religion has helped individuals to cope with unfulfilled aspirations caused by the abrupt decline in social mobility coupled with rising inequality and poverty (Binzel and Carvalho 2017).

Some evidence is consistent with the theory that extreme cases of unfulfilled aspirations may lead to unrest (Ray 2006, 2016). The Tunisian Revolution, a series of street protests in 2010-11, is thought to have resulted from higher aspirations given increased educational attainment but stagnating job opportunities (Campante and Chor 2012). The causes of protest are always complex and multiple, but the high unemployment, which, including many jobseekers with university degrees, is thought to have been one of the factors that sparked tensions. A similar phenomenon happened in Chile in 2012 when higher access to universities, largely financed through scholarships and student credits, had initially raised high expectations in the population for their future but then converted in discontent after stagnant social mobility, labor market conditions, and poverty (Urzúa 2012; Flechtner 2017). Somewhat paradoxically, protests and social tensions are more likely to improve when countries improve their conditions (e.g. higher economic growth, less inequality) because people raise their aspirations, which may then not be fulfilled (Ray 2006).

Further, the study of international recruits of the terrorist organization of the Islamic State of Iraq and the Syria (ISIS) brings a suggestive example that is consistent with the theory that extreme cases of unfulfilled aspirations may also lead to terrorism. Using personal records from the terrorist organization, a study shows that higher unemployment rates for a given educational level in a given country — an indicator of a lack of opportunities — for 59 countries from which the recruits were

> 9 Adolescents who aspired for an education level and occupations with wages too close to their initial situation had close to three fewer years of education completed seven years later, at age 19, and scored lower on exams of math, English, and Telugu (the local language) between 0.6 and 0.9 standard deviations.

17