rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6 Sectoral distribution of informal workers‟ main job, by European region  and employment status. 2004-2009**

|||**East**|||**South**|||**West**|||**North**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Employees**|**Self-employed**|**Total**|**Employees**|**Self-employed**|**Total**|**Employees**|**Self-employed**|**Total**|**Employees**|**Self-employed**|**Total**|
|Agriculture &|||||||||||||
|Forestry|5.4|15.5|11.3|3.9|19.8|13.3|1.7|15.6|11.1|3.8|21.4|17.7|
|Manufacturing|14.7|9.6|12.0|13.4|9.2|11.0|14.2|8.0|10.2|14.6|6.7|8.5|
|Construction|13.0|8.9|10.8|11.6|8.3|9.5|8.5|11.7|10.4|14.8|12.4|12.9|
|Trade, Auto Repair,|||||||||||||
|Hotels & Restaurants|20.7|27.0|24.2|22.2|34.1|29.4|25.5|18.5|21.7|17.4|19.8|19.3|
|Transport, finance,|||||||||||||
|& business activities|10.6|15.4|13.0|12.6|11|11.6|14.5|20.2|17.7|16.2|19.4|18.6|
|Education, Health &<br>Social Care, Public|||||||||||||
|Administration,<br>Utilities, Post &|||||||||||||
|Communications|16.2|4.7|9.8|20.3|3.3|10.4|24.0|10.3|15.3|22.8|8.0|11.4|
|Community,|||||||||||||
|Personal & Household|||||||||||||
|Services|9.9|10.5|10.0|13.4|10.1|11.3|7.8|10.3|9.1|5.6|9.9|8.7|
|NA|8.5|7.2|7.7|2.7|4.2|3.5|2.7|4.6|3.8|3.3|2.3|2.5|
|Total|100|100|100|100|100|100|100|100|100|100|100|100|
|N obs.|1857|2136|4152|2036|2337|4540|1703|2680|4567|391|1137|1561|

_Notes:_ See Table 1 for definitions of informal employment. Columns ̳Employees‘ and ̳Self-employed‘ here correspond, respectively, to categories 5 and 3 defined in Table 1; column ̳Total‘ includes also family workers. Countries are not weighted by population size. Design weights corrected for variation of sample size across countries in each round are applied (i.e., all respondents are assumed equally likely to be sampled; countries which did not participate in some rounds are, however, under-represented). _Source:_ Calculation with ESS data.

**Table 7 Perceived discrimination on  grounds of race, ethnicity, religion, language, nationality, age or disability among formal and informal workers, by European region. 2004-2009**

||<br>**_South_**|<br>**_East_**|<br>**_West_**|<br>**_North_**|**%**<br>**_Total_**|
|---|---|---|---|---|---|
|[1] Formal Employees|3.5|3.8|4.1|2.3|3.6|
|[2] Informal Employees|7.6|6.4|5.3|2.8|6.2|
|[3] Informal Self-employed|3.9|4.0|3.9|2.3|3.7|
|[2]/[1]|2.18|1.70|1.30|1.24|1.74|
|_t-test_: [2] = [1]|0.000|0.002|0.057|0.540|0.000|
|_t-test_: [3] = [1]|0.505|0.638|0.749|0.970|0.609|

_Notes:_ Countries are not weighted by population size. Design weights corrected for variation of sample size across countries in each round are applied (i.e., all respondents are assumed equally likely to be sampled; countries which did not participate in some rounds are, however, under-represented). _Source:_ Calculation with ESS data.

54
attended the course prior to our booster incentive interventions and (ii) a dummy variable for whether the individual attended either before or after we offered the incentives.<sup>15</sup> The bottom row of table 3 shows that the take-up rate increased from 20.8 percent to 28.5 percent after the incentive intervention (in our sample of financial institution clients). Two characteristics are strongly and positively correlated with attendance: education and age. Individuals who have a bachelor’s degree or higher are up to 14 percentage points more likely to have attended the training. The take-up rate among this group was 38 percent after the incentive intervention. Older individuals are also more likely to have participated in the training. In addition, we find weak evidence that females and individuals who are clients of our partner financial institutions as opposed to another institution are slightly more likely to have attended the training.

# **6. Impacts of Financial Literacy Training**

We first discuss our data and estimation methodology, and then turn to impacts of the financial literacy training on financial knowledge, behaviors and outcomes.

# **6.1 Follow-Up Survey**

We conducted a follow-up survey between February and July 2012 to measure post-training financial knowledge, behavior and outcomes. We kept the questionnaire relatively short (about 15 minutes) to encourage participation. The questions focused on concepts and behaviors taught in the course. We discuss specific questions and outcome variables below.

For logistical reasons, we first attempted to conduct the follow-up survey over the phone. If the person did not respond to the survey during the first attempt, we offered them a 500 pesos (US$36) Walmart gift card for completing the survey during the second attempt. If we were still not able to interview the person over the phone, a surveyor visited their house to conduct a face-to-face interview. If the participant was not at home, the surveyor delivered a letter with information about our study and instructions for how to contact us to participate in the survey and to receive the Walmart gift card. Surveyors made two more attempts (three attempts in total) to conduct a face-to-face interview if a respondent was not at home.

We were able to interview 72.8 percent of our sample during the follow-up survey. The attrition rate was slightly higher in the treatment group (29 percent) than in the control group (25.3

> 15 We do not include income among the variables displayed in Table 3 since this variable has many missing values and including it would thus reduce the sample size. When we add income to the analysis, it is not correlated with take-up and the coefficients on the other variables remain similar to the ones shown in Table 3.

19
longer binding. Complementing this liberalization was the lifting of internal trade restrictions of rice. Before this domestic reform, the flow of rice from South (where more rice is produced and there is a surplus) to North was restricted to encourage self sufficiency in each region. The internal trade ban coupled with the binding export quota caused low prices of rice in the South and high prices in the North. The increase in export quota and the lifting of internal restrictions has led to increased rice prices (in real terms) everywhere but proportionally more in the South (Benjamin & Brandt, 2004). Along with rice, fertilizer markets were also liberalized which led to a decline in fertilizer prices.

The response in Vietnam to these reforms was overwhelmingly positive. Between 1993 and 1998 (for which there is household survey data), real incomes of rural households increased by almost 60 percent. This is unusually rapid growth for rural communities, corresponding to a real income growth of almost 10 % per annum. Moreover, more than half of this growth in rural areas was caused by the growth in agricultural incomes (Isik-Dikmelik, 2006). The household response to these reforms could be conditional on the household characteristics and service availability. It is important to find out the reasons for this rapid response and to determine who responded by how much.

The determinants of the rate of supply response can be separated into factors that are intrinsic to the household, and factors that are external to the household. Intrinsic to the household are the household size, the education and skills of the household members, and the capital stock of the household including land. Factors external to the household have to do with the availability of services and infrastructure, both physical and market, in rural communities. Thus, the survey data allows the measurement of the rate of supply response of households with different initial characteristics to these reforms and also measure the role of the availability of services infrastructure on these response rates.

Many farm households may not be able to shift into commercial crop production and sales as a response to trade reform even if it is a much more profitable activity because (small scale) farmers face high transaction costs in marketing their products or receiving modern inputs (Key, Sadoulet, and de Janvry, 2000). Transaction costs differ across households due to differences in the availability and efficiency of markets for their products and inputs, assets ownership, access to other services, market information, and 3
no two countries use the same questionnaire to measure it, and this also affects comparability. There is a large literature that documents significant variation in estimated consumption resulting from seemingly innocuous variation in, for example, the number of prompts or questions posed (Beegle et al., 2012, Browning et al., 2014, Jolliffe, 2001, Pradhan, 2009, and Winter, 2003), the recall period (Beegle et al., 2012, Deaton and Kozel, 2005), or the time of year of the fieldwork (Jolliffe and Serajuddin, forthcoming; Khandker, 2012). Because the SPL proposed in this paper is in part defined by the measure of wellbeing used in a particular country, the societal poverty threshold assigned to each country is partially harmonized with the measure of wellbeing.

# **IV. Profile of Societal Poverty**

Current global counts of absolute poverty are typically based on data primarily from PovcalNet (e.g. Ferreira et al, 2016), as are the estimates of societal poverty presented in this paper. For the global count of absolute poverty, many high-income countries are assumed (not estimated) to have no extreme poverty, as measured by the international poverty line. While this may be a reasonable approximation for the percent of people living on less than $1.90 per day in some rich countries, it is not an acceptable assumption for societal poverty. To address this concern, we supplement the PovcalNet reference-year distributions for 131 low- and middle-income countries with data from 24 high-income countries not included in the World Bank’s extreme poverty counts.<sup>30</sup> These 24 high-income countries represent 928 million people in 2013. To estimate the

> 30 We produce estimates from 100 surveys based on data from 24 high-income countries, including: Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and the United States. These surveys are from PovcalNet, but are not included in the reference-year aggregations. For details on the regional groupings used in PovcalNet and by this paper, see: http://iresearch.worldbank.org/PovcalNet/data.aspx.

24
39

# **Table 21. Impact of Decentralization on Fiscal and Economic Performance**

|Regressors / Dependent Variable|Consolidated regional<br>budget balance as<br>percent of the<br>consolidated regional<br>budget expenditures|Real industrial output<br>growth<br>**(%**to previous year)|
|---|---|---|
|Constant||74.93<br>(15.11)|
|Percent of rural population in 1994|-0.17*<br>(-2.23)||
|Local governments total revenues as<br>|-0.15<br>|0.15*<br>|
|percent of the consolidated regional<br>budget revenues|(-5.44)|(2.32)|
|Regional transfers to local governments<br>as percent of local governments total<br>expenditures|<br>0.22<br>(2.62)||
|Inflation|0.04<br>_(9.05)_|-0.03<br>(-6.14)|
|Real per capita income||0.001<br>(3.1)|
|Sample|1994 1996|1994 1996|
|Total Panel (Unbalanced) Observations|250|243|
|Adjusted R-squared|0.34|0.19|
|Durbin-Watson|1.64|2.25|
|White's Heteroskedasticity Test|85.61<br>(0.00)**|26.21<br>(0.00)**|
|Standard Error of Regression|8.80|10.79|
|Root Mean Squared Residual 1994|7.90|10.43|
|Root Mean Squared Residual 1995|7.13|13.05|
|Root Mean Squared Residual 1996|10.75|7.59|

Notes: Unless otherwise indicated, numbers in parenthesis are t-statistics calculated with the White heteroskedasticity consistent standard errors of OLS coefficients Unless otherwise indicated, OLS coefficients are statistically significant at the 99% level of confidence

* Statistically significant at the 95% level of confidence

** P-value

The OLS coefficients of real per capita income and the rate of inflation are statistically robust as they remain statistically significant and close in value to the full-sample estimates, when re-estimated with all possible sub-samples of 84 out of 85 regions. Figure 2 presents the OLS coefficients and the 95% confidence intervals of the share of local governments in consolidated regional budget revenues. In all sub-samples the above coefficient is positive and close to its full-sample estimate, but on one occasion (when the Altay Republic is removed from the sample) it becomes marginally insignificant at the 95% level of confidence. Consequently, while the positive link between revenue decentralization and the real
We find that a congestion toll and a gasoline tax, both generating equal revenue, impose a very similar incremental cost per car trip and work in the desired direction qualitatively. However, they have significantly different effects quantitatively because they work on different margins of consumer behavior. The fuel tax is more effective as a policy instrument for reducing gasoline consumption and emissions because it works directly on the demand for gasoline by raising its after-tax price significantly. The congestion toll works on excess travel delay which is the source of the negative externality of traffic congestion. We also show that a policy to improve the fuel efficiency standard of cars is more efficient than a fuel tax or a congestion toll to reduce the same amount of CO2 emissions in Beijing.

The paper is organized as follows: In section 2 the model is laid out in all its technical details. Subsection 2.1 presents the consumer preferences and the three-stage utility maximization problem. Subsection 2.2 presents the demands for travel and housing derived from the utility maximization. Subsection 2.3 discusses how the cost components of travel enter the model. These include the delay due to traffic congestion, the monetary cost of gasoline, and how the speed affected by the congestion delay determines fuel consumption and emissions. Subsection 2.4 presents the market equilibrium formulation in which the market for traffic demand and the market for housing are jointly equilibrated by solving for rents and congested equilibrium travel times. Section 3 explains how the model was calibrated from the aggregate 2005 data for Beijing, focusing especially on the calibration of the two key parameters of the congestion function, choices of which affect the results. In section 4, simulations of the effects of the congestion toll and the gasoline tax are presented and the results are compared. The same section also presents the results of two other policies (a fuel tax, and an improvement in vehicle fuel efficiency) that match the emission reductions achieved under the congestion toll. Section 5 offers some conjectures as to how the results might be modified in a setting in which more geographic detail could be included.

# **2. Setting up the model**

Our model is derived from a consistent microeconomic formulation of the consumer’s utility maximizing behavior in which discretionary trips made are choice variables because they are complementary to consumption goods that are the direct objects of choice, while non4
indicates that provincial governments facing large fiscal deficits are more likely to privatize their public provincial banks than those facing smaller deficits. This result is consistent with findings in World Bank (1995) which suggest that reform is more likely when the government is facing a fiscal crisis -- provincial governments facing large deficits are less willing, or able, to subsidize public provincial banks.<sup>25</sup> In general, the hazard rate appears more sensitive to changes in the bank quality variables than to the provincial deficit (See Table 4). 26 A I% increase in net worth over liabilities decreases the (unobserved) hazard rates for privatization by 3%, and a 1% increase in normal loans as a percentage of total loans decreases the hazard rate for privatization by 4%.27 In contrast, a 1% increase in provincial deficit (as a share of total revenues) decreases the hazard rate for privatization by less than 1 %.

**Table 4: Elasticities of Estimated Hazard Rate with Respect to Continuous Independent Variables**

||**Elasticity**|**95% Confidence**<br>**Interval**|
|---|---|---|
|_Net_**_worth over liabilities_**|-3.03**|**(-5.27, -0.78)**|
|_Public_**_loans as percentofall loans._**|1.08**|(0.13, 2.03)|
|_Total_**_"normal" loans as percent of all loans_**|-4.37**<br>|(-7.03, -1.71)|
|**_Provincial government deficit as percent of_**|0.73**|(0.15, 1.31)|
|**_revenues_**|||

**+** Elasticities are calculated at the means of all variables

++ Coefficient estimates are from Table 5, Column 2.

_25_ World Bank (1995), Chapter 4.

26 Results from exponential and Weibull hazard models are similar to the results presented for the proportional hazard model. The main differences are: in the exponential hazard model, the coefficients on the share of loans to the public sector and on provincial deficit as a share of provincial revenues drop to a 10% significance level. In the Weibull hazard model, the coefficient on provincial deficit as share of provincial revenues becomes insignificant at conventional levels (but share of loans to the public sector remains significant at a 5% level). However, changes in national laws and institutions and exogenous shocks such as the Tequila crisis make Cox's proportional hazard formulation appear more attractive than the fully parametric models which force the base hazard rate to follow less general time-dependent paths. Further, as noted in footnote 39, hypothesis tests appear to favor Cox's proportional hazard model over the two fully parametric models.

27 Elasticities are calculated at the means of all variables.

15
- Jayne, Thomas S., Sieglinde Snapp, Frank Place, and Nicholas Sitko. 2019. “Sustainable Agricultural Intensification in an Era of Rural Transformation in Africa.” _Global Food Security_ 20 (March): 105–13. https://doi.org/10.1016/j.gfs.2019.01.008.

- Kondylis, Florence, Valerie Mueller, and Siyao J. Zhu. 2017. “Seeing Is Believing? Evidence from an Extension Network Experiment.” _Journal of Development Economics_ 125: 1–20. https://doi.org/10.1016/j.jdeveco.2016.10.004.

- Kondylis, Florence, Valerie Mueller, and Siyao J. Zhu. 2015. “Measuring Agricultural Knowledge and Adoption.” _Agricultural Economics_ 46 (3): 449–62. https://doi.org/10.1111/agec.12173.

- Maertens, Annemie, Hope Michelson, and Vesall Nourani. 2020. “How do Farmers Learn from Extension Services? Evidence from Malawi.” _American Journal of Agricultural Economics_ 103(2): 569–595; doi:10.1111/ajae.12135

- Magruder, Jeremy R. 2018. “An Assessment of Experimental Evidence on Agricultural Technology Adoption in Developing Countries.” _Annual Review of Resource Economics_ 10 (1): 299–316. https://doi.org/10.1146/annurev-resource-100517-023202.

McKenzie, David. 2012a. “Beyond Baseline and Follow-up: The Case for More T in Experiments.” _Journal of Development Economics_ 99 (2): 210–21. https://doi.org/10.1016/j.jdeveco.2012.01.002.

- ———. 2012b. “Tools of the Trade: A Quick Adjustment for Multiple Hypothesis Testing.” World Bank Development Impact Blog. 2012. https://blogs.worldbank.org/impactevaluations/tools-of-the-trade-a-quick-adjustment-for-multiple-hypothesis-testing (accessed April 11, 2021).

Nakano, Yuko, Takuji W. Tsusaka, Takeshi Aida, and Valerien O. Pede. 2018. “Is Farmer-toFarmer Extension Effective? The Impact of Training on Technology Adoption and Rice 41
**Table 6: Distribution of Learning Studies Included in each Review by Publication Status**

||**Conn**<br>**2014**|**Glewwe et**<br>**al. 2014**|**Kremer,**<br>**Brannen, &**<br>**Glennerster**<br>**2013**|**Krishnaratne,**<br>**White, &**<br>**Carpenter**<br>**2013**|**McEwan**<br>**2014**|**Murnane &**<br>**Ganimian**<br>**2014**|**Studies in this**<br>**category**<br>**across**<br>**reviews**|
|---|---|---|---|---|---|---|---|
|_Journal articles with learning outcomes_||||||||
|Number of studies|34|62|18|23|32|46|142|
|As percentage of all studies with LO in this review|61%|93%|60%|53%|48%|50%|63%|
|_Working papers with learning outcomes_||||||||
|Number of studies|15|5|11|16|33|44|73|
|As percentage of all studies with LO in this review|27%|7%|37%|37%|50%|48%|32%|
|_Reports with learning outcomes_||||||||
|Number of studies|7|0|1|4|1|2|12|
|As percentage of all studies with LO in this review|13%|0%|3%|9%|2%|2%|5%|
|Total|||||||227|

Note: LO stands for learning outcomes.

28
only whether it is possible to obtain the desired spike but also whether it is possible to co-ordinate treatment efforts in consumer countries to provide resources so that the system is able to take advantage of the short-term opportunity.

# **Strategic Location**

It is plausible to suggest that even programs that succeed in raising the price of coca and opium will fail to substantially reduce world consumption of cocaine and heroin. The reason is simply that the elasticity of retail price with respect to the price of opium or of coca paste is too low; raising Afghan opium prices by 50 percent may generate, even in Iran (a middle income neighbor of Afghanistan), no more than a 5 percent increase in retail price<sup>41</sup> and thus a very modest decline in consumption. That has important policy implications, as it suggests that control efforts will result in shifts in location rather than reductions in the volume of production. Afghanistan’s decline in production will be compensated, perhaps with a lag, by increases in production elsewhere.

Drug production then becomes a global public bad, like toxic waste disposal. Some nations will have to bear the consequences of the global demand for drugs so long as that demand cannot be suppressed. In the case of waste disposal, there are compensation mechanisms that hopefully do not distort decisions. For other global public bads, such as the sex trade, no such compensation mechanisms have been developed, in part because what is conspicuous is the revenue generated by the trade rather than the institutional and social problems that accompany it.

The global policy decisions are then whether it is (a) desirable to have production dispersed across many countries or concentrated in a few; (b) desirable to have production stably located in specific countries or moved around; (c) possible to determine which countries are likely to suffer the least bad consequences from becoming major producers and traffickers; and (d) possible to develop compensation mechanisms for those nations that end up with the industry.

_Many or few?_ It can be argued that many countries with a small opium industry will result in less total harm than a few countries each with a large industry. A few hundred opium farmers scattered across a broad area will generate only opportunistic corruption and the funds available from the farmers will not be sufficient to purchase central government protection.

> 41 The retail price of a kilo of pure heroin in Iran (sold in small and somewhat dilute units) in the year 2000 was about $5.000-10,000 at a time when the 10 kilograms of opium required to produce that kilo of heroin cost less than $500.  The figures on Iranian prices come from the UNODC _Global Illicit Drug Trends_ ; they show broad ranges.  For example, the 2002 report quoted the street price as $0.70-2.30 for a gram that was 4-20% pure.

27