rafmacalaba/datause-encoder-data huggingface dataset:
Table 5.2:

# RankMeasuresCorrelationCoefficientsUsing Different ScalesBetweenof Activity:Intensity Lower-BoundToxic Water Pollution Intensity

- ee eee [| rotai vaiue of shipments | 2-00 | o.929 | ge | vatueadea | ss] too | ce |Logs _smptoynent |e | tc

## 5.3 Medium-Specific Intensities

Medium-specific indices are useful for two reasons. First, they provide a better indication of the ecological stress and health risks imposed by pollution than estimates which do not distinguish the medium of discharge. Second, they allow analysis of the extent to which inter-medium substitution of waste disposal is possible within a given sector, an important consideration in comprehensive pollution control.

Current development of IPPS has drawn on plant-level pollution information from all of the previously mentioned US EPA pollution data bases: Toxic Release Inventory (TRI), Aerometric Information Retrieval System (AIRS) and National Pollutant Discharge Elimination System (NPDES). Using the corresponding economic data from the LRD, intensities have been calculated for 14 different pollutants. These intensities, calculated as pounds of pollutant released per unit of production in each industrial sector, are listed in Table 5.3.

40
Table 6 Income, wage and price changes, vanilla region, 1999-2001

|Decile a/|<sup>Per-capita</sup><br>income b/|Income<br>from<br>vanilla<br>sales c/|Wage<br>change<br>1999-2001<br>d/|<br>Producer price<br>change 1999-<br>2001 e/|
|---|---|---|---|---|
|1<br>|220'391|28'628|4.04|0.51|
|2<br>|337'558|32'135|0.35|-0.12|
|3<br>|427'348|45'412|3.99|4.83|
|4<br>|521'138|38'283|2.02|-0.44|
|5<br>|627'169|148'841|3.42|0.13|
|6<br>|735'808|101'400|2.46|0.87|
|7<br>|866'146|102'574|1.74|0.14|
|8<br>|1'066'679|114'097|1.96|0.63|
|9<br>|1'361'700|152'066|2.25|0.16|
|10<br>|2'462'838|257'644|0.69|0.91|

Notes a/ Ranked from poorest to richest.

b/ Decile’s average per-capita income in real (CPI-deflated) FMG, 1999.

c/ Decile’s average income from vanilla sales, real FMG, 1999.

d/ 1999-2001 wage change was calculated for pseudo-panels defined by the narrowest geographical areas for which we had observations in both years.

e/ Calculated as the decile’s average of the individual weighted-producer price change, itself _P P_ calculated as Δ _p_ = ∑ _j w_ Δ _p_ where i indexes households in the decile, j indexes crops, and _ij ij j_ _P w ij_ is the weight of crop j in household i’s income.

It can be seen that income from vanilla sales represents about 10% of per-capita income except in deciles 4 (7%) and 5 (23%). Real wage changes over 1999-2001 (the only period where vanilla producer prices went significantly up) show little reaction, with rates between 2% (toward the top of the distribution) and 4% (towart the bottom, a very slightly progressive evolution). The evolution of producer prices other than vanilla, shown in column e, is essentially noise. Together, these numbers suggest little spillover of the rise in vanilla producer prices on other prices in the region.

Table 7 shows estimates of Gini indices, poverty headcount, and poverty gap for the vanilla region (ignore for now the simulated numbers given for 2001). Starting with the poverty headcount, calculated here as the number of individuals with full incomes (i.e. overall consumption including self-produced) lower than what is necessary to purchase a 2’100 calories-a-day diet at current prices (see Paternostro et al., 1999, for a discussion). Between 1993 and 1999, the period 26
time. In particular, there is a slight increase in the levels of education achieved and a reduction in the proportion of single individuals. There is a 9%-10% reduction in informality rates between the first and second SISBEN. In our empirical strategy we look at the evolution of informality rates among those eligible for the SR relative to people who are not eligible.

# **IV Effect of the Subsidized Regime on Informal Employment**

## **Survey Data Results**

Assuming that the reform was effectively in place when the municipality began conducting SISBEN interviews, we estimate the impact of the reform on informal employment. We construct an indicator variable, _post_ , that aligns the starting date of SISBEN interviews for each city and is equal to one on and after the year when the municipality began conducting interviews. In this way, we are able to disentangle the effects of national-level reforms from health reforms. Specifically, we estimate probit models with the following specification: where the sub-indices correspond to _i_ for an individual, _h_ for a household, _j_ for a municipality, and _t_ for a year. Here, _inf_ corresponds to an indicator variable for being informal, defined as a worker who does not contribute to health insurance through employment; _γ_ represents municipality effects; and _σ_ represents year effects, which capture any reform and shock at the national level. _X_ is a vector of controls that vary across municipalities and over time, such as population, working-age population, economically active population, employment, and statelevel GDP. These controls serve to capture variation due to business cycles and labor market fluctuations. _Z_ corresponds to a vector of individual controls, such as age group, schooling, 14
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the discussion here is to focus on the empirical context and highlight features of Bangladesh’s rural economy that help address some of the confounding factors that have dogged a ‘clean’ explanation of the IR in other developing settings.

_Table 1: Characteristics of Bangladesh agriculture_

||**2000**|**2004**|**2008**|
|---|---|---|---|
|**Household characteristics**||||
|Households with farm income (%)|79.9|80.8|87.2|
|Households with non‐farm income (%)|83.1|89.1|77.4|
|HHs with both farm and non‐farm income (%)|62.9|69.9|64.5|
|Family size|5.40|5.23|4.94|
|Number of earners|1.56|1.63|1.58|
|Number of agricultural workers|0.89|0.93|0.84|
|Number of non‐agricultural workers|0.67|0.69|0.73|
|Female heads of household(%)|5.89|6.94|13.53|
|**Land**||||
|Total owned land (ha)|0.53|0.48|0.47|
|Total cultivated land (ha)|0.42|0.38|0.32|
|Per capita cultivated land (ha)|0.07|0.07|0.06|
|Per agric. worker cultivated land (ha)|0.42|0.37|0.35|
|Proportion of irrigated land|0.66|0.77|0.80|
|**Inputs and mechanization**||||
|Percent of cultivator HHs using fertilizer|96.8|96.4|97.7|
|Percent of cult. HHs using high‐yield varieties|83.9|86.6|84.5|
|Percent of cultivator HHs mechanized|66.2|82.3|88.7|
|Percent of HHs with electricity|46.1|61.3|82.5|
|Agricultural capital/agric. worker (2008 BDT)|8,158|8,434|11,758|
|Non‐agric. capital/non‐agric. worker(2008 BDT)|15,523|11,514|12,939|

_Source:_ Authors’ calculation using the 62-village panel survey _. Note_ : The averages for each characteristic are calculated across all rural households in the survey (i.e., farm and non-farm households). BDT=Bangladesh Taka One striking feature of Bangladesh’s agriculture is the widespread use of technology as embodied in modern inputs – almost all households use fertilizer, a vast majority have adopted high yielding varieties, and have increasingly mechanized over time. Mechanization is not necessarily with owned machinery (though, on average, farm capital has gone up). Most households hire mechanization services, the market for which has grown rapidly, allowing even the poor to costeffectively rent in services that were once presumed out of their reach (due to capital constraints or simply the small scale of farming preventing large investments in lumpy technology). These indicators are important, as one of the counter-arguments for the IR is that it may hold in 6
and where θ�� is the population weight for cell g at time t, v�� is the variance in cell g at time t, y�� is the average log wage in cell g at time t and y� is the average of the log wage at time t. The within component measures variation in wages within a group, whereas the between component measures variation across groups. We use two definitions for group g. The first is an age/education group and the second is an age/education/industry group. The results are reported in Table 4.

Three main results emerge from Table 4. The first is rising inequality between 1990 and 2000 and falling inequality between 2000 and 2010. The second main result is that most of the inequality in Mexico (in every period and sample) is within, rather than between age/education groups. For example, the within component makes up over 81% of total inequality for the whole sample in 1990 (0.646/0.791=81.7%). The third is that differences between industries contribute little to overall inequality. The differences between the top three rows and the bottom three rows is smaller than the differences in the between and within components of inequality.

Wage densities in Panel A of Figure 9 show falling inequality: the 2010 distribution of log wages is more compressed than the distributions of earlier years. The log-wage residuals from a Mincerian wage equation follow a similar pattern of falling inequality over time. Panel B of Figure 9 shows a compression of the residuals in 2010 relative to earlier years.

In Panel A of Figure 10 the changes in the distributions over time show that the frequency of values close to zero increased, which is consistent with the fall in inequality. This pattern is clearer with the residual plots in Panel B of Figure 10. Values farther from zero became less frequent and values closer to zero became much more prevalent.

_Household Surveys_ 18
and others (2011) find that the evidence supports the existence of an effect of food prices on weight outcomes, but that those price effects are small. Mytton, Clarke, and Rayner (2012) advocate taxes on unhealthy food, but they estimate the tax needs to be at least 20 percent to have a significant impact on health. Overall, taxing unhealthy food is more problematic than taxing alcohol and tobacco because while those two products are not necessary for survival, food is a necessity and in many contexts the cheapest food is not the healthiest.

The use of financial incentives to discourage obesity has also gained in popularity, but the evidence regarding efficacy is decidedly more mixed (see e.g. Follick and others., 1984; Jeffery and others, 1978; and Jeffery and others, 1984). For example, Volpp and others (2008) found significant weight loss from a lottery-based incentive program, but it was not sustained 4 months after the program’s end; similarly, John and others (2011) observed that matched commitment contracts led to significant weight loss after 36 weeks, but again it was not sustained during a 32-week post-incentive period. A systematic review of randomized controlled trials of treatments for obesity (Paul-Ebhohimhen and Avenell, 2007) showed no significant effect of the use of financial incentives on weight loss or maintenance at 12 months and 18 months. However, further sub-analysis indicated that large transfers (greater than 1.2% of personal disposable income) had greater impact, as did incentives for behavioral change rather than weight loss, per se, and rewards based on group performance rather than individual results.

One recent example emphasizing behavioral change is the study by Loewenstein, Price and Volpp (2016) conducted in U.S. elementary schools testing whether providing short-run incentives could create habit formation in matters of diet composition in children. Over a 3- or 5- week period, students received an incentive in the form of redeemable tokens for eating a serving of fruits or vegetables during lunch. Relative to an average baseline rate of 39%, providing small 17
- Lo Bello, S., M. L. Sánchez-Puerta, and H. Winkler. 2019. “From Ghana to America: The Skill Content of Jobs and Economic Development.” World Bank Policy Research Working Paper 8758, World Bank, Washington, D.C.

- Mazza, Jacqueline, Wendy Cunningham, Nga Thi Nguyen, and Audrey Stienon. 2019. “Functional Review of Employment Services Centers in Vietnam: Analysis and Recommendations.” World Bank, Washington, D.C.

- Moroz, Harry, Nga Thi Nguyen, and JJ Naddeo. 2021. “Digital Skills in a Digitizing Vietnam: Assessing the Demand for Digital Skills in Vietnam _._ ” World Bank, Washington, D.C.

- Moroz, Harry, Nga Thi Nguyen, and Lan Chu. 2019. “Skills Profiling of Priority Occupations in Vietnam _._ ” World Bank, Washington, D.C.

- Moroz, Harry, Nga Thi Nguyen, and Lan Chu. 2019b. “Using Targeted Labor Market Information Tools To Improve Employment Services in Vietnam.” World Bank, Washington, D.C.

- OECD. 2016. _The Survey of Adult Skills: Reader’s Companion._ Second Edition. OECD Skills Studies. Paris: OECD Publishing. <u>http://dx.doi.org/10.1787/9789264258075-en</u>

- Peterson, N.G., M.D. Mumford, W.C. Borman, P.R. Jeanneret, and E.A. Fleishman (Eds.). 1999. _An Occupational Information System for the 21st Century: The Development of O*NET_ . American Psychological Association. <u>https://doi.org/10.1037/10313-000</u>

- Pierre, G., M.L. Sanchez Puerta, A. Valerio, and T. Rajadel. 2014. _STEP Skills Measurement Surveys Innovative Tools for Assessing Skills_ . Social Protection and Labor Discussion Paper no. 1421. Washington, DC: World Bank.

- Rivkin, D., P. Lewis, and S. Cox. 2001. _Pilot Test Results: Testing Subject Matter Expert Methodology for Collecting Occupational Information for O*NET._ O*NET Center.

- Tsacoumis, S., and C.H. Van Iddekinge. 2006. _A_ c _omparison of Incumbent and Analyst Ratings of O*NET Skills._ HumRRO.

- World Bank. 2022. “The Impact of Digital Transformation on Outputs and Labor in Vietnam.” World Bank, Washington, D.C.

- World Bank. 2021. _Indonesia’s Online Vacancy Outlook 2020. Technical Report: from online job postings to market intelligence._ Washington, DC.

World Bank. 2021b. _Vietnam: Adapting to an Aging Society_ . Washington, D.C.: World Bank.

27
more likely to report willingness to give part of their income for the environment (77 percent compared with 51 percent). They were also more likely to be amenable to tax increases if the government provided environmental protection. Furthermore, the Peruvians interviewed were no less likely than Americans to “agree strongly” with these statements, a potential proxy for the level of commitment to environmental action, suggesting that strength of norms or additions is unlikely to provide a clear explanation for the null result. Other environmental questions in the survey asked respondents about their views on the importance of various local and global environmental problems. The respondents surveyed in the United States exhibited no more concern about these issues than those in Peru. Although these numbers can provide only a rough proxy for specific attitudes about recycling, they suggest that this is not an obvious rationale for the differing results between earlier American trials and our study. Potentially, however, because these numbers reflect more serious global and local environmental concerns, they may not accurately capture views on recycling. Peruvians who are faced with more serious problems close to home might feel that recycling is much less serious than other concerns and may be less willing to expend energy on it.

A possible related explanation is that the messages, which were modeled on those from several different studies in the United States, failed because the subjects of this experiment had norms to which they wanted to conform. For instance, they may have felt more or less desire to conform to the expectations of the church or a local authority. Once again, the World Values Survey allowed us to examine very rough proxies for several of the messages on the basis of church and authority (national instead of local), and there was no evidence that Peruvians felt less strongly than Americans. This argument also seems less valid than other possibilities because none of the messages had any significant 19
**Table 14: Impact on Income by Pre-Reform Occupation**

||D<br>|ependent variab<br>Logincome<br>|le:|
|---|---|---|---|
||Nom|inal|Real|
||(1)|(2)|(3)|
|SARE*Past registered owner|-0.0317*|-0.0387**|-0.0353**|
||(0.0180)|(0.0172)|(0.0165)|
|SARE*Past non-regist. owner|-0.0177|-0.0062|-0.0038|
||(0.0211)|(0.0213)|(0.0212)|
|SARE*Past wage earner|-0.0047|-0.0065|-0.0035|
||(0.0082)|(0.0074)|(0.0082)|
|R-squared|0.376|0.382|0.376|
|No. of municipalities|34|20|20|
|No. of observations|570,154|419,709|419,709|

_Note:_ Standard errors in parentheses (clustered at municipality level). Past occupation variables are pre-reform occupations when the person was first observed. The regressions drop all individuals who were initially not employed since their past income is not observed. Regressions include quarter and municipality fixed effects, as well as dummies for firm characteristics interacted with quarter and municipality fixed effects, and individual background variables. They also include 1999 municipality log GDP per capita, log number of economic establishments per 1000 capita, log fixed assets per capita, and log investment per capita, all interacted with a linear time trend. Column 1 includes all 34 municipalities from the main sample. Columns 2 and 3 include only the 20 municipalities for which the price index is available. Significance levels: * 10%, ** 5%, *** 1% 46
# **VI. Conclusion and Policy Implications**

While interest rate caps are often implemented to lower the burden of high interest rates on borrowers, such policies can lead to unintended consequences that negatively impact both the aggregate banking sector and borrowers themselves. The analysis presented in this paper highlights some of the short-term impacts on the Kenyan economy after the interest rate caps of 2016.

While we cannot definitively attribute the slowdown in loan growth and compositional changes among Kenyan banks to the interest caps alone, we certainly do not find any economic attenuation effects that many advocates of the interest rate caps promote. Instead, we find a continued downward trend in loan growth and adverse compositional changes in loan and deposit maturity after the caps. These findings are consistent with a parallel cross-country study examining recent trends in the use of interest rate caps (Ferrari, Masetti, and Ren, 2017). Analyzing trends across five countries, the authors find that while interest rate caps result in lower nominal rates, they also lead to higher non-interest fees and commissions, lower transparency, and reduced credit supply especially for small and risky borrowers.

From a microeconomic perspective, our analysis highlights detrimental impacts on financial inclusion, with the crowding out of SME borrowers. Future analysis should focus on the longerterm impacts of the interest rate caps as more data become available from formal banks. Also, to fully understand the effects on financial inclusion, additional data from private sector associations and informal lending markets would greatly help expand the impact analysis.

From a policy perspective, our analysis suggests that interest rate caps can have important and unintended consequences in the economy. There are several policy alternatives that can both protect borrowers from excessive interest rates and limit the negative consequences of interest rate caps.

Foremost, reforms aimed at improving the efficient functioning of financial markets can work to counteract perennially high interest rates for borrowers. For example, the universal adoption of credit scoring and sharing can improve bank lending policies. A credit scoring system can allow banks to properly price loans rather than charge the same rate to all customers. This reform, coupled with a well-functioning credit bureau can further improve pricing transparency among banks, and broadly lower interest rates.

In addition to increasing the risk-pricing ability of lenders through more robust credit reporting, risk premia can be reduced by more efficient loan foreclosure procedures, including in the space of small claims, summary procedures for uncontested debt, and other resolution procedures that reduce the costs of debt collection. Reforms around movable collateral can also reduce interest rate charges, especially for SMEs. Allowing firms and individuals to leverage movable property as a security against debt, and simultaneously promoting a registry to make public those claims can provide lenders more confidence in lending, and borrowers more incentives to repay.

16
point Likert-type scale that government health services had gone from much worse to much better over the previous year, with higher values on the scale associated with perceptions of greater improvements in of the quality of government health services. Again we average these responses to EA level to create our second dependent variable. We consider public perceptions of the quality of government health services an important area of inquiry separate from actual usage of these services. The decentralization process in Sierra Leone was at least partially motivated by the belief that many grievances during the civil war were related to the centralized nature of the state, leading ultimately to a lack of government accountability and low levels of trust among citizens. Because fiscal decentralization was intended to increase public knowledge of government led service provision, whether citizens actually observe and approve of changes in these services is an important indicator of the success decentralization initiatives, and, therefore, a potentially important antecedent to trust in local government (see Sacks and Larizza 2011).

Our third dependent variable is access to public education services, which will indicate the degree to which our findings are consistent across different types of public goods. We measure the provision of public education as whether families with school age children report that their children attended government schools on the 2011 NPS. Although local councils did not receive direct transfers for education until 2007 (Casey 2009), this function was devolved to the cohort of councilors included in our surveys, causing us to expect a substantive impact in this policy area as well. Ideally we would also include a dependent variable measuring variation in public goods that remain in the domain of the chiefdoms, such as security and the maintenance of order, to assess whether power-sharing dynamics affect the provision of these goods. Unfortunately, the NPS does not include indicators of this nature.

**Table 1** reports the correlations between our three dependent variables, which indicate the extent to which the three measures of service delivery are related to each other six years after the Local Governance Act initiated the process of decentralization. We note that the use of government health services and government schools are moderately positively correlated in our data, suggesting public investments in government and education generally move together. Further these values are not correlated in 2008 (r = -0.007), suggesting that the correlation in the 2011 usage levels are related to simultaneous improvements in public health and education provision at the council level.

14