rafmacalaba/datause-encoder-data huggingface dataset:
19 data for potential criminals, we suggest that the employment rate may capture the opportunity cost of crime. The coefficients on inequality and mean expenditure are virtually unchanged in this specification, while we find no association between unemployment and crime, other than the small negative coefficient on vehicle theft, significant only at the 10% level. These results represent our best effort to control separately for the costs and benefits of crime for potential criminals. Clearly, the mean expenditure and unemployment variables may have other, more complicated relationships to crime rates. For example, unemployment may capture not only opportunity costs for potential criminals, but also part of the value of transferable assets, and thus the benefits of crime. An insignificant or weak negative relationship between unemployment and crime is not an uncommon finding in the literature (Kelly, 2000; Ehrlich, 1973) and we revisit the issue later in the paper (see Table 9).

Next we consider the impact of the rank of a jurisdiction's wealth among its neighbors. In Table 5, we include a dummy variable equal to one if the jurisdiction has the highest per capita expenditure among jurisdictions in its criminal catchment area. Controlling for inequality and mean expenditure in own jurisdiction, and unemployment in the catchment area, burglary rates are 20% higher in jurisdictions that are the wealthiest among their neighbors. This is consistent with the hypothesis that burglars travel to neighboring areas where the expected returns are highest. The coefficient on the richest jurisdiction dummy is insignificant for other crimes. The fact that we find a significant link between a jurisdiction's relative rank and burglary, but not other crimes, is not surprising. There is no theoretical reason to suppose that violent crime should be
subscribers were living in urban areas while people in remote rural parts of China remained unconnected.

For a long time before the late 1990s, the Ministry of Posts and Telecommunications was the regulator and main operator of telecom services, and telecom monopoly seriously constrained the development of the industry. In the late 1990s, partly following the worldwide trend (Li and Xu 2004), China started telecom deregulation and liberalization by granting more administrative autonomy to the Post and Telecommunications Bureaus at the regional and local levels, by introducing more competitors to market, and by gradually opening the telecom markets to foreign investors. As a result, the service quality has dramatically improved and charges have fallen substantially, leading to a record growth in landline phone subscribers.<sup>6</sup> As shown in Figure 1, the number of landline phones per 100 people increased from less than 1 in 1990 to more than 12 in 2000, and the number continued to rise to 28.1 by 2006. Meanwhile, with the introduction of new technologies, other telecom modes, such as cellular phones and the Internet, began to penetrate China. For example, the number of cellular phone users surpassed the number of landline phone users in 2003 and peaked at 75 sets per 100 people by 2011. And the number of Internet users has increased nearly 7 times between 2002 and 2011. However, between 1993 and 2000 (our sample period), landline phones were the primary telecom tool, especially in rural areas. The exclusive reliance on landline phones in the rural areas thus allows us to focus on a single telecom technology. Moreover, because the introduction of a landline phone network was largely determined by village characteristics, as we will demonstrate later, we should be able to identify the effects more convincingly than to identify the telecom effects of mobile phones, which involves individual- or household-level selectivity to a larger extent.

> 6 Using cross-country data, Li and Xu (2004) find that both telecom privatization and competition facilitated telecom development, especially when both are done at the same time.

9
Improvements in math might come at the expense of paying less attention to other subjects, as students in the treatment group might be more likely to shift their time and efforts toward studying math. Results in **Table A6** in **Appendix A** rule out this hypothesis.

Only 59 percent of the students in the control group enrolled in the third semester in November 2021. In columns 3 and 4 of **Table 3** , we report ALEKS’ impacts on the probability of enrolling in the third semester on time. The effect is null, irrespective of whether we control for baseline characteristics or not. Among students who enrolled in the third semester, those who had the opportunity to use ALEKS display a lower probability of failing at least one course since they first enrolled in the TTC (columns 5 and 6). The effect size is considerable - as it corresponds to 45 percent of the mean in the control group and is statistically significant at the 10 percent level. The probability of failing any course is only reported for students enrolled in the third semester, thus creating a potential source of selection bias. However, due to the null effect of ALEKS on enrollment, this bias is likely to be the same for students in the treatment and the control group.

The results presented in this section show that for two (out of three) outcomes, the ITT of the ALEKS software was large and statistically significant. Addressing issues related to testing multiple hypotheses leaves our conclusions substantially unchanged. In summary, our results suggest that the possibility of using ALEKS during the first semester of higher education improved students’ math preparedness and reduced the probability of failing a class during the first two semesters. However, it did not result in increased retention, possibly because multiple factors affect this outcome.

# _6.2. Heterogeneous Effects_

To better understand which students benefited the most from ALEKS, we analyzed how the treatment effect varied across different baseline characteristics. We first study whether there are differences in ALEKS effectiveness by gender, and Panel A in **Table 4** reports the results. For male and female students, we find that ALEKS led to large and statistically significant improvements in math test scores (0.28sd and 0.26sd, respectively).

No gender-related differences exist in the treatment effect on enrollment (columns 3 and 4). There is, nonetheless, a significant difference in the treatment effects on repetition. Among male students, ALEKS led to a 14-percentage point reduction in repetition, equivalent to 54 percent of the repetition rate in the control group (column 5), while the effect on female repetition 14
# **Appendix S2: Comparison of our dataset and results to previous metaanalyses**

There are two earlier meta-analyses about financial education: The study by Miller et al. (2015) covers 19 papers due to its extremely restrictive selection criteria. Thus, most similar to our work is the study by Fernandes et al. (2014), which covers 90 effect sizes from financial education reported in 77 papers. Despite an overlap of 44% with their sample of studies, our research differs in four ways which explains our new results: (i) most important is that we analyze determinants of program effectiveness in a broader way by applying respective coding. (ii) Then we code the various outcomes per study and their respective effectiveness. Moreover, (iii) we cover recent and mostly randomized experiments providing evidence of effective interventions; and (iv) we cover additional studies focusing exclusively on financial literacy as the outcome variable. We aim to elaborate on these comparisons in this part of the Appendix.

**Comparison of studied samples** . Our selection-process (see Appendix S1) led us to a final sample of 126 independent intervention studies that report 539 effect sizes. Of these, 90 studies report 349 effect sizes on financial behavior, and 67 studies report 190 effect sizes on financial literacy. Among these 90 plus 67 studies, there are 31 studies reporting effect sizes on both financial literacy and behavior. The sample is comprised of 51 RCTs and 75 quasiexperiments.

As mentioned, Miller et al. (2015) select 19 intervention-studies for their statistical metaanalysis. Their main inclusion criterion is that interventions report on identical outcomes. This limits their analysis to sample sizes of four to six studies (and estimates) per outcome. While informative of magnitude and significance of effect sizes on identical outcomes, such an approach prevents a detailed investigation into the sources of heterogeneity, given the very limited number of studies available. However, we note that the results for size, direction, and significance of the main behaviors studied in Miller et al. 2015 are in line with our results (see Figure 2 in the main text).

40
|**Model**|**Background**|**Further Analysis**|**Aim**|**Modeling approach**|**Production Function**|**Utility Function**|
|---|---|---|---|---|---|---|
|**Diao****_et. al._ 2008**|Roe et.al.(2005)||empirically evaluate the<br>importance of conjunctive<br>management in a general<br>equilibrium setting|Extends Diaa et. al 2005 to to<br>include surface and GW as two<br>"intermediate sectors".|||
|**Beritella****_et. al.,_2005a**<br>**Beritella****_et. al.,_2005b**|GTAP (Hertel, 1997); GTAP-E<br>(Burniaux and Truong,2002)|Beritella<br>_et._<br>_al.,_<br>2005b,2006|<br>role of water resources and<br>water scarcity in the context of<br>international trade<br>to assess a series of water tax<br>policies|multi-region world CGE model|8 level, nested CES function<br>under Armington assumption|<br>3 level nested Cobb-Douglas<br>under Armington assumption|
|**Beritella****_et. al.,_2006**|||to estimate the impacts of the<br>North-South<br>water<br>transfer<br>project on the economy of China<br>and the rest of the world.||||
|**Smajgl****_et. al.,_2005**|||Show that while CGEs allow the<br>quantification<br>of<br>trade-offs<br>between economic sectors,<br>catchments and values, agent-<br>based models make land-use<br>decisions spatiallyexplicit.|combines<br>a<br>Computable<br>General<br>Equilibrium<br>(CGE)<br>model and an agent based<br>model (ABM) for integrated<br>policyimpact assessment.|Nested CES,5 Levels|Supplied by agent based<br>model.<br>Decision<br>making<br>cumulates in a preference<br>based<br>non-market<br>utility<br>function and a costs and<br>revenues based economic<br>payoff function.|

40
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and at worst negative in heavily regulated countries. Results from decadal growth regressions confirm that trade does not stimulate growth in highly regulated economies.

The paper provides one answer to the question of what complementary policies are necessary for trade to benefit an economy. Our results imply that heavily regulated countries must improve their regulatory environment. The good news is that some of the steps are relatively easy. Governments can eliminate excess procedures for registering new businesses. The advent of new technologies, such as online registration, makes this an excellent time for countries to embark on this process. However, as is always the case with deregulation, difficulties will arise because of interest groups that stand to lose from a more flexible environment.

Our finding that trade might actually hamper growth in highly regulated countries warrants further research. One possibility is that in these inflexible economies, trade tends to expand in the wrong goods as countries grow, leading to immiserizing growth. The intuition follows directly from the theory of the second best. With one distortion in place (excessive regulation), reducing a second distortion (such as trade barriers or transport costs) need not make the country better off. If the regulatory environment severely distorts domestic prices then expanded trade can lead to lower income. This would be the case, for example, in an economy where regulations raise the price of manufactured goods and tariffs raise the price of agricultural goods. As tariffs come down and trade expands, production growth would primarily occur in the manufacturing sector, which could potentially lower welfare. Provided the world relative price of manufactured products is sufficiently below the domestic price, the value of output would fall as the share of production devoted to them expands. This is akin to Johnson’s (1967) example of 23
**Table 17: Policy response to Covid-19 by IT and non-IT central banks**

||IT|Non IT|India|
|---|---|---|---|
|# of countries|27|43||
|Average policy rate at end 2019|4.70|5.27|5.15|
|Average policy rate change<br>between December 2019 and<br>May2020 (percentage points)|-1.31|-0.90|-1.15|
|Average inflation rate during<br>2019|3.13|3.19|3.7|

Source: Haver, authors’ calculations. Inflation is the monthly average during 2019. Policy rate is as of the end 2019; the change in policy rate is between end May 2020, and end December 2019 levels.

So did IT give central banks, the RBI and in general, more room for maneuver? In Table 17 we show policy rate changes between January and May 2020, together with the 2019 rate of inflation, for 74 emerging and developing countries, distinguishing IT and non-IT central banks. The cut in policy rates is larger for IT than non-IT central banks, despite the fact that IT central banks had less “space” (their policy rates started out closer to zero). The contrast is suggestive of greater anti-inflation credibility that makes for more policy room for maneuver.

**Table 18: Change in Policy Rate during the COVID Crisis**

|Dependent Variable||Change in Policy Rate||
|---|---|---|---|
||**I**|**II**|**III**|
|Inflation Targeting|-0.41|-0.48<sup>**</sup>|-0.47<sup>*</sup>|
|dummy|(1.57)|(2.04)<br>|(1.96)<br>|
|Policy rate at end 2019||-0.12<sup>***</sup><br>(2.82)|-0.09<sup>*</sup><br>(1.97)|
|Inflation at end 2019|||-0.06|
||||(0.94)|
|Constant|-0.90<sup>***</sup>|-0.25|-0.23|
||(5.74)|(1.18)|(1.02)|
|Observations|70|70|70|
|Adjusted_R_<sup>2</sup>|0.02|0.23|0.22|

Note: Robust t statistics in parentheses; *, **, ***, indicate significance at 10, 5 and 1 percent respectively.

In Table 18 we regress the change in the policy rate over the same period on a dummy variable for IT central banks, the lagged policy rate, and the lagged rate of inflation. The resulting estimates confirm that IT central banks lowered their policy rates by more, even after controlling for inflation and the level of the policy rate.<sup>47</sup> This suggests that inflation targeting, or more accurately the complex of

> 47 We find the same thing when we construct the dependent variable as the change in policy rate as percent of lagged rate.

30
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Urban infrastructure

## **Challenges**

More attention needs to be paid to urban infrastructure, particularly in slum areas. A two-way comparison between the slums of Nairobi and Dakar provides some important insights. In Nairobi, slum residents have substantially higher levels of education and employment than in Dakar, but this does not translate into better living conditions. Only 3 percent of Nairobi’s slum residents have access to a home with solid walls and a power and water connection, compared with 74 percent in Dakar. Taking a closer look at all aspects of infrastructure provision, coverage for Dakar residents was found to exceed 70 percent versus only 20 percent for Nairobi residents. The only exception was drainage services, where Nairobi residents were significantly better off (figure 10). The explanation lies in contrasting tenure arrangements in the two cities: 92 percent of Nairobi’s slum residents are tenants, and turnover is high. Because settlements are informal, neither landlord nor tenant has much incentive to invest in housing improvements. In Dakar, on the other hand, tenants are just 25.8 percent of the residents, while ownership of buildings (without land) is 13.7 percent and ownership of both land and buildings is 57.6 percent, making the population more stable over time and providing residents with the possibility of gradually investing to improve the quality of their homes.

**Figure 10. Infrastructure services reach a larger share of the residents of Dakar than Nairobi**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>piped electricity toilet sewage garbage drainage<br>water disposal pickup<br>Dakar Nairobi<br>% of population with access<br><!-- End of picture text --> Source: Gulyani, Talukdar, and Jack 2009.

# Information and communication technologies

## **Achievements**

Kenya has made substantial progress with ICT reforms. As of 2006, the country scored around 50 percent on an index of institutional reform, which is close to the African average. More recently, Kenya has privatized its fixed line incumbent, taking the reform process one step further.

19
_Journal of Environmental and Technology and Management_ 5(4): 347‐377.

- Fouquet, R. 2014. “Long‐run Demand for Energy Services: Income and Price Elasticities over Two‐Hundred Years.” _Review of Environmental Economics and Policy_ 8(2): 186‐207.

- Friedman, M. 1954. “The Reduction of Fluctuations in the Incomes of Primary Producers: A Critical Comment.” _Economic Journal_ 64: 698–703.

- Gately, D., and H.G. Huntington. 2002. “The Asymmetric Effects of Changes in Price and Income on Energy and Oil Demand.” _The Energy Journal_ 23(1): 19‐55.

- Gately, D., and S. Streifel. 1997. “The Demand for Oil Products in Developing Countries.” Discussion Paper 359. The World Bank, Washington, DC.

- Gilbert, C.L. 1996. “International Commodity Agreements: An Obituary Notice.” _World Development_ 24(1): 10‐19.

- Goeller, H.E. and A.M. Weinberg. 1978. “The Age of Substitutability.” _American Economic Review_ 68(8): 1‐11.

- Guzmán, J.I., T. Nashiyama, and J.E. Tilton. 2005. “Trends in the Intensity of Copper Use in Japan since 1960.” _Resources Policy_ 30: 21‐27.

- Halvorsen, R. 1997. “Substitution in U.S. Manufacturing.” _The Review of Economics and Statistics,_ 59(4): 381‐388.

- Hamilton, J. 2009. “Understanding Crude Oil Prices.” _The Energy Journal_ , International Association for Energy Economics, 30 (2): 179‐206.

- Hanlon, W.W. 2015. “Necessity is the Mother of Invention: Input Supplies and Directed Technical Change.” _Econometrica_ 83(1): 67‐100.

- Hayami, Y., and V.W. Ruttan. 1970. “Factor Prices and Technical Change in Agricultural Development: The United States and Japan.” _Journal of Political Economy_ 78(5): 1115‐ 1141.

- Heap, A. 2005. “China—The Engine of a Commodities Super Cycle.” Citigroup Smith Bar‐ ney, New York.

- Herman, R., S.A. Ardekani, and J.H. Ausubel. 1990. “Dematerialization.” _Technological Fore‐ casting and Social Change_ 38 (4), 333‐347.

- Hicks, J.R. 1932. _The Theory of Wages_ . New York, Macmillan.

- Hubbert, M.K. 1962. “Energy Resources.” A Report to the Committee on Natural Re‐ sources of the National Academy of Sciences‐National Research Council. Washington DC.

- Humphreys, D. 1987. “Perspectives of Intensity of Use.” _Materials and Society_ 11(3): 333‐ 347.

- Huntington, H., J. Barrios, and V. Arora. 2017. “Review of Key International Demand Elasticities for Major Industrializing Economies.” U.S. Energy Information Admin‐ istration Working Paper, Washington, DC.

_— 27 —_
experiencing either upward or downward mobility, while the rest remain in the same welfare status. These results are consistent with earlier results for the two by two transition matrix (Table 2).

A more general method to study welfare dynamics is to employ the growth incidence curve (GIC) (Ravallion and Chen, 2003), which traces out the growth rate over time for the different percentiles along the whole consumption distribution instead of discretized poverty categories. Consumption growth is then naturally considered to be pro-poor if the poorer part of the consumption distribution, which can be delineated by the poverty line, grows faster than the richer part. Figure 12 plots two GICs, one for the cross sectional data and the other for the synthetic panel data; these two GICs are also known respectively as the anonymous curve and the non-anonymous curve due to their different treatments of the longitudinal nature of the data. The non-anonymous curve is constructed based on the upper bound approach in Dang et al. (2014) and thus provides an upper bound estimate of consumption mobility. Both curves are graphed based on the changes of the median consumption of consumption deciles.

The anonymous curve is rather flat along the consumption distribution, which is consistent with our earlier finding that poverty rates remain stable over time. However, since by construction, the anonymous curve tends to underestimate growth for the poorer part of the consumption distribution while it overestimates that of the richer part,<sup>19</sup> our non-anonymous curve can provide upper bound and lower bound estimates for consumption growth. Thus taking these two curves together, the true growth for the poorer part of the consumption distribution

> 19 A simple example can help illustrate this. Given some upward mobility, some households in the poorest consumption decile in the first year moved to other richer deciles in the second year. Thus the cross sectional growth (underlying the anonymous curve) in the median consumption of the poorest decile in the two survey rounds would not capture these upward mobile households and would underestimate consumption growth for this decile. A similar reasoning results in this cross sectional growth of the richest overestimating consumption growth since some households moved down instead. The (synthetic) panel growth (underlying the non-anonymous curve) can capture these households and can provide better estimates for their consumption growth over time. See, e.g., Glewwe and Dang (2011) for a related discussion in the context of consumption growth for Vietnam.

26