rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: The Price of ICT relative to NICT and Income Per Person <!-- Start of picture text --> (A) Relative Price (2011) (B) Change in Rel. Price. (2005-2011)<br>-8 -6 -4 -2 -7 -6 -5 -4 -3<br>Real GDP Per Capita Real GDP Per Capita<br>2.5 1<br>2<br>.5<br>1.5<br>0<br>Price of ICT Relative to NICT 1<br>Change in Price of ICT Relative to NICT: 2005-2011 -.5<br>.5<br><!-- End of picture text --> _Notes:_ Panel A illustrates the relationship between _rpj_ as defined in equation (1) for 160 countries based on ICP price data from 2011. Panel B illustrates the change in the relative price if ICT. Panel A uses the USA as the benchmark country. In panel panel B we use the UK as the benchmark country because we do not have ICP price data for the US in 2005.

of computers but we do not have data on the price of a sewing machine; instead, we have data on the price of a vehicle (a larger non-ICT item). It would be meaningless to compare the ratio of the computer price to the non-ICT item, because the vehicle represents a more expensive item. However, if we compare the price of computers relative to a benchmark country to the price of the non-ICT item relative to a benchmark country (where we use the US as a benchmark), we are capturing some notion of whether there is a premium associated with ICT items, on average.<sup>12</sup> Panel A of Figure 3 documents a negative correlation between _rpj_ and log real GDP per capita. This suggests that, relative to non-ICT capital goods, ICT capital goods are relatively more expensive in low income countries. We further compute the growth rate in _rpj_ between 2005 and 2011 for a small number of countries for which item level data is available in both ICP waves.<sup>13</sup> Using this measure, Panel B of Figure 3 suggests that, while there is a weak negative correlation between the change in _rpj_ and log income per-capita, the relationship is not statistically significant. Note

> 12We have also experimented with alternative reference countries and find similar results. We report the US here because most of the extant research on ICT prices has thus far focused on the US and it therefore serves as a natural benchmark.

> 13Since data is not available for the US in 2005, we use the UK as a benchmark for this exercise.

14
exclude the observations from the lower half of the schooling distribution to capture the idea that a higher achieving adult is more likely to be the role model a child looks up to and aspires to emulate.

The estimates for the investment equation in Table 7A show that the impact of role models on the educational expenditure is different from that of peers in the urban areas. The coefficient _θm_ is _negative_ and significant at the 1 percent level _; the role model effect is thus a substitute for educational expenditure made by parents in urban Indonesia, implying that δ_ 6<sup>_m<_0</sup><sup>_._In contrast, the evidence in the rural sample cannot reject the null hypothesis</sup> of separability in the education production function, similar to the evidence on peer effect discussed earlier.

The results on intergenerational mobility in Table 7B show that the interaction of role models with father’s education is negative and statistically significant both in rural (at the 5 percent level) and urban (at the 1 percent level) areas. The inclusion of the role model effects in the mobility equation, however, does not reduce the convexity with respect to father’s education.

# **(8) Conclusions**

We incorporate the rural-urban differences in returns to education and school quality in a Becker-Tomes model to study the rural-urban divide in intergenerational educational mobility in Indonesia. Following Becker et al. (2018), the model includes an interaction effect between parent’s education and financial investment in children’s schooling production function which can give rise to a convex CEF. Using household data free of the truncation bias due to coresidency restrictions in surveys, we find that the linear CEF widely used in the current literature is rejected in favor of a convex CEF, both in the rural and urban areas. The rural-urban relative mobility curves cross; while the children from less educated households enjoy higher relative mobility in rural areas, the pattern reverses for the higher educated households. The rural children face lower absolute mobility across the whole distribution of father’s education.

The evidence on the intercepts of the investment and mobility equations when interpreted in terms of the theory suggests that school quality is a substitute for parental invest27
Table 3: Effect on the <u>quantity</u> of alternative contraceptives delivered, by type

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Injectable|Short-|acting|Long-|acting|
||DMPA-IM|Monthly Pill|Condom &<br>Morning Pill|Implants & IUD<br>Introduced|Implants & IUD<br>Removed|
|**_Treat x Post_**|**-5.06**|**-8.67**|**12.15**|**-2.70****|**-0.33**|
||**(5.90)**|**(6.51)**|**(33.50)**|**(1.05)**|**(0.28)**|
||**[0.159]**|**[0.052]**|**[0.494]**|**[0.004]**|**[0.064]**|
|Post|-3.48|2.94|-17.98|4.01***|0.16|
||(4.65)|(4.23)|(26.92)|(0.80)|(0.19)|
|Treat|11.91*|7.88|-27.99|0.55|0.28|
||(6.85)|(5.49)|(38.08)|(1.03)|(0.37)|
|Observations|6,139|6,139|6,139|6,139|6,486|
|Baseline mean|95.37|43.03|293.91|10.11|2.19|

Notes: The table presents results based on the estimation of Equation 1, where the outcome concerns quantities of contraceptives described in the column titles. Standard errors, presented in parentheses, are clustered at the health center level. The stars next to the estimated coefficients follow the usual convention (*** p<0.01, ** p<0.05, * p<0.1). To interpret the statistical significance of the interaction term coefficient, we correct for multiple hypothesis testing following the procedure outlined by Clarke (2021). More specifically, we use the Stata command -rwolf2with 250 bootstrap replications to obtain Romano-Wolf p-values which are presented in brackets.

# **6 Discussion**

Our estimates should be considered lower bounds for the effects of the intervention on SP injections and delivery of alternative methods in the presence of potential spillover effects, such as information spreading from treated health centers to control health centers. While the observed (lower) positive trend in SP adoption for the control group (as indicated by the coefficient of the binary post-intervention dummy variable in Table 2) was anticipated due to the ongoing expansion of the introduced innovation, it is possible that such spillover effects also played a role in the positive trend of SP injections in the control group. The risk of information spillover becomes more pertinent towards the final period of our analysis, as control units were closer in time to receiving the intervention themselves.

The study’s main limitation is related to data constraints and the restricted time horizon available for the analysis. First, the inability to distinguish zero values from missing information in our data implies that our analyses suffer from measurement error in the dependent variable, which yields inflated standard errors. Second, we are unable to ascertain whether women initiating SP receive timely renewals, i.e., maintaining uninterrupted contraceptive protection, since we cannot track them over time with aggregated health facility data. Third, we do not have information on women’s demographics in our data. Consequently, we cannot 16
17 growth and estimate that, if the United States had experienced the same growth as Canada, the cost of providing rail services in 1974 would have been US$13.8 billion (1985 dollars) lower.<sup>7</sup> After railroad deregulation in the United States, Willig and Baumol (1987) estimated that between 1980 and 1985 annual operating expenses dropped 26 percent while traffic volume remained virtually unchanged. Deregulation of the rail sector also led to increases in investment.

Table 6

**Welfare Gains from Deregulation in the United States in 1990 (in Billions of 1990 Dollars)**

|_Industry_|_Consumers_|_Producers_|_Total_|_Further_<br>_Potential Gains_|
|---|---|---|---|---|
|Airlines|8.8-14.8|4.9|13.7-19.7|4.9|
|Railways|7.2-9.7|3.2|10.4-12.9|0.4|
|Road Freight|15.4|(4.8)|10.6|0|
|Telecommunications|0.7-1.6||0.7-1.6|11.8|
|Cable Television|0.4-1.3|-|0.4-1.3|0.4-0.8|
|Brokerage|0.1|(0.1)|0|0|
|Natural Gas||||4.1|
|**Total**|32.6-43.0|3.2|35.8-46.2|21.6-22.0|

Source: Winston (1993) 7 While average total productivity growth for Canadian railroads during the period was 3.3 percent per year, it was only 0.5 percent for U.S. railroads.
24

- We always compute skilled birth attendance and antenatal care utilization rates for births in the past two years. The comparison databases, by contrast, use births in the reference period of the original survey question – e.g. births over the last 5 years for DHS, over the last year in the second MICS wave, and over the last 2 years from the third MICS wave onwards.

- We apply a consistent definition of full immunization in terms of both required vaccines and the age by which a child has to have received them to be considered fully immunized, whereas the vaccines and age-groups vary within and across the DHS and MICS comparison databases. For example, some MICS surveys measure full immunization at age 18-29 months and others at age 12-23 months, whereas we consistently use the 15-23 months age-group.<sup>23</sup> Also, full immunization rates in the MICS comparison databases vary in whether they consider pentavalent vaccination as an alternative to standard DPT vaccination, while we consistently consider it an alternative.

- As mentioned above, we use a consistent definition of ARI (cough and rapid breathing) across all DHS surveys to define the sample for which we compute our ARI treatment indicator.<sup>24</sup>

- We use a consistent definition of modern contraception methods across all surveys in the database which includes the lactational amenorrhea method (LAM). LAM is considered a modern method in STATcompiler and DHS reports but not in MICS reports and the MICS data in WHO’s Health Equity Monitor.

> 23 We depart from the 12-23 months age-group that is used in most DHS and MICS reports to account for the fact that immunization schedule age-ranges for the first dose of measles vaccination vary internationally from 9 to 15 months. Countries with higher measles prevalence are recommended to immunize children at an earlier age, despite the vaccines being more effective when administered later (World Health Organization 2017).

> 24 Our definition of appropriate care-seeking for children with ARIs excludes _other_ public or private providers since it is unclear if a medical consultation took place.
17

# **Table 5 Actual and Conditional Log Hourly Earnings Distributions**

||1<br>(1)<br>_Actual_|988<br>(2)<br>Conditional|(3)<br>_Actual_|1992<br>(4)<br>Conditional|(5)<br>Conditional at<br>1988 means|(6)<br>_Actual_|19<br>(7)<br>Conditional|98<br>(8)<br>Conditional at<br>1988 means|(9)<br>Conditional at<br>1992 means|
|---|---|---|---|---|---|---|---|---|---|
|Q10|_-0.788_|-0.478|_-0.805_|-0.526|-0.444|_-0.550_|-0.246|-0.461|-0.369|
|Q25|_-0.293_|-0.043|_-0.256_|-0.084|-0.013|_-0.086_|0.148|-0.010|0.073|
|Q50|_0.351_|0.431|_0.294_|0.368|0.458|_0.498_|0.577|0.453|0.560|
|Q75|_1.080_|0.924|_0.987_|0.833|0.949|_1.173_|1.019|0.927|1.062|
|Q90|_1.920_|1.408|_1.706_|1.293|1.430|_1.935_|1.464|1.393|1.550|
|Q90-Q10|_2.708_|1.886|_2.511_|1.819|1.874|_2.485_|1.711|1.854|1.919|
|Q75-Q25|_1.373_|0.963|_1.243_|0.917|0.962|_1.258_|0.871|0.937|0.988|
|Q90-Q50|_1.569_|0.977|_1.412_|0.925|0.971|_1.437_|0.888|0.940|0.990|
|Q50-Q10|_1.139_|0.909|_1.099_|0.894|0.903|_1.048_|0.823|0.914|0.929|

Source: computed from results reported in Table 2
after the agreement. The upward pressures related to scale gains are compensated by downward pressure from the arrival and survival of cleaner technology.

# **Figure 3. Decomposition in total emissions – Alternative Scenario**

<!-- Start of picture text --> Pollution decomposition (output) – change in levels Pollution decomposition (output)– change in rates<br>CA CA<br>SLV SLV<br>Scale Scale<br>NIC NIC<br>Composition Composition<br>HDN Technique HDN Technique<br>CRI CRI<br>-20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00<br>Pollution decomposition (export) – change in levels Pollution decomposition (exports)– change in rates<br>CA CA<br>SLV SLV<br>NIC Scale NIC Scale<br>Composition Composition<br>HDN Technique HDN Technique<br>CRI<br>CRI<br>-20.00 -10.00 0.00 10.00 20.00<br>-30.00 -20.00 -10.00 0.00 10.00 20.00 30.00<br><!-- End of picture text --> Source: IPPS and authors calculations The decomposition analysis therefore suggests that the overall direction and size of changes in emissions are largely dependent on assumptions about changes in technology. Nevertheless, some interesting findings arise from the two extreme scenarios:

- Scale effects play a major role in explaining changes in emissions levels and trends after the agreement.

- Composition effects are small and vary in direction across countries.

This effect is the focus of attention of the next exercise. While there seems to be no strong evidence supporting the pollution haven hypothesis in Central America, the exercise shows significant gains from the adoption of cleaner technologies.

# _Sector Composition Exercise_

Table 1 presents the results of the regression analysis measuring changes in the composition of outputs and exports, by sector, for the CA4. The results indicate a positive trend in the relative growth of dirty industries before the beginning of DR-CAFTA negotiations. The trend disappears in the period after.

17
Figure 1: 9th - 1st Decile of Male Return Differences from Quantile Regressions across European Countries and US <!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>9th - 1st decile<br>Portugal Austria USA UK Switzerland Sweden France Finland Netherlands Ireland Spain Norway Denmark Italy Germany Greece*<br><!-- End of picture text -->

- Sources: Pereira and Martins 2000; Martins and Pereira 2004 * Results for Greece based on after tax earnings

Figure 2: 9th - 1st Decile of Male Return Differences from Quantile Regressions Across Countries Outside Europe and Noth America <!-- Start of picture text --> 20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>S.Africa white Chile Brazil Bolivia Colombia Guatemala Singapore Argentina Venezuela Mexico Korea* Indonesia Philippines Mongolia China S.Africa black** Thailand Vietnam Ethiopia Cambodia<br>9th - 1st decile<br><!-- End of picture text --> Sources: Mwabu and Schultz 1996; Girma and Kedir 2005; Lee and Lee 2002 * The 95<sup>th</sup> to 5<sup>th</sup> quantile difference is reported as authors estimates are over the 5<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 95<sup>th</sup> quantiles; also the equation included several controls ** For higher education only The question is what explains the patterns of decreasing returns by earnings quantile. We offer three possible explanations for the deviation from the stylized facts observed in OECD countries. (1) The first is that more job mobility in developed countries explains the differences. That is, higher levels of schooling and higher levels of unobserved skills (therefore higher levels 15
Policy Research Working Paper 8752

# **Abstract**

In the past decade, hundreds of impact evaluation studies have measured the learning outcomes of education interventions in developing countries. The impact magnitudes are often reported in terms of “standard deviations,” making them difficult to communicate to policy makers beyond education specialists. This paper proposes two approaches to demonstrate the effectiveness of learning interventions, one in “equivalent years of schooling” and another in the net present value of potential increased lifetime earnings. The results show that in a sample of low- and middle-income countries, one standard deviation gain in literacy skill is associated with between 4.7 and 6.8 additional years of schooling, depending on the estimation method. In other words, over the course of a business-as-usual school year, students learn between 0.15 and 0.21 standard deviation of literacy ability. Using that metric to translate the impact of interventions, a median structured pedagogy intervention increases learning by the equivalent of between 0.6 and 0.9 year of business-as-usual schooling. The results further show that even modest gains in standard deviations of learning—if sustained over time—may have sizeable impacts on individual earnings and poverty reduction, and that conversion into a non-education metric should help policy makers and non-specialists better understand the potential benefits of increased learning.

This paper is a product of the Office of the Chief Economist, Africa Region and the World Development Report 2018 Team. 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:// www.worldbank.org/research. The authors may be contacted at devans2@worldbank.org and fyuan@g.harvard.edu.

_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
liberalization and the associated direct and indirect secondary effects have benefited the poor. Although we are not able to test empirically what has happened to the welfare level of agricultural wage earners, secondary evidence suggests that they have gained from trade liberalization. That income of non-farmers in rural areas has increased would support this claim.

Despite important data constraints related to the comparability of the 1995/96 and the 2000 surveys, our results shed some light on the somewhat small reduction in overall poverty despite substantial economic growth in Bangladesh during the 1990s. Indeed, the major positive impact of the trade reforms analyzed in the paper falls on the rural landless farmers. This category featured Bangladesh’s highest incidence of extreme poverty in the early 1990s so that the major impact of trade reform may have been to lift a large number of extreme poor to the poverty category. This issue deserves further investigation. The current household survey being finalized could provide useful insights into it.

25
|**variables used to measure Constitutional changes**<br>**% of workers working overtime hours**<br>1983<br>1984<br>1985<br>1986<br>1987<br>1988<br>1989<br>1990<br>1991<br>1992<br>1993<br>1994<br>1995<br>1996<br>1997<br>1998<br>1999<br>2000<br>2001<br>Formal<br>Non Formal<br>**Union enrollment**<br>1986<br>1992<br>1993<br>1995<br>1996<br>1997<br>1998<br>1999<br>ts given by the size of each industrial sector). Definitions of plotted<br>antee Fund). Basically, under the FGTS every firm in Brazil has to<br>01) in an account opened in the worker’s name in a state bank. Other<br>s in case they are fired without a “justified reason”. In this case, they<br>y the employer.|
|---|
|**the**<br>0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>25<br>26<br>27<br>28<br>29<br>30<br>31<br>32<br>33<br>34<br>weigh<br>Guar<br>er 20<br>count<br>ctly b|
|**Figure 8. Evolution of Trade Openness and of**<br>**Trade Openness**<br>1983<br>1984<br>1985<br>1986<br>1987<br>1988<br>1989<br>1990<br>1991<br>1992<br>1993<br>1994<br>1995<br>1996<br>1997<br>1998<br>1999<br>2000<br>2001<br>2002<br>**0**<br>**2**<br>**4**<br>**6**<br>**8**<br>**10**<br>**12**<br>Effective Tariffs<br>**Imports Penetration**<br>**Years of tenure of fired workers**<br>1983<br>1984<br>1985<br>1986<br>1987<br>1988<br>1989<br>1990<br>1991<br>1992<br>1993<br>1994<br>1995<br>1996<br>1997<br>1998<br>1999<br>2000<br>01<br>20<br>With FGTS<br>Without FGTS<br>Notes:<br>Figures correspond to weighted averages of all industrial sectors (with<br>variables correspond to those described in Table 7.<br>FGTS = Fundo de Garantia de Tempo de Serviço (Length of Service<br>deposit 8% of its current formal employees wages (8.5% since Septemb<br>than some exceptions, workers can only withdraw money from these ac<br>have access to the FGTS account balance plus a firing penalty paid dire|
|0<br>20<br>40<br>60<br>80<br>100<br>120<br>1.0<br>1.5<br>2.0<br>2.5<br>3.0<br>3.5<br>4.0|
resource concerns and new technologies will reduce demand for traditional raw materials, as well as encouraging substitutions between them. Based on current trends, metals and foods consumption growth could slow by one-third over the next decade. Energy consumption growth would remain broadly constant at post-crisis rates, and shift towards faster-growing EMDEs. Aluminum and copper consumption would continue to grow steadily. Rice and wheat consumption growth is expected to slow as population growth slows, while rising incomes would result in a shift to foods such as meat, which require growing inputs of maize and soybeans. These trends have already become evident in advanced economies, and a similar path could be expected for the major EMDEs. More modest commodity consumption growth, all else equal, would dampen pressures on prices.

Advances in global technology, shifts in consumer preferences, and policies to encourage cleaner fuels could trigger much steeper slowdowns in global use of some commodities than current trends indicate. A rapid shift away from investment-driven and industrial production-driven growth in China could sharply lower its demand for metals. Similarly, a tightening of environmental regulations could reduce coal use more than in the baseline. Improved technologies (such as electric cars), lower costs of alternative fuels, and policies favoring cleaner fuels, could reduce the use of petroleum in transportation. However, they could also increase demand for raw materials used in the production of these technologies, such as rare earths.

Many EMDEs, especially smaller ones, are heavily exposed to commodity markets. The prospect of persistently lower demand heightens the need for commodity exporters to diversify. Over the medium term, diversification away from resource-based production would help raise GDP per capita and improve growth prospects for commodity-exporting EMDEs. Cross-country studies underscore that greater diversification of exports and government revenues bolsters long-term growth prospects and resilience to external shocks (Lederman and Maloney 2007; Hesse 2008; IMF 2016a). The successful diversification experience of some energy producers (e.g., Malaysia, Mexico) highlights the benefits of both vertical diversification (e.g., in crude oil, natural gas, and petrochemical sectors) as well as horizontal diversification. These involve reforms to improve the business environment, education, and skills acquisition (Callen et al. 2014).

16