rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

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- Anderson, S., Lazicky, C., and Zia, B. (2019). Measuring the Unmeasured: Combining Technology and Behavioral Insights to Improve Measurement of Business Outcomes. _World Bank Working Paper Series_ .

- Anderson, S. J., Chandy, R., and Zia, B. H. (2018). Pathways to Profits : The Impact of Marketing vs. Finance Skills on Business Performance. _Management Science_ , pages 1–25.

- Appadurai, A. (2004). The Capacity to Aspire: Culture and the Terms of Recognition. In Rao, V. and Walton, M., editors, _Culture and Public Action_ , pages 59–84. Stanford University Press, Palo Alto, CAL.

- Bandiera, O. and Rasul, I. (2006). Social Networks and Technology Adoption in Northern Mozambique. _The Economic Journal_ , 116:869–902.

- Beaman, L., Duflo, E., Pande, R., and Topalova, P. (2012). Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India. _Science_ , 335(6068):582–586.

- Beaman, L., Yishay, A. B., Magruder, J., and Mobarak, A. M. (2018). Can Network Theory-based Targeting Increase Technology Adoption? _Cowles Foundation Discussion Paper_ , 2139.

- Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. _Journal of the Royal Statistical Society. Series B (Methodological)_ , 57(1):289–300.

- Berg, G. and Zia, B. (2017). Harnessing Emotional Connections to Improve Financial Decisions: Evaluating the Impact of Financial Education in Mainstream Media. _Journal of the European Economic Association_ , 15(5):1025–1055.

- Bernard, T., Dercon, S., Orkin, K., and Taffesse, A. S. (2014). The Future in Mind: Aspirations and Forward-looking Behaviour in Rural Ethiopia. _CEPR Discussion Paper_ , 10224.

- Bernedo, M., Ferraro, P. J., and Price, M. (2014). The Persistent Impacts of Norm-Based Messaging and Their Implications for Water Conservation. _Journal of Consumer Policy_ , 37(3):437–452.

- Beshears, J., Choi, J. J., Laibson, D., Madrian, B. C., and Milkman, K. L. (2015). The Effect of Providing Peer Information on Retirement Savings Decisions. _Journal of Finance_ , 70(3):1161– 1202.

- Bloom, N., Brynjolfsson, E., Foster, L., Jarmin, R., Patnaik, M., Saporta-Eksten, I., and Van Reenen, J. (2019). What drives differences in management practices? _American Economic Review_ , 109(5):1648–83.

- Bloom, N., Eifert, B., Aprajit, M., McKenzie, D., and Roberts, J. (2013). Does Management Matter? Evidence from India. _Quarterly Journal of Economics_ , 128(1):1–51.

21
**<u>Table 4: QUAIDS Parameter Estimates (Part 2)</u>**

||**All**|**Rur**|**al**|**Urb**|**an**|
|---|---|---|---|---|---|
|||Poor|Non-<br>Poor|Poor|Non-Poor|
|\gamma_{cereal_cereal}|-0.085***<br>[0.006]|-0.101***<br>[0.007]|-<br>0.170***<br>[0.007]|-0.112***<br>[0.005]|-0.067***<br>[0.011]|
|\gamma_{meat_cereal}|0.059***|0.083***|0.117***|0.064***|0.056***|
||[0.004]|[0.011]|[0.006]|[0.006]|[0.008]|
|\gamma_{oil_cereal}|0.005**|0.010***|0.002|-0.017***|-0.027***|
||[0.002]|[0.003]|[0.003]|[0.002]|[0.007]|
|\gamma_{tuber_cereal}|0.097***|-0.002|0.054***|0.064***|0.083***|
||[0.005]|[0.017]|[0.014]|[0.017]|[0.015]|
|\gamma_{oth._cereal}|-0.075***|0.010***|-0.004|0.001|-0.045**|
||[0.008]|[0.004]|[0.015]<br>-|[0.018]|[0.020]|
|\gamma_{meat_meat}|-0.081***|-0.004|0.096***|-0.080***|-0.059***|
||[0.003]|[0.027]|[0.009]|[0.008]|[0.011]|
|\gamma_{oil_meat}|-0.001|-0.008|-0.003|0.016***|0.002|
||[0.002]|[0.008]|[0.004]|[0.003]|[0.006]|
|\gamma_{tuber_meat}|0.031***|-0.078***|-0.017|0.029*|0.049***|
||[0.007]|[0.025]|[0.020]|[0.015]|[0.018]|
|\gamma_{oth._meat}|-0.007|0.006|-0.001|-0.028|-0.048**|
||[0.009]|[0.009]|[0.019]|[0.017]|[0.022]|
|\gamma_{oil_oil}|-0.004***|-0.028***|-0.002|-0.008***|0.036***|
||[0.001]|[0.003]|[0.003]|[0.002]|[0.010]|
|\gamma_{tuber_oil}|0.011***|0.023**|0.009|0.005|-0.052***|
||[0.004]|[0.010]|[0.008]|[0.009]|[0.019]|
|\gamma_{oth._oil}|-0.010*|0.002|-0.006|0.004|0.041**|
||[0.005]|[0.003]|[0.008]|[0.009]|[0.017]|
|\gamma_{tuber_tuber}|-0.049***|0.092***|0.064|0.021|0.078**|
||[0.012]|[0.030]|[0.040]<br>|[0.029]|[0.031]|
|\gamma_{oth._tuber}|-0.089***<br>[0.011]|-0.035***<br>[0.013]|-<br>0.110***<br>[0.027]|-0.119***<br>[0.020]|-0.158***<br>[0.028]|
|\gamma_{oth._oth.}|0.181***|0.016***|0.122***|0.143***|0.210***|
||[0.011]|[0.005]|[0.028]|[0.021]|[0.041]|
|Observations|21,239|6,766|4,785|5,743|3,945|

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 Source: 2012 1-2-3 Survey, DRC Tables 3 and 4 present estimates of the parameters of the QUAIDS for rural and urban households by poverty status. Most estimates are statistically different from zero, showing an overall good fit of the model. Estimated parameters differ in magnitude and sometimes in sign between rural and urban areas, highlighting the need to conduct a stratified analysis by area of residence and poverty status. These estimated parameters do not have a straightforward economic interpretation. The fact that most  coefficients are statistically different from zero further supports the choice of the QUAIDS that captures well the nonlinear relationship between the budget share devoted to aggregate commodities and logged expenditure, which is noticed in figures 3.2a-3.2e.

15
# **6. The Intensity based Targets for Atmospheric Stabilization**

It would be interesting to investigate how much cuts in emission intensities would be required if the Scenario 1 and 2 were to achieve through intensity targets meaning that de-carbonization of economies without sacrificing economic outputs (i.e., GDP)<sup>17</sup> . Figure 5 presents percentage reductions of CO2 intensities required in 2020 and 2030 from current levels to cause global CO2 emissions decline by 2025 (Scenario 1) and by 2020 (Scenario 2).

In order to cause global CO2 to decline by 2025 (Scenario 1) without negatively affecting the expected economic growths, CO2 emission intensities of most Annex I countries and China need to be reduced by 50 percent or more from their 2004 levels by 2025. The CO2 intensities of these countries plus India are required to be cut by more than 60 by 2030 to maintain that declining trend of global CO2 emissions through 2030. If the global CO2 emissions were to decline by 2020 (Scenario 2) without sacrificing economic outputs, the CO2 intensities of most Annex I countries plus China and India would be required to be cut by more than 40 percent in 2020, more than 50 percent in 2025 and more than 65 percent in 2030.

Since CO2 intensities are also expected to decline in BAU case, the additional cuts in CO2 intensities above their BAU varies significantly across countries/regions. For example, under Scenario 2, most OECD Annex I countries would require to further cut their CO2 intensities 50 percent above their BAU reductions in 2030. On the other hand,

> 17 More precisely, without impacting negatively the expected (or BAU) economic (i.e., GDP) growth.

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12 system, such as early retirement ages and wide coverage, have remained intact and why discussions on possible reforms have remained limited. Thus the first basic question to examine is whether this view is realistic and whether the country can restore the old pension benefit schemes once the economy recovers? If not, two questions arise: would the pension system be sustainable if one were to keep its most recently introduced features or should one envisage deeper structural reforms ?

# 4.1. Methodology and Basic Assumptions

# _A. The Model_

To address these questions, a simulation model has been constructed. It integrates four modules: pension fund, labor market, macroeconomic, and demographic modules (see **Appendix** A for a detailed description of the model). The model covers the period 1995-2030 and provides a consistent framework for analyzing the balance of the Pension Fund under current provisions as well as for assessing the impact of various policy reforms. It allows for taking into account the interactions between the social security system and other key economic variables The Pension Fund module calculates the Fund balance for each year based on expenditure and contribution data provided by the other modules. The number of pensioners depends on demographic factors and eligibility rules set by the pension system. The level of contributions to the fund depends not only on the level of employment, wages and the social security tax but also on the relative importance of the informal sector. It is assumed that workers in the informal sector do not contribute to the pension fund although they may be future beneficiaries either because they have accumulated rights in the past (prior to, or in the early stages of the transition) or because they are eligible for social pensions under the present set of regulations. The Pension Fund is linked to the consolidated government account. So a large and persistent deficit can undermine fiscal balance and threaten macroeconomic stability.

The labor market module defines trends in employment and real wages responding to the evolution of macroeconomic conditions. It calculates total labor income in the formal economy. This in turn determines the revenues of the Pension fund for a given level of the social security tax. The flow of workers exiting the labor force and eligible for retirement determines the stock and age composition of pensioners. The allocation of workers between the formal and informal sectors responds to changes in relative wages (net of social security contributions) in these two economic sectors.

The production sector is divided into two parts: formal and informal. It is assumed that shifting production to the informal sector reduces overall economic efficiency. So the effect of investment on growth declines as the size of the informal sector expands. Lower efficiency in the informal sector originates from greater uncertainty and ill-defined property rights which raise
of Inpres schools in West Sumatra, and compare and contrast with the estimates from the other primarily patrilineal provinces separately in Tables 6 (panel B) for higher secondary completion. The estimates are strikingly dierent between the matrilineal vs. patrilineal islands. The crowding out of girls we observed at the national level seems to be driven solely by the patrilineal islands, while there is no negative impact for daughters in West Sumatra. The sons in West Sumatra experienced a signicant negative impact of Inpres at the secondary level, consistent with the expectation of a reversal of gender-based crowding out observed in the patrilineal islands (and at the national level).

# (8) Alternative Models of Mobility: Normalized Schooling and Ranks

Our analysis so far is based on years of schooling as a measure of children's nal educational attainment. However, it is sometimes argued that years of schooling divided by generation specic standard deviation of schooling is preferable because the normalization makes crosssectional inequality constant (equal to 1) across generations (Salvanes (2023), Torche (2019)). Some recent studies on intergenerational educational mobility adopt ranks in schooling distribution in each generation, following the inuential work on intergenerational income mobility in the United States by Chetty et al. (2014) (see, for example, Hilger (2015) on the United States, Asher et al. (2023) on India, and Andrade and Thomsen (2018) on Denmark and the United States). A recent paper by Ahsan et al. (2022) provides a comparative analysis of these alternative models of educational mobility with empirical evidence from China, Indonesia, and India. They show that the common argument that the rank-rank model removes the changing inequality across generations is valid for income but not for education. Percentile ranks in schooling distribution calculated by mid rank method often fails to equalize inequality across generations, because, unlike income, schooling is a discrete variable with limited support. They also show that the shape of the rank-based CEF may be fundamentally dierent from the husband joins the family of the wife after marriage. A substantial anthropological literature suggests that matrilocal (and neolocal) kinship norms in South East Asia including Indonesia are important for better health and educational outcomes of women, especially when compared to the patrilineal and patrilocal kinship norms in South Asia (Dube (1997)). Evidence on the importance of kinship norms for women's education in the more recent cohorts in Indonesia is, however, conicting: Levine and Kevane (2003), using 2000 IFLS, nd no signicant dierences between matrilocal vs. patrilocal communities, but Rammohan and Robertson (2012), using the 1997 round of IFLS, report that patrilocal residence has a negative eect on women's educational attainment in Indonesia. Recent evidence also indicates signicantly higher decision making autonomy for women in matrilineal tribes in Indonesia. See, for example, the evidence based on the 2000 round of IFLS by Rammohan and Johar (2009).

27
on the standard indicators of banking and capital market development. Benchmarking based on the correlates of financial development in other developing countries reveals a substantial gap between predicted and actual levels of African financial development. In addition, both country-level and firm-level tests indicate that the determinants of banking development in Africa differ from the rest of the world in telling ways.

For example, inflation and the current account balance explain no variation in African financial development although they do so in other developing countries, and measures of institutional development explain less variation in Africa than elsewhere. Why this collection of macroeconomic fundamentals is not strongly linked to African financial development remains unclear. However, if these macroeconomic and institutional mechanisms could be made to function, our results suggest that the levels of financial development would rise.

Most importantly, population density appears to be more important in Africa than elsewhere, and our firm-level study on the access to external financing indicates that population density is linked more to bank branch penetration in Africa than in the rest of the developing economies. We also find a nonlinear relationship between population density and banking sector development, with the largest gap between Africa and other developing countries observed for those African countries ‘trapped’ in the low density area. Presumably, bank branch penetration figures remain low in Africa because of difficulties in achieving minimum viable scale in sparsely populated, low-income areas. Therefore, technological advances, such as mobile banking, that enable users of financial services to be located far away from their financial institutions, provide a promising way to facilitate African financial development outside major cities, a topic that has been studied in the context of Kenya, where the mobile payments services of M-PESA are now widely used (Mbiti and Weill, 2010; Jack and Suri, 2010).

38
**Table 3: Estimation results.**

||(1)<br>Trade Costs|(2)<br>Endowments|(3)<br>Institutions|(4)<br>No Transport|(5)<br>No Construction|
|---|---|---|---|---|---|
|log�distanceij�|-0.202***|-0.214***|-0.221***|-0.259***|-0.234***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|contiguousij|0.026|-0.004|-0.003|0.078|-0.004|
||(0.534)|(0.928)|(0.952)|(0.189)|(0.942)|
|common official languageij|0.027|0.049|0.048|0.072*|0.049|
||(0.331)|(0.120)|(0.129)|(0.052)|(0.163)|
|colonyij|0.168***|0.135***|0.125***|0.130**|0.115**|
||(0.000)|(0.004)|(0.010)|(0.038)|(0.026)|
|RTAij|0.132***|0.146***|0.142***|0.143***|0.156***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|log(high−skill labori)<br>∗log(high||||||
|−skill intensityk)||0.020***|0.018**|0.017*|0.006|
|||(0.006)|(0.017)|(0.058)|(0.547)|
|log(non−ICT capital stocki)||||||
|∗log(non−ICT capital intensityk)||0.041***|0.039***|0.028*|0.039***|
|||(0.001)|(0.002)|(0.055)|(0.003)|
|log(internet users per 100 peoplei)||||||
|∗log(ICT capital intensityk)||0.021|0.065**|0.065**|0.079***|
|||(0.407)|(0.018)|(0.041)|(0.006)|
|legali∗complexityk|||0.115***<br>(0.000)|0.026<br>(0.359)|0.212***<br>(0.000)|
|legali∗Herfindahlk|||0.061***<br>(0.000)|0.099***<br>(0.000)|0.107***<br>(0.000)|
|Observations|39879|32008|31524|28163|28140|
|Pseudo-R2|0.389|0.379|0.380|0.430|0.392|

_Note: The dependent variable is exports in all cases. Estimation is by Logit with fixed effects by exporter, importer, and sector. Prob. values based on standard errors corrected for clustering by country pair are indicated in parentheses below the coefficient estimates. Coefficients are reported as marginal effects at the mean in all cases. Statistical significance is indicated as follows: * (10%), ** (5%), and *** (1%)._ 35
Our use of epidemiologic studies also assumes that the spatial relationship between pollution monitors and population that exists in the original studies is generally similar in Jakarta. Thus, with these assumptions, the relationship between the levels of air pollution and subsequent health effects in the cited studies can be extrapolated to estimate the health impact in Jakarta.

# m. REVIEW OF HEALTH EFFIECTS AND PROVISION OF DOSE-RESPONSE FUNCTIONS

## A. Selection Critena

For this report, dose-response fimctions have been identified and adapted from published epidemiologic and economics literature. These functions allow the estimatiou of the change in health effects that would be expected to occur with changes in ambient pollution levels. For each health effect, a range is presented within which the estimated effect is likely to fall. The central estimate is typicafly selected from the middle of the range reported in a given study, or is based on the most recent study using the most reliable estination methods available. When several different studies are available for a given health effect, the range reflects the variation in results observed across the studies. When only one study is available, the range is based on the statistical confidence that can be placed on the reported results.

The reported epidemiologic investigations involve two principal study designs: statistical inference based on time-series and cross-sectional dat sets. Time-series analysis examines changes in a health outcome within a specific area as air pollution levels fluctuate over time. A cross-sectional analysis compares the rate or prevalence of given health outcomes across several locations for a given point in time. The time-series studies have the distinct advantage of reducing or elimiating the problems associated with confounding or omitted variables, a common concem in the cross-sectional studies. Since the population characteristics are basically constant over the study period, Ebe only factors that may vary with daily mortality are environmental and meteorologic conditions. In general, researchers are able to
from the rest of the world.<sup>15</sup> Our focus is supply driven export surges, and condition (c), that market share abroad increases, makes this unlikely. Still it could be the case that both supply and demand forces are at work. If import growth from the rest of the world increases dramatically during the surge then foreign income growth or foreign liberalization may be an important part of the story. Figure 8 shows average and median total imports and import growth from the rest of the world. For both developing and developed countries there is some evidence of higher import growth especially in the first year of the surge, but growth is very volatile. In addition, for developing countries import growth slows markedly as the surge progresses. This suggests that import demand is not a major factor for maintaining surges in developing countries.

In sum, the exchange rate displays the clearest and strongest relationship with the export surge, especially in developing countries. On average, a 30 percent real depreciation precedes export surges in developing countries. Sizeable undervaluation and reduced volatility also accompany the surge.

# **V. Composition**

The purpose of this section is to understand how the real depreciation influences trade patterns using disaggregated 4-digit SITC (Rev. 2) bilateral trade data. The large real depreciation of the exchange rate has three potential effects. First, it makes existing domestic goods cheaper abroad, increasing demand for them. This is the standard effect. Second, it makes entries into new markets or new export products more profitable, which would generate more entries. And third, for a given level of entries, it reduces the

> 15 The top 5 markets for each exporting country were identified by calculating the share of each importing country (destination market) in the exporting country’s total trade during the period between 1985 and 2006 and selecting the five markets with the highest share throughout this period.

15
Table 1: The ICT sector in the West Bank

||ISIC|industry|Estab.|Employed|
|---|---|---|---|---|
|Repair of computers and peripheral equipment|9511|S|244|325|
|Repair of communication equipment|9512|S|183|340|
|Other telecommunications activities|6190|J|160|282|
|Wired telecommunications activities|6110|J|96|1,337|
|Wireless telecommunications activities|6120|J|88|1,888|
|Computer programming activities|6201|J|79|957|
|Radio broadcasting|6010|J|47|454|
|Television programming and broadcasting<br>activities|6020|J|25|351|
|Publishing of newspapers, journals and peri-|5813|J|22|244|
|odicals|||||
|Other information technology and computer<br>service activities|6209|J|18|112|
|Motion picture, video and television pro-|5911|J|18|87|
|gramme production activities|||||
|Data processing, hosting and related activities|6311|J|13|101|
|News agency activities|6391|J|9|57|
|Computer consultancy and computer facili-<br>ties management activities|6202|J|8|132|
|Publishing of books|5811|J|6|22|
|Sound recording and music publishing activities|5920|J|5|15|
|Other Publishing activities|5819|J|4|10|
|Satellite telecommunications activities|6130|J|3|6|
|Other information service activities n.e.c.|6399|J|3|22|
|Manufacture of measuring, testing, navigat-|2651|C|1|1|
|ing and control equipment|||||
|Manufacture of communication equipment|2630|C|1|0|
|Motion picture, video and television pro-|5912|J|1|6|
|gramme post-production activities|||||
|Web portals|6312|J|1|1|
|Total, excluding maintenance and trade|||520|4,197|
|Total|||947|4,862|

_Notes:_ In the column “industry,” we follow the ISIC classifications for the sections: C is manufacturing, J is information and communication, and S is other service activities. _Sources:_ PCBS, authors’ calculations.

11