rafmacalaba/datause-encoder-data huggingface dataset:
the same: the same set of fixed effects are included ( _μijs_ , _νist_ , and _νjst_ ), and we estimate the RTA-specific NTB effects for the sample that covers the years 2000 to 2015 and the three broad sectors manufacturing, agriculture, and services, we use information on tariffs from the new GTD, and include time-varying border controls<sup>L2015</sup> _t_ =2001 _Globijt_ . The approach allows us to separately identify the NTB effect for every RTA while simultaneously controlling for tariffs and other omitted variables such as general globalization trends.

Our baseline results deliver distinct NTB effects for 258 RTAs. Table 2 provides summary statistics for the 258 estimated coefficients. Besides the number of coefficients, the tables also display the mean, median, the 25<sup>_th_</sup> - and 75<sup>_th_</sup> -percentile. Table A4 in the Appendix shows the same summary statistics when only including estimates that are significant at the 90%-level and at the 99%-level, respectively.

**Table 2:** Estimated NTB-Effects: Summary Statistics

|||A<br>|ll Estim<br>|ates<br>||||95%-Lev<br>|el<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Nr.|(2)<br>Mean|(3)<br>pc(50)|(4)<br>pc(25)|(5)<br>pc(75)|(6)<br>Nr.|(7)<br>Mean|(8)<br>pc(50)|(9)<br>pc(25)|(10)<br> pc(75)|
|All|258|0.121|0.089|-0.113|0.319|83|0.308|0.376|-0.218|0.623|
|Positive|157|0.350|0.276|0.128|0.459|59|0.604|0.515|0.348|0.731|
|Negative|101|-0.235|-0.167|-0.318|-0.081|24|-0.421|-0.341|-0.467|-0.260|

**Note:** The table summarizes the results for the 258 RTA-specific NTB-effects when estimating Equation (6).

Examining the separately estimated coefficients, shows that the NTB effects are extremely heterogeneous across RTAs: first, although most of the estimated NTB effects are positive, for some RTAs they turn out to be negative. This finding is in line with Baier, Yotov, et al. (2019) who find “widely differing” effects for RTAs, too. Second, from the large difference between the 25<sup>_th_</sup> - and 75<sup>_th_</sup> -percentile as well as the large difference between the mean and median we can conclude that even within the set of RTAs featuring positive NTB effect, the magnitude varies drastically. Lastly, more than two-thirds of the RTAs do not have an effect on NTBs at all, as the number of insignificant coefficients suggests.

For 68% (175/258) of all RTAs we find insignificant NTB effects. Out of the remaining 83 significant RTA-specific NTB effects, 59 are significantly positive (5%-level) and 24 negative. Hence, we can only identify positive and significant NTB effects for 23% (59/258) of all RTAs that entered into force between 2000 and 2015. For these RTAs, the trade-creating effect due to the reduction of NTBs is large: on average, trade increases due to the RTA by 60%, and the median NTB effect translates into a 51% increase. For 9% of all RTAs, we find negative NTB effects, so, conditional on tariff reductions, countries traded less after the respective RTA entered into force. One potential explanation could be over-regulation through RTAs.

21
|of infrastructure|||||||
|---|---|---|---|---|---|---|
|Non-self average logarithm of|.235|-.026|.241|-.193|.196|-.171|
|expenditures|(.189)|(.219)|(.206)|(.174)|(.207)|(.177)|
|Non-self female education|.006|.184-|.002|.170"|.008|.171"|
||(.032)|(.042)|(.031)|_(.035)_|(.030)|_(.035)_|
|Water supply|||||.114|.255|
||||||(.240)|(.221)|
|Sanitation|||||.186|-.135|
||||||(.165)|(.167)|
|Neighborhood water supply|||||-.270|.414|
||||||(.249)|(.666)|
|Neighborhood sanitation|||||.471**|1.108-|
||||||(.232)|(.559)|
|Neighborhood water supply||||||-.584|
|squared||||||(.687)|
|Neighborhood sanitation||||||-1.317"|
|squared||||||(.604)|
|R-square|.167|.256|.191|.217||.220|
|Number of observations|799|587|1110|974||974|

Robust standard errors in parentheses. *** significant at 1 percent level. ** significant at 5 percent level. **a** Sample restricted to households with both types of infrastructure.

**b** Samnple restricted to households with neither type of infrastructure.

An analogous argument can be made regarding the rural population. Since few households in the rural areas command both types of infrastructure, the regression in column two can not be specified exactly as with the urban regression. Thus, for the rural population we confine the sample to that subset of the rural population which has neither water nor sanitation infrastructure (587 observations). In this regression we find that those rural households with neither sanitation nor piped water benefit from being located near households with both of these. We note, however, that we only observe an important neighborhood effect for a combination of water and sanitation; the coefficient of having one type of neighborhood infrastructure is not significant in column two.<sup>6</sup> Another way to allow for the fact that the impact of neighborhood access is mediated by the household's own investment is to use the full urban sample but to limit the parameter estimates to only those households that have a given level of infrastructure supply. We do this in a specification for urban areas by interacting the neighborhood _14_
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

# Why infrastructure matters

For the period 2003–08, all East African countries experienced economic growth, albeit at different rates. On average, East African countries grew at an annual rate of 5 percent, and EAC countries at 4 percent, from 2003 to the onset of the global financial crisis. During this period, infrastructure contributed one percentage point to growth among East African countries. As in other regions of Africa, most of that growth can be traced to the introduction and spread of mobile telephony (figure 1.1a). Yet unlike other regions where power infrastructure has constrained growth, in East Africa power infrastructure has contributed around 0.2 percentage points per capita to growth per year.

**Figure 1.1a . Infrastructure’s historic contribution to economic growth, 1995-05**

<!-- Start of picture text -->
1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>Telecom Power Roads<br>Source: Calderon 2008.<br>Africa<br>per capita economic growth<br>Additional percentage points of<br>North Africa West Africa East Africa Southern Africa Central Africa<br><!-- End of picture text -->

**Figure 1.1b. Infrastructure’s potential future contribution to economic growth (% GDP per capita per year)**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>North West East Africa Southern Central Africa<br>Africa Africa Africa Africa<br>Telecom Power Roads<br>Source: Calderon, 2008.<br>growth per capita<br>Potential of better infrastructure on<br><!-- End of picture text --> Infrastructure could potentially contribute much more to economic growth than it has in the past (figure 1.1b). Simulations suggest that if East Africa’s growth infrastructure were upgraded to the level of the best-performing country in Africa (Mauritius), per capita growth would increase by almost 6 percentage points. Interestingly, improvements in infrastructure make a greater contribution to per capita growth in East Africa than in the other regions of Africa. While all areas of infrastructure—ICT, power, 2
# **1 The Challenge of Measuring the Effects of Family Planning**

Many countries, especially in Africa, continue to have high fertility rates and most of the predicted increase in the world’s population until 2100 comes from these high-fertility countries (United Nations 2011). High fertility has potentially significant implications for women’s and children’s health as well as for economic development more broadly. Motivated by these concerns, policy discussions often focus on the role of family planning programs in helping individuals manage their fertility. Standard economic models of fertility decisions suggest, however, that many people in developing countries have little incentive to reduce the number of children. The cost of women’s time is low and children are potentially productive on the family farm or can serve as old age security. Furthermore, there is a lack of empirical evidence that family planning programs are effective. As a result, rather than focusing on the supply of family planning, economists instead emphasise factors that influence fertility demand such as household poverty and girls’ schooling (Pritchett 1994; Das Gupta, Bongaarts, Cleland and Joshi 2011).

The lack of convincing empirical evidence showing that family planning programs reduce fertility may be attributed to the challenge of measuring their impact. First, studies of family planning programs have often covered periods of rapid economic development and fertility decline, making it difficult to isolate the effects of family planning programs. Second, existing studies have largely ignored heterogeneous impacts, especially with regard to how family planning affects women with different education levels. Evidence from the US shows that although better-educated women are not more efficient users of modern contraceptives than less-educated women, better-educated women are more efficient at using “ineffective” contraceptive methods such as withdrawal or rhythm (Rosenzweig and Schultz 1989). The effect of family planning is therefore conceivably stronger the lower the education levels and family planning may thus substitute for education in reducing fertility at lower education levels. Finally, rigorous study is hampered by the challenge of non-random program placement (Rosenzweig and Wolpin 1986; Pitt, Rosenzweig and Gibbons 1993). If the government places programs in areas that are more “receptive” to reducing fertility, simply comparing fertility in areas with and without family planning will overestimate the impact of expanding the program. If the government places programs in high fertility areas and information on prior fertility is not available, comparing fertility across areas will underestimate the effectiveness of the program. Without information on the placement process it is difficult to assess the direction of the potential bias.
However, Moene and Wallerstein (2001, 2003) have used the 90-10 ratio (income at the 90<sup>th</sup> percentile vs. income at the 10<sup>th</sup> percentile), and Iversen and Soskice (2006) have used the 90-to-median ratio. The exact measure chosen has varied from author to author, seemingly depending on author’s preferences, availability of the data (OECD provides the 90-10, 90-50 and 50-10 gross wage ratios), or perhaps contingent on the formulation that yielded the desired result. Barenboim and Karabarbounis (2008) used three measures: gross earnings at the 90<sup>th</sup> percentile, 10<sup>th</sup> percentile and at the median, each normalized by the mean. It is hard to see which one of these numerous ways to define the median voter and to compare his or her income with income of somebody else makes more or less sense. This has lead to a fundamental problem of an insufficiently empirically circumscribed hypothesis, and to “groping” for that particular position in income distribution that the “decisive” voter might occupy as well as with whom exactly he or she might compare own income.

# **2. The redistribution hypothesis defined**

There is a way, which I used in my 2000 paper to reformulate the hypothesis in order to avoid the arbitrariness implicit in the search for the elusive “median” or “decisive” voter. My 2000 paper formulated two hypotheses that I propose to call jointly the “redistribution hypothesis”:

- (i) More market-income unequal situations are associated with greater redistribution, and,

- (ii) An increase in the market income share of a given decile is associated with a lower sharegain for that decile.

The first part of the hypothesis is easily and clearly tested by looking at, on the one hand, an inequality index (say, Gini) of market income and, on the other hand, the decrease in that index as we move from market to disposable income. We expect to find a positive relationship: the higher the initial (market) Gini, the greater its subsequent reduction, that is.

6
- Duflo, E., P. Dupas, and M. Kremer. 2011. "Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya ." _American Economic Review_ 10 (5): 1739-74.

- Duncan, G.J., and K. Magnuson. 2013. "Investing in preschool programs." _Journal of Economic Perspectives_ 27 (2): 109-32.

- Evans, D.K., and A., Popova. 2016. "What really works to improve learning in developing countries? An analysis of divergent findings in systematic reviews." _The World Bank Research Observer_ 31 (2): 242-270.

- Evans, D.K., and F. Yuan. 2021. "What We Learn about Girls' Education from Interventions that Do Not Focus on Girls." _The World Bank Economic Review._

- Evans, D.K., P. Jakiela, and H.A. Knauer. 2021. "The impact of early childhood interventions on mothers." _Science_ 372 (6544): 794-796.

- Fernald, L.C., A. Weber, E. Galasso, and L. Ratsifandrihamanana. 2011. "Socioeconomic gradients and child development in a very low income population: evidence from Madagascar." _Developmental Science_ 14 (4): 832-847.

- Fernald, L.C., E. Prado, and Kariger, P. Raikes, A. 2017. _A toolkit for measuring early childhood development in low and middle-income countries._ Washington D.C.: The World Bank.

- Galasso, E., and A. Wagstaff. 2019. "The aggregate income losses from childhood stunting and the returns to a nutrition Intervention aimed at reducing stunting." _Economics & Human Biology_ (Policy Research Note World Bank) 34: 225-238.

- Gallego, F.A., E. Näslund-Hadley, and M. Alfonso. 2021. "Changing Pedagogy to Improve Skills in Preschools: Experimental Evidence from Peru." _The World Bank Economic Review_ 35 (1): 261286.

- Ganimian, A.J., K. Muralidharan, and C.R. Walters. 2021. _Augmenting State Capacity for Child Development: Experimental Evidence from India._ (No. w28780): National Bureau of Economic Research.

- Garces, E., D. Thomas, and J. Currie. 2002. "Longer-term effects of Head Start." _American Economic Review_ 92 (4): 999-1012.

- Gelber, A., and A. Isen. 2013. " Children's schooling and parents' behavior: Evidence from the Head Start Impact Study." _Journal of Public Economics_ 101: 25-38.

- Haacker, M., T.B. Hallett, and R. Atun. 2020. "On discount rates for economic evaluations in global health." _Health Policy and Planning_ 35 (1): 107-114.

- Hamlin, J., T. Ullman, J. Tenenbaum, and N. Baker, C. Goodman. 2013. "The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model." _Developmental Science_ 16 (2): 209-226.

- Havnes, T., and M. Mogstad. 2011. "No child left behind: Subsidized child care and children's long-run outcomes." _American Economic Journal: Economic Policy_ 3 (2): 97-129.

- Heckman, J.J. 2006. "Skill formation and the economics of investing in disadvantaged children." _Science_ 312 (5782): 1900-1902.
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Power

During the Liberia’s recent civil conflict period, the country’s key generation, transmission, and distribution assets were almost entirely looted or destroyed. Prior to the conflict, Liberia’s installed generation capacity was relatively high at 67 megawatts per million of people. Today the country runs on a single new small diesel plant of 9.6 megawatts. This level of power generation capacity amounts to less than 3 mega-watts per million people—barely a tenth of what is found in neighboring countries. Moreover, access to utility power is close to zero, even in the capital city where only 0.1 percent of households are connected. Due to some prevalence of household generators, the 2007 Demographic and Health Survey reports that 3 percent of households have access to electricity. Liberia’s mining and natural resource concessionaires have significant isolated generation capacity of their own, although the exact amount is not known.

**Table 8. Benchmarking power indicators**

||Unit<br>Res|ource-rich co|untries<br>**Liberia**|Low-income countries|
|---|---|---|---|---|
|Installed power generation capacity|MW/mil. people|43.2|**2.7**|20.2|
|Power consumption|kWH/capita|205.7|**87.0**|107.4|
|Power outages|day/year|14.5|**12.7**|10.4|
|Firms’ reliance on own generator|% consumption|44.9|**97.1**|21.2|
|Firms’ value lost due to power outages|% sales|7.0|**2.9**|6.5|
|Access to electricity (from utility)|% population|46.1|**0.0**|15.0|
|Urban access to electricity|% population|79.4|**0.0**|57.6|
|Rural access to electricity|% population|28.0|**0.0**|3.9|
|Growth in access to electricity|% population/year|2.4||0.8|
|Revenue collection|% billings|81.1|**93.0**|93.1|
|Distribution losses|% production|25.8|**24.8**|23.7|
|Cost recovery|% total cost|53.9|**55.8**|84.4|
|Total hidden costs|% of revenue|168.3|**158.0**|68.8|
|U.S. cents|**Liberia**||Predominantly thermal<br>generation|Other developing regions|
|Power tariff (residential at 75 kWh)|**43.0**||14.5|5 – 10|
|Power tariff (commercial at 900 kWh)|**43.0**||18.8||
|Power tariff (industrial at 50,000 kWh)|**43.0**||14.2|5 – 10|

Source: Eberhard and others 2009. Derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

## **Achievements**

Despite high tariffs, revenue collection ratios for Liberia’s power utility are very high at 93 percent (table 8). This can largely be attributed to the practice of charging for power up front, one month ahead of actual consumption. By comparison, the utility only collected 50 percent of billed revenues prior to the war. The power utility also seems to be performing relatively well in terms of distribution losses. System 17
|**Appendix Table 9.5: Impact on Ex**|**ternal Invest**<br>|**ment**<br>|||||
|---|---|---|---|---|---|---|
||Taken on <br>new<br>debt|Made deal<br>with<br>investor|Received<br>at least<br>25,000|Amount of<br>new investment<br>received|Received<br>incubator <br>grant|Aggregate<br>index|
|**Panel A: Impact at Six Months**|||||||
|Assigned to Treatment|‐0.118**|‐0.024|‐0.032|‐11232*|‐0.036|‐0.152*|
||(0.057)|(0.033)|(0.028)|(6425.486)|(0.037)|(0.087)|
|Sample Size|276|279|277|277|269|279|
|Control Mean|0.419|0.083|0.068|13452.273|0.090|0.084|
|Control S.D.|0.495|0.276|0.253|62358.927|0.288|0.741|
|**Panel B: Impact at Two Years**|||||||
|Assigned to Treatment|‐0.059|0.050|‐0.024|n.m.|‐0.005|0.003|
||(0.048)|(0.049)|(0.041)||(0.036)|(0.080)|
|Sample Size|278|330|317||268|330|
|Control Mean|0.182|0.244|0.168||0.076|0.018|
|Control S.D.<br>Note: n.m. denotes not measure|0.388<br>d in this surv|0.431<br>ey round.|0.375||0.267|0.698|

71
Table 3: Differences By Country Data Quality Grade.

||ln(GDP)<br>(1)|ln(GDP)<br>(2)|ln(GDP)<br>(3)|ln(GDP)<br>(4)|
|---|---|---|---|---|
|Grades|All|A, B|C|D, E|
|ln(NO2)|0.116***|0.135***|0.079**|0.121**|
||(0.025)|(0.039)|(0.032)|(0.055)|
|ln(Lights)|0.031**|-0.013|0.016|0.059**|
||(0.016)|(0.012)|(0.023)|(0.029)|
|_H_0: Same Efect All Grades (_F_-Stat./[_p_-val.])|||||
|NO2:|0.69<br>[0.503]||||
|Lights:|3.00<br>[0.053]||||
|Observations|2,752|464|1,681|562|
|Countries|178|29|109|37|
|Year|05-20|05-20|05-20|05-20|
|R-squared|0.806|0.807|0.784|0.814|

_Notes._ All specifications include country and year fixed effects, and country time trends. When nights lights are used, fixed effects for either OLS (average over DMSP satellites; pre-2014) or VIIRS (2014-2020) are included. Robust standard errors, clustered by country are shown in (parentheses) and _p_ -values in [brackets]. R-Squared is adjusted and within fixed effects. Three countries, Cura ̧cao, Palestinian Territory, and Sint Maarten, are ungraded.

and Nordhaus (2011). On the other hand, the effect of NO2 is statistically significant across all grades. We reject the null hypothesis that the effect is the same across-grades for lights with a _p_ -value of 0.053, but fail to do so at any reasonable significance level for NO2. NO2 is the only signal which consistently has explanatory power for cyclical economic fluctuations—even when other signals are available—across all countries, and with a common effect size.

**Data Manipulation.** Martinez (2019) uses night lights to quantify the global extent of data manipulation in GDP. This is achieved using the Freedom in the World (FiW) index of autocracy to augment the error-measurement framework for proxy estimation.<sup>16</sup> Building on this insight, the FiW index is now added to the baseline specification in Table 2 in levels, squared, and as an interaction with lights. The effect on the interaction, known as the autocracy gradient, is the key parameter. When positive, this is indicative of data manipulation, because GDP increases more per unit increase in light radiance in autocracies than in democracies, and there is no reasonable alternative explanation but manipulation.

Table 4 Column 1 replicates Martinez (2019)’s results for annual fluctuations over 19922008. The interaction between lights and the FiW index is positive and strongly statistically

> 16Freedom in the World (FiW) index: `https://freedomhouse.org/report/freedom-world` .

18
<!-- Start of picture text --> Negative correlation between Storage Level and<br>Damage Ratio<br>200%<br>150%<br>100%<br>50%<br><!-- End of picture text --> <!-- Start of picture text --> 0%<br>2012 2014 2015<br>—=@=— Damage Ratio for Yala} <@®=—=Deviation from normal average storage for Yala<br><!-- End of picture text -->
# PRE Working Paper Series

||Author<br>tDAor||Contact<br>patpe[|
|---|---|---|---|
|WPS758 Is There Excess Co-Movement of<br>Primary Commodity Prices? A<br>Co-integration Test|Theodosios B. Paiaskas<br>Panos N. Varangis|<br>August 1991|D. Gustafson<br>33714|
|WPS759 The Profamilia Family Planning<br>Program. Columbia. An Economic<br>Perspective|Jesus Amadeo<br>Dov CherrichovsKy<br>Gabriel Oleda|August 1991|0. Nadora<br>31091|
|WPS760 How Confl;cting Definitions of<br>"Manufactures" Distort Output and<br>Trade Statistics|Alexander J. Yeats|September 1991|J. Jacobson<br>33710|
|WPS761 Uncertainty and the Discrepancy<br>between Rate-of-Return Estimates<br>at Project Appraisal and Project<br>Completion|Gerhard Pohl<br>DubravKo Mihal)ek|September 1991|P. Lee<br>81950|
|WPS762 Debt, Debt Relief, and Growtn:<br>A Bargaining Approach|Danie! Cohen<br>Thferry Verdier|September 1991|S. King-Watson<br>31047|
|WPS763 A Valuation Formula for LDC Debt|Daniel Cohen|September 1991|S King-Watson<br>31047|
|WPS764 African Financing Needs in the 1990|s Jorge Culagovski<br>Victor Gabor<br>Maria Cristina Germany<br>Charles P. Humphreys|||
|WPS765 Withholding Taxes and International<br>Bank Credit Terms|Harry Huizinga|.Srptpmhprlqql|S Kinn-Watson<br>31047|
|WPS766 Economic Crisis. Structural<br>Adjustment and Health in Africa|Francois Diop<br>Kenneth Hill<br>lsma;!<br>Sarageldin|September 1991|0. Nadora<br>31091|
|WPS767 Framework for Macroeconomic<br>Analysis (Applied to Kenya)|Colin A. Bruce<br>David Ndii|September 1991|M. Lynch<br>34046|
|WPS768 Going to Market: Privatization in<br>Central and Eastern Europe|Manuel Hinds<br>Gerhard Pohl|September 1991|L. R. Hovsepian<br>37297|
|WPS769 Entry-Exit, Learning, and<br>Productivity Change: Evidence fron<br>Chile|Lilt LiJ|September 1991|D. Baliantyne<br>37947|
|WPS770 Privatization in Eastein and Central<br>Europe: Objectives, Constraints,<br>and Models of Divestiture|Farid Dhanji<br>Branko Milanovic|September 1991|CECSE<br>37188|