rafmacalaba/datause-encoder-data huggingface dataset:
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Table 6: Poverty Reductions from New Information: Urban C6te d'Ivoire

|Initial<br>Information|Model I|N<br>Model 2|ew Informat<br>Model 3|ion<br>Model 4|Model 5|Perfect<br>Infornation|
|---|---|---|---|---|---|---|
|T=10,000,000<br>1. None (untargeted)|||||||
|a=1|0.0043<br>(-6.0%)|0.0048<br>(-6.6%)|<br>0.0044<br>(-6.1%)|<br>0.0067<br>(-9,3%)|<br>0.0071<br>(-9.8%)|0.0089<br>(-12.3%)|
|a=2|0.0030<br>(-10.8%)|0.0036<br>(-12.9%)|<br>0.0031<br> <br>(-11.1%)|<br>0.0039<br>(-14.1%)|<br>0.0045<br>(-16.2%)|0.0099<br>(-35.6%)|
|=3|0.002<br>(-15.3%)|0.0024<br>(-18.3%)|<br>0.0021<br> <br>(-16.0%)|<br>0.0025<br>(-19.1%)|<br>0.003<br> <br>(-22.9%)|0.0076<br>(-58.0%)|
|2. ModelI|||||||
|=1|_|0.0005<br>(-0.7%)|<br>0.0001<br> <br>(-0.1%)|<br>0.0024<br>(-3.5%)|<br>0.0028<br>(-4.1%)|0.0046<br>(-6.8%)|
|a=2|_|0.0006<br>(-2.4%)|<br>0.0001<br>(-0.4%)|<br>0.0009<br>(-3.6%)|<br>0.0015<br>(-6.0%)|0.0069<br>(-27.8%)|
|_a_=3|_|0.0004<br>(-3.6%)|<br>0.0001<br>(-0.9%)|<br>0.0005<br>(-4.5%)|<br>0.0010<br>(-9.0%)|0.0056<br>(-50.4%)|
|T=20,000,000<br>1,None (untargeted)|||||||
|_a_=<br>1|0.0071<br>(-10.3%)|0.0082<br>(-11.9%)|0.0067<br>(-9.7%)|0.0107<br>(-15.6%)|0.0114<br>(-16.6%)|0.0180<br>(-26.2%)|
|a=2|0.0041<br>(-15.8%)|0.0045<br>(-17.3%)|0.0037<br>(-14.2%)|0.0054<br>(-20.8%)|0.0064<br>(-24.6%)|0.0153<br>(-58.8%)|
|a=3|0.0026<br>(-21.5%)|0.0027<br>(-22.3%)|<br>0.0023<br>(-19.0%)|<br>0.0033<br>(-27.3%)|<br>0.0041<br> <br>(-33.9%)|0.0097<br>(-80.2%)|
|2. ModelI|||||||
|a=1|_<br>_|0.0011<br>(-1.8%)|-0.0004<br>(+0.6%)|0.0036<br>(-5.8%)|0.0043<br>(-7.0%)|0.0109<br>(-17.7%)|
|a=2|_<br>_|0.0004<br>(-1.8%)|<br>-0.0004<br>(+1.8%)|<br>0.0013<br>(-5.9%)|<br>0.0023<br>(-10.5%)|0.0112<br>(-51.1%)|
|_a_=3|_|0.0001|-0.0003|0.0007|0.0015|0.0071|
|||(-1.1%)|(+3.2%)|(-7.4%)|(-15.8%)|(-74.7%)|

Note: 1. Poverty reductions are In terms of the index in equation (11).

2. Figures in parentheses give the % decrease in poverty, for a fixed set of funds, due to new Information (relative to old information), and are expressed In terms of negative numbers.
This illustrates the trade-off between collecting cross-sectional data for the purpose of single-difference matching, versus collecting longitudinal data with a lighter survey instrument. An important factor in deciding which method to use is how much we know _ex ante_ about the determinants of program placement. If a single survey can convincingly capture these determinants then PSM will work well; if not then one is well advised to do at least two rounds of data collection and use DD, possibly combined with PSM, as discussed below.

While panel data are not essential for estimating _DD_ , household-level panel data open up further options for the counterfactual analysis of the joint distribution of outcomes over time for the purpose of understanding the impacts on poverty dynamics. This approach is developed in Ravallion et al. (1995) for the purpose of measuring the impacts of changes in social spending on the inter-temporal joint distribution of income. Instead of only measuring the impact on poverty (the marginal distribution of income) the authors distinguish impacts on the number of people who escape poverty over time (the “promotion” role of a safety net) from impacts on the number who fall into poverty (the “protection” role). Ravallion et al. apply this approach to an assessment of the impact on poverty transitions of reforms in Hungary’s social safety net. Other examples can be found in Lokshin and Ravallion (2000) (on the impacts of changes in Russia’s safety net during an economy-wide financial crisis), Gaiha and Imai (2002) (on the Employment Guarantee Scheme in the Indian state of Maharashtra) and van de Walle (2004) (on assessing the performance of Vietnam’s safety net in dealing with income shocks).

Panel data also facilitate the use of dynamic regression estimators for the _DD_ . An example of this approach can be found in Jalan and Ravallion (2002), who identified the effects of lagged infrastructure endowments in a dynamic model of consumption growth using a sixyear household panel data set. Their econometric specification is an example of the nonstationary fixed-effects model proposed by Holtz-Eakin et al. (1988), which allows for latent individual and geographic effects and can be estimated using the Generalized Method of Moments, treating lagged consumption growth and the time-varying regressors as endogenous (using sufficiently long lags as instrumental variables). The authors found significant longerterm consumption gains from improved infrastructure, such as better rural roads.

**_Concerns about DD designs:_** Two key problems have plagued DD estimators.  The first is that, in practice, one sometimes does not know at the time the baseline survey is implemented who will participate in the program.  One must make an informed guess in designing the

36
countries where inheritance rights remain severely biased against women.

A unique dataset containing comprehensive information on demographic characteristics, patterns of inheritance and asset ownership over three generations of individuals allows us to examine intergenerational transfers of physical and human capital following amendments in inheritance legislation in India. We find that the HSAA significantly increased women’s likelihood to inherit land, although it did not fully compensate for the underlying gender inequality. At the same time, the finding of a significant increase in girls’ educational attainment after the HSAA suggest that the Act led to genuine improvement in women’s socio-economic status, rather than a substitution away from human capital to physical capital transfers by parents to their daughters following the legislative amendment.

While this paper provides robust evidence on the impact of changes in inheritance legislation, it is not clear whether strategic behavior on the part of parents in terms of substituting property away from joint to separate could potentially disinherit daughters, leading to an adverse affect on women’s asset ownership. Moreover, in recent economic literature, dowry is viewed as pre-mortem bequests where parents transfer their inheritance to daughters at the time of marriage. In particular, if there are are institutional or legal barriers to women’s ability to inherit property, especially in virilocal societies, dowry may emerge as a culturally sanctioned method of bequest. In such a scenario, the Hindu succession act presents an interesting case, as it removes an institutional barrier of transferring joint property to daughters and recognizes women as coparceners. If credible dowry data are available, future research could accurately measure the dowry response to this legal amendment. Further micro-level research to explore the role of information dissemination, and potential mechanisms that would help in enforcing legal arrangements, would also be of relevance.

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Table 6: Employment growth and tech adoption (Extensive and Intensive Margins)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|ABF Ext|0.099***<br>(0.019)|0.099***<br>(0.020)|||||
|GBF Ext|||0.067***<br>(0.014)|0.073***<br>(0.016)|||
|SBF Ext|||||0.073***<br>(0.017)|0.068***<br>(0.017)|
|Ln (Employment 2016)|-0.092***<br>(0.017)|-0.098***<br>(0.017)|-0.093***<br>(0.014)|-0.104***<br>(0.015)|-0.090***<br>(0.018)|-0.095***<br>(0.017)|
|Observations|1,041|970|1,710|1,616|1,041|970|
|R-squared|0.072|0.096|0.069|0.098|0.067|0.090|
|Firm characteristic|NO|YES|NO|YES|NO|YES|
|Sector FE|NO|YES|NO|YES|NO|YES|
|Region FE|NO|YES|NO|YES|NO|YES|
|ABF Int|0.128***<br>(0.032)|0.109***<br>(0.036)|||||
|GBF Int|||0.149***<br>(0.025)|0.142***<br>(0.028)|||
|SBF Int|||||0.051**|0.037|
||||||(0.024)|(0.025)|
|Ln (Employment 2016)|-0.090***<br>(0.018)|-0.096***<br>(0.017)|-0.102***<br>(0.015)|-0.109***<br>(0.016)|-0.082***<br>(0.017)|-0.091***<br>(0.017)|
|Observations|1,038|967|1,711|1,617|1,038|967|
|R-squared|0.059|0.083|0.076|0.101|0.049|0.076|
|Firm characteristic|NO|YES|NO|YES|NO|YES|
|Sector FE|NO|YES|NO|YES|NO|YES|
|Region FE|NO|YES|NO|YES|NO|YES|

_Note:_ Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 When analyzing the association between technology adoption and growth for specific general business functions, we observe a positive and statistically significant association for all functions in the intensive margin. Figure 29 shows that this association is larger and more precisely estimated for the intensive use of more advanced digital technologies applied to business administration and production planning (e.g. Specialized Software and ERP systems).

One important question for the impact of technology on employment is how adoption of more sophisticated technologies affects the skill composition towards skilled workers; the skill bias technological change hypothesis. To investigate this relationship, we analyze the correlation between the technology index and changes in the skill composition of the firm based on existing occupations in 2016 and 2018. We use as a proxy for high-skill intensity the share of high-skilled (CEOs and managers, professionals, and technicians) on total workers, 37
makers in Latin America, Hirschman felt that “the best method of looking for answers was to scrutinize the record of a few specific, documented, protracted, significant policy problems” (Hirschman 1963, p. 2). This propensity for historically-grounded analyses would remain an important feature of Hirschman’s approach. As he suggested at a 1980 conference: Following in detail the process of a revolution gives us a strong feeling, as the structuralist approach does not, for the many might-have-beens of history [...]. As a result, the event-minded historian is less likely than the sociologist to declare that, given such a structural condition, the outcome was preordained. [This] emphasis on the revolutionary process [...] in effect promises to restore a few degrees of freedom we were in danger of losing to the structuralists (Hirschman 1980, pp. 171-172).<sup>5</sup> The research that resulted in Hirschman’s 1967 book, _Development Projects Observed_ , followed the same approach. Although the book does not contain the stories of the individual projects analyzed, the research had an “intensive concern with ‘cases’” and the projects considered had “an extended history.” “Immersion in the particular,” Hirschman concluded, “proved [...] essential for the catching of anything general” (Hirschman 1967a, p. 3).

This tension between the particular and the general was an answer to the widespread disorientation, among development practitioners and scholars, regarding the state of knowledge about development. General and unifying theories had given way to a much more fragmented body of knowledge, and the promise of better insights had been coupled with the feeling of a lack of vision. A 1964 Brookings Institution report on foreign aid summarized this unresolved tension: Aid is being extended, bilaterally and multilaterally, to 100 countries, democratic and authoritarian, allied and neutral, progressing and retrogressing, without satisfactory standards for evaluating competing claims, promoting particular strategies, harmonizing aid with other available instruments of policy, or appraising the results achieved. There is need for a better political, moral and economic framework within which to review our foreign aid programs and to prepare recommendations regarding their future.<sup>6</sup>

> 5 For one more testimony to Hirschman’s sensitivity to the role of historical reconstruction, see his opinion of Marx’s work: “ _The Eighteenth Brumaire of Louis Bonaparte_ was a particularly fine work. His historical books were much less orthodox than his economic ones. [...] I like to understand how things happen, how change actually takes place” (Hirschman 1998, p. 67).

> 6 The Brookings Institution, Division of Foreign Policy Studies, “Proposal for a Program of Studies Dealing with American Foreign Assistance”, April 1964 (Revised), p. 5, Barcode 1070156 - Projects and Studies - Projects Evaluation - Professor Albert O. Hirschman - Volume 1, Series No. 4225: Operations policy files - Projects and studies - General Files - General 1946-1968, World Bank Group Archives (henceforth World Bank Hirschman Folders Vol. 1).

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Data in fragile and conflict-affected states is scarce. However, legal aid programs enjoy particular success in situations where political space for other forms of justice sector support is low. A review of 199 legal aid studies found only ten that considered legal aid programs in ‘not free’ countries, indicating the scarcity of data and understanding of legal aid programs under repressive regimes (Goodwin and Maru, 2017). Scarce data demands further research in relation to legal aid programs’ medium and long-term effects, their sustainability, their development effect beyond the immediate legal need (including changes in agency), and the use of accountability mechanisms (including human rights commissions, ombuds offices, and public interest litigation) (Goodwin and Maru, 2017). We know little about legal empowerment, despite the multitude of forms it might take. Scarce knowledge surrounding the environments where legal aid programs are successful (at national or community level) demands research that codes according to demography, social norms, power differentials and other political circumstances (Goodwin and Maru, 2017). It also requires consideration of programmatically specific stakeholders and risks across the many forms of legal aid programming relating to many interactions between citizens and formal justice systems, informal justice systems, and formal and informal administrative processes.

The Commission on Legal Empowerment for the Poor found that “...four billion people around the world are robbed of the chance to better their lives and climb out of poverty, because they are excluded from the rule of law” (Albright and de Soto, 2008). We know that there is a link between the strength of the Rule of Law and levels of violence. The cost of violence, is rarely provided as justification for Rule of Law programming. Similarly, the specific economic benefits of empowerment programs are rarely calculated, let alone cited as justification for government interest in their support. Governments (and non-state actors), particularly those that have fought to capture the state, may perceive bottom-up Rule of Law programming as undermining their control over resource distribution, revenue collection and other rule of law-related levers of power (Hackmann, 2012). Persuading governments that bottom-up Rule of Law programming drives growth for all, elevates normative pressure while also appealing to realist self-interest. Increases in income among the bottom 40 percent alongside increases in equality of opportunity has a greater positive impact on growth than that of other income quintiles (Narayan et al., 2013). Inequality drives lower growth and diminished market efficiency, to a significant extent as a result of diminished equality of opportunity for its most productive asset – its people (Stiglitz, 2012).

8
13 Broadly speaking, seignorage is the benefit which accrues to the issuer of money by virtue of being able to issue interest-free currency rather than having to incur interest charges on borrowing. The total value of the currency issue in the UMOA is about $2 billion, in the BEAC zone it is about $1.3 billion. Most of the benefit of the seignorage on this currency issue should flow to member governments through the distribution of BEAC profits, but some is transferred to those who are given subsidized credit from the Central bank.

The easiest way to trace the flow of seignorage is to consider the accounts of the central banks. Taking the BCEAO 1986 balance sheet (Table 7) for example it can be seen that currency is the largest single iteml" representing over half of the BCEAO's liabilities. The principal corresponding assets are claims on banks and on the national treasuries, the latter being little mcre than one-third as important as the former. These claims are interest bearing, while the currency is not. This offers a margin which, after the operating costs of the BCEAO are deducted, represents most of the flow of benefits to member governments arisinG out of seignorage. The remainder of the seignorage is passed through the implicit interest subsidy on government borrowing and on some of the commercial bank borrowing from the BCEA020.

The most natural distribution of seignorage would be in proportion to each member's national currency issue. In accordance with the UMOA statutes, however, the BCEAO's dividend is distributed equally among the member states. Therefore were it not for the subsidy on BCEAO lending rates it would seem that we could say that the seignorage is divided equally among the countries and thus represents a progressive redistribution from the large (and rich) to 2 1 the small countries). But there is an important caveat.

19 Excluding the IMF-related items and the miscellaneous items "comptes d'ordre et divers".

20 There is not enough published information to make a precise calculation of each of the elements in the seignorage. Rough approximations based on 1987 balances and interest rates suggests that about one-third of the flow of UMOA seignorage benefits for that year came through the interest subsidies. For the BEAC the proportion was more like three-fifths.

21 The rough calculations based on 1987 suggest that Cote d'Ivoire could have taken almost 30% of the UMOA seignorage flow and Senegal 16%. In each case the take was not greater than these countries' share (50% and 16%) of total currency in circulation; but it was greater than would be implied by equal distribution (14%). Benin's estimated take (12%) was the third largest, even though it had the lowest share of currency in circulation.
# **Figure A.5. Predicted Near - Poverty Rates Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>13 16<br>12 15<br>14<br>11<br>13<br>10<br>12<br>9 11<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>16<br>12<br>11 15<br>10<br>14<br>9<br>13<br>8<br>7 12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>18 9<br>16 8<br>14 7<br>12 6<br>10 5<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicates that the estimates are statistically insignificantly different from the true near- poverty rates. Dashed lines represent the true near-poverty rates defined as living on an income between 100 and 125% of the poverty line. Dotted lines represent confidence intervals of the true nearpoverty rates. Estimates are obtained using the normal linear regression models.

62
level of exports in the first year the plant has exported. Unfortunately, we cannot use a reciprocal functional form in our regressions because both indexes of plant export experience are zero for the majority of observations in the sample.

Given our definitions of output and export experience, we need to restrict our main estimating sample to plants born in 1981 or later, the first year when information on exports is included in the census.<sup>11</sup> Besides limiting our sample to plants born in or after 1981, we require plants to have a minimum of three years of data and have positive values for the key variables output, labor, intermediate inputs, capital, and wage premium. We exclude plants that do not report data in some year between their first and last year in the survey and plants belonging to industries with less than 100 plant-year observations. In addition, given that our output and export experience measures depend on cumulative output and exports up to _t-1_ , we exclude from the estimating sample the first observation of each plant. Applying these criteria, we obtain a sample of 3,324 young plants and 16,706 plant-year observations. Finally, since our estimation procedures are sensitive to outliers, we reduce further our sample to 3,091 plants and 15,457 plant-year observations. The criteria for the elimination of outliers are described in Appendix B. To compare the effect of export experience on productivity in young and old plants, Section 6 uses a larger sample including both young and old plants. The latter plants appear continuously in the Colombian manufacturing census since 1974. This sample includes 6,171 plants and 46,574 plant-year observations.

> 11 A limitation of this procedure is that some of the “new” plants in 1981 could have actually been born before 1981, but were smaller than the cutoff level of ten employees required to fill out the census form. Similarly, if a new owner acquires a previously operating plant and registers it under a different name, it might be coded in the census as a new plant. Since we do not have information to sort out these potential sources of error, we consider plants that appear for the first time in the census as new plants.

18
23

# **_The Myth of the Dying Golden Goose_**

In recent decades, innovation, technological developments and new sources of economic growth have led some economists to question whether “manufacturing still matters”. Manufacturing’s share of global value added has declined steadily over the past nearly 30 years as the global value added of services has grown. In 1985, manufacturing’s share of global value added was 35 percent. By the late 2000s, it had declined to 27 percent. Services grew from 59 percent to 70 percent over the same period (Unido 2009). However, these trends are mainly observed in highincome countries. They can be explained by several factors: productivity increases and raising standards of living in advanced economies, which have pushed up wages and forced many industries to delocalize their production in lower-costs nations; the decrease in relative prices of consumption goods due to higher levels of efficiency in the world economy and the simultaneous growth of the demand for services such as healthcare, security, or transportation; and, perhaps even more important, the multiplier effect of manufacturing on services jobs—the development of industries everywhere automatically generates a wide variety of new economic activities, from transportation to housing, from restaurant to entertainment.<sup>14</sup> Concerns about the future of manufacturing as a viable source of economic growth have been investigated empirically by Hausmann, Hidalgo, et al. (2011) with a measure of the sophistication of an economy based on how many products a country exports successfully and how many other countries also export those products. Looking at the composition and quantity of a nation’s manufacturing, they observe that sophisticated economies export a large variety of “exclusive” goods that few other countries can produce. To do this, these economies have typically accumulated productive knowledge and developed manufacturing capabilities that others do not have. It therefore appears that national income and economic sophistication (“economic complexity”) tend to rise in tandem, and the linkage between manufacturing, economic complexity and prosperity is highly predictive, with economic complexity being much better at explaining the variation in incomes across nations compared to any other leading

> 14 A study by the U.S. Department of Commerce, Bureau of Economic Analysis, shows that manufacturing has a higher multiplier effect on the American economy than any other sector with US$ 1.40 in additional value added in other sectors for every US$ 1.00 in manufacturing value added. Source: World Economic Forum (2012).