rafmacalaba/data-use-mentions-extended huggingface dataset:
The short-term effects showed that the ban reduced work incidence in paid work activities by 4.1 p.p. or 27 percent. It seems the ban reduced the proportion of boys doing paid work and studying and increased the proportion of boys only studying. We found that these effects lasted for five years, from 1999 to 2003 when boys hindered from entering the formal labor force at age 14 reached age 18.

We pooled the PNADs of 2007 to 2014 to estimate the long-term effects of the ban on educational attainment and labor-market outcomes when the cohorts were age 22 to 29. Our pooled effect estimates suggest the ban did not affect those average outcomes among the entire affected cohort in the long term. It thus seems that the higher proportion of boys only studying just counterbalanced the lower level of accumulated labor-market experience. However, it seems to have detrimental effects on a sub-group of the affected cohort that will likely have lower earnings when adults.

It is important to contextualize the findings of this paper. First, we estimated ITT effects, that is, the average effect of the child-labor ban on the entire cohort of boys who turned age 14 in 1999. Since the child-labor ban affected a small share of affected boys (4 p.p.), the ITT estimate captures the lower bound of a local average treatment effect (LATE), that is, the effect on those who dropped out of the labor force at ages 14 or 15 because of the ban.

Second, although we are confident of the internal validity of our results, one should be cautious to extrapolate to different age groups or cohorts. This cohort faced a particular set of constraints and policies that are not the same faced by other cohorts.

One should not read it as an implicit advocacy towards child labor but as a cautionary note that the law should be designed according to the context and compensatory policies—such as a conditional cash-transfer program and/or an apprenticeship program—should be put in place; otherwise it would make individuals worse off by
analysis. Specifically, educational indicators are averaged over two periods (1975-80 and 1996-2002) and health indicators over two periods (1996-99 and 2000-02). This discrepancy in the sub-period construction is due exclusively to the lack of data for earlier years. The averages are treated as separate observations. The advantages of this approach are threefold. First, the averages may serve as a better measure of the educational and health attainment, which can hardly be substantially improved on a yearly basis; Second, the averaging maximizes the coverage of countries for each period, since one observation of a certain year is sufficient to help the country survive in the cross sectional comparison; Third, the time series thus constructed for each country, although short, facilitates the implementation of econometric techniques on panel data to explore the efficiency variations across countries and through time.

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studies the relationship between survival and observable producer characteristics, namely innovation-related variables, as a way to test the implications of industry dynamics models.<sup>3</sup> First, our dataset allows constructing objective plant-level time-varying measures of product innovation - categorical and continuous - based on the observation of whether a product is newly manufactured by a plant in any year. This is a clear advantage relative to previous studies that mostly use measures of innovation based on subjective perceptions of managers for a cross-section of firms taken from innovation surveys.<sup>4</sup>

> 1 See OECD-Eurostat (2005) for a discussion of different types of innovation: product innovation, process innovation, managerial innovation, and organizational innovation.

> 2 See Gourville (2006) on the failure of new product introductions. Famous examples of failed product launches include New Coke by Coca Cola and Sony‟s Betamax.

> 3 See Doms et al. (1995), Chen (2002), Disney et al. (2003), and Shiferaw (2009) and Manjon-Antolin and ArauzoCarod (2008) on the determinants of firm survival and Esteve-Pérez et al. (2004), Hall (1987), Cefis and Marsili (2006), and Zhang and Mohnen (2011) on the on the role of innovation for firm survival.

> 4 Mairesse and Mohnen (2010) point to the problems with innovation surveys‟ subjective measures that rely exclusively on firm perceptions of whether they have introduced innovations at the process or product levels. They

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are similar without trimming.

> 23 Since we have many control variables, and the coverage of both the Enterprise Survey and cross-country sources differ for some countries, dropping the observations with any variable missing would result in the loss of the majority of the sample, and render our empirical exercises mush less representative.  For variables with significant number of missing observations, we thus resort to imputation, using the predicted value based on the following basic controls: the country-level urbanization level, the regional dummies (Africa, East Asia and Pacific, East Europe and Central Asia, South Asia, Middle East and North Africa), the manufacturing dummy, and firm size dummies (10-20, 20-60, and 60+ employees).  Why do we use the manufacturing dummy in imputation when our analysis largely focuses on the manufacturing subsample?  While we largely use the manufacturing subsample for most of analysis, we do use the service subsample in some sensitivity checks.

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**Figure 17: Export prices, Import prices and Terms of Trade in Togo (Base 2000=100), 1960‐2005.**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>Exports price index (goods and services, 2000 = 100) Imports price index (goods and services, 2000 = 100)<br>Terms of trade (goods and services, 2000 = 100)<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> _Source_ : WDI data, World Bank.

# **4.6 Conclusions from and Implications of the Development Dynamic Analysis**

- Togo’s immediate post independence period (1960‐1973) of prudent, modernizing‐oriented and market‐ friendly economic policies were followed by a decade of interventionist, protectionist and expansionary policies that ended with the international oil shock and external debt crisis in the early 1980s. Economic reforms under an IMF‐sponsored financial stabilization program in the 1980s did not result in increased and sustained growth, mainly because of the burden of debt, economic mismanagement, and confrontations between population and the government, which ended up in removing the political elite in the early 1990s. Social unrest, public authority dysfunction, economic mismanagement and perception of corruption of public officials remained until late 1990s.

- The 2000s has constituted a decade of market oriented economic reforms that have been commended by international financial institutions (IMF, 2009). Prudent fiscal and monetary policies have been set in place. The structural reforms have stressed fiscal governance, financial sector restructuring and public enterprise reform, all with an emphasis on measures that more directly impact economic growth. Yet, reforms have not yielded the expected growth. Consecutive food, energy price increases and the
Chart 3 Characteristics of Rate Cycles: January 1970–May 2024 <!-- Start of picture text --> Duration Number of Rate Changes<br>150 30<br>100 20<br>50 10<br>0 0<br>Tightening Easing Tightening Easing<br>Average Pace Initial Velocity<br>4 3<br>2 0<br>0 -3<br>-2 -6<br>Tightening Easing Tightening Easing<br>Amplitude (total rate change)<br>16<br>8<br>Range (10 - 90 percentile)<br>0 Mean<br>Median<br>-8<br>-16<br>Tightening Easing<br>Months<br>Rate change (in pp)<br>Number<br>Rate change over first 6 months (in pp)<br>Total rate change (in pp)<br><!-- End of picture text --> Sources: Calculated based on the rate cycles defined in the text, with data from January 1970 through May 2024. Notes: The number and pace of rate adjustments only include "in-sync" rate adjustments, i.e., the rate increases for tightening phases and decreases for easing phases. Initial velocity and amplitude are the total changes in rates (in any direction) over the first six months of the phase or the entire phase, respectively. Members of the euro area are included as individual countries through 1998, and then the euro area is included from 1999.

A closer look at the statistics other than the means and medians, as well as individual economy information in Table 1, provide additional insights on these patterns. Although the durations of the shortest tightening and easing phase are similar (around 25 months), the longest easing phase (at 156 months for Japan) is much longer than the longest tightening phase (at 98 months for Spain). The fastest initial
three stages. In the first stage, we regress saving for old age on a rich set of individual characteristics. These include: gender, urban/rural region of residence, age [6 groups], education [3 groups], marital status [3 groups], having financially dependent children, household income per capita centile (by country), and labor market status [4 groups]. In the primary specifications we incorporate region fixed effects

> 13 Data on real GDP per capita, PPP (constant 2011 international dollars) are from the World Development Indicators.

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available for only 49 of the 83 countries that were surveyed; in the remaining 34 countries information was provided incompletely or was not provided at all (as in the case of Lesotho). In any case, more than 12 percent of the countries for which complete information is available use 4 tools of parliamentary oversight, more than 14 percent of them used 5 tools, almost 33 percent of them used 6 tools while the remaining 40 percent used 7. Data are presented in Table 3.

# <u>Oversight Tools and the Forms of Government</u>

In addition to providing information concerning the distribution of oversight tools and the potential for parliamentary oversight, the IPU-WBI data shed some light on how tools and types of oversight are related to another institutional feature, that is the form of government<sup>14</sup> . Here again, an analysis of the data is informative. First of all, the analysis makes clear that the number of oversight tools available varies from one constitutional form of government to another. As shown in Table 4, tools such as committee hearings,

> 14 Respondents were asked to indicate what form of government was in their countries. Responses were coded : Presidential form of government was given value 1, Parliamentary form of government was given value 2, Semi-Presidential form of government was given 3, Parliamentary form of government in countries with a Constitutional Monarchy was given  4, Parliamentary form of government in countries with a Hereditary Monarchy received 5, and other forms of government were all given value 6. In our analysis, we adopted a different coding scheme. Countries that were given value 2, 4 or 5 by the IPU dataset were all considered to have a parliamentrary system and were hence collapsed into a single category.

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4

# log V = a + b, log Y + b2 D (log Y - log Y*)

where V is the dependent variable, Y is per capita income, Y* is the benchmark income level that separates the countries into low-income and high-income groups, and D is a dummy variable that takes the value of zero for low-income countries and one for highincome countries. With this specification, b, is the elasticity for the low-income group, and (b, + b2) is the elasticity for high income groups. Results testing for non-linearity effects of income using piece-wise linear specifications for vehicle ownership, road length, and the vehicle-road ratio (presented in Appendix Tables 6 and 7) are also discussed in the next section. In these equations, we used $5,500 (in 1987 constant US dollars) as the benchmark income level to separate developing countries from high-income countries.

# Controlling Fixed Effects

Both the national and urban data sets contain observations from a wide range of country income levels, but data are available at only two or three points in time, usually at the beginning of decades or when decennial censuses are taken. Ingram and Liu (1997) presented charts indicating that the cross-section and time-series relations among variables such as vehicle ownership and income are sometimes very similar, but sometimes differ dramatically--particularly for road length at the urban level. The cross-section regressions with the use of year dummy variables, reported in Ingram and Liu (1997), do not really exploit the panel nature of the data. In those regressions, the _levels_ of income often explain much of the variations in the _levels_ of the dependent variable. It is often interesting to know how _changes_ in the dependent variable over a given period of time correspond to the _changes_ of the explanatory variables over the same period of time. In addition, it is often desirable to control for country or urban fixed effects which may be associated with the level of the dependent variable. This can be done by estimating the first difference
The poverty and welfare domains of study for the HIES 2016/17 are slightly different from the sampling strata defined for the sampling design. Although the sample includes 20 sampling strata, official poverty statistics will only be produced at the following levels: (i) annual district level; (ii) quarterly national level; and (iii) annual division level for urban and rural areas (but not for CCs separately).

# **3.5. Selecting target variables and accuracy statistics**

One of the first decisions when designing a sample is determining key target variables to study. That then determines the sample size and allows an assessment of the ability of the sample to achieve a certain level of precision to estimate statistics related to the key target variables.<sup>10</sup> In the case of the HIES 2016/17, the main objective is to generate reliable poverty estimates for Bangladesh at different levels of disaggregation. Poverty in Bangladesh is estimated based on per capita nominal consumption expenditures. As a result, three different target variables are used to design the HIES 2016/17 sample: (i) nominal per capita expenditure; (ii) nominal total household expenditure; and (iii) poverty headcount ratio.<sup>11</sup> Table 3 presents summary statistics for the three target variables by division.

> 10 In the case of integrated household surveys with multiple objectives, a decision needs to be made as to what the key target variables are for design of the sample. The number of target variables needs to be kept at a minimum, and must consist of closely related indicators with relatively similar levels of prevalence in the population of interest. Otherwise the proposed sample size would grow dramatically.

> 11 Nominal per capita expenditure is used as a target variable rather than real per capita expenditure in order to adhere to the traditional convention followed by BBS for the design of the samples for the previous three rounds of the HIES collected in 2000, 2005, and 2010.

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a globally consistent database of input-output tables, allowing for the modeling of production, consumption, and trade for 134 countries, 57 sectors, and five factors of production. The GMig2 database contains the latest available model-consistent estimates of bilateral migration stocks, labor earnings, and remittance flows at GTAP 8.1 regional aggregation level as described in Walmsley et al. (2013). The GMig2 database contains economy-wide headcount estimates of skilled and unskilled workers by origin and host region in 2007 for each of the 134 GTAP 8.1 regions as well as estimates of the 6
on the left-hand side of figure in our central scenario (BRIUBD). Most of BRI-induced bilateral trade costs reductions are below 10%, but several exporters experience higher trade costs reductions to most destinations. Those biggest cost reductions on trade costs of exporting are expected in Kyrgyz Republic, Kazakhstan, Ethiopia, the Lao People’s Democratic Republic and Cambodia. We would expect those countries to be the biggest beneficiaries of the BRI. The full bilateral data set of trade costs reductions is presented in Table 4.

When analyzing the bilateral trade costs reductions by using the example of imports by China, we note that the differences across sectors of economic activity are very small, but systematically vary by the source of exports. Again, South Asia experiences the biggest trade cost reductions, while countries that are not part of the BRI such as the United States or Latin American and Caribbean countries (LAC) register very small gains (less than 1%) from better maritime connectivity of China with other regions.

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World Bank 2005a). According to the World Bank’s narrower definition of high-tech export products,<sup>3</sup> Thailand’s and Korea’s levels are on par lagging far behind those of Malaysia and the Philippines (Figure 7.B). Yet, Korea is clearly the most technologically advanced country among this group. The World Bank series also suggest a different trend than the BOT data: Thailand’s high tech export share more than doubled from 16 percent in 1988 to 34 percent in 1998, but subsequently declined to 30 percent in 2003. A similar trend is visible for the manufacturing sector overall rather than manufacturing exports only. Using BOT definitions, Figure 7.C groups the manufacturing sector into heavy industry, high-skill labor-intensive industries, traditional light industries, and food and related industries, and Figure 7.D shows the growth averages by period. The share of the first two industries was in 2003 at 58 percent still about 4 percent below the peak before the Asian crisis, even though the recovery since 1999 is driven by sectors such as motor vehicles, electronic and electrical apparatus and parts as well as chemical products (Figure 7.E). Comparison of manufacturing employment and enterprise numbers based on the 1996/7 and 2001/1 enterprise census data shows also a small decline in heavy and labor-intensive industries (Figure 7.F). These figures suggest that Thailand’s manufacturing is still as much focused today on raw material intensive and labor-intensive industries as it was before the Asian crisis, in spite of a sharp rise in exports over this period.

> 3 The World Bank classification defines high-technology exports as products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery.

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_Panel B: Survey mean vs GDP mean_ <!-- Start of picture text -->
-1  -.5  0  .5  1<br>Gap<br>All surveys  Income surveys  Consumption surveys<br>5<br>250<br>4<br>200<br>3<br>150<br>Density<br>Frequency  2<br>100<br>50  1<br>0  0<br><!-- End of picture text -->

<!-- Start of picture text --> All surveys Income surveys Consumption surveys<br><!-- End of picture text -->

**Note:** The charts show the distribution of the estimated gap between household survey mean and national accounts means. The gap is estimated as the proportional difference (per cent) of survey means with respect to national accounts means. It is estimated as the ratio of per capita survey mean over the per capita national accounts mean, minus one. A negative (positive) gap estimate indicates that surveys is (larger) than national account means. The histograms are unweighted; the density functions are weighted to give each country equal weight, using the same approach as in Table 1. Outlier observations above 1 are excluded (one observation for GDP and three observations for HFCE).
G<br>17 G 28 G<br>11 G 26 G<br>7 G 24 G<br>8 G 7 G<br>6 G 30 G<br>10 G 18 G<br>24 G 9 G<br>30 G 22 G<br>3 G 17 G<br>9 G 13 G<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Participation rate in % Participation rate in %<br>(a) Screening 1 (b) Screening 2<br><!-- End of picture text --> rates of the aspirational videos were, on average, above 90% for both screenings. Only 8 school catchment areas had a take-up between 80%-90% for Screening 1 (first weekend) and three of them remained with a take-up below 90% in the Screening 2 (second weekend). Figure 1 presents take-up rates for Screening 1 and 2 for all areas selected for treatment of aspirational videos ( _T_ 1 and _T_ 2). All families randomly selected for the mobile phones with m-Learning apps ( _T_ 2) picked up the mobile phone (100% take-up) when instructed and assisted the 2-hour-training to explain how to use the phone and the two m-Learning apps.<sup>8</sup>

# **5.1 Data Collection**

Using geo-spatial information provided by the Nigerian Federal Ministry of Education on the location of local schools in Kano and Jigawa states, the authors defined school catchment areas based on a 2-kilometer radius around the closest local school. We then created a sampling frame by producing a list of all households with children between

> 8This evaluation design was pre-registered prior to the baseline data collection at the The American Economic Association’s registry for randomized controlled trials `https://www.socialscienceregistry. org/trials/3619` , AEARCTR-0003619, on December 5,
the MENA region is very limited.**

Banks prefer to provide wholesale financing through specialized microcredit providers. Bankmicrocredit provider linkages build on the relative strengths of both types of entity – banks are able to raise funds from deposits and from the market and often have excess funds looking for returns, while MFIs are closer to clients, have tailored lending methodologies and delivery mechanisms, and lack funds. One of the big three Lebanese MFIs, Ameen, provides financial services in partnership with four Lebanese banks. Moroccan banks have set-up microcredit associations or foundations, rather than provide microloans directly. In Egypt two private sector banks offer microcredit in cooperation with microfinance service companies.

# **State banks are significant providers of financial services for low income people and**

**microenterprises in some MENA countries.** In Algeria, for example, the top 3 providers of loans to micro and small enterprises are public banks, with private banks and NGOs (associations) far behind, although the market share for private banks was growing (IFC survey, 2006).  While in Syria, half of MSEs interviewed for the IFC (2008) used banking services from

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from the LAPOP Surveys. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrillacontrolled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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53%|35%|
|Agricultural|0.093|0.160|0.01%|0.00%|46%|58%|
|Miscellaneous machine operators, n.e.c.|0.078|0.157|0.95%|0.79%|90%|87%|
|Industrial|0.070|0.157|0.23%|0.19%|68%|45%|

Source: Sample consists of Current Population Surveys Merged Outgoing Rotation Group Workers for 1983-2002. Import penetration data are taken from Bernard et al. (2006).