rafmacalaba/data-use-mentions-extended huggingface dataset:
different from most of the available estimates in the literature, as we are interested in how household economic status vary with the education level of the father. Given that IFLS is a panel data set, we have household level income and consumption expenditure information for multiple rounds. To reduce the measurement error, we focus on the household consumption expenditure as a measure of economic status; it is widely-noted that expenditure suffers much less measurement error from transitory shocks because of consumption smoothing and thus is likely to be a more reliable indicator of a household’s economic status (see, for example, the discussion in Deaton (1997)).

The availability of 5 rounds of panel data allows us to provide estimates of returns to education for both the parental and children’s generations: we rely on the first three rounds (1993, 1997, 2000) for the father’s generation, and the last two rounds (2007 and 2014) for the children’s generation. For parents, we use the average of the 1993, 1997, and 2000 rounds of household expenditure (deflated by average rice price) data as a measure of household permanent income and estimate the income equation (equation (1) in the theoretical model in section (2)). The estimating equation for the fathers is given as follows: For the children, we take the average of the household expenditure in 2007 and 2014. The estimates are reported in Table 5. For parent’s generation, the intercepts are not different across rural and urban areas which can be interpreted as evidence that the rural-urban divide does not matter for the subset of households where the father has no schooling (about 5 percent of our sample). In contrast, the interaction of the rural dummy with the father’s education is negative and statistically significant at the 1 percent level. The evidence thus strongly suggests that returns to education at the household level was substantially lower in the rural areas. In terms of the parameters of the income equation (1)
eligible for a second SAP payment (because they were living in areas under ECQ in May of 2020) received it by the end of June (DSWD, 2020), but almost none of the non-4Ps in our sample had received a second SAP payment by this time. By the end of July, about 22% of non-4P SAP recipients had received a second payment.<sup>5</sup> The 4Ps households continued to receive 4Ps transfers every other month (in May, July, and September 2020 during our study period), in addition to the two SAP top-ups. The government waived the program requirements during our entire study period and paid households their full transfer amount each pay period. These bimonthly cash transfers were much smaller than the SAP transfer amount, especially during summer vacation (April-May), when households do not receive the education component of the 4Ps grant.

# **3 Data**

We rely on a panel survey of low-income households across the Philippines. The baseline wave of this survey was conducted, in person, starting in November of 2019 for a separate project on cash transfers and domestic violence (Sahay et al., 2023). The sample consists of households with children and which, at the time of an impact evaluation of the program in 2014, had poverty scores just below and above the 4Ps eligibility cutoff. Cho et al. (2021a), which includes a more detailed description of the data, shows this sample is fairly representative of the poor and near-poor in the Philippines – specifically, the bottom 40% of the region-specific per capita income distribution.

The remaining survey waves were conducted during the pandemic and therefore over the phone. Wave 1 took place at the end of April, wave 2 at the end of June, wave 3 at the end of July, and wave 4 at the end of October. The timeline in Figure 1 depicts the events discussed in the previous section along with the timing of each survey wave. We also report the share
?

- 6 Is a collateral registry in operation that is unified geographically and by asset type, as well as indexed by the grantor's name of a security right?

- 7 Do secured creditors have absolute priority to their collateral outside bankruptcy procedures?

- 8 Do secured creditors have absolute priority to their collateral in bankruptcy procedures?

- 9 During reorganization, are secured creditors' claims exempt from an automatic stay on enforcement?

10 Does the law authorize parties to agree on out of court enforcement?

When compared across regions, MENA is clearly behind all of the other regions, including Sub-Saharan Africa (see Table 3). Egypt, Jordan, West Bank and Gaza, Bahrain and Qatar have begun to consider substantial overhaul of their systems, and are in early stages of reform.

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# Table B.2: **Effects of Inpres Schools on Intergenerational Educational Mobility: Comparison Cohorts: 1958-1962**

**Panel A: Dependent Variable: Primary Completion**

||**Linear**|**Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.205***|0.215***|0.196***|0.199***|
||(0.041)|(0.043)|(0.042)|(0.046)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.022***|-0.014**|-0.039***|-0.019**|
||(0.006)|(0.006)|(0.011)|(0.010)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.002***<br>(0.001)|0.001**<br>(0.001)|
|R2|0.105|0.170|0.116|0.186|
|Observations|1190828|837014|1190828|837014|

# **Panel B: Dependent Variable: Years of Schooling**

||**Linea**|**r Model**|**Quadra**|**tic Model**|
|---|---|---|--
and thus increased agricultural income by 16% in the average district (Donaldson, 2018). In current times, Asturias et al. (2015) estimate that the Golden Quadrilateral, a large infrastructure project in India connecting Delhi, Mumbai, Chennai, and Kolkata with highways established from 2001 onwards, lead to real income gains in the manufacturing sector of 2.7%, about 7% of which was caused by gains in allocative efficiency.

In addition to reductions in transport cost, better road infrastructure can also reduce transaction costs for firms. In the US, a marginal dollar invested in highway capital has been estimated to decrease firm‐level raw material inventory by seven cents in the 1970s, although the return shrunk to two and 0.3 cents in the 80s and 90s, respectively (Shirley and Winston, 2004). Using the World Bank Enterprise Survey (ES) data for India, Datta (2012) shows that businesses in cities affected by the Golden Quadrilateral project could reduce their average inventory by production inputs worth 6‐12 days between 2002 and 2005 and are more likely to have switched their supplier, again hinting at improvements in allocative efficiency. Using firm‐level data for Chinese manufacturers, Li and Li (2013) confirm the existence of this effect channel. They find that for every dollar invested in roads, affected firms can reduce their inventory costs by about two cents.

Empirical evidence exists for several other ways in which transport networks can boost productivity. The quality of physical infrastructure has been found to substantially impact the entrance of new firms into the market (Ghani, Kerr and O’Connell, 2014; Ghani, Goswami and Kerr, 2016). Martincus et al. (2017) use the Inca road network to assess changes in road infrastructure in Peru and estimate that 26% of growth in firm exports between 2003 and 2010 can be attributed to upgrades of domestic transport infrastructure. In addition to these direct effects on productivity, Wan and Zhang (2018) identify an indirect effect
prior to benchmarking, and therefore is unaffected by benchmarking. Benchmarking has minor effects for in-sample municipalities because of the presence of many state-level dummy variables in the model. Conversely, state dummy variables were not included for many out-of-sample municipalities, and as seen in the mean poverty column in Table 2, the non-benchmarked predictions significantly underestimate poverty in these cases. As in Masaki et al. (2022), the mean squared error was multiplied by the same scaling factor that was used for benchmarking in each municipality, which increases mean squared error and improves coverage rates, particularly in outof-sample municipalities. For in-sample municipalities, benchmarking improves the correlation with the CONEVAL estimates by about a percentage point, from 0.853 to 0.862, and slightly reduces root mean squared deviation. For out-of-sample prediction municipalities, meanwhile, benchmarking has a more substantial beneficial effect on accuracy, as the correlation with the CONEVAL estimates improves by about 3 percentage points from 0.669 to 0.701, and root mean squared deviation falls from 0.196 to 0.181. Overall, benchmarking improves the accuracy of the estimates, particularly for out-of-sample municipalities, and the benchmarked estimates are therefore preferred.

# _6.2 Weights_

Table 5 considers the impact of using sample and population weights when estimating the household-level model. The population weights in this case are estimated average household size at the state level, obtained by the MCS-ENIGH survey. As noted in section 3, this is assigned to each household in the synthetic census. Sample population weights, defined as the provided household weight times household size, are provided with the MCS-ENIGH data, and are used in the preferred specification when estimating the mixed effect model. Table 8 shows that failing to use sample weights decreases the average poverty rates prior to benchmarking from about 28.1%, which nearly matches the direct estimates, to about 26.4%. This suggests that failing to incorporate weights can moderately increase bias. Interestingly, the use of sample weights makes the estimates slightly less accurate for in-sample
comparison of informal firms to India. We compare the reported constraints on firm growth for rural and informal sector firms with those of formal sector firms, which were used in the previous analysis in the paper. The informal sector survey of India includes only manufacturing firms.

For the formal/rural comparison, we pool the rural sector firms for the three countries. For both the formal/rural and formal/informal comparisons, the comparison group are the firms in the Enterprise Surveys for the relevant set of countries, which we call “formal”. Only manufacturing firms in the Enterprise Surveys are included for the comparison with informal firms.

We do two exercises for the formal/rural and formal/informal comparisons. The first is to compare the levels of the reported constraints in the two groups of firms (Tables 16 and 18. The second is to examine whether there are any differences between formal and rural or informal sector firms in how constraints are ranked (Tables 17 and 19).

In the left hand panel of Table 16 (specification 1), we show the unconditional mean evaluations of each obstacle for formal sector firms and informal or rural sector ones. To facilitate comparison between the formal and informal/rural sectors, we show the results for small formal sector firms (dropping from the samples those firms with more than 20 employees). The column headed “Diff?” shows whether there is a statistically significant difference between the mean for the small formal and the informal/rural sector firms.

The right hand panel of Table 16 (specification 2) includes a size control for firm characteristics interacted with an informal or rural dummy (plus country-survey fixed effects). This allows the two groups of firms being compared (formal vs. informal and formal vs. rural) to have their own coefficients on the size control. We can interpret the constant or the constant plus the dummy as means conditioned on size.

The benchmark firm is the same as in the rest of the analysis except that it is centered at
(Labor income)|18.15|18.02|
||(0.61)|(0.78)|
|demean|-1.63e-07|4.86e-07|
||(0.61)|(0.76)|
|Ln(Education Expenditure)|12.21|12.19|
||(4.78)|(4.82)|
|Observations|2,282|10,405|
|_Child-level variables_ (6 _≤age ≤_24)|||
|Age (female)|14.47|14.55|
||(4.96)|(5.01)|
|Age (male)|14.41|14.28|
||(4.88)|(4.86)|
|Years of schooling: girls|7.26|7.68|
||(3.84)|(4.10)|
|Years of schooling: boys|7.24|7.38|
||(3.75)|(3.87)|
|% In school: girls|77.51|78.63|
|% In school: boys|79.28|81.30|
|% In school: girls 6-18|93.01|93.36|
|% In school: boys 6-18|92.84|93.60|
|Observations|6,295|25,076|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: The table reports summary statistics of household and child-level data by treatment status. Standard deviations are in parenthesis. Family incomes and expenditures are deflated by CPI. Treated: Oil and Gas, Energy Supply industries. Synthetic control: Information (0.148), Education (0.169), and Health (0.683) industries.

15
|Nigeria|Togo|
|Cameroon|Liberia|Republic of Congo|Tunisia|
|Chad|Madagascar|Rwanda|Uganda|
|Côte d'Ivoire|Malawi|Senegal|Zambia|
|Democratic Republic of the Congo|Mali|Sierra Leone|Zimbabwe|
|Djibouti|Mauritania|Somalia||
|Ethiopia|Morocco|South Africa||

Source: USGS data.

34
use the United States to compute the natural external dependence and then we confirm our results using Canadian data to compute the natural external dependence of industries.

The data are from Standard and Poor's _Compustat for_ U.S. firms in 36 industries. This database contains only publicly listed firms. A firm's dependence on external finance is defined as the share of investmen **t** hat cannot be financed through internal cash flows; or as capital expenditures minus cash flow from operations divided by capital expenditures. Both numerator and denominator are averaged over the 1980s to smooth temporal fluctuations. The industry values are calculated as medians rather than means to thus prevent outliers from dominating the results. Table 1 lists the external dependence for all 36 industries. The drug industry is the industry most dependent on external finance, whereas the tobacco industry has no demand for external finance, i.e. our dependence measure is less than zero. Table 1 also lists three alternative measures of external dependence that we will discuss in the section on robustness tests.

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# 1. Introduction

Measuring poverty trends is a complex exercise that requires data on household welfare that can be compared over time. Limited availability of household-level data and changes, or improvements, to the methodology used to measure household consumption can affect the comparability of survey-based poverty estimates. These issues arise frequently in poverty measurement exercises (Deaton, 2005; Beegle et al. 2012) and can severely limit the availability of evidence needed to guide policies for reducing poverty.

This paper considers this issue in the context of Nigeria and seeks to understand the country’s poverty dynamics for the period between 2009 and 2019. Nigeria contains the largest number of extreme poor – that is, those living below the international poverty line of US$1.90 per person per day – in Sub-Saharan Africa, the world’s poorest region (World Bank, 2020). As such, understanding poverty trends in the country is crucial to inform policy at the country, regional, and global levels. Moreover, Nigeria has faced a series of compounding negative shocks to households’ well-being: even prior to the COVID-19 pandemic, the country suffered frequent conflict and climate shocks, as well as a deep recession following the collapse of global oil prices 2016. These crises are likely to have affected Nigeria’s progress towards poverty reduction.

Despite its high levels of poverty and its importance for regional and global poverty reduction, the household data needed to measure poverty have not been collected frequently in Nigeria over the past decade. In 2020, the Nigerian National Bureau of Statistics (NBS) released the first official poverty estimates in the country in almost a decade, calculated using data from the 2018/19 Nigerian Living Standards Survey (NLSS; see NBS 2020). The 2018/19 estimates show that 39.1 percent of Nigerians lived below the international poverty line of US$1.90 per person per day, corresponding to 78.5 million people (Castaneda et al., 2020).<sup>2</sup> While
Examining patterns of total jobs in exports highlighted the importance of domestic supplying sectors in creating jobs and different patterns of sectoral specialization in high-income versus emerging countries. In manufacturing, employment in exports is higher in supplying sectors (e.g., services or agriculture) than in the exporting sectors themselves. Emerging countries added more jobs in manufacturing exports, while high-income countries created more jobs linked to business services exports. The latter made up for falling jobs in manufacturing exports (e.g., the US and Western Europe). The findings also highlight the growing number of jobs in business services exports across all countries, reflecting the structural transformation away from manufacturing and towards services in the sample of HMICs. A few emerging countries showed higher growth of manufacturing-linked jobs than business services-linked jobs in exports, including Türkiye, Mexico, Chile, Czechia, Indonesia, the Slovak Republic, and China.

Adding the occupational dimension to sectoral specialization patterns allowed us to assess different upgrading trajectories at the level of activities. Services activities in exports, such as support, engineering, and managerial services, are becoming increasingly important as countries get richer, while production activities continue to dominate the exports of lower-income countries. The analysis highlighted the fundamental shift in manufacturing activities taking place between 2000 and 2018, offering more opportunities for structural change in lower-income countries. The analysis also suggested that GVCintensive countries such as Vietnam, Mexico, Poland, and Ethiopia tend to have larger shares of production activities in exports compared to resource-intensive countries, like Indonesia, Kyrgyz Republic, Brazil, and Ghana. Even among countries that export similar products, the activities that workers perform have different implications for growth. For example, in Cambodia, textile workers have been stuck in low value-added production activities for two decades. On the other hand, in Türkiye, the labor composition of textile exports is more diverse and includes sophisticated managerial and engineering services.

Tailored policies are essential to harness the job creation benefits from trade. While trade policies are a fundamental instrument, long-term success also relies
different transport costs and times. For instance, some carriers may have driven faster than others. The figure shows average speeds recorded by two Kazakh shipping carriers in 2013. Origins, destinations and stopped points can be different across carriers. Even if they are different (e.g., between Shymkent and Merke), the speed (or time) data are available at the road segment level. Similarly, the transport costs differ across carriers and the cost data are also disaggregated at the road level.

> 4 See ADB (2014) for detailed data collection methods.

> 5 While 527 locations are identified as origins, 516 locations are georeferenced as destinations.
developing countries should look to the experience of developed economies in order to streamline the process and achieve the desired results.

It is not always the size of the patent portfolio that matters. For example, China ranks fourth worldwide in terms of patents for green technologies according to a recent analysis of patent applications filed in 76 countries between 2000 and 2005 but it falls to tenth rank when the analysis is restricted to patents filed internationally (i.e. _claimed priorities_ in more than the home country). Brazil also ranks eleventh and then plummets to thirty-first according to the same criteria (Dechezleprêtre et al. 2011). Some patents are also simply less valuable than others in terms of their commercial potential. The fact that Kenya and Botswana pay more in royalties than they receive could suggest both countries simply own IP of lower commercial value or technological relevance. This raises important questions as to why the patent application was filed in the first place. Again, the critical issue of patent quality arises. However, assuming adequate patent quality, the situation suggests the necessary skills and infrastructure to monetize IP internationally do not exist in Botswana or Kenya.

The IMF data provides a rough picture of global monetary flows associated with IP but it is inadequate to make concrete statements regarding the state of each national IP system in detail. Yet, the figures do suggest that the majority of nations struggle with IP commercialization. The

> 5 http://www.economist.com/node/17257940

9
or equity contracts. A firm production process may therefore be influenced by both the availability and type of finance. Besides, external funding can assume different forms and it may bring ancillary benefits (e.g., managerial skills) that may propel a firm’s innovation process.

Using survey data on more than 17,000 firms in 104 countries, we investigate the cross-country variation in the relationship between financial structure and innovation distinguishing between the development of invention and adoption and diffusion of new technologies, products, and services. We find that bank funding of fixed assets is strongly and positively associated with both the decision to innovate and the intensity of firm-level innovation. The relationship is stronger for invention. Moreover, funding by non-bank financial institutions (NBFI) is strongly associated with the overall extent of innovation and adoption, especially in countries with more developed financial markets and institutions.

2
. As can be seen, by adding this option, the complete `icio` output has been saved into a file called `wdr_4_9b.xls` within the current working directory.

Lastly, we consider the analysis of value-added in the total imports of a country. To quantify the German GDP potentially exposed to US tariffs vis-`a-vis all partners, according to WIOD 2014 data, we can use the following syntax ``` .icio_load Loadingtablewiod2014...loaded .icio,origin(deu)imp(usa)output(va) Decompositionofgrossimports: Perspective:importer Importer:USA Origin:DEU Exporter:totalUSAimports Output:Value-Added ```

||`Millions of $`|`% of import`|
|---|---|---|
|`Value-Added`|`133064.91`|_`5.53`_|

The Stata output indicates that around $133 billion of Germany’s value-added are imported, directly and indirectly, by the United States (around 5.5% of the total US imports), and thus could be exposed to US trade barriers, according to WIOD 2014 data. German GDP exposure to these trade barriers can be computed taking the ratio with respect to the total German GDP - obtained with `icio, origin(deu)` . Thus, around 3.7% of German GDP could be affected by these 24
questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block.

Each outcome is described in greater detail in Appendix C. The frequency with which each outcome is collected is also presented in Appendix C.

**Multiple hypothesis testing** We utilize two approaches to address the issue of multiple hypothesis testing. First, we present our primary outcome, psychosocial well-being, as an inverse-covariance weighted index variable following Anderson (2008). We also generate index variables for other outcomes in which this is possible, such as the cognitive index, the household power index, and the work rights index. Our second strategy is to report the sharpened False Discovery Rate (FDR) q-values for all outcomes within a particular table, which control for the expected proportion of rejections that are type I errors, likewise

12