rafmacalaba/data-use-mentions-extended huggingface dataset:
# Information Deprivation

National level information deprivation has been drastically decreasing over time, in the urban and the rural areas alike (see Figure 36). In 2010, 33 percent of rural children of school going age were, according to the GHS data, deprived of information, such as phone, TV and radio, while this share had dropped to 9 percent over the subsequent 6 years period. This large reduction in severe information deprivation of children, holds, as Figures 37 a and 37 b show, across all geo-spatial zones. While the estimates from the MICS 2016/2017 survey, do not continue the trend of the third wave of the GHS survey these are very much in line with the GHS wave 2 estimates. One explanation for this discrepancy may be large heterogeneity at the state level, that the GHS, only representative at the regional level, cannot capture.

Unlike other deprivations, there is not such a clear north-south divide in terms of information deprivation. While North Eastern and North Western rural zones have the largest shares of information deprived children, the share of information deprived children in the North Central urban and rural areas is according to both surveys in the last wave comparable to the South Eastern and South Western zones. Figures 38 and 39 rather show that particular states stand out. Ebonyi stands out with about 15 percentage points more children being severely information deprived compared to neighboring states in the South East. Children living in the at the time of the survey accessible areas of Borno on the other hand are one of the least information deprived, even compared to those in the least deprived southern states and the FCT. Considering that most accessible enumeration areas of Borno were in and around it’s state capital city Maiduguri and exclude IDP camps, results hence suggest that children living in these areas are despite the conflict in the state still doing quite well in terms of household access to information. The surge in information deprivation in the urban North East from wave 3 to wave 4 may however be linked to the rising
contrasts kernel-density estimates for these outcomes and reveals a clear shift to the left for these distributions when focusing on children of illiterate mothers. These are meaningful differences which indicate that the children of illiterate mothers are worse off.

The ECED survey also collected child outcomes on a battery of cognitive tests and examinations of school-curriculum materials. Children were administered tests to assess their ability in terms of Bahasa Indonesia, mathematics and abstract reasoning. Two versions of the overall test were administered: a shorter test for 6 and 7-year-olds and a longer test for 8 and 9-year-olds. In this paper, we use the common set of items that were included in both versions of the test. We standardized the percentage of correct answers for each test to have a mean of 0 and standard deviation of 1, using the age-specific mean and standard deviation of the entire ECED sample.<sup>7</sup> The language test consisted of two sections. The first section (match pictures) evaluated children’s phonological awareness (i.e., whether they can match pictures that start with a given sound) and letter recognition (i.e., whether they can match pictures that start with a given letter). The second section (mention objects) assessed children’s vocabulary skills (i.e., whether they can name the word associated with a given image). The mathematics test included two sections. The first section (summation) evaluated children’s ability to add and subtract (i.e., whether they can add to or subtract away from a set of objects). The second section (order numbers) assessed children’s ability to recognize patterns (i.e., whether they can order one- to two-digit numbers in ascending and descending order). Finally, the abstract reasoning section was modeled on the Ravens Progressive Matrices. Students were presented with an image that was missing a small section and asked to select the missing pieces from six options, based on color, pattern,
##

# **Account penetration among the poorest**

Adults living on less than $2 a day by whether with or without a formal account (as % of all adults) <!-- Start of picture text --> 100<br>80<br>Below $2 a day<br>WITH<br>60 ACCOUNT<br>40<br>WITHOUT<br>ACCOUNT<br>20<br>0<br>HIGH-INCOME EUROPE & LATIN AMERICA EAST ASIA MIDDLE EAST SOUTH SUB-SAHARAN<br>ECONOMIES CENTRAL ASIA & CARIBBEAN & PACIFIC & NORTH AFRICA ASIA AFRICA<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012; Gallup World Poll, 2011.

Consider Ghana and Benin. Both have a GDP per capita of about $560.<sup>5</sup> But while 29 percent of adults in Ghana report having a formal account, only 10 percent in Benin do. Thus even among economies with similar income levels and in the same region there can be significant differences in account penetration.

Indeed, when the analysis is restricted to the bottom 50 percent of economies by income level, GDP per capita explains only 22 percent of the variation in account penetration among economies. This suggests that the variation across economies is not determined solely by national income as proxied by GDP per capita.

At the individual level, household income—both absolute and relative—plays an important part in explaining the variation in account penetration. The role of absolute household income can be assessed by looking at the share of adults living on less than $2 a day who have a formal account (figure 1.3).<sup>6</sup> Worldwide, only 23 percent of adults in this income category report having an account at a formal financial institution. Economies in South Asia and in East Asia and the Pacific have been most successful in expanding financial services to this group. In these regions
within 2-digit industry _j_ at time _t_ . _AnnualEarningsijt_ measures annual earnings of individual _i_ working in industry _j_ ’ at time _t_ . _GGExpjt_ and _GGImpjt_ measure the share of green goods export and import, respectively, in total exports and imports within 2-digit ISIC industries. _σj_ is the industry fixed effect; _μ_ is the constant term; _AD_ is the average duty; and finally, _εjt_ / _εijt_ is the error term.

To facilitate interpretation, these percentages are multiplied by 100. The calculations are based on the Labor Force Survey, incorporating appropriate weights. While incorporating additional control variables would enhance the precision of our estimation, we encountered limitations in doing so due to data constraints. Despite attempts to merge the data with the firm-level survey conducted by the Philippines Statistical Authority, the lack of identical sampling frames between the Labor Force Survey and the firm-level survey posed challenges in the merging and weighting procedures.

The annual earnings data is obtained from a cross-sectional Labor Force Survey. The richness of this database allows us to incorporate various observable characteristics in a Mincer-style regression, including gender, age, marital status, education, and industrial affiliation. However, due to the cross-sectional nature of the data, we are unable to control – for unobservable characteristics that may impact earnings, such as inherent talent which could introduce biases. The annual earnings variable is represented in natural logarithm form, while the green goods trade variables are expressed as shares out of 100. The gender variable assumes a value of zero for male workers and one for female workers. Education level is categorized into eight different groups, ranging from no schooling to incomplete elementary education, elementary graduate, incomplete high school, high school graduate, vocational education, incomplete college, and college graduate and above. The marital status variable is a dummy variable that takes a value of one if an individual is married and zero otherwise. Age is a self-explanatory variable that represents the age of the individual.

Estimating the impact of green goods trade on labor market outcomes is susceptible to endogeneity issues. Firstly
0.004)|(0.004)|(0.003)|(0.004)|(0.004)|(0.004)|(0.003)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Education|No|No|Yes|Yes|No|No|Yes|Yes|
|Employment|No|No|No|Yes|No|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.342|0.350|0.389|0.791|0.204|0.209|0.260|0.731|
|Panel B Adj.R<sup>2</sup>|0.221|0.234|0.305|0.782|0.120|0.129|0.209|0.726|
|Nr. of countries|19|19|19|19|19|19|19|19|
|Nr. of hh|372027|372027|372027|372027|372027|372027|372027|372027|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a share of household workers employed in a respective sector. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in
and 1 percent on “printing and related support activities”.

Looking at the heterogeneity of digital goods tariffs across countries in AfCFTA, Figure A3 reveals that the level of tariff protection on digital goods correlates negatively with income per capita across African countries, with economies like South Africa imposing tariffs as low as 0.9 percent, while the Central African Republic and Cameroon apply tariffs as high as 15 percent. We also observe that tariffs on digital goods are relatively high in large countries (as measured by population size), including Ethiopia, Nigeria and Egypt. Therefore, the scope to reduce the relatively high tariffs on digital goods in AfCFTA is especially high in low-income countries in the region, and such reduction would impact a sizable share of Africa’s population.

# **4 The quality and quality-adjusted prices of Africa’s imports of digital goods**

It is well-documented that the unit values of internationally traded goods are strongly influenced by differences in quality (Schott, 2004; Hummels and Klenow, 2005; Hallak, 2006; Hallak and Schott, 2011; Feenstra and Romalis, 2014). To undertake meaningful cross-country comparisons of import prices of digital goods, it is therefore important to adjust them for these quality differences. In this section, we present estimates of the quality and quality-adjusted prices of AfCFTA imports of digital goods. We first briefly outline the main features of the methodology we use to estimate the quality and quality-adjusted prices of Africa’s imports of digital goods. We then present the corresponding results.

## **4.1 Methodology**

We estimate the quality and quality-adjusted import prices of digital goods following the methodology proposed by Feenstra and Romalis (2014). This approach exploits a pervasive supplydriven feature of trade data to decompose unit values of traded goods into quality and qualityadjusted price components, exploiting both differences in the demand and supply for quality. Previous studies relied mainly on the demand side to identify quality together with a simple supply side to control for the extensive margin (Schott, 2004
“shocks”—that is, changes in monetary policy that do not represent a response to changes in the U.S. macroeconomic environment. However, as noted by Nakamura and Steinsson (2018), Cieslak and Schrimpf (2019), and Jarociński and Karadi (2020), changes in interest rates in response to Fed announcements may reflect the market’s interpretation of the motivation for those announcements, e.g., changes in prospects for economic activity, prospects for inflation, or shifts in the Fed’s reaction function. Only a few papers have examined how differences in these types of shocks, as identified using financial market data, may lead to different spillovers to EMDEs.

Arteta et al. (2015) decompose daily movements in 10-year yields into two components: one reflecting real U.S. growth shocks and another reflecting U.S. monetary shocks (that is, both inflation and reaction shocks), based on a vector autoregressive (VAR) model with sign restrictions. The study then uses a panel VAR to show how these shocks impact EMDE indicators, finding that adverse U.S. monetary shocks lead to higher EMDE bond yields, lower equity prices, and weaker currencies, whereas positive U.S. real shocks raise EMDE equity prices and industrial production. Hoek, Yoldas, and Kamin (2021, 2022) categorize changes in U.S. Treasury yields around FOMC announcements as either real, inflation, or reaction shocks, based on the simultaneous movements in equity prices and inflation compensation in TIPS markets. They find that monetary surprises generated by rising inflation expectations have the greatest impact on EMDEs exchange rates, equity prices, bond yields, and CDS spreads, whereas real shocks are generally benign. IMF (2021) looks at the impact of U.S. employment and CPI releases on U.S. and EMDE financial conditions. Consistent with Arteta et al. (2015) and Hoek, Kamin, and Yoldas (2022), it finds that good employment
true for domestic firms. Firms with productivity above the median represent a disproportionate share of entries. Lastly, firms that have previous importing experience (from any foreign market) make up most of the entries into new markets in the data.

# **3.4 Data**

Using data on 38,907 unique Serbian firms from the Serbian BRA we construct an unbalanced panel of 31,482 firms to include in the final analysis. The data includes balance sheet information of firms, for example, their revenue, capital, employment, age, and ownership.

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Maternal anemia has been identified as the world’s most common nutrition-related health problem (Kalaivani, 2009). Recent estimates suggest that about half of the pregnant women in low-income countries are anemic (Daru et al. 2018; Stevens et al. 2013; Balarajan et al. 2011; WHO 2004). The high incidence of anemia is attributed to factors that include dietary iron deficiency, hemoglobinopathies,<sup>2</sup> macronutrient deficiencies, and infections such as malaria, HIV, and hookworm infestation (McLean et al. 2009). Ezzati et al. (2002) find that South Asia accounts for about one-half of global maternal deaths due to anemia, and India accounts for about 80% of the South Asian total. In 1990, 19% of the maternal deaths in India were related to anemia (Anand, 1995), which also contributes to maternal deaths caused by hemorrhage, septicemia, and eclampsia (Rukuni et al. 2016).

We perform our analysis with a pooled sample of data from the just-published India National Family Health Survey for 2015-16 and the most recent Bangladesh DHS (2011) that includes measures of maternal anemia. We fit appropriate regression models to observations on child wasting and maternal anemia, using independent variables that have been identified by published empirical studies for Indian and Bangladeshi women and children. To ensure comparability, we use measures that are identical in the two national surveys. The results enable us to assess the health impacts of several determinants that are commonly cited in the literature: mothers’ education and age, children’s birth order, household wealth, and seasonal factors. We investigate the potential importance of unobserved geographic factors by incorporating fixed effects for Indian states and Bangladeshi provinces in our regression models. Then we re-estimate the models without regional effects and search for spatial clusters by computing and mapping mean regression residuals for the 197 districts in Bangladesh, West Bengal, Bihar and Jharkhand.

> 2 Hemoglobinopathies are abnormally-structured globin chains of the hemoglobin molecule.

3
**Table 7:** Reservation wage correlates

||Re|servation|Wage (Lo|g)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Wage growth belief|-0.031<sup>_∗_</sup><br>(0.018)||||
|Discount factor||-0.417<sup>_∗∗_</sup><br>(0.169)|||
|Discount factor (Inc.)|||-0.004<br>(0.109)||
|Liquidity Constrained||||-0.103<sup>_∗_</sup><br>(0.055)|
|Observations|533|1362|619|552|
|R square|0.01|0.00|0.00|0.01|

Notes: Source: Endline survey. The dependent variable is the log of reservation wages reported at endline. _Wage growth belief_ measures the perceived percentage increase over a six months spell. _Discount factor_ measures how much people discount future payments, collected in the inperson edline. _Discount factor (Inc.)_ captures the incentivized measure of how much people discount future payments, collected in the SMS edline. _Liquidity constrained_ is a dummy measuring whether people are not able to raise funds for a risk free profitable investment. Robust standard errors are presented in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01

# **5.5 Other explanations**

In addition
This paper uses the historical military routes of Armenia during the Russian Empire times to account for the endogeneity of modern day road quality.

The research focuses on Armenia to fill the gap in the literature for several reasons. In Armenia the agricultural employment rate is still high, has reduced from 40.4% in 1991 to only 33.6% in 2016.<sup>2</sup> Moreover, most people engaged in agriculture in Armenia, like in many other low- and middle-income countries, are best characterized as engaging in subsistence or quasi-subsistence agriculture, meaning that they consume most of the goods they produce. Given this, identifying mechanisms of structural change is very important. In addition, Armenia, has been receiving funding for rehabilitating and improving existing deteriorated roads, as well as for building new ones. Therefore, it is policy relevant to study the impacts of these road projects. And lastly, the unique dataset on road quality and the historical setting of Armenia give an opportunity to identify impacts of road quality on rural employment outcomes.

# **2 Conceptual framework**

In regional research, the importance of transportation costs has long been recognized as one of the major factors of economic development. Von Th ̈unen’s land rent model and its subsequent modifications predict concentric circles of specialization in agriculture surrounding cities (Von Th ̈unen, 1826). Building on this, regional economic models predict that improving rural connectivity would facilitate moving from agricultural to nonagricultural employment. For example, the Alonso-Muth-Mills model predicts an urban perimeter beyond which agriculture would be the primary employment sector. The urban perimeter takes into account that urban wages, deduced with transportation costs, would be lower than agricultural wages (Brueckner, 1987). Therefore, if we consider road quality improvements as a source of decreasing commuting costs in a given location, we expect improved road quality to expand the urban perimeter.

Starting from Lewis (1954) model, researchers have argued that labor market imperfections prevent people employed in the agricultural sector from
- Faggian, Alessandra and Philip McCann. 2006. Human capital flows and regional knowledge assets: a simultaneous equation approach. _Oxford Economic Papers_ 58(3): 475–500.

- Ferreyra, María Marta and Mark Roberts. 2018. _Raising the bar for productive cities in Latin America and the Caribbean_ . Washington, dc: The World Bank.

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- Greenwood, Michael J. 1997. Internal migration in developed countries. In Mark R. Rosenberg and Oded Stark (eds.) _Handbook of Population and Family Economics_ , volume 1B. Amsterdam: Elsevier, 647–720.

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- Matano, Alessia and Paolo Naticchioni. 2016. What drives the urban wage premium? Evidence along the wage distribution. _Journal of Regional Science_ 56(2): 191–209.

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trends in wage inequality.

To develop our strategy, we set up unusual historical data sets of trends in trade reforms, trends in wages, and trends in wage inequality in Argentina. The data span the period 1974-2001. We construct a time series of tariffs, for different sectors in different years, and a time series of labor force surveys with data on individual wages. This is the first instance in this literature in which such a historical record of trade reforms is put together with a historical micro data set of workers and wages.<sup>2</sup> The outcome is almost 30 years of data on sectoral tariffs and individual wages that, we believe, provide a different, useful, and compelling identification strategy.

In addition to exploring the overall match between trends in tariff reforms and trends in wage inequality, we use these data to compare the two liberalization episodes of the 1970s and 1990s and to examine some key differences between them. This is a contribution to the literature on trade liberalization in Latin America, which has focused almost entirely on the 1990s. However, in Argentina, both the increase in wage inequality and the trade reforms were much more marked in the 1970s than in the 1990s. The skill premium, for instance, doubled in the 1970s but increased by 42 percent in the 1990s. In addition, the average tariff reduction of the 1970s was of nearly 70 percentage points (from 100 to 30 percent) but was of 12 percentage points (from 30 to 18 percent) during the 1990s. Further, the share of wage earnings in GDP declined from 45 percent to 30 percent during the 1970s (to the benefit of the capital share), and it remained below 40 percent afterwards. It seems that an analysis based solely on data spanning the 1990s may miss important links between tariffs and wages. Here, we fill this gap by pursuing a historical investigation of these episodes of reforms.

Our findings confirm that trade reforms, ceteris paribus, have i) decreased wages and ii
_<mark>Source:</mark>_ <mark>Authors’ simulation based on expanded version of CKW 2017 model.</mark> When considering the status quo by sector, women’s contribution to the modern sector was USD 436.4 million or 10.8 percent of GDP while women represented 32.7 percent of its workforce with an average yearly wage of USD 1,582 (Table 3). In contrast, men’s total contribution to the modern sector was more than double that of the female contribution and amounted to USD 957.6 million or 23.65 percent of GDP. Men represented 67.3 percent of the workforce allocated to the modern sector and had average yearly wage of USD 1,686.

**Table 3. Labor Market Statistics for Baseline Scenario (2020)**

|**3. Decomposition by Labor Supply**|
|---|
|3.1. Total Labor Force<br>1,905,372|
|3.2. Female Labor Force<br>888,824|
|of which, modern (%)<br>20.69%|
|of which, traditional (%)<br>79.31%|
|3.3. Male LF<br>1,016,547|
|of which, modern (%)<br>37.25%|
|of which, traditional (%)<br>62.75%|
|**4. Decomposition by Earnings**|
|4.1. Modern-sector wage<br>$1,652|
|4.2. Modern-sector, female wage<br>$1,582|
|4.3. Modern-sector, male wage<br>$1,686|
|4.4. Traditional-sector wage<br>$1,030|
|4.5. Traditional-sector, female wage<br>$919|
|4.