rafmacalaba/data-use-mentions-extended huggingface dataset:
**Import** is the share of raw material imports in total material inputs.

**Importer** is a dummy variable that takes the value 1 if the firm imports and 0 otherwise.

**State ownership** is the share of ownership of a firm held by the government.

# **A.3 Definition of Industry-Level Variables**

**Dependence on external finance** ( _EFD_ ) is the median of the ratio of capital expenditures minus cash flow from operations over capital expenditures in US industries. Source: Rajan and Zingales (1998).

**Entry rate** ( _Entry_ ) in industry _j_ at time t is the number of all new firms in our data at time _t_ divided by the total number of firms at time _t – 1_ .

**Entry regulation** in industry _j_ is a dummy variable that equals 1 if industry was subject to entry restrictions (source: Presidential Decrees issued in 1993, 1995, and 2000 in Indonesia); it is an indicator of stringent entry regulation.

**Exit rate** ( _Exit_ ) in industry j at time _t_ is the number of all firm that do not exist in our data at time _t +1_ divided by the total number of firms at time _t_ .

**Export** in industry j is the share of total exports out of total output. **Foreign market share (** **_MS_ )** is the share of output in industry j produced by firms with majority foreign ownership: ∑ <u>, j</u> , where is a dummy variable indicating a firm with a majority of foreign ownership ∑ ii ii∈ FFii∙YYii and is the output of firm ii,ii∈jj YYii _i_ FF. ii **Herfindahl-Hirschman Index** ii _(HHI)_ in industry j at time _t_ is defined as the sum of the squared market YY 2 shares of firms in an industry: H = ∑ O <u
Source: Estimates based on the _Enquête Nationale sur l’Emploi_ (ENE; Labor Force Survey) Source: Estimates based on the _Enquête Nationale sur l’Emploi_ (ENE; Labor Force Survey) Labor market variables differ depending on geography. Employment distribution varies across regions. For example, 58 percent of employment in Drâa-Tafilalet was in agricultural jobs in 2014, while in the Oriental region this share was just 30 percent while 45 percent was in services (Figure 2.13a).

**Figure 2.13a Employment distribution varies across regions (2014).**

Percentage of total employment <!-- Start of picture text --> 100%<br>90% 21%<br>27% 30% 32% 33% 35% 31%<br>Services 80% 42% 45% 41%<br>Public administration 70% 11%<br>9%<br>Construction 60% 6% 4% 10% 10% 10% 20% 9%<br>50% 11% 7% 8% 11%<br>8%<br>Utilities 12% 9%<br>40% 17% 12%<br>Manufacturing 8% 10%<br>30% 55% 58% 5%<br>Extractive industries 20% 45% 45% 44% 45%<br>Agriculture, forestry 10% 30% 30% 30% 28%<br>and fishing<br>0%<br>Oriental<br>Khénifra<br>Béni Mellal- Drâa-Tafilalet Fès-Meknès Settat Souss-Massa Hoceima<br>Grand Casablanca- Marrakech-Safi Rabat-Salé-Kenitra Southern Regions Tanger-Tetouan-Al<br><!-- End of picture text --> At the same time, those regions with the highest shares in agricultural jobs (Béni MellalKhénifra, Drâa-Tafilalet, and Marrakech-Safi) have the lowest unemployment rates. These shares are stable over time, compared to the distribution of 2000 (Figure 2.13b).

**
234)|
|Internal trade x Ln(GDP cap)||||-0.097||-0.221|
|||||(0.135)||(0.146)|
|Internal trade x Ln(GDP)||||-0.251***||-0.259***|
|||||(0.086)||(0.088)|
|Internal trade x Institutions WGI||||-0.014**||-0.009|
|||||(0.007)||(0.008)|
|Obs|496570|496676|496570|490371|496570|490371|

_Notes: The dependent variable is the value of bilateral imports. Annual data from 2000 to 2018. All columns include importer-year and exporter-year fixed effects. Column (2) include country-pair (directed) fixed effects. Columns (5) and (6) include interactions between the internal trade dummy and year dummies. Standard errors three-way clustered by undirected country-pair, importer and exporter in parenthesis. * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level._ In Table 3, we show the results of the gravity estimations using each of the three indicators of protectionism in government procurement separately (lack of national treatment clause – “discrimination” ; preferential treatment and domestic sourcing). We report the coefficient on the internal trade dummy, but omit for brevity the coefficients on all the other bilateral covariates (the results are similar to Table 2). Like for the cross-country regressions, we find that preferential treatment provisions have strong and significant effect on protectionism in trade data, as measured by the difference between internal and international trade. The estimates suggest that for importers whose laws have preferential treatment for domestic bidders in government procurement, domestic purchases are around 16 times bigger
Not all countries will have adopted the GFSM 2001 functional classification, COFOG, and necessitating a code-mapping exercise as described earlier in the Guide. The basis for this code-mapping exercise should be fully described in the country report. The best way to do this is through a tabular presentation (as shown for Kenya in table 3.5 and for Rwanda in table 3.6).

An assessment of the practical problems that officials face in presenting data from the government‘s existing budget presentation, aligned with the GFSM 2001 reporting framework, should consider whether these problems arise from poor data capture (on the part of the Ministry of Finance, for example), from weak accounting systems, or simply from weak data reporting through the budget structure.

# _Pre-field templates._

In the report embed<sup>11</sup> the Excel files containing the pre-field templates:

- Template 1. Jurisdictional responsibilities in infrastructure service delivery

- Template 2. Special funds financing infrastructure service delivery

- Template 3. Private participation in infrastructure

- Template 4. ODA in infrastructure service delivery

_Qualitative field templates._ In the report embed the Excel files containing the qualitative templates:

- Template 5. Basic budgetary institutions, national level

- Template 6. Budgetary cycle diagram, national level

- Template 7. Governance of public operators

_Quantitative field templates._ In the report embed the Excel files containing the quantitative templates:

- Template 8. Macroeconomic parameters for normalization

- Template 9. Macroeconomic parameters for budgetary context of infrastructure spending

- Template 10. Functional and economic classification of government expenditures (central and local)

- Template 11. Public operators‘ financial data

## **d. Concluding remarks**

The report should provide concluding remarks on the data collection process, emerging messages and other country-specific issues that might be of interest, as next steps for the country in terms of data generation, institutional transparency, etc.

11 To embed an Excel file into a Word file:

> Insert  Object  Select ―Create from File‖ tab
conditions.

With the help of the growing accessibility of satellite data and advancements in machine learning techniques, the costs associated with local knowledge support can significantly diminish, enabling widespread availability. For instance, e-extension services can utilize predictive machine learning technology to offer insights based on highly detailed soil nutrient status.<sup>10</sup> Even when regenerative agriculture is suitable and profitable based on the local climate and soil conditions, it can still take time to raise awareness among farmers and train farmers to learn how to utilize the technologies. Demo plots may

> 10 For example, iSDAsoil provides soil and agronomy maps with a 30-meter resolution covering the entire African continent. In addition, Global Agro-Ecological Zones (GAEZ), supported by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA), can identify suitable agricultural land utilization options at a  9 x 9 km grid cell level for the entire globe.

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(1982). Since then many of these national models have become far more sophisticated, and in particular have added regional, occupational and household disaggregations and have become dynamic (as for example in the transforming of the Australian ORANI model into the MONASH model – see Dixon and Rimmer 2002). The latter feature a **l** ows forecasting though time and hence can show paths of adjustment to shocks – something about which politicians are especially anxious.

Global CGE models were slower in coming, since they require so much more data than national or regional models. Early examples are Whalley (1985) and Deardorff and Stern (1986, 1990), with the latter having more country and commodity detail. The Australian Government’s Industry Commission also began building a global CGE model for trade negotiating purposes (the SALTER model – see Jomini et al. 1991). A copy of that model was taken to Purdue University and, since the early 1990s, it has been improving constantly and been made publicly available as the so-called GTAP model and database (Global Trade Analysis Project – see Hertel 1997). The extraordinary efforts by Tom Hertel to train users and recruit willing helpers to revise and update the production, trade and protection data and improve the theory in the model has resulted in hundreds of people becoming persistent users and thousands of simulation experiments being published since its creation (see www.gtap.org). That openness, which has been characteristic of some other CGE modelling groups too, has been a great spur to modelling innovations.

The basic global GTAP model is similar in architecture to the Australian ORANI model, but more complex versions are being developed all the time. Among the modifications that have been incorporated for particular applications are scale economies and imperfect competition (Francois 1998), dynamics through capital accumulation (Francois and McDonald 1996), and those plus foreign direct investment (Dee, Hanslow and Phamduc 2000). In addition, computational tools for practical policy analysis have been developed to enable systematic sensitivity analysis
absence of such data sources for Afghan refugees and Iranians, we focus on a specific dimension of social cohesion that can be measured using items in HEIS. In particular, we look into assistance received by Afghan households from other households as a measure of trust between two communities. Pham et al. (2021) use participatory research methods to identify the elements of the predominant understanding of social cohesion in the Democratic Republic of the Congo. Their study suggests that helping each other and sharing (generosity and charity) represent strong indicators of social cohesion. Accordingly, although our measure of social cohesion departs from its general definition, it captures one of the most important aspects of a broad concept.

Having our definition of social cohesion in mind, we compute different variables using HEIS to measure trust between the communities. **Aid** is an unearned income item corresponding to aids received from NGOs and charity institutions. **Transfer** is another item in the unearned income table that comes from direct monetary transfers from other households. Moreover, in HEIS expenditure tables, in addition to the quantity and value of the items, their method of obtaining (purchase, home production, from work, free) is also inquired. Using this information, we compute **free items** as the sum of all expenditure items that their method of obtaining is mentioned as ‘free’. Finally, we sum over household expenditure items for charitable giving and build variable **donation** that measures the transfers and contributions that are given to other households. According to rows (16) to (18), the means of aid, transfer, and free items are significantly higher among Afghan households, and it raises above their monthly expenditure per capita. However, the share of Afghan households who receive aid, transfer, and free goods are respectively 3.2%, 6.6%, and 14.4%, which is significantly lower than these shares for Iranians.

## **4.2
# **4.1 Spatial price gaps**

Under the theoretical framework described in section 3.1, if markups did not vary across locations the pass-through rate would be constant across trading pairs and equal to one, i.e. _ρ_<sup>_k_</sup> _od_<sup>= 1.Fromequation(3),thisrestrictionimplies</sup> We estimate equation (8) through OLS imposing a linear relationship between distance and trade costs, i.e. _τ_ ( _xodt_ ) = _βxod_ + _γt_<sup>_k_+</sup><sup>_χk_</sup> _odt_<sup>,where</sup><sup>_γ_</sup> _t_<sup>_k_areproduct-timefixedeffects</sup> that capture economy-wide product-specific shocks to trade costs and _χ_<sup>_k_</sup> _odt_<sup>capturesother</sup> components of trade costs that are unrelated to distance. With the assumption of perfect competition, differences in prices across locations should reflect trade costs and, therefore, we would expect _β_<sup>ˆ</sup> to be positive. A negative _β_<sup>ˆ</sup> could arise in the more general case of imperfect competition with variable markups. Yet another potential explanation for observing price gaps that decrease with distance is that local distribution or logistic costs in the destination are higher at destination locations that are closer to the origin. Results are presented in Table 3. Except for Rwanda, the results show a positive relationship between origin-destination price gaps and distance.

Table 3: Estimates of the relationship between distance and trade costs using origindestination price gaps

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Distance to origin (log km)|0_._070<sup>_∗∗∗_</
Using georeferenced Demographic and Health Survey (DHS) data from nineteen countries that have been affected by violent conflict, this paper analyzes the association between conflict and child marriage. For the purposes of this paper, violent conflict is defined as lethal armed force by an organized group. We measure the intensity of conflict primarily using four categories of conflict intensity, namely tertiles of conflict (low, medium, and high conflict) versus no conflict. These tertiles are based on fatalities recorded by the Uppsala Conflict Data Program (UCDP). Fatalities are measured for a particular year and in a particular location at the second level administrative geography. We also present alternative results accounting for (1) four years of lagged conflict using tertiles, (2) country-specific tertiles of conflict, and (3) how many of the preceding five years had conflict. We use discrete-time hazard models and variation in conflict over time and space to examine the relationship between conflict and the hazard of child marriage.

We find that conflict is associated with an increase in child marriage in some countries, in others it is associated with a decrease in child marriage, and in some countries there is not a statistically significant relationship. This overall pattern persists across a variety of approaches to measuring conflict. Thus, while conflict is a potential risk factor for child marriage, conflict’s impacts can be heterogenous and may interact with local economic, social, and demographic environments.

Our paper deepens knowledge about the relationship between conflict and child marriage rates by expanding the number of countries and time periods studied. By undertaking crosscountry research with comparable data and methods, we can assess whether the mixed results of past research are due to varying methods and data across single-country studies, or whether there really is country heterogeneity in the relationship between conflict and child marriage.

In the first section of the paper, we review the existing literature on the relationship between child marriage and conflict. The following section describes our data and methods, before presenting and discussing the results from our models of conflict
br>2014)|NSSO,<br>Government of<br>India|1|Usual monthly<br>consumption<br>expenditure of<br>the household|January to<br>July 2014|Education and health specific survey.<br>Sample: 65,932 households|
|Periodic Labor<br>Force Survey (PLFS)|NSSO,<br>Government of<br>India|1|Usual monthly<br>consumption<br>expenditure of<br>the household|July 2017 to<br>June 2018|Starts in 2017-18 to replace<br>employment-unemployment surveys.<br>Cross-sectional in rural areas and panel<br>in urban areas. Sample: ~56,000<br>households|
|India Human<br>Development<br>Survey (IHDS)|NCAER &<br>University of<br>Maryland,<br>Indiana<br>University and<br>University of<br>Michigan|52|MRP|Two and half<br>rounds: 2004,<br>2011 and<br>subsample<br>round in<br>2017|Household panel containing income and<br>expenditure questions. Sample: ~<br>41,500 households|
|Consumer<br>Pyramids (CP)|CMIE, private<br>data collection<br>agency|~ 80|Consumption<br>recall over last<br>three months|Starts in 2014<br>(and every<br>quarter since)|Starts in 2014. Household level panel<br>with three-monthly period recall.<br>Sample: ~174,000 households|

_Table 2._ Estimated poverty rate in 2017/18 ($1.90 per day poverty line) using survey-to-survey imputation methods, comparing different models

|**Sector**|**Model 1**|**Model 2**|**
20 years. Trade in primary goods includes exports and imports of agricultural raw materials, food, fuel, mineral ores and metals. Trade in manufacturing goods comprises exports and imports in SITC sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). Trade in primary and manufacturing goods is expressed as a percentage of GDP and the data are collected from the World Bank’s World Development Indicators.

The (median) value of trade in primary goods increased in Sub-Saharan Africa from 19.6 percent of GDP in 1990-94 to 22.3 in 2010-14 while trade in manufacturing goods grew from 16.8 percent of GDP in 1990-94 to 21.9 percent of GDP in 2010-14. This implies that the intensity of trade in manufacturing goods increased at a faster pace in Sub-Saharan Africa (by about 5 percentage points of GDP). In the case of resource rich countries, there is a sharp decline in the trade of primary goods (from 65.8 percent of GDP in 1990-94 to 38.7 percent of GDP in 2010-14) while trade in manufacturing goods increased (from 15.1 percent of GDP in 1990-94 to 23.9 percent of GDP in 2010-14). Finally, trade in both primary and manufacturing goods (as percentage of GDP) increased among non-resource rich countries over the past 20 years. As expected, trade in primary goods is smaller in non-resource rich countries (relative to resource rich countries) and trade in manufacturing goods for this group is higher than that of primary goods.

# _Regional trade: Patterns of inter-regional and intra-regional trade_

To close the characterization of the integration with global markets of goods, it matters how much, what and to whom you have trade linkages with. Table 1 shows the (median) amount of trade conducted by Sub-Saharan Africa with other world regions (inter-regional trade) and the amount of trade undertaken within the region (intra-regional trade). The
# **1. Introduction**

Poverty reduction is one of the key objectives of socioeconomic development. The first World Development Report (WDR) argued that development efforts should be aimed at the twin objectives of rapid growth and poverty reduction<sup>1</sup> (World Bank 1978). This vision of development has been reiterated in one form or another in subsequent reports culminating in a conception of development as _opportunity equalization_ presented in WDR 2006 (World Bank 2005). In this context, equity is defined in terms of a level playing field where individuals have equal opportunities to pursue freely chosen life plans and are spared from extreme deprivation in outcomes. In this sense, the pursuit of equity also entails that of poverty reduction.

A recent review of poverty trends across the world has shown that poverty had been on a steady decline for a wide variety of countries from the late 1990s up until 2009 (when the financial crisis hit the world economy). The evidence on inequality reduction, a key determinant of poverty outcomes, is however mixed. From the policymaking perspective, it is important to understand the factors driving these observed outcomes.

Focusing on the fact that distributional statistics are computed on the basis of a distribution of the living standards which is fully characterized by its mean and the degree of inequality, several authors have proposed counterfactual decomposition methods to identify the contribution of changes in the _mean_ and in _inequality_ to variations in overall poverty. These decompositions include the Datt-Ravallion (1992) method, which splits the change in poverty into distribution-neutral growth effect, a redistributive effect and a residual interpreted as an interaction term. The Shapley method proposed by Shorrocks (1999) is analogous to that of Datt and Ravallion, but does not involve a residual. Kakwani (2000) has proposed an equivalent approach. Ravallion and Huppi (1991) offer a way of decomposing change in poverty over time into intrasectoral effects, a component due to population shifts and an interaction term between sectoral changes and population shifts. We present a
from East Asia and highlights important questions on retirement behavior in developing countries that may be addressed from new panel data initiatives currently underway.

# **Employment Patterns and the Retirement of Older Workers**

In China, long-term urban residents with formal sector employment can expect to receive a pension upon retirement, but face mandatory retirement at a relatively young age.<sup>5</sup> Where urban employed men confront mandatory retirement at age 60, women in blue collar occupations are frequently required to retire at age 50, those in white collar occupations at age 55, with women in some categories (e.g., university professors) able to work until age 60. Among current retirement-age residents of urban areas, a large share is receives relatively generous pension support. By contrast, rural elderly, who had lower incomes during their working lives and less accumulated wealth than their urban counterparts (Kanbur and Zhang, 1999; Ravallion and Chen, 2007), do not typically have pension income. According to the 2005 one percent population sample, 45.4 percent of urban residents over age 60 report pension income as their most import source of financial support, but only 4.6 percent of rural residents note an important role for pension income. Instead, 38 percent of rural respondents over age 60 report that income from own labor is their most important source of support.<sup>6</sup> The stark difference in employment rates of rural and urban residents reflects differences in both pension wealth and mandatory retirement provisions across urban and rural areas.<sup>7</sup> As evident in Panel A of Figure 1 below, which presents locally weighted regression (LOWESS) estimates of employment rates by age, China’s rural residents are far more likely to be employed well after the mandatory retirement ages faced by urban residents. From the China Health and Retirement

> 5We define a long-term urban resident as an urban dweller with an urban (non-agricultural) residential registration ( _hukou_ ) status.
Skill subset**|**1**|**2**|**3**|**Uniqueness**|
|1Digital|Advanced|0.06|0.78|-0.03|0.38|
|2Digital|Intermediate|0.28|0.81|0.09|0.26|
|3Digital|Basic|0.76|0.28|-0.32|0.24|
|4Cognitive|Thinking|0.75|0.43|-0.18|0.22|
|5Cognitive|Communication|0.88|0.08|-0.10|0.21|
|6Cognitive|Organization|0.72|0.30|0.20|0.36|
|7Socioemotional|Relationships|0.66|0.24|-0.31|0.41|
|8Socioemotional|Emotions|0.70|-0.36|0.10|0.37|
|9Socioemotional|Personal growth|-0.10|0.04|0.96|0.07|

_Sources_ : Authors’ calculations based on SEAD data. _Note_ : The factor analysis is based on varimax rotation for orthogonal factors.

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