rafmacalaba/data-use-mentions-extended huggingface dataset:
It would be tedious to characterize textually the many differences in approach we find in the IPAs considered here. Accordingly, we summarize them in Table 1, contrasting agreements involving the United States, the EU or EFTA, and others. The World Bank database identifies 130 detailed IPRs provisions, broken into 16 broad categories. These provisions are simply coded as either 0 (absent in the PTA) or 1 (present). In Panel A we list eight categories that seem relevant for studying potential trade impacts.<sup>5</sup> For example, there are 15 international treaties or conventions covering intellectual property that members of IPAs might be required to gain accession or ratify. The United States is far more likely to negotiate agreements demanding such activity, with the average IPA listing 11.5 conventions, compared to 3.3 for the EU/EFTA IPAs and 2 for the others. Indeed, US-involved IPAs stand out across the board, embedding far more provisions within nearly all categories than do the others. This difference is particularly stark in trademarks, patents, test data protection, copyrights, and enforcement.

In Panel B we turn to a subset of categories and, within those, list the number of provisions that are widely considered to be “TRIPS-Plus” in that they are considerably more prescriptive than what is found in TRIPS (Maskus, 2012). There are 32 such provisions, which we collectively call BTRIPS (for “Beyond TRIPS”) to avoid confusing the standard TRIPS-Plus terminology with the World Bank’s “WTO-Plus” designation.<sup>6</sup> These 32 provisions were selected because they feature prominently in debates over elevated IPRs norms, but the list is somewhat ad hoc. With this caveat in mind, there are five BTRIPS norms in patents, including such items as patents for new uses of known products, patent-term adjustments, and patent linkage. Similarly, there are five standards in test data protection, including protection for trial data in pharmaceuticals and biologics. The
**|.464**|
|||(4.42)|(5.47)||(10.8)|(11.5)|
|CRI mean|||.098**|||.051**|
||||(6.70)|||(11.1)|
|GPP dummy *|||-.258**|||-.655**|
|CRI>0.5|||(-2.86)|||(-5.42)|
|4-digit sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Size, Age, SOE|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.740|0.739|0.740|0.370|0.369|0.370|
|Adjustd R2|0.740|0.739|0.739|0.369|0.368|0.368|
|Obs total|780,653|780,653|780,653|1,439,689|1,439,689|1,439,689|

_Source_ : Firm census panel data from 2010-2018 matched with public procurement transaction data. _Note_ : Second, we restrict the sample only to firms that obtained (any type of) public procurement contracts to estimate if GPP contracts are more often allocated to more productive firms than non-GPP contracts. Again, we test if that relation depends on the corruption risk involved in awarded GPP and other contracts.

Table 6 shows that firms awarded GPP contracts tend to be more productive than same sector, fully privately-owned firms with a comparable size and age that did not obtain any GPP contract (columns 1 and 5). The positive relation between GPP and firm productivity is stronger and significant
right-censoring patterns<sup>30</sup> ). Such methods have not been successful however at this stage.

Univariate analysis shows for example that standard parametric distributions (Gamma, Lognormal, and Weibull) are not fitting the dwell time data well. Data is processed to try and attempt to reduce some discrete patterns but neither seasonality nor simple stratification improves distribution fit. We also fit a Cox Proportional Hazard (PH) model (refer to Hosmer, Lemeshow 1999 for details) - a semi-parametric model – to Cargo Dwell Time (CDT) with help of covariates identified in earlier sections (e.g. fiscal regime, full-container-load, density of value, cargo type, C&F agents

> 30 Survival techniques enable the consistent analysis of expected time before occurrence of a discrete event such as illness, death or in our case, exit from port terminal. They have not been use yet to our knowledge for the analysis of cargo dwell time in ports and further research would be very useful to make use of the powerful scope of analysis. In particular, survival theory copes with biases attached to right or left censoring. In the case of cargo dwell time right censoring is needed since many containers in the sample are not cleared before cut-off date but have to be included in the modeling structure.

24
<br>observations|969|969|724|969|724|724|724|

Note: Data on average labor costs per worker are from the _Banque de Donnees Financieres,_ which is composed of about two thousand medium and large firms. There is one observation per industry per year, over period 1976-1994. Values in parentheses are "t" statistics. Significant coefficients at the _5_ and 1 percent level are indicated by one and two asterisks respectively.

21
official time required to complete the process, and its official cost. Substantial empirical evidence suggests that more procedures and longer delays reflect bribe extraction and/or capture (make entry less attractive to potential competitors). Predictions regarding the cost indicator are ambiguous. A benevolent social planner may finance screening of market entrants from direct fees, thus rendering the latter as an imperfect measure of costs. On the other hand, higher fees are frequently used to deter entry. Indicators of the reforms in exit conditions would include, at least, the legal reform of bankruptcy procedures, a measure of efficiency of the bankruptcy agencies and officers, and its average cost (e.g., annual number of sentences as shares of filings, time required and official costs to sentence). The World Bank is assembling an index of the efficiency of the bankruptcy process, based on the procedures for filing, the petition hearing, the court's decision, the appointment of an insolvency practitioner, the assessment of claims and their ordering by priority, and the sale of assets.

Assessing the impact of reforms on the efficiency of the judiciary system for civil procedures is quite difficult. Djankov et al. (2002b) develop indicators based on the analysis by law firms of two specific legal procedures (the eviction of non-paying tenants and the collection of a bounced check). A measure of the reforms would be the change in the average time and the monetary cost involved in accomplishing each goal. In similar fashion, three indicators of contract enforcement are the number of independent procedural steps counted from the moment the lawsuit is filed in court until the contract is fulfilled; the associated time of the procedures; and their cost as a percentage of GNP.

**_3. Outcome indicators_** . Evaluating the impact of changes in legal and regulatory frameworks is difficult because of the microeconomic, specific nature of most reforms and also because changes occur slowly due to the presence of substantial adjustment costs. Hence, it is necessary to focus on intermediate measures of
damental differences in their magnitudes. We can then use statistical tests to determine whether the homogeneity assumption and the unity restrictions on the _βi_<sup>_gdp_</sup> and _βi_<sup>_de f_</sup> parameters across countries, which are imposed when the credit-to-GDP ratio is used on the left hand side of (11), are supported by the data. It is well known that imposing restrictions on a subset of parameters that are not supported by the data leads to substantial distortions in the estimates of the remaining unrestricted parameters.

# **3.1.3. Second-stage cross-country regression**

We are particularly interested in the cross-country variation of the long-run coefficients on real GDP and the GDP Deflator in (11); that is, the variation in the _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> and _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> coefficients and also the speed of adjustment coefficient _α_ ˆ _i_ which measures how fast deviations from the are eliminated. Once estimates have been we long-run equilibrium computed, proceed by relating the cross-country variation in _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> , _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> and _α_ ˆ _i_ to a set of development indicators. These are obtained from various sources, such as the FinStats database of AlHussainy _et al._ (2010) and the Financial Structure data set of Beck _et al._ (2000). We also add economic development indicators to this set. No well developed economic theory exists to guide us in the selection of possible drivers of the variation in _β_<sup>ˆ</sup> _i_<sup>_gdp_</sup> , _β_<sup>ˆ</sup> _i_<sup>_de f_</sup> and _α_ ˆ _i_ . We
—and those of the International Red Cross and Red Crescent Movement—and the resolutions of the International Conferences of the Red Cross and Red Crescent.

84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security.

85 See: popstats.unhcr.org.

86 IDP data are only included from 1998 onwards.

> 87 See: <u>http://data.unhcr.org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan),</u> DRC regional refugee response, Mediterranean (refugees/migrants emergency response), CAR, Côte d’Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal.

88 IOM’s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum).
variables as obstacles for firms’ current operations: high tax rates, labor laws, inadequately educated workers ( _Skills Obstacle_ ), and lack of proper transport facilities.

We now consider macro-level controls. Unless stated otherwise, we use 1 year lagged (from the year the ES was conducted) values of all the macro-level variables.

We argued above that one of the ways in which trade affects women’s employment is by raising incomes and overall economic development. However, differences in the level of development across countries cannot be solely attributed to trade. This implies that at least part of the observed relationship between exports and women’s employment may be spuriously driven by aspects of overall economic development unrelated to exporting activity. To guard against this problem, we control for the (log of) GDP per capita, PPP adjusted and at constant 2011 international dollars. The data source for the variable is WDI, World Bank.

As discussed above, greater competition in the domestic markets makes discrimination against female workers more costly to the employer. Thus, a positive relationship between greater competition domestically and female workers is expected. This could bias our main results if exporting activity happens to vary systematically with the level of competition in the domestic markets. Thus, we control for two proxy measures of the competitive pressure faced by the firm in the domestic markets. The first measure is based on entry regulations as measured by the World Bank’s Doing Business sub-indicator Starting a Business. We use the “distance to frontier” summary measure for the Starting a Business indicator. Higher values of the variable imply fewer entry restrictions and therefore greater competition in the domestic markets. The second measure of competition that we use is the traditional Herfindahl-Hirschman index for the annual sales of the firms in the ES. The index is defined at the country-ES time-industry level. Industry grouping is at the same level as used for computing cell averages for our exports variable. Note that higher 16
affordability. It can help inform the design of housing subsidies to make them more equitable and efficient and provide a way to refine residential mortgage underwriting to reflect a more accurate assessment of risk (Valdez 2009).

It is uncontested that the RIM is conceptually sounder than any of the ratio-based benchmark approaches to measure housing affordability. However, what remains as one of the main challenges in the adoption and operationalization of the RIM is the complexity of developing a budget standard appropriate to specific contexts and locations. A budget standard is a list of goods and services that a family of a specified size and composition would need to live at a designated level of well-being, together with the costs of those goods and services (Fisher 2007). Although every household has its own unique characteristics, it is possible to define some socially agreed assumptions of what constitutes a minimum, adequate, or decent standard of living. A budget standard indicates the level of income a specific family living in a specific place at a specific time needs to achieve a particular standard of living. It is derived by specifying every item needed by the family, pricing each item, and summing to produce the overall budget. “For housing, food and most other items, data from consumer expenditure surveys, expert opinion and, in some cases, opinion surveys and focus groups are used to establish a minimal standard... The physical standard for each item is then priced to determine a total (after-tax) minimal budget” (Stone 2006). In principle, any household with an income equal to or greater than the total budget should be able to meet its basic needs, including housing, at the physical quantity and quality represented by the budget standard (Saunders and Bedford 2017). The budget standard thus attempts to move beyond relative comparisons of economic well-being and derives the absolute amount of resources that households need to meet their basic needs. The core question that forms the basis of the budget standard is: How much income does a family need to live at
Costinot, A., D. Donaldson, and I. Komunjer (2012). “What Goods Do Countries Trade? A Quantitative Exploration of Ricardo’s Ideas,” The Review of Economic Studies, vol. 79(2), pp. 581–608.

Costinot, A., A. Rodriguez-Clare (2014). “Trade Theory with Numbers: Quantifying the Consequences of Globalization,” Handbook of International Economics, Edited by G. Gopinath, E. Helpman and K. Rogoff, Chapter 4, pp. 197–262.

Deaton, A. (1989). “Rice Prices and Income Distribution in Thailand: a Non-parametric Analysis,” Economic Journal, 99 (Supplement), pp. 1–37.

Deaton, A. (1997). _The Analysis of Household Surveys - A Microeconometric Approach to Development Policy_ . Baltimore: Johns Hopkins Press.

Deaton, A. and O. Dupriez (2011). “Purchasing Power Parity Exchange Rates for the Global Poor” American Economic Journal: Applied Economics, 3 (2):137-66.

Faber, B. (2014). “Trade Liberalization, the Price of Quality, and Inequality: Evidence from Mexican Store Prices,” mimeo, Department of Economics, University of California at Berkeley.

Fajgelbaum, P. and A. Khandelwal (2016). “Measuring the Unequal Gains from Trade,” Quarterly Journal of Economics, vol. 131, pp. 1113–1180.

Galle, S., A. Rodriguez-Clare, and M. Yi (2017). “Slicing the Pie: Quantifying the Aggregate and Distributional Effects of Trade,” mimeo Berkeley University.

Nicita, A. (2009). “The Price Effect of Trade Liberalization: Measuring the Impacts on Household welfare,” Journal of Development Economics, vol. 89(1), pp. 19–27.

Nicita, A., M. Olarreaga, and G. Porto (2014). “Pro-Poor Trade Policy in Sub-Saharan Africa,
_ 1. Within treatment arms the covariates are not jointly significant, as seen from the joint test reported at the base of the table. Furthermore, 12 (21.1%) of the tests of joint significance have a _p < ._ 1.

Overall, therefore, there is minor imbalance. We control for all baseline covariates in all treatment effects regressions in the paper to account for this.

### **A.2 Neighborhoods and recruitment**

Table A.3 describes each of the study neighborhoods where we recruited, along with population estimates. We report the estimates of the number of all adult males, as well as our low-end estimates of the number of target males in each neighborhoods—men 18 to 35 in the bottom decile of income.

### **A.3 Tracking and attrition**

We achieved tracking rates of roughly 93% over a year.<sup>2</sup> Given that this was such a transient population, we took special measures to minimize attrition.

> 1We maintained the Phase 1 baseline survey for all Phases for the sake of consistency and completeness.

> 2Rates of 80, 90 or even 95 percent are not uncommon in developing country field experiments and panel surveys. For example, the Indonesia Family Life Survey reached 94% of households and 91% of target individuals after four years. The Kenyan Life Panel Survey made contact with 84 percent of target respondents over a seven-year period. Similarly, in the US, researchers were able to reach 98% of the Perry Pre-school children at age 19 and 95% at age 27. One reason is that a small sample is easier to track intensively. Another reason is that enumerator wages are lower in Liberia in the U.S. and this means that intensive sleuthing and tracking is affordable.

i
16 developing countries. By comparing public expenditures as a percent of GDP in 1990 and 2000, it is easy to see that at best resource allocation stayed almost constant, but in most countries it fell. A much more systematic study of the evolution of the public sector role in financing infrastructure by Calderon and Serven (2004) shows that for eight Latin American countries, public sector investment dropped from 3% of GDP to 0.8% during the 1990s.

**Figure 2:** Public Expenditure in Infrastructure (% of GDP)

<!-- Start of picture text --> 16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>0% 5% 10% 15%<br>Source: IMF Government Finance Statistics, various years<br>1990<br>Low income Lower-middle income<br>Upper-middle income 45-degree line<br>2000<br><!-- End of picture text --> The political economy of this larger than average contribution of the infrastructure sector to fiscal adjustment is not difficult to understand – postponing large and costly infrastructure investments is far easier for a Ministry of Finance than cutting current expenditures such as wages and debt service. Figure 3 shows that in the 16 countries sample, the relative share of infrastructure in total public expenditures has declined.

It is possible that some of the decline in public investment in infrastructure was financed by subnational governments (which may not be captured in the Government Finance 18
and fruits. Weights are based on developing countries’ export values in 2002–04. Indices are based on nominal prices in U.S. dollars.

Many governments in developing countries control petroleum product prices. In the face of mounting subsidies, a number of governments seriously explored options for pricing reform in the period leading up to mid-2008. The budgetary pressure to press on with reform subsided briefly following the price collapse in late 2008, but those governments that had done little were caught by rising prices again soon thereafter. To the extent that price increases on the world market have been transmitted to the domestic market, soaring prices have led to calls on governments across the world to take action, ranging from providing greater safety nets to the poor and increasing the minimum wage to releasing oil from strategic reserves, reducing taxes, and granting outright price subsidies.

Given the high share of household expenditure on food—in some low-income countries, the food share can be as high as half or more, and even in middle-income countries it is typically 20–30 percent (World Bank 2012b)—rising food prices have made fuel price reform, politically difficult under any circumstances, all the more challenging. Conversely, rising fuel prices have compounded the political difficulties of reducing food price subsidies, as households face rapidly increasing expenditures on other basic goods. As a result, some countries have seen people taking to the streets to protest both high food and energy prices.
emissions multipliers for all sectors. CC cĉ The level of emissions per worker by sector is estimated as the level of emissions divided by the number of workers aged 15 to 64 years in each sector. In this section, three primary sources of information are implemented to estimate GHGE per worker by sector: the Socioeconomic Database for Latin America and the Caribbean (SEDLAC),<sup>11</sup> the 2018 edition of the Organization for Economic Co-operation and Development (OECD) country-level Input-Output Tables (IOTs),<sup>12</sup> and the Climate Watch (CAIT) Historical

> 11 SEDLAC is a database of harmonized socio-economic statistics constructed from the Latin American and Caribbean (LAC) household surveys. SEDLAC includes information from over 300 household surveys carried out primarily in 18 LAC countries for which a comparable income aggregate (for welfare analysis) can be created: Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. The SEDLAC database and project were jointly developed and are jointly maintained by CEDLAS (Universidad Nacional de La Plata) and The World Bank’s LAC Team for Statistical Development (LAC TSD) in the Poverty and Equity Global Practice.

> 12 Available in https://stats.oecd.org/Index.aspx?DataSetCode=IOTSI4_2018.

8
<br>SERVICE ACTIVITIES 0.00<br> PROFESSIONAL, SCIENTIFIC AND<br>TECHNICAL ACTIVITIES 0.36<br> REAL ESTATE ACTIVITIES 0.02<br> FINANCIAL AND INSURANCE<br>ACTIVITIES 0.03<br> INFORMATION AND<br>COMMUNICATION 0.00<br> ACCOMMODATION AND FOOD<br>SERVICE ACTIVITIES 0.00<br> TRANSPORTATION AND STORAGE 1.30<br> WHOLESALE AND RETAIL TRADE;<br>REPAIR OF MOTOR VEHICLES AND... 2.18<br> CONSTRUCTION 0.40<br> WATER SUPPLY; SEWERAGE, WASTE<br>MANAGEMENT AND REMEDIATION... 0.01<br><!-- End of picture text --> <!-- Start of picture text --> Ural<br> ADMINISTRATIVE AND SUPPORT<br>SERVICE ACTIVITIES 0.00<br> PROFESSIONAL, SCIENTIFIC ANDTECHNICAL ACTIVITIES 0.18<br> REAL ESTATE ACTIVITIES 0.01<br> FINANCIAL AND INSURANCE<br>ACTIVITIES 0.00<br> INFORMATION AND<br>COMMUNICATION 0.01<br> ACCOMMODATION AND FOOD<br>SERVICE ACTIVITIES 0.00<br> TRANSPORTATION AND STORAGE 0.41<br>REPAIR OF MOTOR VEHICLES AND... WHOLESALE AND RETAIL TRADE; 0.13<br> CONSTRUCTION 0.08<br> WATER SUPPLY; SEWERAGE, WASTEMANAGEMENT AND REMEDIATION... 0.00<br><!-- End of picture text --> Source: RUSLANA. Note: Per capita density is per million population.

47
GDP)|-0.11***<br>(0.02)|-0.06***<br>(0.02)|
|Initial Educ. Spend (% of Gov’t)|-0.17***<br>(0.04)|-0.24***<br>(0.04)|
|Income Group Fixed Effects|No|Yes|
|Region Fixed Effects|No|Yes|
|N|852|852|
|R-squared|0.13|0.21|

_Source:_ World Bank calculations based on UIS and IMF data. _Notes_ : The table shows regression estimates of five-year change in total education spending in constant PPP-adjusted dollars (dependent variable) on initial country conditions. Standard errors in parentheses.

* p<0.10; ** p<0.05; *** p<0.01. All variables are logged.

# _3.2 Evidence on How Governments Have Translated Spending into Education Outcomes_

In the previous section, we explored how countries have increased their spending and how this was influenced by their initial conditions. However, having the _ability_ to mobilize resources for education is not enough to guarantee that those resources _should_ be mobilized from an efficiency point of view or that they _will_ be mobilized even if they should. So, the first question is: Should a country always aim for an ambitious increase in resources for its education sector? The answer is no. The desired level of public spending on education can depend on numerous factors, including the importance of competing demands on the public purse. However, a crucial consideration in determining whether a country should spend more on education, regardless of other demands, is the outcomes that can be realized from such an increase.

# **_Defining Our Outcome of Interest: Learning-Adjusted Years of Schooling (LAYS)_**

The first step in answering these questions is to establish the outcomes and