rafmacalaba/data-use-mentions-extended huggingface dataset:
- (i) gender: women or men

- (ii) education: complete lower-secondary or less; upper-secondary; or tertiary education

- (iii) experience: less than three years working in the firm; three to ten years; or more than ten years

- (iv) training: none; in-house; or external formal training

- (v) belonging to a union: no or yes

- (vi) type of contract: temporary or permanent

The rationale for the choice of the above variables has been well documented in several studies since they have an impact in either wages or productivity of the workers. Gender might reveal information on a possible gender wage gap (Katz and Correia, 2001). Human capital is important to enhance productivity and long-term economic growth (Dearden, Reed, and Van Reenen, 2004). A more educated work force increases worker productivity and facilitates the adoption and use of new technologies. Higher skilled workers can more easily adjust to changes in the economy than less skilled workers. Institutions such as union membership might be important in determining wages (Maloney and Pontual, 1999). Long-run employment relationships might be beneficial for both employers and employees, helping to build and retain firm-specific skills (Verner, 2000), which implies that permanent contracts might affect productivity. Also, we have controlled for other worker and firm characteristics, such as size, region, sector of activity, R&D, export orientation, technology adoption, firm ownership, and quality control.

Thus, the composition of the workforce would be characterized by the share of workers in each of the 216 possible combinations or types of labor. Consideration of many variables poses a critical challenge in terms of the data since the characterization of the workforce encompasses 216 categories. From ENESTYC we cannot obtain the number of workers in every narrowly defined subgroup (i.e. number of women with lower secondary or less, less than three years of experience, with no training, not belonging to a union and with a temporary contract
0.00328)|
|Quality of land administration|0.000160<br>(0.00163)|||
|Rural land tenure insecurity||0.00629<br>(0.00696)||
|Enabling the business of agriculture|||0.000927†<br>(0.000566)|
|GDP per capita|-5.60e-07|-1.67e-07|-2.19e-06**|
||(1.02e-06)|(7.55e-07)|(1.00e-06)|
|Gini index|-0.00187†|-0.00245***|-0.00305**|
||(0.00118)|(0.000927)|(0.00119)|
|Constant|0.103*|0.136***|0.139***|
||(0.0537)|(0.0408)|(0.0465)|
|Observations|100|82|63|
|R-squared|0.051|0.112|0.149|

_Note: The regressions presented in this table are applied to the sample of pixels that were not cropland in 2003 but located within 3 kilometers of existing 2003 cropland pixels. Cropland per capita is from MODIS 2018; the Quality of Land Administration is from Doing Business 2020; GDP per capita is in current US$, the Gini Index uses the latest value reported in the World Development Indicators. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1,_ † _p<0.15_ .

15
―Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between: ... Cheating on tax if you have the chance‖. The question leads to a ten scale index of tax morale with the two extreme points ―never justified‖ and ―always justified‖.

A second important data set that has been used is the Latinobarómetro (Torgler, 2005a). It is an annual public opinion survey carried out in 17 Latin American countries (since 1996). It reports the opinions, attitudes, and behaviors of the around 400 million inhabitants of the region. The survey started with 8 countries in 1995 and was extended to 17 countries in 1996. It covers most of Latin America with the exception of Cuba, the Dominican Republic, and Puerto Rico. This data set is not as well known as the WVS though economists have referred to it recently (see, e.g., Graham and Pettinato, 2002, who contributed to the happiness research). This data set has integrated a similar question which allows to measure tax morale: ―On a scale of 1 to 10, where 1 means not at all justifiable and 10 means totally justifiable, how justifiable do you believe it is to: Manage to avoid paying all his tax‖.

In addition some studies have worked with the International Social Survey Programme (ISSP) (see, e.g., Torgler, 2005b). Similar to the previous data sets, it is a continuing annual program of cross-national collaboration. It started in 1983 and has grown to include more than 30 nations (mostly European countries). The RELIGION II (ISSP 1998) module covers, e.g., the following question: ―Do you feel it is wrong or not wrong if a taxpayer does not report all of his or her income in order to pay less income taxes? (1= not wrong, 2= a bit wrong, 3= wrong,
# **B.3 Global Consumption Database**

A limitation of our core sample of 31 countries is that it tends to be geographically clustered and in does not contain countries in Asia. To obtain an idea on particular whether our results might be relevant for all low and middle income countries, we compare food expenditure with that of the Global Consumption Database (GCD). The GCD is the most data source on consumer in comprehensive spending patterns developing countries to date, by assembling all available representative household expenditure surveys across countries. In particular, it includes most countries in Asia. The dataset is curated by the World Bank: aggregatef consumption statistics and further details on sources and methodology are available at http://datatopics.worldbank.org/ consumption/.

We obtained access to the Global Consumption Database microdata, in order to comfood in our core of 31 countries to the 79 low and middle pare expenditure sample income countries available in the GCD. From our sample of 31 countries, 21 countries with the GCD and have the exact same as an source. overlap usually survey original With this enhanced dataset, which represents 51% of the world population,<sup>49</sup> we measure food consumption as a share of total consumption and the slope of the food Engel curve. First we note that for the 21 countries we curve overlapping find Engel slopes within 5% of the GDC estimate. Second we compare food expenditure patterns in our core sample 31 countries to the 58 countries which only appears in the GCD. We find remarkably similar food expenditure shares and food Engel curve slopes, as a function of development, which supports that our core sample informality measures could be informative to the entire population located in developing countries, with the caveat that informality patterns might still differ geographically.

> 49We exclude rich countries by design, but a few populous countries such as China, Egypt and Iran are not part of the GCD. This explains the lion’s share of the missing population in the GCD
Group</u>**

||Changei|n Rural FL|FP Rate|Change|inUrban FL|FP Rate|Differe|ncein Diff|erences|
|---|---|---|---|---|---|---|---|---|---|
|Age|1993-05|2005-12|1993-12|1993-05|2005-12|1993-12|1993-05|2005-12|1993-12|
|15 to 24|-2.9|-14.2|-17.1|2.6|-6.3|-3.7|-5.5|-7.9|-13.4|
|25 to 34|1.4|-13.6|-12.3|3.5|-3.8|-0.3|-2.1|-9.8|-11.9|
|35 to 44|2.8|-12.9|-10.1|3.5|-6.5|-3.0|-0.7|-6.4|-7.1|
|45 to 65|2.8|-9.7|-6.9|0.5|-3.5|-3.1|2.3|-6.1|-3.8|
|65 plus|1.4|-3.5|-2.1|0.7|-1.7|-1.0|0.7|-1.7|-1.0|

Source: Authors’ own estimate based on NSSO data. Note: Diff-in-Diff in the last three columns are calculated by deducting the change in urban FLFP rate from the change in rural FLFP rate for respective years.

Historically in India, the education of female children, especially
# **1. Introduction**

This paper studies labor markets in Zimbabwe. Zimbabwe has one of the most highly developed industrial sectors in Africa. It contributes more than 25 percent to GDP. The effectiveness of economic policy in generating economic growth and improving standards of living rests on the capability of creating more employment. This paper analyzes wage and productivity determination. Among the questions addressed are the following:

- Do experience, training and education impact wages and productivity?

- Does discrimination -- related to gender or ethnicity of employees -- exist in the Zimbabwean labor markets?

- Is the labor market competitive?

- Does trade unionization affect wages and productivity?

- Are wages and productivity affected by enterprise characteristics?

- Does international trade change wages and productivity?

The questions are analyzed jointly applying an employer-employee dataset from the manufacturing sector in Zimbabwe. The introduction of productivity and enterprise data adds a new dimension to wage analyses.

Most standard wage regressions do not control for firm characteristics that can affect the wage determination process. The reason is that information on firns and employees are often not available. We control for firm characteristics in this paper. Additionally, this paper goes a step further in that we jointly estimate wage and production functions. This approach allows not only for assessing the marginal impact of demographic and other characteristics on wages but also for comparing the impact of these variables on productivity and wages.

2
.

The legacy of the boom is therefore one of missed opportunity, overhanging macroeconomic vulnerability, and increasing concentrations of the world’s poor in resource-rich Sub-Saharan Africa.

Most alarmingly we find that by 2030, on current projections even after adjusting all countries for the impacts of COVID-19, 80 percent of the world’s poor will reside in SubSaharan Africa, while 75 percent of the world’s poor will be found in resource-rich countries. Taken together, 62 percent of the extreme poor could be concentrated in resource-rich Sub-Saharan Africa. This is up from only 13 percent in 2000.

The findings of this study are in line with those documented in the literature on the resource curse. Recent surveys such as Van der Ploeg (2011) and Ross (2015) point towards the challenges resource-rich developing countries have faced translating price booms into sustained prosperity. While the cross-country empirical evidence does not support the hypothesis that this is a problem restricted to African countries, studies do suggest that weaker political and economic institutions may exacerbate the challenges countries face harnessing resource wealth for economic development (Mehlum, Moene, and Torvik 2006).

The rest of the study is organized as follows. The next section 2 examines the four main outcomes of the boom relating to i) resource dependency, ii) economic growth iii) poverty 6
ing on recent scholarship to investigate conceptions of masculinity and their influence on individuals’ preferences and behavior could be a path toward addressing the latter obstacles.

# **2 Background**

In MENA as elsewhere, reconciling responsibilities at work and at home can be challenging, especially for women who have traditionally shouldered the majority of unpaid household labor. A high burden at home may pose an impediment to female labor force participation, which remains low by global standards in the MENA region despite women’s rising educational attainment (Assaad, Hendy, Lassassi, and Yassin 2020). Expectations that women will take on the dominant role caring for the home and raising children lead many women to exit the labor force upon family formation (Assaad, Krafft, and Selwaness 2022). State-funded childcare facilities are rare, and private childcare provision is usually informal, in-home, and minimally regulated. Where public childcare is available, insufficient operating hours may nonetheless inhibit or even decrease women’s labor force participation (Krafft and Lassassi 2020). Cross-national comparisons of timeuse surveys show that the ratio of time spent on unpaid labor by women versus men in the MENA is among the highest in the world (Charmes 2015, 2019). The COVID-19 pandemic also increased women’s unpaid work and care burdens in the region (Hendy and Yassin 2022; Moghadam 2021; OECD 2020).

At the same time, norms around household task burden-sharing are in flux, and active efforts to change norms are underway. In Morocco, for example, recent anthropological research has explored how female labor force participation contributes to the renegotiation of expected household responsibilities (Bouasria 2013, 2017; Dike 2021). Community associations working on women’s empowerment in Morocco have also undertaken awareness campaigns focused on de-stigmatizing men’s contribution to household labor (Barnett 2022). During the early phases of the pandemic, Facebook posts in the country went viral highlighting how men could relieve the burdens of lockdown by contributing 4
7 benefits provided through preferential utility prices and rates of asset lease, etc.<sup>6</sup> It is estimated from data of the State Tax Service that regional governments provided more than 1% of GDP in tax exemptions in 1995, which constitute about 20% of their funded budget subsidies (Table 3). In Tatarstan, which is probably an extreme case of regional government intervention, a case study revealed that in 1995 off-budget subsidies, i.e. the most implicit part of the overall subsidy program, exceeded 50% of those which were reflected in the budget (Table 4)7.

_Table 4. Tatarstan: various subsidies in 1995._

||In trln. rbl.|As % of total|
|---|---|---|
|Subsidies from the regional|3.65|64.83|
|budget, o/w:|||
|-consumer<br>-producer|1.95<br>1.70|34.64<br>30.20|
|Subsidies to enterprises from regional<br>extrabudgetary funds|1.65|29.31|
|Tax benefits granted to local enterprises|0.33|5.86|
|TOTAL|5.63|100.00|

Source: the World Bank In addition, a number of case studies suggest that the regional allocation of transfers to industrial enterprises is biased towards regionally dominating "crisis" industries and strong sectoral interest groups. For example, in Kemerovo oblast subsidies for the regional coal company Oblkemerovougol amounted to 6.6% of all expenditures in the 1996 draft oblast budget, and budgeted subsidies per ton of coal produced were higher than the average per ton federal coal subsidies provided by the federal budget (Bodnar et al., 1996). Correspondingly, there is evidence that some regions in Central Russia, depending on peculiarities of their industrial structure, provide relatively more in budget support for local textile
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# VI. Conclusion

The taxonomy put forward in this paper provides a common ground on which to compare countries over time and in a more granular way than before. Countries that have deregulated, unbundled, and privatized their electricity sectors, for example, will only feature an SOE in the natural monopoly segments (transmission and distribution), and this can now be differentiated from a country that still has an SOE operating in every electricity subsector. Similarly, paired with data on SOEs, this taxonomy has the potential to provide valuable insights on the sectoral footprint of the state across different countries, as in the World Bank’s new Businesses of the State (BOS) database and provide guidance on the options for SOE reforms (World Bank, PSD Toolkit, forthcoming).<sup>3637</sup> Preliminary cross-country analysis (World Bank 2022, forthcoming) shows that the state’s footprint in competitive sectors is bigger than previously captured: on average across 80 economies covered in the BOS database, more than half of businesses with state ownership operate in fully competitive sectors.

There are important caveats to the taxonomy, however, which relies on the NACE Revision 2 and is therefore subject to the same shortcomings as any other industry classification or methodology based on industry classification. For example, the disaggregated sector classification of one specific SOE is not necessarily representative of the main economic activity in which that SOE is engaged, nor does the classification accurately capture multi-product SOEs that operate across sectors. SOEs may have changed their main business line entirely and may serve in multiple (related) sectors in different ways. These shortcomings are the same for public entities and private entities.

Perhaps more challenging is the fact that sector characteristics (and market failures) are not constant across countries, across markets of different sizes, or across countries and markets at different stages of development. Electricity generation, for example, may be a natural monopoly in a small economy, but when the economy grows, the sector may be able to profitably sustain more
There are a number of empirical approaches to controlling for MR in estimation. First, since MR terms are country-speci...c, they can be controlled for by including country ...xed e¤ects. This is not appropriate for our purposes as we want to identify the e¤ect of country-speci...c logistics quality. Second, some attempts have been made to control for MR by using price data to construct the appropriate price indices. Data limitations are among the problems with this method. As noted in Feenstra (2004), published price indices are typically stated relative to an arbitrary base period, making comparison of levels impossible. Furthermore, they tend to include too many non-tradeable goods and thus fail to capture the additional costs embedded in internationally traded goods (Anderson & van Wincoop, 2004). Third, Anderson & van Wincoop (2003) propose solving for a system of price indices together with the gravity equation, but this involves a potentially problematic customized non-linear program. The research community has shown that, while gravity models are popular in empirical trade, estimating the entire non-linear system is not.<sup>3</sup> Fourth, Baier & Bergstrand (2009) introduce a method by which the MR terms are approximated using a ...rst-order Taylor expansion, yielding a log-linear expression for MR which contains exogenous variables only. These approximated MR terms can then be included in a single linear equation. This approach has the advantage of yielding tractable comparative statics; in particular, the role of country size in determining the appropriate comparative static e¤ect is made explicit, while Baier & Bergstrand show that the approximation error associated with this method is small for the majority of country pairs.

# 2.1.2 Firm heterogeneity

Models of monopolistic competition with ...rms of heterogeneous productivity predict selection into export markets in the presence of ...xed costs of trade. The reason is that the least productive ...rms do not generate pro..
# **II Estimating the Innovation Function**

# _Data_

Our dataset consists of a panel of 49 developed and developing countries from the 1960s to the present. We first review the measures of innovation output and input.<sup>8</sup> _Innovation Output:_ A large literature uses patents as an imperfect measure of innovation output although the limitations of the measure are well known (see for example, Griliches 1990, Trajtenberg 2001, Jaffe and Trajtenberg 2002). Of perhaps greater concern for the present work is that patents granted by national agencies are not comparable due to differences in national standards, costs of applying for patents, levels of intellectual property protection, pecuniary benefits from patenting, and other country-specific institutional features. To ensure comparability we follow Jaffe and Trajtenberg (2002), Branstetter (2001), Furman, Porter and Stern (2002) and use the number of patents granted by the United States Patent and Trademark Office (USPTO).<sup>9</sup> In the absence of a global patenting agency, the US remains the principal locus of patenting activity and the USPTO offers reliable panel data for the period 1963 to 2000 for a large number of countries. Granted patents are assigned by country of origin based on the country of residence of the first inventor.

That said, countries may differ systematically in their propensities to apply for patents in the US. Those with a large volume of exports to the US have a greater interest in patenting any invention that may be embodied in their exports.<sup>10</sup> Similarly, country endowments and economic structure may also impact the ratio of innovations to patents: manufacturing in general may generate higher patents per innovative dollar than natural resource based sectors. To control for these effects we include in our specifications the volume of merchandise exports to the US, drawn from the IMF Direction of Trade Statistics, and Leamer’s (1984) index of natural resources endowments, net exports of

> 8 See Lederman and Saenz
than other surveys where respondents are tracked and re-interviewed irrespective of where they move within the sample area (e.g., as in the Indonesia Family Life Survey, see Thomas, Frankenberg and Smith 2001).

We include only the children who were successfully followed up _in the same barangays_ in Rounds 2 and 3 in the analysis because those who were in Round 1 but not in the two other rounds (with the exception of the few refusals or incomplete interviews) died or experienced changes in treatment status when they moved. Table 4 shows the sample sizes for children who remained in the same sample barangay for all three surveys and were not missing critical data in Round 3. This group totals 6,693 children. The sample in non-program areas thus includes: 1) children living in barangays in regions 6 and 7 who did not receive the program by Round 3 and 2) all children who reside in region 8. As can be seen from Table 4, very few sample children residing in regions 6 and 7 were not living in program barangays by Round 3. Thus, our measure of treatment is essentially (though not identical to) whether a child resides in regions 6 or 7.

**Table 4. Sample Sizes for Program and Non-Program Children by Region, Philippines ECD** **<u>Study</u>**

|Region and Program or Non-program Areas|N|
|---|---|
|_Program Areas_||
|Region 6|1,682|
|Region 7|2,458|
|Total|4,140|
|_Non-Program Areas_||
|Region 6|145|
|Region 7|49|
|_Total_|194|
|Region 8|2,359|
|Total|6,693|

For the purposes of this evaluation, a central question is whether attrition is selective with respect to treatment status. Several studies on the U.S. and developing countries show that non-random attrition generally does not have a