rafmacalaba/datause-encoder-data huggingface dataset:
# **2.4 Information Collected in FAT**

The survey collects information in three broad areas: the business functions conducted by an establishment, the use of technologies in each business function, and information on the establishment’s financial statements, workers, and management.

**Business functions.** The business functions that comprise the horizontal dimension of the grid cover the key tasks involved in production. Explorations conducted at the piloting stage of the survey as well as the responses to the questions on the use of technologies in GBFs demonstrate that these functions are conducted in-house and that respondents are aware about the technologies their establishments use in the GBFs.<sup>7</sup> We formally explore the relevance of each sector-specific business function in each establishment through a screener question that asks whether a sector-specific function is conducted in that establishment. This information helps us assess the relevance of establishment-level measures of technology sophistication based only on the technologies used in functions conducted in-house.

**Technology questions.** The survey has two types of questions about the technologies used to conduct each business function. First, it asks whether the establishment uses each of the technologies listed in the grid. After identifying the technologies that are used by the establishment in a business function, the survey asks which technology is the most widely used in that function. The answers to these questions permit us to differentiate between the range of technologies present in the business function vs. the intensity with which they are used.

FAT also asks whether the establishment uses “other technologies” in the business function in addition to those contained in the grid. Only in 3.6% of the business functions establishments declare that “other” technologies are used in the business function, and only in 0.8% of the business functions "other" is the most widely used technology. The low frequency of “other” demonstrates the comprehensiveness of the technologies in the grid.

**Other variables.** The survey also includes other standard questions about financial statements’ information, employment, education of the employees, and education and experience of the manager. The survey collects information on four management practices from MOPS, including the presence of formal incentives, number of key performance indicators (KPIs), 7Due to space constraints in the survey and the information revealed during the pre-pilot, we decided to not directly ask about whether establishments conduct each GBF in FAT. Proxying the fraction of GBFs that are not conducted in house by the share of GBFs for which the establishment responds that either "does not use" or "does not know if it uses" to all the technologies in the grid for the BF, we find that only 3.9% of GBFs are not conducted in-house.

10
**Table 3. Estimates of the Potential Demand for Emissions Reductions and Size of the CDM Market by 2010.**

|Study|Annex B Countries'<br>Demand for Kyoto Units<br>under the Protocol<br>mtCO2e/year|Potential Size of the<br>CDM Market<br>mtCO2e/year|
|---|---|---|
|Blanchard, Criqui, and Kitous<sup>a</sup>(2002)|688−862|0−174|
|Eyckmans et al.<sup>a</sup>(2001)|1,414−1,713|261−499|
|Grutter<sup>a</sup>(2001)|1,000−1,500|0−500|
|Haites<sup>b</sup>(2004)|600−1,150|50−500|
|Halsnæs<sup>b</sup>(2002)|600−1,300|400-520|
|Holtsmark<sup>b</sup>(2003)|1,246−1,404|0−379|
|Jotjo and Michaelowa<sup>a</sup>(2002)|1,040|0−465|
|Van der Mensbrugghe (1998)|1,298|397|
|Vrolijk<sup>b</sup>(2000)|640−1,484|300−500|
|Zhang<sup>b</sup>(1999)|621|132−358|
|Range|600−1,713|0−520|

Note:<sup>a</sup> The forecasts assume that only the United States does not ratify the Kyoto Protocol;<sup>b</sup> the forecasts assume that Australia and the United States do not ratify the Kyoto Protocol. Sources: Haites (2004) and Zhang (1999).

16
# **1. Introduction**

Most developing and emerging countries have implemented maternity leave policies to help women balance careers with childbearing and childrearing activities (World Bank 2022). Maternity leave policies have the potential to contribute significantly to the development process by improving women’s labor market participation. However, the efficacy of these policies is yet to be properly established. Theory suggests competing effects of maternity leave on female employment. The available empirical evidence is inconclusive, confined mostly to developed countries, and often fails to distinguish between women in the formal vs. informal sector, even though maternity leave laws only apply to the formal sector workers. The present paper contributes to the literature using firm-level survey data on private registered or formal firms in 111 developing and emerging countries. We estimate the impact of the duration of paid maternity leave (henceforth, maternity leave) on the share of workers in a formal private sector firm that are females (henceforth, female workers). The results indicate a large, positive, and statistically significant relationship between the two. We uncover important heterogeneities in this relationship depending on who bears the cost of maternity leave (government or employer), and the share of females in the country who are in the childbearing age group of 20-29 years. We argue that these heterogeneities serve the additional purpose of raising our confidence against endogeneity concerns. We also find strong distributional implications of maternity leave in that maternity leave benefits more women at the relatively low end of the skills and wage spectrum.

The existing empirical studies on the impact of maternity leave on women’s labor market outcomes are largely restricted to the developed countries (see Albagli and Rau 2019, Dahl et al. 2016, Baum and Ruhm 2016, Zvegligh and Rodgers 2003, Ruhm 1998). With a few exceptions, studies on developing countries tend to focus on a single country or a handful of countries with no 2
**Table 1. First-step regression: Fixed effects panel regression of annual changes in employment ratios**

|||**I**|**II**|**III**|**IV**|**V**|
|---|---|---|---|---|---|---|
|**Dependent**<br>**variable:**<br>**change in EWAP**|**Annual**|**Aggregate**|**Female**|**Male**|**Non-**<br>**agriculture**|**Agriculture**|
|Labor productivity growth||-0.1192***|-0.1043***|-0.1308***|-0.0404***|-0.0797***|
|||[0.0218]|[0.0192]|[0.0250]|[0.0146]|[0.0130]|
|Working-age population gro|wth||||||
|Total||-0.1947***|||-0.1210***|-0.0730***|
|||[0.0368]|||[0.0342]|[0.0194]|
|Female|||-0.1879***||||
||||[0.0355]||||
|Male||||-0.2520***|||
|||||[0.0492]|||
|Lagged EWAP|||||||
|Total||-0.0622***|||||
|||[0.0124]|||||
|Female|||-0.0589***||||
||||[0.0101]||||
|Male||||-0.0607***|||
|||||[0.0189]|||
|Non-agriculture|||||-0.0512***||
||||||[0.0190]||
|Agriculture||||||-0.0485***|
|||||||[0.0146]|
|Constant||4.4334***|3.4728***|5.2174***|2.2074***|1.4587***|
|||[0.7700]|[0.5260]|[1.4031]|[0.6268]|[0.4167]|
|Observations||2058|2058|2058|2058|2058|
|Numbers of countries||103|103|103|103|103|
|Adjusted R-squared||0.234|0.192|0.208|0.082|0.154|
|Year dummies||Yes|Yes|Yes|Yes|Yes|
|Countryfixed effects||Yes|Yes|Yes|Yes|Yes|

Sources: Authors’ estimates. International Labour Organization; Penn World Tables (database); WDI (database); World Bank.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Robust standard errors reported in brackets. Country fixed effects and year dummies are included. Columns show the results from regressions of the change in aggregate, women’s, men’s, non-agriculture, and aggregate employment ratios. Sample includes 103 emerging market and developing economies (EMDEs) that are not small states during 2000–19.

22
**<u>Table 4 Public expenditure on health as percentage of total government expenditure</u>**

||**Value**|**Year**|
|---|---|---|
|Albania|7.3|2001|
|Bosnia and Herzegovina|12.8|Average of 1996-2002|
|Macedonia, FYR of|14.4|Average of 1996-2002|
|Kosovo|11.5|2004|
|Serbia and Montenegro|14.0|Average of 2000-2002|
|Croatia|13.6|Average of 1996-2002|
|Slovenia|14.4|Average of 1996-2002|
|EU-15|12.9|Average of 1996-2002|

Source: IMF Government Finance Statistics; Bank estimates for Kosovo

# _<u>D1.4 The functional composition of public expenditure is marked by inefficiencies</u>_

While strict cross-country comparisons are not possible since categories of expenditure in available data sources differ across countries<sup>15</sup> , it is clear that the composition of health care expenditure is skewed towards inpatient care. Typically, such an imbalance results in a crowdingout of expenditure on outpatient and preventive care – care that is typically more cost-effective. Still, the ratio of inpatient to outpatient care, in most cases, is not that much higher than in the EU-15 and OECD countries. As a percentage of total public spending on health, the EU-15 spends about 38% on inpatient care and 31% on outpatient care. The ratio is identical in OECD countries which, on average, allocate most of their health expenditure to inpatient care (38%), followed by outpatient services, including ancillary services and homecare (31%) (Orosz and Morgan 2004). Very similar ratios are observed in the Federation of Bosnia and Herzegovina and in Montenegro. In the FYR of Macedonia and Republika Srpska, though, the percentage of the health insurance fund budget spent on inpatient care is almost 10 percentage points higher than in the EU-15 and in other countries of the sub-region.

Pharmaceutical expenditures are also very high and an important driver of medical inflation (see next section)<sup>16</sup> . Also, actual total expenditure on drugs are higher than these figures reveal because most pharmaceutical estimates tend to reflect only outpatient and retail pharmacy drug expenditure, and not the large drug expenditures that are incurred while in hospital and classified as part of inpatient care expenditure.

While a conclusive statement can not be made since data are not available for all countries, capital expenditures in the sub-region tend to be lower than is needed to maintain physical infrastructure and medical equipment in good condition and to purchase up-to-date medical technology.

> 15 This reflects differences in the national health accounting systems since the standard National Health Accounts expenditure reporting methodology is not yet used in most of the countries of the sub-region. The one exception is Serbia where a first set of National Health Accounts have recently been completed, although the process is yet to be institutionalized.

> 16 See the Policy Note on the Pharmaceutical Sector in the Western Balkans for a more in-depth description of the expected increase in spending on pharmaceuticals in the sub-region in the next few years.

19
# **2. Data**

The main data source for this study is the _Pesquisa Nacional por Amostra de Domicílios_ (PNAD), and the period of analysis is 1995-2012. The PNAD is an annual, nationally representative household survey, covering both rural and urban areas.<sup>8</sup> It is fielded by the Brazilian Census Bureau ( _Instituto Brasileiro de Geografia e Estatística_ , IBGE) every year, except for census years. The descriptive analysis uses all available years in the period 1995-2012, but for the decompositions we use the PNAD datasets for six years: 1995, 1996, 2002, 2003, 2011 and 2012.

The following filters were applied to the data to generate our working sample. Our sample includes all workers aged 18-65 who reported positive earnings during the survey’s reference week. Our measure of earnings is total monthly earnings from all jobs, and it is expressed in real values using the CPI deflator with base-year 2005. Monthly earnings are trimmed at the 1<sup>st</sup> and 99<sup>th</sup> percentiles. Sample sizes vary somewhat over time, but about 130,000 individuals per year are included on average.

All variables used in the analysis are categorical, except for earnings, schooling and experience in the labor market. The last two are measured in years. Demographic worker characteristics include a gender dummy and a three-way categorical variable for race (white, black and other). The analysis also distinguishes between rural and urban workers. Rural and urban areas are classified in accordance with the Brazilian census definitions. Spatial measures also include dummies for the five main geographic regions of Brazil: North, Northeast, Center-West, Southeast and South. Finally, regarding sectoral distribution, workers are divided into 17 different sectors of economic activity (see Table 2 below).

In terms of institutional factors, we distinguish three characteristics of the job. Workers are classified as formal employees if they have a job that is properly registered in their work-card or “ _carteira de trabalho_ ,” which provides workers with various benefits including rights to pensions, unemployment insurance and severance payments. Those employees whose employers have not registered their job in the “carteira de trabalho” are considered informal employees, and we also include in the analysis own-account or selfemployed workers (“ _conta própria_ ”) as a separate category. Self-employment in developing countries is often considered as an indicator of informality, although it also encompasses workers engaged in liberal professions and some entrepreneurs with no employees of their

> 8 Except for the rural areas of Acre, Amapá, Amazonas, Pará, Rondonia and Roraima states, which correspond to the Amazon rain forest. These areas, which according to census data account for 2.3 per cent of the Brazilian population, were excluded from the survey before 2003. To preserve sample comparability, we exclude these areas from our sample in all years.

6
The factors account shows the income distribution matrix (households, factors), that distributes the remuneration of factors to households. Part of the capital is owned by the rest of the world.

For the demand side, we summarize the matrix of household expenditures (activities, households), government consumption (activities, government), private and public investments (activities, investments) and the vector of exports (activities, ROW). The matrices (household, household) and (household, government) correspond to transfers between agents.

Private savings, public savings and foreign savings are added up to finance investments. The row BNI closes the model and it represents the superavit/deficit of every agent; it corresponds to financial transactions as of 2006.

# **4 BASIC CHARACTERISTICS OF THE GENERAL EQUILIBRIUM MODEL**

Our model is organized in ten representative households, 29 production sectors, one consolidated public sector and the rest of the world, and a thorough decomposition of the tax structure and regulatory regimes. It takes into account different degrees of factor mobility and several technologies that compete to produce the same good or service. The information has been updated as of 2006 and it includes a dynamic recursive component to take into account economic growth. Also discussed here are specific characteristics of the economy of Argentina, like unemployment and significant export taxes for crops and oil<sup>7</sup> . It allows simulating the economy-wide impacts of large scale production of biofuels in the country. The model is numerically solved using GAMS/MPSGE<sup>8</sup> . A more detailed description of the model is presented in Appendix A.

Since it is necessary to take into account the opportunity costs of land and alternative allocation of crops (producing vegetable oil or exporting grain directly) the model is structured to have a more detailed and realistic representation of the biofuel industry and the potential trade-offs and opportunity costs, focusing specially on alternative uses of land. A detailed representation of

> 7 The model is flexible to address different elasticities and parameters, as well as different degrees of factor mobility. Also, different mobility of factors can be taken into account in the model; this is relevant for capital in agriculture which is taken as mobile only among agricultural sectors.

> 8 The solution of the model is obtained using the representation of General Equilibrium and using the Mixed Complementarity Approach –see Ferris and Pang (1997) for a survey of the mathematical method and Böhringer and Rutherford (2008) for a recent description on the usefulness to model energy sectors in CGE. The model is developed in the environment of GAMS/MPSGE (see Rutherford (1999)). At present it can be used in interface with GAMS (see Brook _et al_ ,1992).

10
# **The Role of Trade Costs in Global Production Networks.**

# **Evidence from China’s Processing Trade Regime**

Alyson C. Ma,<sup>a</sup> Ari Van Assche,<sup>b,*</sup> aUniversity of San Diego bHEC Montréal and Katholieke Universiteit Leuven

*Corresponding author.  HEC Montréal, Department of International Business, 3000 Chemin de la Côte-Sainte-Catherine, Montréal (Québec), Canada H3T-2A7. Phone: (514)340-6043. Fax: (514)340-6987. E-mail: ari.van-assche@hec.ca.

## ARTICLE INFO

JEL Classification: F12, F14, F23 Keywords: vertical specialization, global production networks, trade costs, China, processing trade, export platform FDI, oil prices, Global Recession.
25

|Intervention # Sud-Kivu Province||-0.076||0.022||0.184|
|---|---|---|---|---|---|---|
|||(0.286)||(0.044)||(0.158)|
|Child's sex|||0.010|0.010|0.134**|0.135**|
||||(0.014)|(0.014)|(0.062)|(0.062)|
|Child's age (years)|||-0.043***|-0.043***|-0.233***|-0.234***|
||||(0.004)|(0.004)|(0.033)|(0.033)|
|Constant|0.272|0.324|0.235***|0.268***|-1.323***|-1.248***|
||(0.168)|(0.214)|(0.039)|(0.044)|(0.182)|(0.204)|
|Observations|329|329|4072|4072|2142|2142|
|Wald_F||0.790||3.169||3.022|
|Wald_p||0.502||0.027||0.032|

Coefficients and standard errors from linear models of the primary outcomes on intervention group, fixed effects for randomization strata, fixed effects for province, and intervention-by-province interaction terms. Standard errors are clustered by cluster (group of villages). There were 121 clusters in total. The WASH institutions index was calculated by rescaling each variable in the index (e.g., presence of WASH committee) so that higher values imply better outcomes, then standardizing relative to the control group, following Kling _et al_ . Effects are in standard deviation units. The Wald F is a test statistic for the null hypothesis that all of the interaction term coefficients are zero. * p<.1, ** p<.05, *** p<.01"
reach it. Both approaches can be either static or dynamic. While endogenous technology adoption can be part of the model, CO2 concentration can also be addressed via level of production only.

Springer notes a variety of common outcomes from most modeling exercises. For example, most models find that trade in permits substantially lowers the cost of meeting Kyoto objectives, while trade restrictions increase costs and potentially lead to market power concerns. The withdrawal of the US is expected to substantially lower the environmental efficacy of the climate change framework. Dynamically, most modeling exercise reveal what Nordhaus (2007) describes as a climate-policy ramp, whereby policies aimed at slowing climate change tighten over time.

Another important modeling dynamic involves the treatment of technical change.<sup>44</sup> In most modeling efforts, technical change most often enters climate change policy models exogenously. In bottom-up models technical change consists of optimizing among a fixed set of engineering technical relationships. The same can be said of some of the models that rely on abatement cost curves built up from information on the energy structure of economic regions. In top-down models, technology is reflected in the parameters of the modeled economic relationships, which are expected to change with shifting technologies. The most straightforward way is to think in terms of the parameters of a production function where the parameters imply an underlying technology. For this reason, Löschel (2002) argues that endogenous technical change is more easily modeled within a top-down structure. Still, shifting production function parameters are also consistent with the endogenous adoption of existing technologies (Mundlak 1993).<sup>45</sup> Moreover, efficiency gains can also come about because of a changing input composition as capital levels change over the longer term (Sands 2004).

Consequently, what distinguishes the endogenous technology change models is a structural link between research expenditures and innovation. Endogenous technical change models that take into account research and development investments include Goulder and Schneider (1999), Buonanno, Carraro and Galeotti (2003) and Nordhaus (2002).

One primary purpose to which models have been put is the development of a schedule of carbon prices that are consistent with different carbon concentration levels and that lead to stable but different long-run climates. Estimates gathered for the Fourth IPCC Assessment suggest a carbon dioxide price of from $US 20 – 80 per ton by 2030 and rising to $US 30-155 by 2050 is consistent with scenarios that stabilize atmospheric carbon concentrations at 550 ppm, a level thought to be consistent with moderate climate change. Importantly, models that allowed for endogenous technical change suggested that the same level of atmospheric concentration levels could be obtained at

> 44 See Carraro and Galeotti (1997), Löschel (2002) and references therein.

> 45 The diffusion of known technologies is well illustrated by Oda, Akimoto, Sano and Tomoda’s (2007) bottom-up study of the Japanese steel industry.

23
Schwartz, S. (1977). Normative influences on altruism. In L. Berkowitz (Ed.), _Advances in experimental social psychology._ Vol. 10. New York: Academic Press.

Schwartz, S. H. (1994). Are there universal aspects in the structure and contents of human values? _Journal of Social Issues_ , 50, 19–45 _._ Shafir, S., Bechar, A., & Weber, E. U. (2003). Cognition-mediated coevolution: Contextdependent evaluations and sensitivity of pollinators to variability in nectar rewards. _Plant Systematics and Evolution, 238_ ,195-209.

Simon, H. A. (1957). _Models of man: Social and rational_ . New York: John Wiley and Sons.

Simon, H.A. (1990). Invariants of human behavior. _Annual Review of Psychology, 41,_ 1- 19.

Sloate, P. L. and Voyat, G. (2005). Language and imitation in development, _Journal of Psycholinguistic Research, 12_ , 199-222.

Sloman, S. A. (1996). The empirical case for two systems of reasoning. _Psychological Bulletin, 119_ (1), 3-22.

Slovic, P. (1997). Trust, emotion, sex, politics, and science: Surveying the riskassessment battlefield. In M. Bazerman, D. Messick, A. Tenbrunsel, & K. WadeBenzoni (Eds.), _Psychological Perspectives to Environmental and Ethical Issues in Management_ (pp. 277-313). San Francisco, CA: Jossey-Bass.

Slovic, P., Kunreuther, H., & White, G. F. (1974). Decision processes, rationality and adjustment to natural hazards. In G. F. White (Ed.), _Natural hazards, local, national and global_ (pp. 187-205). New York: Oxford University.

Slovic, P., Lichtenstein, S., & Fischhoff, B. (1984). Modeling the societal impact of fatal accidents. _Management Science, 30_ , 464-474.

Stern, P.C., Dietz, T., Abel, T., Guagnano, G.A., and Kalof, L. (1999). A value-beliefnorm theory of support for social movements: The case of environmentalism. _Human Ecology Review_ , _6_ , 81-97.

Sunstein, C.R. (2003). _Why Societies Need Dissent_ . Harvard Univ. Press, Cambridge.

Sunstein, C.R., Hastie, R., Payne, J.W., Schkade, D.A., Viscusi, W.K. (2002). _Punitive Damages: How Juries Decide_ . Chicago, IL: University of Chicago Press.

Sunstein, C. R. (2006). The availability heuristic, intuitive cost-benefit analysis, and climate change. _Climatic Change, 77,_ 195-210.

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