rafmacalaba/data-use-mentions-extended huggingface dataset:
types of firms with respect to private banks. This is equivalent to having government-owned banks specifically targeting firms with certain characteristics.

It is plausible that government-owned banks are better managed and more likely to fulfill their mandate when they are located in countries with good policies and institutions. To test this hypothesis, we interact the government-ownership variable with a set of indexes of institutional quality from the World Governance Indicators (we focus on Control of Corruption, Rule of Law, and Government Effectiveness). Formally, we estimate variants of the following model: Where IQct is a country-year level measure of institutional quality rescaled to range between 0 (lowest possible institutional quality) and 1 (highest possible institutional quality) and all other variables are defined as above. The main effect of IQct cannot be estimated separately because it is absorbed by the country-year fixed effects.

In the set-up of Equation 2, β indicates whether government-owned banks are more likely to target firms with certain characteristics in countries that have the highest possible level of institutional quality and β+ δ indicates whether government-owned banks are more likely to target firms with certain characteristics in countries that have the lowest possible level of institutional quality. Hence, δ indicates whether institutional quality affects the likelihood that government-owned banks fulfill their mandate of lending to credit constrained firms or firms that generate positive externalities.

# _Lending over the business cycle_

As mentioned, there is evidence that state-owned banks contribute to smoothing credit over the business cycle. Our firm-bank level matched data allow us to test whether this mechanism affect firm-specific outcomes and if it is particularly important for credit constrained firms.

While our matched data do not have information on the amount of credit disbursed by a given bank to a given firm, we do have information on an important firm-level outcome: employment growth. We thus test whether having a relationship with a government owned bank reduces the correlation between macroeconomic shocks and firm-level employment growth and if this effect is particularly important for credit constrained firms. We start by estimating the following model: 11
2, 195–221.

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- Deaton, A., K. Parikh, and S. Subramanian (1994): “Food Demand Pattern and Pricing Policy in Maharashtra: An Analysis Using Household Level Survey Data,” _Sarvekshana_ , 17, 11–34.

- Deaton, A. and C. Paxson (1998): “Economies of Scale, Household Size, and the Demand for Food,” _Journal of Political Economy_ , 106, 897–930.

- DeSoto, H. (1989): _The Other Path_ , Harper & Row New York.

- Diamond, P. A. (1975): “A Many-Person Ramsey Tax Rule,” _Journal of Public Economics_ , 4, 335–342.

- Donovan, K., J. Lu, T. Schoellman, et al. (2018): “Labor Market Flows and Development,” in _2018 Meeting Papers_ , Society for Economic Dynamics, vol. 976.

- Ebrill, L. P., M. Keen, J.-P. Bodin, V. P. Summers, et al. (2001): _The Modern VAT_ , International Monetary Fund.

- Enste, D. H. and F. Schneider (2000): “Shadow Economies: Size, Causes, and Consequences,” _Journal of Economic Literature_ , 38, 77–114.

34
Delhi, as shown in Appendix Figure C1.<sup>8</sup> Appendix Table D5 provides some summary statistics of the survey sample. Compared to non-users, bus users (both new and continuous) are predominantly aged between 31 and 40, more often homemakers, more likely to reside in households with a monthly income exceeding |20,000, and less inclined to travel for work. Given these differences, we constructed a comparable sample of nonusers, new users, and continuous users using propensity score matching. We match women on the following variables: age, occupation,<sup>9</sup> total average monthly household income and expenditure on travel, ownership of private vehicles, and driving knowledge.

Our study sample of the Delhi primary survey is the matched sample ( _n_ = 1, 290) consisting of 184 non-users, 182 new users, and 924 continuous users. Compared to the treated sample in the CPHS data, the matched sample is characterized by higher levels of education and annual household income. Approximately 68 % of the bus users in the matched sample hold a graduate degree, and 85 % of them report an annual household income of over |240,000.

**Perceptions of Buses** In Table A1, we compare perceptions of buses between non-users and users (panel A), as well as between new users and continuous users (panel B) in the post-scheme period. We examine the differences in their perceptions of five aspects: 1) affordability and availability of bus transit; 2) safety regarding accidents, crashes, threats, and thefts; 3) connectivity; 4) bus frequency, waiting time, travel duration, and unnecessary stops; 5) accessibility to bus stops. In panel A, we can see that non-users consistently give lower ratings across all five perspectives; they find bus travel less satisfactory across all five dimensions compared to users. They are particularly concerned about safety issues. The average rating concerning safety among non-users is 1.63,
The Resource Blocks appear to have fewer SC/ST (Scheduled Caste/ Scheduled Tribe) members and slightly higher rates of home ownership, but most differences between the two groups are small and not statistically significant, particularly when it comes to existing levels of SHG membership.

Our qualitative research suggests that RRLP staff and program directors had some knowledge and familiarity with NGOs that were working in at least two of the intensive blocks. However, the operational scale of these institutions was small, and since the selection of blocks for the two

> 21 We originally sent survey enumerators to Andhra Pradesh to interview the SERP facilitators while they were home and taking a break from their rotations in Rajasthan. The head of the organization however, denied our surveyors access and refused to allow any surveys in the state of Andhra Pradesh. We were able to complete our surveys with tremendous difficulty only when the women went back to Rajasthan. The Government of Rajasthan allowed the surveys and overruled the NGO’s directives. The busy schedules of the SERP facilitators however, made it impossible to interview all women on a particular day – the division of labor between the women required some women to be constantly working on either cooking, cleaning, washing clothes, going out into the village to conduct mobilizations, and/or record key data and report this to headquarters.

> 22 2,914 control households in 6 villages sampled per block in each of the 19 non-RRLP blocks in the same districts as the treatment villages were also surveyed, but we do not analyze the control data in this paper.

10
most labor intensive of sectors/ Transport costs are calculated using a linear relationship from the transport time (in days) τ�� � 1 � λ<sup>�</sup> time��� , as in Alder (2017) and Roberts et al., (2012). Costs are highest from non‐tradeable services which are produced and sold predominantly in the same location, and lowest for manufacturing, for which longer travel times do not lead to much loss in terms of the value of the good. θ is chosen at 0.75 , reflecting similar assumptions elsewhere in the literature such as for rural goods in China in Roberts et al. (2012). There are other ways of modeling the impacts of transport times on transport costs, such as through an exponential model in Lall and Lebrand (2019).

The GVA data from the UN National Accounts, and the consequent Gross Output values calculated from input‐output matrices, combined with the net trade flows from each country, gives a total value of output of each sector consumed within each country. From these, national consumption value shares for each sector, β�, β�, β� , are calculated.

# _Parameters_

||Primary<br>Goods|Manufacturing|Non‐Tradeable<br>Services|
|---|---|---|---|
|Input Shares||||
|Primary Goodsα�|0|0.2|0.1|
|Manufacturingα�|0.1|0.2|0.1|
|Non‐Tradable Servicesα�|0.1|0.1|0.1|
|Landα�|Rural 0.25|Urban 0.1|Urban 0.3|
|Labourα�|0.55|0.4|0.4|
|Transport Costs||||
|λ<sup>�</sup>|0.18|0.
# **2. Climate Change and Agricultural Crop Yields**

# _Selecting climate change scenarios_

General Circulation Models (GCM) produce a wide range of future climate change scenarios, especially when examined at the country-level (see Solomon et al. 2007). Apart from differences in the science of modeling global climate systems, there is also uncertainty about other key variables such as how the global economy will evolve in coming decades. To account for this, GCMs typically employ different “emission scenarios” based on assumptions about future populations, technological advances, and global agreements to reduce carbon emissions.

To capture a range of possible climate change realizations, we select four projections with different temperature and precipitation outcomes averaged over all land areas of the country. The scenarios employed are presented in Table 1. In addition to temperature and precipitation deviations, the table presents the Climate Moisture Index ( _CMI_ ) (Willmott and Feddema, 1992), which is an indicator of a region’s aridity, at the national level. The CMI depends on average annual precipitation ( _P_ ) and potential evapotranspiration ( _PET_ ). A climate is classified as semi-arid (semi-humid) and then arid (humid) as _PET_ increases (decreases) relative to precipitation. The CMI is defined as: A _CMI_ of -1 is very arid whereas a _CMI_ of +1 is very humid.

# [Insert Table 1]

The scenarios are labeled WET, DRY, COOL, and HOT. The COOL scenario is relative to the other scenarios, and projects a mean average temperature increase of 1.1 degrees Celsius by 2041-50 compared to 1.9 degrees Celsius in the HOT scenario. Three of the four scenarios project an increase in precipitation reflecting an analysis of climate futures for Tanzania conducted by the Tyndall Center (2010). While precipitation rises in three scenarios, the _CMI_ remains fairly constant in two of the three because rising temperatures increase _PET_ . It is of some interest to note that the WET scenario for Tanzania is the same scenario identified by the
services. We show that a constant returns to scale model without FDI in business services (that we believe to be an inappropriate model specification given the importance of FDI commitments in Russian WTO negotiations) would produce estimated gains dramatically smaller. In Rutherford, Tarr and Shepotylo (2004) (RT&S), we extend the Jensen, Rutherford and Tarr model by incorporating all the 55,000 households of the Russian Household Budget Survey as agents in our general equilibrium model. That is, we build a “real household” model of the Russian economy with endogenous productivity effects. In that paper, we show that WTO accession could be expected to benefit virtually all the poor, but an inappropriately specified, constant returns to scale model, without liberalization of barriers against FDI in services and endogenous productivity effects would produce much smaller average welfare gains and the wrong sign for the about 7 percent of the Russian population.

In this paper we employ the same model and dataset that we employed in RT&S to examine the impact on poverty in Russia of the Doha Development Agenda as well as a result of global free trade. Crucially, we compare these impacts with the impact on Russia of its own liberalization through the commitments it will make as part of its WTO accession. In this process we are able to assess the relative benefits to Russia of liberalization of countries in the rest of the world of their tariffs or subsidies with liberalization in Russia of its barriers against FDI and goods imports. We examine impacts on Russia overall, at the decile level, as well as on the entire distribution of Russian households through our “real household” model of Russia.

As with the other country case studies in Hertel and Winters (forthcoming), the starting point for the analysis of the Doha agenda and global free trade impacts are the vectors of percentage changes in the price of exports and imports for Russia as a result of the completion of 2
their domestic markets and export less, Brazil is the least open country and significantly below its benchmarked openness based on different econometric specifications and even after controlling for country size and distance to main partners (Lederman et al, 2014).

<!-- Start of picture text --> Figure 1: Trade to GDP: 2000-2011(%) Figure 2: Trade to GDP: 2012-2017 (%)<br><!-- End of picture text --> <!-- Start of picture text --> Brazil<br>Brazil<br>3 3.5 Log GDP per capita (PPP adjusted -avg 2012-17)4 4.5 5<br>3 3.5Log GDP per capita (PPP adjusted -avg 2000-11)4 4.5 5<br>Source: World Bank staff elaboration using WDI data Source: World Bank staff elaboration using WDI data<br>400 400<br>300 300<br>200<br>200<br>Trade to GDP(%)avg2012-17<br>Trade to GDP(%)avg2000-11<br>100<br>100<br>0<br>0<br><!-- End of picture text -->

> 5 For example, provisions of the tax code that vary with firm size; employment protection measures; product market regulation limiting size or market access; and tariffs applied to specific categories of goods.

> 6 Discretionary provisions made by the government or other entities (such as banks) that favor or penalize specific firms; for instance, subsidies, tax breaks, low interest loans granted to specific firms.

> 7 For example. monopoly power, market frictions, and enforcement of property rights.

3
the informal sector is associated with migration from rural areas to urban centers. New migrants arrive without jobs, providing a large pool of labor for informal "employment." In Mongolia, however, migration is not currently a driving force of the informal sector - although net migration to Ulaanbaatar has increased considerably since 1991, the levels remain fairly small, about 2% of Ulaanbaatar's population annually?<sup>2</sup> But migration can not be dismissed entirely for several reasons. (i) Migrants may not be officially registered with the police, implying that 2% net migration is an understatement of true migration. Indeed, some officials believe that the true level of netmigration is much higher. (ii) Unregistered migrants can not legally work in the formal sector. Thus it is a virtual certainty that un-registered migrants work informally or not at all. Several respondents to the informal sector surveys conducted by the author (particularly at the "black" market) said they avoid questioning by the _duureg_ on employment status because they are not legally permitted to live in Ulaanbaatar. (iii) An important constraint on migration is that migrants have difficulty finding living space, and this constraint may soon be alleviated. Until very recently, there was a small market for housing due to the slow progress on housing privatization and legal prohibitions on the sale of publiclyowned apartments. Development of new, private, housing stock was similarly hindered by the lack of a market for urban lands and difficulties obtaining utility connections?<sup>3</sup> In general, Mongolians wishing to migrate to Ulaanbaatar can only do so if they have relatives in the city who volunteer enough space in their living quarters for the new migrants. Housing privatization, urban land privatization, and improvements in the energy sector may be expected to lead to a more fluid market for living space in Ulaanbaatar - existing apartments will become more easily reallocated, and the improved access to utility connections and the market for urban land will accelerate the development of new
production, support, and other activities across all industries. Panel A suggests that at lower levels of economic development, production activities account for a major part of export income. The production share steadily declines when countries grow richer: from more than 50 percent of total exports at GDP per capita levels below 5,000 US$ to about 30 percent at levels above 40,000 US$. Engineering, managerial, and support activities account for the majority of labor income from exporting at higher levels of income. Panel B further details production activities in four broad industry groups. It shows that income from production activities within agriculture, mining, and “light” manufacturing industries (including food and textiles industries) shifts towards production

> 7 For the purpose of Figure 1, data for eight countries has been added to the dataset, in particular data for some low-income countries, see Appendix G. However, the data for these eight countries have a lower level of detail and hence are not used in the rest of the paper.

9
Cooper and Haltiwanger (2006). The labor supply side is characterized by a rational expectations optimization problem of workers facing mobility costs and time-varying idiosyncratic shocks. The labor demand side is characterized by the rational expectations intertemporal prot maximization problem of rms facing costs for adjusting their capital stock and time-varying technology shocks. To deal with trade shocks, our model features multiple sectors. To deal with general equilibrium eects and labor market responses, we endogenize equilibrium wages across sectors.<sup>3</sup> We t our model to plant-level panel data and household survey data from Argentina for the 1994-2001. We use the rm-level data to identify the technology and capital adjustment cost parameters that dene labor demand. We use the panel component of the household survey data to identify the labor mobility costs parameters. We recover the structural parameters that characterize the frictions faced by both workers and rms. A major feature of our estimation strategy is the joint estimation of these parameters: rms internalize workers decisions when choosing investment and workers internalize rm decisions when choosing sector of employment. Finally, we use the estimated parameters to compute counterfactual stationary adjustments of investment, capital, labor allocations and wage distributions across sectors following a cut in taris. We use these counterfactual adjustments to carefully assess the implication of imperfect capital adjustment when the economy responds to trade shocks.

We focus on taris on textiles, a major import sector in Argentina that enjoyed signicant tari protection during the 1994-2001 period. In the benchmark simulation, we work with the full elimination of an initial tari on Textiles of 19.4 percent. This reduces textile prices and decreases protability in the sector. Capital gradually declines, as textile rms disinvest, and employment gradually declines, as workers are displaced. The capital stock decreases by 9.41 percent initially and by 25.47 percent in the new steady state. Employment decreases by 2.91 percent initially and by 4.64 percent in the new steady state. These are sizeable
Development (CSD), 1996. Their survey of 174 households in the _Songino Hairhan Duureg_ during the winter of 95/96 found that 41% had some income from micro enterprise activities, as contrasted with the estimates of 47-51% presented in the text. The difference can easily be explained by a number of factors including the 10 month gap between the CSD survey and the referenced SSO household surveys, the difference in _duuregs_ sampled, and statistical discrepancies.

The estimates of 30% and 33%, presented in the text, are based on SSO household survey data from October and November, 1996, respectively. As an illustration, the adjustment for October was made as follows. The probability that a household will have some informal income equals one minus the probability that the household has no informal income. The probability that a household has no informal income (approximately) equals the probability that a single member has no informal income raised to the power of the number of working age household members. Since 46.9% of households had some informal income, and the average household had 1.78 working age members, the estimate of employment in the informal sector equals one minus X, where X solves the equation: 0.469 = I - X'<sup>78</sup> . Then I - X = 30%.

33.The focus here is on the aggregate contribution of the informal sector to household incomes. The earnings of informal activities relative to formal activities will be discussed later in the report.

34.The sample of areas chosen is designed to be proportional to the fraction of population living in such areas, and includes nearly every _aimag_ plus Ulaanbaatar. Within a given area, the households are selected at random from official records of households living in the area. (Thus recent migrants, likely candidates for informal sector labor, could not be sampled unless they were officially registered which is not a trivial task, especially in Ulaanbaatar.) The households are then surveyed on the details of
**Table 7   Unemployment rates are particularly high among young workers**

||Youth unemployment<br>as % of labor force 15-<br>24|Youth unemployment<br>rate/overall<br>unemployment rate|
|---|---|---|
|**CEE**|||
|Bulgaria|35.5|2.0|
|Czech R.|16.9|2.3|
|Estonia|17.7|1.9|
|Hungary|11.9|2.1|
|Latvia|24.6|1.9|
|Lithuania|21.4|1.6|
|Poland|41.7|2.1|
|Romania|18.5|2.6|
|Slovakia|37.3|2.0|
|Slovenia|15.3|2.6|
|Turkey|17.8|1.6|
|**SEE**|||
|Croatia|34.5|2.4|
|Serbia and Montenegro|||
|a)|58.0|3.1|
|**Memorandum**|||
|EU15|14.7|1.9|
|OECD Europe|17.6|2.0|
|Total OECD|13.1|1.9|

Note: Labor Force Survey data a) The unemployed include persons who in the reference period did a temporary job Source: CEE and EU: Eurostat Turkey and OECD: Employment Outlook, OECD Croatia: Central Bureau of Statistics, Labour Force Survey 2002 (second half), Bank staff calculations. Serbia and Montenegro: Republic of Serbia, Republican Statistical Office, Labor Force Survey 2002 (October), Bank staff calculations For example, in Poland youth unemployment rate is above 40 percent. In Bulgaria and Slovakia it is between 35 and 37 percent. On average, the youth
Does Paternity Leave Matter for Female Employment in Developing Economies? Evidence from Firm Data Mohammad Amin<sup>a</sup> Asif Islam<sup>b</sup> Alena Sakhonchik<sup>c</sup> JEL: J16, J08, J21, K31 Keywords: Gender, Paternity, Women, Employment, Laws a Corresponding author, World Bank Country Office in Kuala Lumpur, Malaysia and Washington DC, USA. Email: mamin@worldbank.org. Phone: +60 179092271.

> b Enterprise Analysis Unit, DECIG, World Bank, Washington DC. Email: <u>aislam@worldbank.org</u>

> **c** Women, Business and the Law, DECIG, World Bank, Washington DC. Email: asakhonchic@worldbank.org.
**Figure 5. Services are gradually becoming tradable (Service exports as a share of service value addition, 1980 - 2007)**

<!-- Start of picture text --> 11<br>10<br>9<br>8<br>7<br>6<br>World Developing countries Advanced countries<br>5<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors‘ calculations.

**Figure 6. Index of goods exports/industry value added and service exports/service value added**

<!-- Start of picture text -->
1.3<br>1.25<br>1.2<br>1.15<br>1.1<br>1.05<br>1<br>Goods Services<br>0.95<br>2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text -->

_Source_ : World Development Indicators, World Bank, 2010 and authors‘ calculations Note: 2000 is indexed to 1.

10