rafmacalaba/datause-encoder-data huggingface dataset:
Second, it is easy to see how both type 1 and type 2 errors could introduce noise into our measure. We may over-predict the share of family firms in countries with a small group of very common surnames. Reporting requirements for the number of management staff may also differ across regulatory bodies. Our regional regressions, which rely on within-country variation in regional trust, difference out any such factors that vary at the country-level. Moreover, since this affects all firms equally and is unlikely to be correlated with measures of contracting institutions, we do not believe this influences our results beyond reducing the precision of our estimates.

Figure A1 and Table A1 in the Online Appendix correlate our measures of family-management with those in the World Management Surveys, that explicitly ask about family ownership and family management, for 1,349 firms which we were able to match to our dataset. The correlation between our measures reaches as high as 0.653 at the country-level and 0.433 at the firm-level. We find this high correlation at the country-level encouraging, given that our definition is likely to be noisy at the firm-level but should be well-positioned to capture changes in the relative share of family-managed firms across countries.

To further motivate our empirical analysis, Figure II provides an overview of the relationship between the share of family firms, and GDP per capita as well as our three proxies of contractual institutions across countries. These correlations confirm that poorer countries, and countries with weaker contracting institutions, are characterized by a higher prevalence of family firms.

# V. RESULTS

## **_V.A. Cross-Country_**

_Management Mode Choice._ In order to test Prediction 1, that improvements in contractual institutions decrease the probability of being FOFM, we run where _FOFMick_ is a dummy for whether firm _i_ in country _c_ and industry _k_ is family-owned-familymanaged, _Lick_ is firm employment as reported in BvD Amadeus and Rule of Law _c_ is the WGI rule of law index (measured in standard deviations). The control vector _Xic_ includes the logarithm of GDP per capita and the logarithm of firm age, and _δk_ are 4-digit industry fixed effects. Standard errors are clustered at the country-level.

Columns (1) and (2) in Table II show that an improvement in contractual institutions is associated with a reduction in the probability of being family-managed - a 1 standard deviation increase in the rule of law index is associated with a 16.3% drop in the probability a firm is family-managed.

To test Prediction 2, that the effect of improving institutions on management mode is attenuated for large firms, we extend specification (8) by adding an interaction term between rule of law and firm size. Column (3) reports the result. The interaction term is positive confirming that the effect of contractual institutions on the management mode decision is attenuated for large firms.

13
# **5 Theoretical framework**

Our theoretical base is a principal-agent model in which the principal, the landlord, devises a contract to serve his interests and presents a take it or leave it offer to the agent, the tenant, assumed to be a landless laborer. The latter decides whether to accept the contract, comparing the contract with his outside opportunities (reservation utility) and conditional on accepting, decides on the amount of effort to put in.<sup>18</sup> To integrate the role of the absentee landlord, we modify this base as a double incentive problem, as first introduced by Eswaran and Kotwal (1985), in which the landlord too puts labor effort into the production of crops and needs to decide on his degree of engagement. We build on the interpretation of Singh (1989 and 2004).<sup>19</sup> We consider this to be a realistic model in the Indian context. Reddy (1996) notes that the type of tenancy, the quantity and quality of produce or amount in cash, the details of transport and delivery methods are commonly determined by the landlord.<sup>20</sup> Other village level studies, such as Bliss and Stern (1982) and Walker and Ryan (1990), concur.

> 18Perhaps this choice of model needs some justification. Why, for instance, do we not start with a general equilibrium setting instead? As first noted by Newbery (1974), the share in a sharecropping contract cannot be viewed as a price in the standard Walrasian equilibrium sense, as, if it were a price, the tenant taking the share as given will demand more land, up to the point where the marginal product of land is zero. An equilibrium share would then be determined as equating this demand to the village supply. Unless land is abundant, this equilibrium will not exist.

> 19We do not consider screening effects as in Hallagan (1978) and Newbery and Stiglitz (1979): while it is the case that in-migration might increase the percentage of unknown tenants, hence tempting landlords to use a menu of agreements to screen the tenants; the dominance of one agreement over another in most villages seems to conflict with this screening explanation.

> 20Reddy (1996) distinguishes between three contract components, the main lease (kaul), the mutchalika and the rent receipts. It is only the mutchalika which is joint written by the tenant.

16
<!-- Start of picture text --> Loss function: financing needs<br>0.1 10 year event<br>0.08<br>0.06<br>0.04<br>0.02 100 year event 200 year event<br>0<br>0 2,000 4,000 6,000 8,000 10,000 12,000<br>Losses in LCU<br>Financing sources: financing supply<br>12<br>International bonds<br>10<br>Borrowing<br>from IFIs<br>8<br>resource<br>6 Domestic bonds gap<br>and credit<br>4<br>Diversion<br>2<br>Grants<br>0<br>Amount available<br>Probability<br>Marginal cost<br><!-- End of picture text --> Fig. 7: Illustration for calculating the disaster resource gap As shown in figure 7, while enough funding may be available over time, yet there may be a sporadic resource gap, as generally in the aftermath of a disaster event, urgent expenditure needs are high and immediately available financial resources often very limited. As an example how the resource gap is calculated here we refer to box 3.

Due to the focus of this work in step 3, we do not go into more detail on steps 4 and 5 in this report but rather present a small overview for the sake of completeness of the CATSIM methodology.

# **_3.4 Step 4: Illustrating the developmental consequences of a resource gap_**

Financial vulnerability can have serious repercussions on the national or regional economy and the population. If the government cannot replace or repair damaged infrastructure, for example, roads and hospitals, or provide assistance to those in need after a disaster, this will have longterm consequences. The consequences on long-term economic development can be illustrated by the CATSIM tool. Generally, economic welfare will be higher on average if the government does not allocate its resources to catastrophe insurance or other risk management, but the economy has fewer extremes and is more stable with public sector insurance. Investing in the risk financing instruments can thus be viewed as a trade-off between economic growth and stability. Budgetary resources allocated to catastrophe reserve funds, insurance and contingent credit (as well as to preventive loss-reduction measures) reduce the potential resource gap, and thus can ensure a more stable development path. On the other hand, ex ante financing and prevention measures come at a price in terms of other investments foregone and will inevitably have an adverse impact on the growth path of an economy.

19
degree. To the best of our knowledge, this is the first paper to analyze the causal impact of the Covid shock on labor market dynamics in a developing economy, over a fairly long (16 months) time horizon since the end of the initial lockdowns in the summer of 2020. The findings are also likely to have relevance for other developing countries whose labor markets share some similarities with India.

Our paper also relates to the broader literature on the impact of economic crises in general on labor outcomes, the longer-term impacts of which can be particularly harmful for inequality and socioeconomic mobility. There is evidence to suggest that loss of jobs can leave lasting impacts on vulnerable workers, and particularly on young entrants to the labor market. For example, Guvenen et al. (2017) use data from the Social Security Administration in the United States to show that individuals who go through a long period of non-employment suffer large and long-term earnings losses compared to individuals with similar age and previous earnings histories. Because of this effect, a cohort that enters the labor market during a recession is likely to face long-term disadvantages that lead to lower lifetime earnings. Moreover, employment shocks that are unexpected in nature can be particularly harmful – studies have shown workers who are displaced by unexpected firm closures to experience significant and long-lasting reductions in earnings. A crisis also tends to affect workers unequally because of differences in their socioeconomic background, widening existing inequalities. Youth from disadvantaged backgrounds may be forced to enter the labor market at a time when few economic opportunities are available, compared with youth in households that are more well off or enjoy better access to credit, who are more likely to postpone labor market entry, accumulate more schooling or unpaid work experience, and improve their prospects of upward mobility.

This paper does not attempt a comprehensive analysis of the policy response by the Indian government but only focuses on the extensive vaccination campaign and its role in helping labor markets return to normal. For a review of India’s relief package - the Prandhan Mantri Garib Kalyan Yojana (PMGKY) - refer to Bhattacharya and Sinha Roy (2021) and Chatterjee et al. (2022). In the next sections of this paper, we describe the data and methods used for our main analysis. Section 4 presents our main results on labor market impacts looking at transitions across employment status, earning levels, employment types, sector and location. Section 4 also examines how education and location may have mitigated some of these transitions and contains some additional results on the effect of vaccination on individual employment outcomes. The last section concludes.

# **2 Data and descriptive statistics**

The Consumer Pyramids Household Survey (CPHS) is a continuous panel survey of approximately 170,000 households in each wave. The surveys are conducted by the Centre for Monitoring the Indian Economy (CMIE), a private think tank and data collection 5
(Gbadamosi (1967), Hiskett (1975)). Yet Hailey observed that the education of girls was relatively more advanced in West Africa though the increase is more evident in respect of the total numbers and not in the proportion of girls to boys.

For example in 1954, of the 494,334 children in primary schools, 150,118 were girls; of the 8,602 pupils in Government secondary schools, 1,296 were girls. In that year 785 boys and 121 girls passed the examination for the Cambridge School Certificate (Hailey (1957: 1187)).

For instance, in Ghana, one of the few countries for which we have education statistics and records over the colonial period, enrollment rates were abysmally low and exhibited a strong gender bias. While primary enrollment rates for boys averaged 0.52% of the total population in 1901, girls‘ enrollment rates were even lower, less than 0.11%. And over the first decades of the 20<sup>th</sup> century, the gender gaps in primary enrollment widened, with more boys attending schools (0.84%) than girls (0.18%). Interestingly, that gender gap inherited in the colonial era has persisted over time as illustrated by Figure 1 below.

**Figure 1: Enrollment in Primary and Middle Schools in Ghana**

<!-- Start of picture text --> 500000<br>450000<br>400000<br>350000<br>Boys Girls<br>300000<br>250000<br>200000<br>150000<br>100000<br>50000<br>0<br>1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915-9 1920-4 1925-9 1930-4 1935-9 1940-4 1945-7 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960<br><!-- End of picture text --> Source: Kay (1972: 407-8).

French Africa would witness major educational reforms in the aftermath of the Brazzaville Conference of 1944 and the French Union of 1946, as the French government directed its 28
|P129945|2012|2015|1/27/2015|3 Closed|Central America|
|---|---|---|---|---|---|
|P129945|2012|2016|3/21/2016|4 Closed|Central America|
|P129961|2014|2014|4/12/2014|1 Closed|Bosnia and Herzegovin|
|P129961|2014|2015|11/29/2014|2 Closed|Bosnia and Herzegovin|
|P129961|2014|2015|6/15/2015|3 Closed|Bosnia and Herzegovin|
|P129961|2014|2016|12/4/2015|4 Closed|Bosnia and Herzegovin|
|P129961|2014|2016|5/28/2016|5 Closed|Bosnia and Herzegovin|
|P129961|2014|2017|12/2/2016|6 Closed|Bosnia and Herzegovin|
|P129961|2014|2017|6/1/2017|7 Closed|Bosnia and Herzegovin|
|P129961|2014|2018|12/5/2017|8 Closed|Bosnia and Herzegovin|
|P129961|2014|2018|6/7/2018|9 Closed|Bosnia and Herzegovin|
|P129961|2014|2019|12/18/2018|10 Closed|Bosnia and Herzegovin|
|P129961|2014|2019|5/30/2019|11 Closed|Bosnia and Herzegovin|
|P129968|2012|2013|1/2/2013|1 Closed|Mexico|
|P129968|2012|2015|7/4/2014|2 Closed|Mexico|
|P129968|2012|2016|2/19/2016|3 Closed|Mexico|
|P129969|2018|2012||0 Dropped|Albania|
|P129969|2018|2013||0 Dropped|Albania|
|P129983|2009|2013||0 Closed|Vietnam|
|P129983|2009|2020|12/10/2019|1 Closed|Vietnam|
|P129992|2014|2015|9/23/2014|1 Active|Dominica|
|P129992|2014|2015|2/14/2015|2 Active|Dominica|
|P129992|2014|2016|8/27/2015|3 Active|Dominica|
|P129992|2014|2016|3/3/2016|4 Active|Dominica|
|P129992|2014|2017|9/29/2016|5 Active|Dominica|
|P129992|2014|2017|4/6/2017|6 Active|Dominica|
|P129992|2014|2018|10/16/2017|7 Active|Dominica|
|P129992|2014|2018|1/28/2018|8 Active|Dominica|
|P129992|2014|2019|7/26/2018|9 Active|Dominica|
|P129992|2014|2019|1/25/2019|10 Active|Dominica|
|P129992|2014|2019|6/18/2019|11 Active|Dominica|
|P129992|2014|2020|2/3/2020|12 Active|Dominica|
|P129992|2014|2021|7/22/2020|13 Active|Dominica|
|P130012|2013|2014|2/26/2014|1 Closed|Nigeria|
|P130012|2013|2015|11/15/2014|2 Closed|Nigeria|
|P130012|2013|2020|5/2/2020|3 Closed|Nigeria|
|P130013|2013|2014|11/7/2013|1 Active|Kazakhstan|
|P130013|2013|2015|7/2/2014|2 Active|Kazakhstan|
|P130013|2013|2015|3/9/2015|3 Active|Kazakhstan|
|P130013|2013|2016|9/18/2015|4 Active|Kazakhstan|
|P130013|2013|2016|4/15/2016|5 Active|Kazakhstan|
|P130013|2013|2017|10/28/2016|6 Active|Kazakhstan|
|P130013|2013|2017|6/20/2017|7 Active|Kazakhstan|
|P130013|2013|2018|1/12/2018|8 Active|Kazakhstan|
|P130013|2013|2019|8/21/2018|9 Active|Kazakhstan|
|P130013|2013|2019|4/9/2019|10 Active|Kazakhstan|
|P130013|2013|2020|10/17/2019|11 Active|Kazakhstan|
micro-simulation model to derive the distributional effects of the scenarios; and section 6 concludes.

# 2. Methodology

## **2.1. CGE model**

Building on recent work of Petri and Plummer (2016), Petri et. al. (2012) and World Bank (2016a and b), the backbone of the economic modeling would be obtained by using the global dynamic CGE model LINKAGE (van der Mensbrugghe, 2011). The analysis includes 17 production sectors and 35 countries/ regions (see Table A1 in the Annex) and simulates the impacts of policy changes up to 2030, including reduction of tariffs and of NTMs in goods and services.

The core specification of the model replicates largely a standard global dynamic CGE model. Production is specified as a series of nested constant elasticity of substitution (CES) functions for the various inputs – unskilled and skilled labor, capital, land, natural resources (sector-specific), energy and other material inputs. LINKAGE uses a vintage structure of production that allows for putty-semi putty capital. In the labor market we assume fixed unemployment and labor participation rates. Demand by each domestic agent is specified at the so-called Armington level, i.e., demand for a bundle of domestically produced and imported goods. Armington demand is aggregated across all agents and allocated at the national level between domestic production and imports by region of origin.

The standard scenario incorporates three closure rules. First, government expenditures are held constant as a share of GDP, fiscal balance is exogenous while direct taxes adjust to cover any changes in the revenues to keep the fiscal balance at the exogenous level. The second closure rule determines the investment-savings balance. Households save a portion of their income, with the average propensity to save influenced by elderly and youth dependency rates, as well as GDP per capita growth rates. The savings function specification follows Loayza, Schmidt-Hebbel, and Serven (2000) with different coefficients for developed and developing countries. In the case of China and the Russian Federation, we target projections of investment or savings rates up to 2030 from World Bank regional reports. Since government and foreign savings are exogenous, investment is savings driven. The last closure determines the external balance. We fix the foreign savings and therefore the trade balance, hence changes in trade flows result in shifts in the real exchange rate.

This modeling framework allows to incorporate the complex interactions of productivity differences at the country, sector or factor level, shifts in demand as income rises, as well changes in comparative advantage and trade flows following trade liberalization. The applied multiregional dynamic CGE model accounts simultaneously for interactions among producers, 5
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 9. Provision for maintenance and rehabilitation, 2006**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>Ethiopia Tanzania Uganda Kenya<br>maintenance rehabilitation<br>spending as % of requirements<br><!-- End of picture text --> _Source:_ Gwilliam and others 2009.

## **Challenges**

As of 2006, fuel levy collection was a major issue for Tanzania’s Road Fund, which was collecting only 39 percent of the required amount; one of the worst collection rates among the countries in SubSaharan Africa that had adopted road funds at that time. Despite this, and due to the addition of resources from the public budget, spending at that time remained adequate to meet maintenance and rehabilitation needs (figure 9). Over the period 2007/10, this issue has been addressed and the Road Fund reports a steep increase in the collection of fuel levy resources.

Although the trunk network provides basic regional and national connectivity, rural accessibility is another important issue for the country. Only 24 percent of Tanzania’s rural population lives within two kilometers of an all weather road. This is somewhat above the low-income country benchmark but less than half the level found in middle-income countries. Reaching 100 percent rural accessibility would entail doubling the classified network, which would be a major challenge. If instead the network were expanded to provide connectivity to those areas responsible for 80 percent of the country’s agricultural production value, the required expansion would be only 50 percent of the existing network—a significant but manageable challenge.

# Rail

## **Achievements**

Tanzania’s rail corridors are key conduits for bulk freight in the region. They ease the pressure on roads, in particular on the north-east corridor connecting Sudan-Ethiopia-Kenya-Tanzania-Uganda; the east-south corridor connecting Tanzania-Rwanda-Democratic Republic of Congo-Uganda and as Dar Es Salaam-Kigoma-Burundi; and the east-south corridor connecting Tanzania-Zambia-Zimbabwe17
13 The highly differentiated attitude towards privatization is manifested in serious disproportions in the demographic composition of the population living in privatized and non-privatized housing (see Fig. 1.1 below). In the privatized sector, 33.7% of the dwelling space is held by pensioners, and 44.2%, by households with children. In municipal or enterprise-owned housing stock (that is, in the non-privatized sector), the share of pensioner households is comparatively low (10.5%), while the proportion of households with children is very high (68.5%). However, in the housing market, the ratio of these two categories is essentially different. Sociological surveys (Guzanova, 1994) show that families with children are more active on the market given a greater urgency of their housing problem, a better financial status, and readiness to pay for better-quality housing.

**Fig. 1.1.** Types of households living in privately owned (i) and non-privatized (ii) housing Moscow, 1994.

<!-- Start of picture text --> 33.7% 68.5%<br>M Pensioners<br>/-^10-105%<br>* Adults without<br>children<br>22.1 . l 44.2% l l l t 21.0% D Familieswith<br>children<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, December 1994.

_c._ **_Households' value orientations_** and their willingness to acquire their own housing influence intentions vis-a-vis privatization, and these, in turn, essentially depend on the level of education attained by members of the household. The Moscow survey demonstrates that the higher the level of education, the greater the inclination to privatize one's dwelling. The privatization rate among respondents with a graduate academic degree was 53%; among those with a university education, 45%; and among respondents without higher education, 28%.

d. **_Household income_** is another factor influencing privatization. In the low-income group we find childless families and single pensioners, while in the high-income group there are households with children. The desire to bequeath their housing to heirs stimulated privatization rates in the low-income group that were higher than among high-income households.

On the face of it it would appear that the low-income groups were the gainers, judging by the degree to which they engaged in privatization. However, this is not so because they mainly privatized low-cost, lowquality dwellings. However, as will be shown later on (Fig. 1.2 below), over time the high-income group in Moscow appears to be becoming more involved in the process of privatization.

# **Recent trends in housing privatization** in Moscow

In 1995, Muscovites privatized only 3% of the overall number of apartments. The fourth wave of the Moscow Longitudinal Household Survey conducted in late 1995-early 1996 showed that the reasons for privatizing and the agents involved had experienced some changes while some characteristics of the process remained unchanged, as demonstrated by Tables 1.9 to 1. 11 which show the dynamics in privatization in Moscow between 1994 and 1996.
<!-- Start of picture text --> 450 20<br>400 18<br>ee<br>=<br>8 300<br>a / 12<br>3ww 250 a —/ i q<br>4wo 200 il aia<br>a 8<br>Wl<br>a iso<br>o&io) 100 | :<br>50 : 3<br>u<br>: i<br>1990 1991 1992 1995 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>GEM New capital flows (Equity and IPO) New Capital flows (Cat Bonds and Sidecars) ===Nat cat reinsurance rates<br><!-- End of picture text -->