rafmacalaba/data-use-mentions-extended huggingface dataset:
12 variable ranging from 0 to 100 into N categories<sup>9</sup> . Second, we assume that the contribution to social capital of being a member of each group was greater if the group was more heterogenous across kinship groups, more inclusive and horizontal, and better functioning.

Hence the contribution of each group to social capital is an equally weighted sub-index of these three characteristics. The village level social capital index is the product of the average number of groups with the average characteristics of those groups. Since the absolute scale for this index is arbitrary we re-normalize the index to have mean zero and standard deviation

**10** one

Before examining the specific hypotheses about social capital, some information on the situation and activities of the people surveyed will set the context. Rural Tanzania is a clear case of arrested economic development. Tanzanians are very poor, the average per person consumption expenditures reported in the 1993/94 HRDS in rural areas is 50 _cents_ a day ($180 per person per year)". Most of the population is employed in traditional agriculture, with a substantial subsistence component as the imputed value of production for own consumption accounts for half of consumption expenditures. Nearly all agriculture is rain fed and uses 9 The numerical value to being the k' of the N, categories for the lI indicator is: _V, = (IO0/N)*k_ - IOOI(N *2) . As long as the value assigned to each category is the mean of the observations in that category this procedure will not induce inconsistency in the resulting estimates.

**'°** This is done using the mean and standard deviation for all 87 clusters. Therefore the samnples used below may not have exactly mean zero/standard deviation one.

" The World Development Report, 1995 reports Tanzania as tied with Mozambique for the lowest GNP per capita of $90.
from _low_ to _high_ . The intuition is that when there are no changes in aggregate shocks ( _D_ 1 = 1 ), wages follow an AR(1) with correlation coecient _ρw_ . When instead there are changes in aggregate conditions ( _D_ 2 = 1 or _D_ 3 = 1 ); wages jump discretely downwards or upwards in the rst period (they overshoot) and adjust gradually afterwards. This rst-period response occurs in models with imperfect labor mobility such as Artuc, Chadhuri and McLaren (2010) and Dix-Carneiro (2014).

Product prices of tradable sectors are determined in international markets. Domestic prices are equal to international prices plus taris. Sectors in which supply is larger than demand are net exporters, whereas sectors in which supply is smaller than demand are net importers. Gross trade ows are not determined. In the non-tradable sector, prices are determined endogenously by the equilibrium of domestic supply and domestic demand.

The previous equilibrium conditions hold for all time periods and all vectors of aggregate state variables. We are also interested in dening a stationary equilibrium, which we will use in simulation exercises to study trade shocks. In a stationary equilibrium, there are rm-specic productivity shocks _Aijt_ and worker-specic utility shocks _εlt_ , but there are no aggregate prot shocks _bjt_ . As a consequence, while we observe uctuations in rm-level labor demand, investment and output, and in worker-level mobility, there are no uctuations at the aggregate level. Labor allocations, aggregate capital, output, wages, prices of non-tradables, and the distribution of rms are time-invariant in a stationary equilibrium.

# 3 Estimation

In this section we discuss the estimation of the model structural paramters. We use two sources of data from Argentina. The rst is the Encuesta Industrial Anual (EIA, Annual Industrial Survey), from INDEC. This is a panel of rms spanning the period 1994 to 2001, with information on investment, disinvestment, employment, revenue, production and materials. The second source of data is the Encuesta Permanente de Hogares
because the poverty impacts are just 0.5 million in an agriculture-only scenario. The second scenario, Doha-ag-sp, shows the impacts of allowing all countries to exempt 2 percent of their tariff lines as sensitive products (plus another 2 percent in developing countries for special products). This greatly dilutes the gains from a potential agricultural deal. Indeed, according to the estimates in table 17.7, there could even be a poverty _increase_ . A final scenario assesses the impacts of the core Doha scenario in a comparative static framework. This would slightly diminish the poverty reduction to 1.7 million individuals lifted out of poverty (from 2.5 million in the corresponding dynamic scenario).

These simple calculations are not a substitute for the more detailed analysis that has been generated by the individual country case studies in Hertel and Winters (2005) through use of detailed household surveys. However, it is useful to have a global assessment, and one that provides a sense of the possible range of poverty impacts. Moreover, the elasticity approach<sup>32</sup> may be somewhat conservative because these are derived elasticity estimates from the baseline scenario.<sup>33</sup>

# **7. Conclusions**

The global economy of 2015 is likely to look quite different from the world economy of 2001 or 2005—particularly if the growth assumptions underlying this chapter’s baseline scenario obtain. The changing structure of production, demand, trade, and comparative advantage will

> 32. The World Bank’s headcount index forecast is derived from an estimated Lorenz curve based on the most recently available household survey and not from an elasticity approach. A forecast of the growth of per capita consumption is plugged into the Lorenz curve–based functional form for the headcount index, assuming distribution neutrality. See Datt (1998) for a derivation of this methodology.

> 33. Elasticities at the low end of the income spectrum should be rising over time as mean incomes rise; therefore the marginal elasticity in 2015
and Development).

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Hallegatte, Stephane, Julie Rosenberg, Jun Rentschler Claire Nicolas and Charles Fox (2019), “Strengthening New Infrastructure Assets. A Cost-Benefit Analysis,” Policy Research Working Paper 8896, World Bank, Washington, DC.

Hallegatte, Stephane, Jun Rentschler and Brian Walsh (2018), _Building Back Better: Achieving resilience through stronger, faster and more inclusive post disaster reconstruction_ , World Bank.

Heal, Geoffrey and Antony Miller (2013), “Uncertainty and Decision in Climate Change Economics”, Working Paper 18929, National Bureau of Economic Research.

Herndon, Thomas, Michael Ash and Robert Pollin (2014), “Does high public det consistently stifle economic growth? A critique of Reinhart and Rogoff”, _Cambridge Journal of Economics_ , vol 38, no 2, pp257-279.

International Monetary Fund (2021A), “Fiscal Policies to Address Climate Change in Asia and the Pacific”, IMF Departmental Paper, No. 21/207, Washington DC.

29
# **_A. Across Income Groups_**

In this section we examine how our results vary across country income groups. In Table 4, in cols. 1-4 we look at employment growth, in cols. 5-8 we look at sales growth and in cols. 9- 12 we look at productivity growth.

We find that across all income groups, controlling for size and age in all regressions, small firms have higher employment growth than large firms. Small firms have significantly higher sales growth compared to large firms in middle-income countries but no significant difference in the low and high income countries. We also find that firms that small firms have significantly lower productivity growth than large firms across all income groups.

# **_B. Size of Informal Sector_**

In this section we examine whether the contribution of size and age to growth varies depending on the size of the informal sector in the economy. Of the 103 countries for which we have data on firm growth rates, we have data on the informal sector’s contribution to GDP in 92 countries from Schneider et al. (2010). In Table 5, in cols. 1-3 we report results for countries with a large informal sector (above the median value) and in cols. 4-6 we report results for countries with a small informal sector (below the median value).

Table 5 shows that when we look at countries with large informal sector we find that the smallest firms have higher employment growth, higher sales growth but lower productivity growth than firms with more than 99 employees. We find similar results in the sample with small informal sectors in cols. 4-6. This suggests that the size of the informal sector does not make a material difference to our results.

15
The positive and significant coefficient for _MRP_ suggests that NSS design may account for at least some of the discrepancy between the two data sources.

However, it is also important to note that the gap between the consumption aggregates from these two sources does not necessarily imply that the NSS overestimates poverty. Some of the gap is due to errors in NAS consumption, which is determined residually in India, after subtracting other components of domestic absorption from output at the commodity level. There are also differences in the definition of consumption, and there are things included in NAS consumption that one would not use in measuring household living standards.<sup>36</sup> Some degree of under-reporting of consumption by respondents, or selective compliance with the NSS’s randomized assignments, is inevitable. However, it is expected that this is more of a problem for estimating the levels of living of the rich than of the poor.<sup>37</sup> We will look into the implications of the growing drift between NAS and NSS consumption in regressions that use (sectoral) growth variables from the NAS.

# **3. Summary statistics**

India’s urban population share has risen steadily over the 60 years, at about 1% per annum (0.25 percentage points per annum) over the 60-year period; Figure 1 plots the urban population share. (The extent of the linearity is possibly deceptive given that interpolations are used between census years, as noted in Section 2.) As can also be seen from Figure 1, poverty incidence showed no significant trend up to about 1970, but fell after that. (The series for the two poverty lines track each other closely.) Both higher growth rates and a higher pace of poverty reduction are evident in the post-1991 period; Table 1 provides the trend rates of growth.

Much of the faster growth post-1991 has occurred in the “tertiary” sector of India’s economy, which is primarily services and trade. The growth rate of tertiary
37 Lekfuangfu, W.N., Piyapromdee, S., Porapakkarm, P., and Wasi, N. 2020. On COVID-19: New Implications of Job Task Requirements and Spouse's Occupational Sorting. Covid Economics: Vetted and Real-Time Papers, May 1.

38 Estupinan, X., Sharma, M., Gupta, S., and Birla, B. 2020. El impacto de la pandemia de COVID19 en la Oferta Laboral, los Salarios y el Valor Agregado Bruto en India (Impact of COVID-19 Pandemic on Labour Supply, Wages and Gross Value Added in India). Available at SSRN: https://ssrn.com/abstract=3637880 or <u>http://dx.doi.org/10.2139/ssrn.3637880</u> 39 Aucejo, E.M., French, J.F., Ugalde Araya, M.P., and Zafar, B. 2020. The Impact of COVID-19 on Student Experiences and Expectations: Evidence from a Survey. NBER Working Paper No. 27392.

40 As formulated by Becker, G.S. 1975. Human Capital, Chicago: University of Chicago Press; and Schultz, T.W. 1961. Investment in human capital. American Economic Review 51: 1-17.

> 41 Mincer, J. 1974. _Schooling, Experience, and Earnings,_ New York: National Bureau of Economic Research.

42 See Banerjee, A.V. and Duflo, E., 2005. Growth theory through the lens of development economics. _Handbook of economic growth_ , _1_ , pp.473-552; Peet, E.D., Fink, G. and Fawzi, W., 2015. Returns to education in developing countries: Evidence from the living standards and measurement study surveys. _Economics of Education Review_ , _49_ , pp.69-90; and 42 Psacharopoulos, G., and Patrinos, H.A. 2018. Returns to investment in education: a decennial review of the
||{21.0}|{16.9}*|{23.2}|{16.5}|
|Daughter Dummy|151.6|240.3|111.2|275.2|
||(103.1)|(100.6)**|(120.7)|(117.1)**|
||[116.9]|[110.3]**|[132.5]|[121.1]**|
||{147.9}|{126.6}*|{168.4}|{135.9}*|
|Intercept|648.0|1939.7|705.7|2173.6|
||(71.0)***|(117.9)***|(83.3)***|(132.7)***|
||[88.1]***|[171.4]***|[100.0]***|[186.5]***|
||{86.4}***|{229.6}***|{87.9}***|{246.5}***|
|No. of Children||-716.8||-838.0|
|||(49.7)***||(56.8)***|
|||[87.2]***||[100.1]***|
|||{123.2}***||{141.5}***|
|No. Observations|4326|4326|3632|3632|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in
set.<sup>16</sup> Information about gross fixed capital formation was provided directly by the Economic Research Department of the Banco de Guatemala. The data is compiled using the somewhat dated 1953 UN System of National Accounts, which is currently under revision.<sup>17</sup> In line with other studies for Latin America, such as Loayza et al. (2002) and Morales (1998), the overall depreciation rate is assumed at 5 percent. This is still a rather high estimate when compared with more commonly used thumb values.

However, regarding the armed conflict, which has lasted for 36 years, and several periods of high violence in Guatemala, it was found useful to adopt a high depreciation rate in order to account for both capital _destruction_ and _distraction_ from productive use. For example, the latter may have resulted in unprofitable military spending, several forms of non-productive investments, or temporary spare capital because of infrastructure deficiencies. As to be shown in the following section, the results of the regression analyses are not sensitive to moderate adjustments in the depreciation rate. In terms of data availability over a long time period, and given the robustness to alternative assumptions about depreciation, the capital stock series with aggregated investment data is adopted in the later regression analyses.

# **_Estimate with Disaggregated Investment Data_**

Based on the PIM, Morán and Valle (2002) present a second approach for Guatemala. In their model the capital stock is estimated for eight broad asset groups for 19712000. However, presumably because of too high depreciation rates for public and private construction, they seem to underestimate the genuine level of the capital stock.<sup>18</sup> Following their methodology but applying different depreciation rates and taking into account the initial benchmark estimate from Nehru and Dareshwar (1993), a second capital stock series has been calculated with disaggregated investment data for the period 1970-2002.

The initial values are obtained from a pre-estimate starting in 1950. The data gaps for the
Taking a regional approach requires the use of standardized hazard definitions and methods for matching hazard and household data. This is done by considering the main hazard measures and techniques used in comparable country studies, and systematically assessing the choice of hazard, the hazard-household matching approach, and the functional form used in analysis. Specifically, we use gridded historical data on precipitation, vegetation, evapotranspiration, and soil moisture, using measures such as the Normalized Difference Vegetation Index (NDVI), the Standardized Precipitation Evapotranspiration Index (SPEI), and WRSI. These measures reflect the hazard to livelihoods from lack of rain in different ways. Some are direct measures of climate conditions (e.g., precipitation, soil moisture, and SPEI), others aggregate climate data through crop models (WRSI), and others measure vegetation outcomes (NDVI). The gridded hazard measures are then merged to harmonized household surveys from nine countries (Ethiopia, Lesotho, Malawi, Mauritania, Mozambique, Niger, Nigeria, Zambia, and Zimbabwe) with location coordinates for either the household or the community. The surveys used are the ones that are also used for official poverty estimates. A consistent approach to temporal matching was used to take into account the timing of the survey and agricultural seasons. Different approaches to spatial merging were tested.

There are important methodological insights from the analysis that emerge. First, we find that soil moisture and greenness measures provide more consistently reliable results than evapotranspiration or rainfall measures. This is an important finding given most of the extant literature is based on evapotranspiration or rainfall metrics. Second, we find that a linear specification performs adequately for the types of moderate shocks that are well-represented in the survey data used. Third, we find that a hazard measure based on 20 km or 50 km radius around each household performs better than those that use smaller radii, perhaps because a larger radius allows local market effects to also be considered.

The relationship between hazard measures and welfare outcomes is estimated separately for different livelihood zones across SSA. Separate
products has not been forthcoming as expected. Thus, access to finance may still be a major hurdle for microenterprise growth. Using household survey data over a period of 10 years (2000-2010), this paper addresses a number of issues critical for understanding the sources and potential of microenterprise growth, such as whether and how better access to finance matters in the profitability and further growth of microenterprise in Bangladesh.

Our approach in this paper is related to a broader literature on access to finance and its role in microenterprise growth. A large literature has documented that access to better finance (in terms of better terms and conditions of loans as well as a reliable source of finance) is an essential predictor of improved productivity and growth in any economy. For example, Butler and Cornaggia (2009) find that in countries with better access to finance, corn producers increased productivity when there was an exogenous increase in ethanol demand. Thus, financial sector development is a robust determinant of growth (e.g., Johnson et al., 2002, Levine et al., 2000, McMillan and Woodruff, 2002, and Cull and Xu, 2005). While other obstacles are also important, lack of access to finance consistently emerges as one of the most important and robust underlying factors constraining firm growth. Better access to finance fosters greater firm innovation and dynamism, entrepreneurship, more efficient asset allocation and the ability to exploit growth opportunities, and growth of incumbent firms to a larger size (Beck, Demirguc-Kunt and Honohan, 2006; Beck, Demirguc-Kunt and Maksimovic, 2005).

Studies in varied settings show that access to finance also promotes a firm’s entry into the market and thus contributes to growth. For example, Mukherjee and Zhang (2007, 2005) explore the factors that affect the share of rural non-farm employment in total employment over time in both China and India, finding that the RNF sector has followed different paths in China and India over the past two decades simply because of differential access to institutional finance. Similarly
---|
|**Armenia**|Integrated Living Conditions Survey|2016|PoP often missing|
|**Bosnia & Herzegovina**|Household Budget Survey|2007|PoP asked as a purchasing habit|
|**El Salvador**|Encuesta de Hogares de Propositos Multiples|2010|PoP often missing; limited product categories|
|**The Gambia**|Integrated Household Survey|2003|No Data access to PoP|
|**Gerogia**|Integrated Household Survey|2018|Limited product categories|
|**Ghana**|Living Standards Survey|2006|No Data access to PoP|
|**Guatemala**|Encuesta Nacional sobre Condiciones de Vida|2000|PoP often missing; limited product categories|
|**Mauritius**|Household Budget Survey|2012|PoP asked as a purchasing habit|
|**Namibia**|Namibia Household Income and Expenditure Survey|2015|limited product categories|
|**Nicaragua**|Encuesta Nacional de Hogares sobre Medicion de Nivel|2014|PoP asked as a purchasing habit; limited product categories|
|**Tajikistan**|Household Budget Survey|2016|limited product categories|
|**Turkey**|Household Income and Consumption Survey|2009|No Data access to PoP|

Table B3 lists the 31 countries which we could include, with summary statistics and the structure of each survey. Any slight deviation from our inclusion criteria is outlined in the last column.

74
levels of urbanization across countries.

The degree of urbanization defines an urban area as a contiguous set of 1 km<sup>2</sup> gridded population cells in which each cell has a population density of at least 300 people per km<sup>2</sup> and the overall population of the set of cells is at least 5,000. As such, unlike the dartboard approach, which is a relative approach, the degree of urbanization is an absolute approach to urban area identification and delineation.<sup>37</sup> It will also be noted that the use of a 1 km<sup>2</sup> gridded population layer is part of the definition of the degree of urbanization. In applying the degree of urbanization, we therefore aggregate the constrained WorldPop data to this resolution rather than the 250 m*250 m resolution that we use for the dartboard approach.

**Figure 2: Correlation with official urban population shares across 46 SSA countries – dartboard approach and degree of urbanization**

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**Note:*
Nene, N., 2015. _Budget speech by Minister of Finance_ , National Assembly, 25 February, Cape Town.

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