rafmacalaba/datause-encoder-data huggingface dataset:
# FIGURE 6 Drivers of potential growth around the global recessions of 2009 and 2020

## A. World: Contributions to potential growth

<!-- Start of picture text --> Percentage points<br>2.0 Capital TFP Labor<br>1.5<br>1.0<br>0.5<br>0.0<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br><!-- End of picture text -->

## B. Advanced economies: Contributions to potential growth

<!-- Start of picture text --> Percentage points<br>1.2 Capital TFP Labor<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br><!-- End of picture text -->

- C. EMDEs: Contributions to potential growth D. EMDEs excluding China: Contributions to

## potential growth

<!-- Start of picture text --> Percentage points<br>4.0 Capital TFP Labor<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br>Sources: World Bank; World Economic Outlook.<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>2.5 Capital TFP Labor<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>t-2 t-1 t=0 t+1 t+2 t-2 t-1 t=0 t+1 t+2<br>2009 global recession 2020 global recession<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies. Figures show the contributions of capital, total factor productivity (TFP), and labor to potential growth around t=2009 and t=2020. Unbalanced sample of 30 advanced economies and 25 EMDEs for 2007-21.

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all countries, the data contain over 90 percent of employment of the old firm manufacturing sector in 1992.

For a more detailed description of the job flows and job flow productivity data, see Brown and Earle (2004). Biletsky et al. (2002) contains more information on the data for worker and job flows. Brown, Earle, and Lup (2004) gives details about the Romanian new small firm survey. See Brown, Earle, and Telegdy (2004a, 2004b) for further information about the privatization data.

# **II.3.1 Job and Worker Flows and Firm Turnover**

The first results are the basic job flow rates for the Russian and Ukrainian manufacturing sectors in Figure 1. The job creation rate is negligible in Russian manufacturing during the Soviet period, but by the late 1990s both the Russian and Ukrainian rates are well within the range of creation rates of the full manufacturing sectors in the U.S. and other market economies.

The calculated job destruction is also very low in the Soviet period, but the rate rises rapidly in the early reform period, quickly reaching the upper end of the typical range of rates found in the U.S. The Russian and Ukrainian creation and destruction rates show very similar patterns, the main difference being that Ukrainian destruction did not rise as quickly as Russian destruction at the beginning of the reform period. These results suggest that labor market flexibility increased substantially in response to market reforms.

Brown and Earle (2002) measure the persistence of these employment changes among continuing firms, i.e., the extent to which jobs added or subtracted from the firm remain gained or lost in future years. Results are displayed in Table 1. Persistence rates are calculated for one- and two-year periods, and both have fallen slightly during the reform period by comparison with Soviet socialism. Apparently, the planners had little tendency to reverse their decisions on changing employment levels! As in other countries, the persistence rate is lower for job creation than for job destruction, but for the latter it is quite high by international standards. Thus, while creation persistence falls substantially during the transition, the measured flows do not appear to be the result of highly volatile behavior or noise in the data. They are not primarily temporary phenomena, but rather appear to be signs of longer-term restructuring.

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subsample, the sample of large manufacturing firms, the average of these Solow shares is almost exactly identical to the parameter estimates we obtain by means of OLS. However, for urban manufacturing microenterprises, the average Solow share on capital is .14 points larger, while the Solow share on labor inputs is .09 points smaller. These differences are still within the 95% confidence interval for our parameter estimates. On balance then, it seems that endogeneity may be a moderate problem in the urban manufacturing data, but that the resulting bias is unlikely to be large. Unfortunately, there is little we can do to solve the endogeneity problem since convincing instruments for material inputs and labor usage are not available.

Turning to the investment climate variables, the results suggest that problems with accessing credit are associated with significantly lower productivity for manufacturing firms in rural areas as well as for large manufacturing firms in urban areas. For transport and utilities constraints, the null hypothesis that they do not matter is never rejected. Finally, it is noticeable that some investment climate constraints have the “wrong” sign; for example, the severity of transport problems is positively correlated with the productivity of small manufacturing firms, though not statistically significantly so. As pointed out by Carlin et al. (2006), however, we have to be very careful when interpreting self-reported investment climate variables. For example, subjectively reported credit constraints might well be endogenous to firm performance. It could be that financial institutions locate near productive enterprises. Alternatively, they may only ration credit to the most productive enterprises.

The fact that small urban manufacturers and rural manufacturers use similar technologies enables us to pool the data. Moreover, it enables us to test how TFP varies with the degree of market integration. We focus on the group of small urban firms only as this

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03 to 2.4 in 2011-13, based on the sample registration system (SRS). In urban areas, the TFR was below replacement at 1.9 in 2011-13 (Registrar General of India, 2014). As a result, the number of children per household has fallen dramatically. The size of the average urban household in the National Sample Survey (NSS) during the period 2004-2011 declined from 4.3 to 4, and only a little over 26 percent of households had at least one child of pre-school age (under 6 years old) in 2011. Educational attainment has also increased across the board, despite rising concerns about the quality of education.

The Indian conundrum of low female labor force participation despite the existence of what are considered enabling factors, has garnered considerable attention among scholars and policy makers. Several studies and reportage have sought to explain these peculiar patterns. Some discussion focuses on the possible under-measurement of women’s work (Kapsos et al. 2014; IAMR and ILO 2013; Sudarshan and Bhattacharya 2009), while another set of explanations points to the fact that more women are now enrolled in educational institutions, which accounts for their unavailability for market work (Himanshu 2011; Rangarajan et al. 2011). However, enrollment in higher education does not explain low rates of employment for those who have completed schooling (e.g. women aged 25 and over) (Mahapatro 2013). Still other explanations point to an “income effect,” where women withdraw from the labor market as household income rises, especially in a culture where males are regarded as the main breadwinners (Klasen and Pieters 2015). The so-called U-shape where female labor force participation declines initially with economic development, but then flattens and rises again, reflecting structural economic shifts has also been implicated in explaining the Indian conundrum (Goldin 1994). Yet it is not clear at what point of rising incomes should the “U” veer upwards, as it has not done so for India as yet (Lahoti and Swaminathan 2015).

It is likely that the jobs available to women are not in keeping with either their aspirations or their ambition; in other words, there are not enough “good jobs” that women would consider engaging in (Chatterjee et al. 2015; Kapsos et al. 2014). It may also be possible that women’s reservation wage is higher than that of men, given the many pressures on the former’s time. There is widespread recognition in feminist circles, of constraints from (real and perceived) safety and security, inadequate housing and transport that contribute to the overall opportunity cost of engaging in the labor market. This is compounded by the fact that employers may have an implicit, or in many cases an explicit bias against women doing certain kinds of work. Over a decade ago 3
34 Laffont, J.J. and J. Tirole (1993), **A theory of Incentives in Regulation and Procurement** , MIT press, Cambridge Laffont, J.J. (2000), **Incentives and Political Economy** , Oxford University Press Laffont, J.J. (2004), **Regulation and Development** , Cambridge University Press (forthcoming) Legros, P. and E. Iossa (2004)), “Auditing and Property Rights,” _Rand Journal of Economics_ , forthcoming.

Lewis, B. (2003), “Local Government Borrowing and Repayment in Indonesia: Does Fiscal Capacity Matter?” World Development, 31, 6, 1047-1063 Litan, Robert E.; Pomerleano, Michael; Sundararajan, Vasudevan, eds. (2003), The future of domestic capital markets in developing countries , Washington, D.C.: Brookings Institution Marrison, Chris. "Risk Measurement for Project Finance Guarantees." _Journal of Project Finance_ . 2001. Summer Newberry (2000 **), "Privatization, Restructuring, and Regulation of Network Utilities** , MIT Press, Cambridge Von Hirshchausen, C. (2002) **, Modernizing Infrastructures in Transition economies: Paving the Way to European Enlargement,** Cheltenham: Edward Elgar

# **Bibliography for Urban vs. Rural poor**

Booth, A. (2004), “Africa or Asia? The Development Challenges Facing Eastern Indonesia and East Timor”, _Oxford Development Studies,_ 32 (1), pp19-35 Cohen, B. (2004), Urban Growth in Developing Countries: A review of current trends and a caution regarding esitsing forecasts”, _World Development,_ 32 (1), 23-51 Ellis, F. (1998), “Household strategies and rural livelihood diversification”, The Journal of Development Studies, 35(1), pp1-38 Fan, S.; Zhang, L.; Zhang, X. (2004), Reforms, investment, and poverty in rural China. _Economic Development and Cultural Change_ 52(2): 395-421 Fan, S.; Jitsuchon, S.; Methakunnavut, N.. (2004a). The importance of public investment for reducing rural poverty in middle-income countries : the case of Thailand. (Discussion Paper) Washington, D.C.: International Food Policy Research Institute (IFPRI), available at <u>http://www.ifpri.org/divs/dsgd/dp/papers/dsgdp07.pdf</u> Fan, S.; Zhang, X.; Rao, N. (2004b) Public expenditure, growth, and poverty reduction in rural Uganda (Discussion Paper) Washington, D.C.: International Food Policy Research Institute, available at http://www.ifpri.org/divs/dsgd/dp/papers/dsgdp04.pdf Fan, S. and C. Chan-Kang (2004c), Road Development, Economic Growth and Poverty Reduction in China, (Discussion Paper) Washington, D.C.: International Food Policy Research Institute, available at: http://www.ifpri.org/divs/dsgd/dp/papers/dsgdp12.pdf Gaiha, R. (2003), “ Are Millenium Goals of Poverty Reduction Useful?, _Oxford Development Studies_ , 31 (1), 59-84 Harvard, L. M. Ruel nd J. Garrett (1999), « Are urban poverty and undernutrition growing? Some newly assembled evidence”, _World Development_ , 27 (11), 1891-1904
implying that disruptions in production reduced imports from countries which had these measures early on during the pandemic.

**Table 1: Average event-study coefficients for Indonesia’s exports and imports of by external lockdown indicators**

|||**Exports**|||
|---|---|---|---|---|
|**Period**|**Workplace**|**Stay-at-home**|**Internal movement**|**International travel**|
|Overall|-5.613|-1.872|3.889|2.697|
|1-4 months|-5.688|-5.778|-5.142|-7.227|
|5-8 months|-4.379|-4.872||-6.794|
|9-12 months|-4.180|-4.087|8.793|6.267|
|13-16 months|-5.015|7.250|8.016|10.617|
|17-19 months|-8.805|x|x|10.624|
|||**Imports**|||
|Overall|-9.974|-8.683|-2.812|-24.005|
|1-4 months|-18.906|-21.535|-16.768|-9.567|
|5-8 months|-22.226|-18.412|-18.033|-20.651|
|9-12 months|-13.876|-13.134|-11.706|-25.121|
|13-16 months|-8.222|-8.688|9.039|-28.598|
|17-19 months|13.360|18.353|23.409|-36.088|

Note: All coefficients are significant at 10% level. x indicates that all coefficients are not significant in a specific period. Source: Authors’ compilation

# **Import and export prices**

The second mechanism we test entails establishing the effects of domestic and external lockdowns on export and import prices. This will further attempt to show differences in quantities and values that some results may have shown. Prices are calculated as the ratio of value to quantity at the product-partner-month level for both exports and imports. As before, coefficients are transformed into elasticities.

Effects of the domestic lockdowns on import prices are shown in the fourth column of Table A2 (only coefficients for lags are presented due to space constraint). Import prices significantly declined from the third month to the eighteenth month by a monthly average of 316% due to Indonesia’s lockdown policies. As we did not find that domestic lockdowns 29
6 a poverty-city size gradient can be observed both directly from survey data and from small area estimation techniques. We conclude that the inverse relationship is thus not driven exclusively by our reliance on a particular estimation method. Section 8 offers tentative conclusions and discusses some of the questions that this descriptive paper raises for further research into urban poverty.

# **2. Methodology**

The economic analysis of the distribution of living standards in developing countries relies almost entirely on household surveys. If their samples are selected appropriately, these surveys can collect detailed information from a relatively small number of households (perhaps 0.1% of the country’s total population), and yet generate information that is representative of the population as a whole. The law of large numbers ensures that the uncertainty about the population which results from sampling (the ‘sampling error’) becomes very small at sample sizes that are still cost effective. This enables researchers the world over to ask detailed questions from small groups of people, at a fraction of the cost that would be required if entire populations needed to be polled.

But there is one drawback: the samples that are designed to be representative of large populations are not, in general, representative of specific non-random sub-divisions of that population. Indeed, the typical (nationally representative) household survey is not representative of sub-national units, such as states, provinces or districts. There are exceptions, mostly in large countries, such as China, India or Brazil. But even in those countries, the problem is simply shifted down one level: living standards will vary enormously across different localities of (or cities in) the states of Uttar Pradesh or Minas Gerais; but the Indian _National Sample Survey_ and the Brazilian _Pesquisa Nacional por Amostra de Domicílios_ are not representative at those levels.

A number of small-area estimation techniques have been developed to seek to address this missing data problem. In this paper, we rely on application of the “poverty mapping” approach developed by Elbers, Lanjouw and Lanjouw (2002, 2003). This approach typically involves a household survey and a population census as data sources. First, the survey data are used to estimate a prediction model for either consumption or
# _2.2.1 Geographical data_

The district boundaries for each of the countries investigated in this study came from a variety of sources. Shape files were provided by USAID, FEWS, EDC-International Program and the US Geological Survey under the Africa Data Dissemination Service (ADDS) and Yale University. The Ghana and Kenya maps were provided by UNESCO (1997) through the UNESCO/GRID-Sioux Falls and Yale University. Land use data was obtained from the Land Use group at IIASA in Vienna. IIASA’s dataset consists of 13 classes, aggregated into six classes (Figure 4; Table 3).

A digitized river network was used to estimate the river density index for each of the districts (Section 2.6). The network used was the ‘Rivers of Africa’ coverage obtained from the FAO geonetwork website. This coverage was developed by FAO as part of the Atlas of Water Resources and Irrigation in Africa. An irrigated area map of Africa indicating (as proportional coverage within a 0.5<sup>o</sup> grid) where irrigation infrastructure and equipment has been installed in the past was also obtained from FAO (Figure 5). The map, also obtained from the geonetworks website, was used to estimate the percentage of each district with potential for irrigation (Section 2.7).

# _2.2.2 Climate data_

Average monthly rainfall and the climate variables required to compute potential evapotranspiration (i.e. wind speed, vapor pressure, temperature and cloudiness) were taken from a database provided by the Climate Research Unit (CRU), University of East Anglia, Norwich, UK. These data, provided on a 0.5<sup>o</sup> grid, represent the World Meteorological Organization’s (WMO) standard reference ‘baseline’ for climate change impact studies. Most climate change scenarios (i.e. plausible descriptions of how things may change in future) are expressed as anomalies from this baseline (see Section 3).

# _2.2.3 Physiological data_

The physiological data required were obtained from a number of geographically referenced datasets.

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Ghani, Ejaz (ed.) The Poor Half Billion (Oxford, UK: Oxford University Press, 2010a).

Ghani, Ejaz (ed.) The Services Revolution (Oxford, UK: Oxford University Press, 2010b).

- Ghani, Ejaz, Arti Grover, William Kerr, and Stephen O'Connell, “Urban-Rural Migration in Indian Manufacturing”, Working Paper (2011).

- Ghani, Ejaz, William Kerr, and Stephen O'Connell, “Promoting Entrepreneurship, Growth, and Job Creation”, in Ejaz Ghani (ed.) Reshaping Tomorrow (Oxford, UK: Oxford University Press, 2011a).

- Ghani, Ejaz, William Kerr, and Stephen O'Connell, “Spatial Determinants of Entrepreneurship in India”, NBER Working Paper 17514 (2011b).

- Ghani, Ejaz, William Kerr, and Stephen O'Connell. “Local Industrial Structures and Female Entrepreneurship in India”, NBER Working Paper 17596 (2011c).

- Glaeser, Edward, and William Kerr, “Local Industrial Conditions and Entrepreneurship: How Much of the Spatial Distribution Can We Explain?”, Journal of Economics and Management Strategy 18:3 (2009), 623-663.

- Guha-Khasnobis, Basudeb, Ravi Kanbur, and Elinor Ostrom, Linking the Formal and Informal Economy Concepts and Policies (Oxford, UK: Oxford University Press, 2006).

- Gulyani, Sumila, and Debabrata Talukdar, “Inside Informality: The Links Between Poverty, Microenterprises, and Living Conditions in Nairobi’s Slums”, World Development 38:12 (2010).

- Haltiwanger, John, Ron Jarmin, and Javier Miranda, “Who Creates Jobs? Small vs. Large vs. Young”, NBER Working Paper 16300 (2010).

- Harris, John, and Michael Todaro, “Migration, Unemployment and Development: A Two-Sector Analysis”, American Economic Review 60:1 (1970), 126-142.

- Hasan, Rana, and Karl Jandoc, “The Distribution of Firm Size in India: What Can Survey Data Tell Us?”, ADB Economics Working Paper 213 (2010).

Henderson, Vernon, “Cities and Development”, Journal of Regional Science 50 (2010), 515-540.

- Hsieh, Chang-Tai, and Peter Klenow, “Misallocation and Manufacturing TFP in China and India”, Quarterly Journal of Economics 124 (2009), 1403-1448.

- Hsieh, Chang-Tai, and Peter Klenow, “The Lifecycle of Plants in India and Mexico”, Working Paper (2011).

- Iyer, Lakshmi, Tarun Khanna, and Ashutosh Varshney, “Caste and Entrepreneurship in India”, Working Paper (2011).

- Kanbur, Ravi, “Avoid Informality Traps”, in Ejaz Ghani (ed.) Reshaping Tomorrow (Oxford, UK: Oxford University Press, 2011).

- Kanbur, Ravi, “Conceptualising Informality: Regulation and Enforcement,” Indian Journal of Labour Economics 52:1 (2009), 33-42.

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Wickens, M. (1996) “Interpreting cointegrating vectors and common stochastic trends”, _Journal of Econometrics_ , Vol. 74, 255–71.

Williamson, J. (1985) “The exchange rate system”, Institute for International Economics, Washington DC: Policy Analyses in International Economics, No. 5.

Williamson, J. (1994) “Estimates of FEERs”, in Williamson, J (ed), _Estimating equilibrium exchange rates_ , Institute for International Economics.

Yajie, W., Xiaofeng, H., and A. S. Soofi (2007) “Estimating reminbi (RMB) equilibrium exchange rate”, _Journal of Policy Modeling_ , 29, 417–429.

Zhang, Z. (2001) “Real exchange rate misalignment in China: an empirical investigation”, _Journal of Comparative Economics_ , 29, 80–94.

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discount rates (based on intergenerational ethical arguments) obtain damages as high as $85 per ton or more (e.g., Aldy et al. 2010, Newbold et al. 2009, Tol 2009, IWG 2010). Especially contentious is the treatment of extreme catastrophic risks due to the possibility of unstable feedback mechanisms that might cause a runway warming effect, for example, due to warming-induced releases of underground methane, itself a greenhouse gas. In theory, these risks could imply damages per ton that are arbitrarily large in expectation (Weitzman 2009). However, this consideration does not provide specific guidance on an appropriate value for the social cost of CO2. To be conservative, we adopt a benchmark value of $10 per ton which is an approximate lower bound estimate from the literature.

A gallon of gasoline and a gallon of diesel produce about 0.009 and 0.0011 tons of CO2 respectively.<sup>18</sup> Thus, our benchmark damage assumption amounts to 9 cents per gallon of gasoline and 22 cents per gallon for diesel.

_Passenger car equivalents_ . We adopt the same value for the passenger car equivalent for an extra vehicle mile by a public bus as assumed for London by Parry and Small (2009), namely 5. For microbuses, which have a size about half way between that of a car and a public bus, we assume a passenger car equivalent of 3.

_Marginal congestion costs_ . Marginal congestion costs depend on the added to delay to other road users caused by an extra vehicle mile and how motorists value travel time.

We begin with the following commonly used functional form relating travel delay per automobile mile ( _T_<sup>_A_</sup> ) to road traffic volume ( _V_ ), where the latter includes microbus and bus vehicle miles in passenger car equivalents: _α_ and _θ_ are parameters and<sup>_T_</sup> _fA_<sup>is the time per auto mile when traffic is free flowing. A typical value for</sup> the exponent _θ_ is 2.5  5.0 for urban centers (Small 1992, pp. 70–71). We assume _θ_ = 4, the same assumption as in the Bureau of Public Roads formula, which is widely used in traffic engineering models. The average delay due to congestion—that is the addition to travel time per mile over time per mile at free flow speeds—is _T A_  _TfA_  _TfA_  _V_  . From differentiating (A1) with respect to _V_ , the

> 18 The carbon content of these fuels is from <u>http://bioenergy.ornl.gov/papers/misc/energy_conv.html). One ton of</u> carbon produces 3.67 tons of CO2.

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