rafmacalaba/datause-encoder-data huggingface dataset:
# **1. INTRODUCTION**

Urban expansion in the developing world has been dramatic, both in absolute and relative terms. Between 1950 and 2015, the total urban population in developing countries increased tenfold from about 300 million to 3 billion, while the urban share tripled from about 17 to 50 percent (United Nations, 2013). There are many similarities with the urbanization process of developed countries in the 19th century. Yet, there are also important differences.

First, urban growth has been substantially faster in today’s developing world. In Europe, urbanization accelerated with the advent of the Industrial Revolution, rising from 15% in 1800 to 40% in 1910. Both Africa and Asia reached the same rate in half the time, moving from 15% in 1950 to<sup>_∼_</sup> 40% in 2010. Second, while urbanization is positively correlated with income across countries (Henderson, 2010), the world is becoming more and more urbanized at a constant income level (Glaeser, 2013; Jedwab & Vollrath, 2015 _b_ ). In 1960, the 35 countries whose income per capita was less than $2 a day had an average urbanization rate of 15% (WDI, 2013). In 2010, the 34 countries with comparable incomes had an average urbanization rate of 30%. So, why has post-1950 urban expansion in the developing world been so fast and does the speed of urban growth matter for the people’s welfare?

Standard models explain urbanization largely by rural-urban migration in response to an expected urban-rural wage or utility gap (Harris & Todaro, 1970; Lall, Selod & Shalizi, 2006). Migration flows could be the result of a _rural push_ . If the country experiences a Green Revolution, the rise in food productivity releases labor for the urban sector (Schultz, 1953; Gollin, Parente & Rogerson, 2002). Rural poverty due to land pressure or natural disasters causes rural migrants to search for better lives in the cities (Barrios, Bertinelli & Strobl, 2006; da Mata et al., 2007; Henderson, Storeygard & Deichmann, 2013). Then there are various _urban pull_ factors. If the country experiences an Industrial Revolution, the urban wage increases, which attracts workers from the countryside (Lewis, 1954; Lucas, 2004; Henderson, Roberts & Storeygard, 2013). If the government adopts urban-biased policies, urban utility also increases (Henderson, 1982; Ades & Glaeser, 1995; Davis & Henderson, 2003). A country that exports natural resources also urbanizes if the resource rents are spent on urban goods and services (Gollin, Jedwab & Vollrath, 2013; Jedwab, 2013; Cavalcanti, Mata & Toscani, 2014). While the Green Revolution, Industrial Revolution and resource export theories find that urbanization is associated with development, the rural poverty and urban bias theories imply that urbanization occurs “without growth” (Fay & Opal, 2000).

The aforementioned theories remain silent on the role of demography in the urbanization 1
# 5.4.3 Community Fixed Effects Results

In this section, we present the estimates based on community fixed effects. The estimates are presented in Table 6. For income, the effects of the survey rounds, as well as the effects of the interactions between the rounds and INDH are statistically insignificant. For household consumption, there is a significant interaction between 2011 and the INDH of some DH 444 (0.122 percent) but also significant decrements in consumption in the main effects for 2011 and 2013.

For household wealth, the positive gains arising from interaction between the INDH and the survey round are not statistically significant. Other improvements over time are more likely linked to the survey rounds and consequently to measurement issues across rounds, differences in the survey dates, and prevailing economic conditions, not necessarily to the INDH. Overall, there do not appear to be significant or persistent gains in these economic measures after accounting for community fixed effects and any existing impact in 2008.

**Table 6. Economic Outcomes Community Fixed Effects OLS Results by Outcome**

||**In**<br>|**2013 Morocc**<br>|**an Dirham**<br>||**As Log (Perce**<br>|**ntages)**<br>|
|---|---|---|---|---|---|---|
||**HH incom**|**e HH wealth**|**HH consump**|**tion HH incom**|**e HH wealth**|**HH consumption**|
|**Round (2008 omitted)**|||||||
|**2011**|347|6,051***|˗383**|˗0.032|0.782***|˗0.116**|
||(320)|(2,176)|(172)|(0.053)|(0.118)|(0.047)|
|**2013**|238|10,308***|˗719***|˗0.003|1.170***|˗0.184***|
||(252)|(2,009)|(142)|(0.050)|(0.096)|(0.039)|
|**INDH and round**|||||||
|**interactions**|||||||
|**2011 and INDH**|326|5,747|444*|0.103|0.046|0.122**|
||(410)|(3,961)|(238)|(0.069)|(0.179)|(0.061)|
|**2013 and INDH**|478|4,657|˗56|0.095|0.068|˗0.023|
||(363)|(3,579)|(211)|(0.072)|(0.144)|(0.060)|
|**Constant**|4,801***|12,348***|3,961***|8.269***|8.449***|8.136***|
||(110)|(1,158)|(65)|(0.020)|(0.050)|(0.018)|
|**N (Observations)**|26,590|26,552|26,596|26,489|26,543|26,587|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

In conclusion, by using regression, RDD, and fixed effects approaches to measure the impact of the INDH on three economic outcome variables (income, consumption, and household wealth), we find limited evidence of a positive economic impact due to the INDH program. Although the estimated effect of the INDH is generally positive, it is moderate in size and often statistically insignificant. This is particularly notable given that we are essentially implementing multiple tests over multiple methods. There may have been some greater impact in 2011 but only on certain economic measures, and effects did not clearly persist into 2013. While the RDD models suggest larger impacts, given their sensitivity to the bandwidth chosen and the observed fluctuations along the forcing variable, these larger impacts are not robust.

23
that in housing increased by 56 percent, contributing most of the rise in the aggregate capitaloutput ratio (and thus in the ICOR) (Figure 17). By comparison, the capital-output ratio in the business sector went up by about a quarter (Herd, 2020).

# **Figure 16 Gross capital formation by sector**

# **Figure 17 Capital output ratio by sector**

(ratio of capital to total GDP) (share of total gross capital formation, current prices, percent) <!-- Start of picture text --> Business Housing Infrastructure Government<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> Source: Herd (2020).

<!-- Start of picture text --> Government Business Infrastructure Housing<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>1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> Source: Herd (2020).

The public investment stimulus also contributed to high corporate leverage. Credit to the nonfinancial sector grew by more than twice the pace of GDP. A significantly larger share of new credit went to infrastructure in 2014–16 (Figure 18). In 2016, infrastructure, construction, and real estate accounted for 50 percent of the outstanding stock of debt (Figure 19).

# **Figure 18 Share of new bank credit to infrastructure, construction, and real estate**

# **Figure 19 Share of infrastructure, construction, and real estate debt, 2016**

(percent of selected types of corporate debt stocks) (percent of all new bank credit to the corporate sector) <!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2012 2013 2014 2015 2016<br>Construction and real estate Infrastrcture<br><!-- End of picture text --> Source: CBRC anunal report; authors’ calculations.

<!-- Start of picture text --> Corporate bonds<br>Bank WMPs<br>Bank loans<br>Trust loans<br>0 20 40 60 80<br><!-- End of picture text --> Source: CBRC annual report; Wind Info; CEIC; China Bank WMP annual report; Trustee Association of China; authors’ calculations.

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|Table2:Policyimpleme|ntation has“determination”and an “|action”steps, examples|
|---|---|---|
|**_Notional Policy_**<br>**_mapping_**|**_Determination_**phase,<br>declaration of administratively<br>relevant state ofthe world|**_Action_**phase|
||**Tax Collection**||
|_Import Duties:_<br>Tariff code from types<br>of goods to tariff rates|What type of good is it in the<br>tariff code classification? Is it in<br>an “exempt” category? What is<br>theimport value?|Apply the specified rate to<br>the category from the<br>tariff code and collect<br>appropriate tax|
|_Property Tax_<br>Value of property to<br>amount owed|What is the taxable value of the<br>property? Is it exempt?|Collect the tax resulting<br>from applying rate to<br>assessed value|
||**Regulation**||
|_Urban Zoning_/<br>_Planning_Specification<br>of spatially allowed<br>types ofactivities|Is the activity of the designated<br>type, Residential? Commercial?<br>Industrial?|Issue zoning certificate for<br>the designated activity|
|_Driver’s license_<br>Personal qualifications<br>to type of license|Does the applicant meet the<br>specified criteria (e.g. age,<br>residence, competence) for the<br>type of license?|Issue driver’s license|
|_Allowable Rate of_<br>_Return Utility_<br>_Regulation_<br>Conditions of firm<br>providing to allowable<br>rates/services|What is the value of the installed<br>capital?|Allow charges sufficient<br>to generate the stipulated<br>return on the capital|
|**Ser**|**vice Delivery/Program Implemen**|**tation**|
|_Old Age Pension_<br>From characteristics<br>(e.g. contributions) to<br>eligibility and amount|Is the person eligible? What<br>were their total<br>contributions/relevant earnings?|Issue a check in the<br>appropriate amount|
|**Cont**|**racting for public works/service p**|**rovision**|
|_Road Construction_|Who are the qualified bidders?|Issue the contract to the|
|From “lowest cost<br>qualified bid”to contract|Which is the “best” bid on the<br>specified criteria?|chosen bidder.|

_Closed Ordered Deals._ The distinction with open ordered deals are deals that are ordered—in that they are roughly predictable both _ex ante_ and _ex post—_ but which are 18
Finally, column 6 in Table 1 presents the difference-in-differences estimates of the impact of the treatment on the implicit regulatory labor tax on firms. As discussed in Section 3.4, we conceptualize the regulatory burden associated with permanent employees as an implicit variable labor cost. Within-firm changes in this hidden cost can be backed out using the firm’s cost minimization problem; they are proportional to changes in the ratio of expenditures on labor and materials. The estimated change in the log of the implicit labor cost is -0.13, significant at the 10 percent level. This implies that the implicit labor cost fell by approximately 14 percent.

Table 2 presents the estimated impacts on the log of output, fixed capital, value added and value adder per worker. The results indicate that the repeal of IDA Chapter V-B did not have a detectable affect on output, investment and labor productivity in the treated firms.<sup>33</sup> Table 1: Firm Level Panel DiD on Employment Outcomes

||(1)<br>Log<br>total workers|(2)<br>Log<br>regular workers|(3)<br>Log<br>contract workers|(4)<br>Share of<br>contract workers|(5)<br>Likelihood of<br>usinganycontract work|(6)<br>Implicit cost<br>of Labor|
|---|---|---|---|---|---|---|
|RJ * Treatment L * Post|0.031|-0.177*|0.807***|0.098***|0.140***|-0.136*|
||(0.065)|(0.101)|(0.241)|(0.028)|(0.050)|(0.075)|
|Treatment L * Post|-0.044***|-0.055***|-0.045|0.004|-0.004|0.022*|
||(0.011)|(0.014)|(0.039)|(0.004)|(0.008)|(0.012)|
|RJ * Post|-0.001|0.069|-0.278|-0.019|-0.048|0.048|
||(0.054)|(0.079)|(0.194)|(0.021)|(0.040)|(0.060)|
|Observations|66,909|66,909|66,909|66,909|66,909|66,909|
|R-squared|0.024|0.023|0.008|0.006|0.005|0.083|
|Number of fact<br>~~n~~um|10,470|10,470|10,470|10,470|10,470|10,470|
|Firm FEs|Y|Y|Y|Y|Y|Y|
|Year Dummies|Y|Y|Y|Y|Y|Y|

Note: Sample restricted to firms with more than 100 workers. Treatment refers to firms with 100-300 workers. All dependant outcome variables represented in log forms here have been assigned an underlying value of 1 when they take a value of zero. Share of contract workers is defined as the share of contract worker in total worker hours used by the firm for that year. Implicit cost is defined using the expenditure on all raw materials as a base. Standard errors in parentheses, clustered at the firm level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.)

# **6.2 Robustness analysis**

Tables 3, 4, 5 and 6 present some robustness checks on the key panel results regarding employment. In Table 3, we include industry-year dummies to control for differential shocks or trends by industry. This may matter, for example, if Rajasthan’s industrial composition is tilted towards industries in which medium-size firms are replacing permanent workers with contract workers

> 33Instead of taking logs, we use the inverse hyperbolic sine (arcsine) transformation of value added as this variable can have negative values in the data. The inverse hyperbolic sine transformation is similar to the logarithmic transformation and has the same interpretation, but has the advantage that it can also be applied to zero and negative values.

22
than 5 cm DBH (1.3 m height from ground). Trees were counted on each plot (sample mean of 14.3 trees per plot) and tree heights were measured using clinometers. Measured trees were marked with enamel or chalk to avoid double counting.

Forest carbon is comprised of above ground biomass (AGB) made up of above ground tree biomass (AGTB) and sapling biomass, below ground biomass (BGB), leaf litter, dead wood and soil organic carbon (IPCC, 2006), but in this paper only AGB is estimated. Biomass was converted to carbon by multiplying the biomass by the 0.5 (IPCC, 2006) conversion factor. The forest carbon inventory methodology used is similar to that of the International Center for Integrated Mountain Development (ICIMOD) REDD+ pilot project in Nepal<sup>5</sup> and relies on allometric equations from Chave et al. (2005) that take account of diameter at breast height (DBH), tree height and density to calculate AGTB. The allometric equations are the following<sup>6</sup> : AGTB = 0.0509 * D<sup>2</sup> H for moist forest stands<sup>7</sup> AGTB = 0.112 * ( D<sup>2</sup> H)<sup>0.916</sup> for dry forest stands AGTB = 0.0776 * ( D<sup>2</sup> H)<sup>0.940</sup> for wet forest stands Where, AGTB = above ground tree biomass in kg;

- = specific gravity of wood in (g/cm<sup>3</sup> )

D = DBH in (cm) H = Tree height in (m)

> 5 The “Design and Setting Up of a Governance and Payment System for Nepal’s Community Forest Management under Reducing Emissions from Deforestation and Degradation (REDD)” project is implemented in three Districts with financial support from the Norwegian Agency for Development and Cooperation (NORAD) <mark>. This pilot project seeks to evaluate the feasibility of REDD payment mechanisms in CFs.</mark>

> 6 If trees are lopped, as can be important in Nepal, branch biomass will be missing and AGTB will be overestimated.  We address this issue by analyzing a variety of forest quality measures, including percentage of canopy cover.  Such measures are less subject to lopping bias.

> 7 District average annual rainfall data was used to categorize stands.  Dry stands have average annual rainfall of <1500 mm. Moist stands are those having average annual rainfall of 1500-4000 mm. Wet stands are in areas with average annual rainfall of >4000 mm.

**13 |** P a g e
# Appendix I.A.7 Historical Demographic Data, Chile 1895-1985

|Year|1895|1920|1930|1940|1952|1960|1970|1985|
|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>populationover65yearsold|19.95|17.77|17.20|16.84|14.69|13.00|11.06|10.78|
|Population aged 20 to 59/<br>population over60years old|10.91|7.89|8.12|7.97|7.17|6.44|5.69|5.88|
|Percentage<br>of population<br>over 60 years old|4.09|5.79|5.69|5.87|6.48|6.79|7.55|8.47|
|Percentage<br>of population<br>under 15 years old|40.75|37.94|37.19|37.15|37.35|39.63|39.19|31.47|
|Women over 60 years old/<br>Men over60years old|1.08|1.17|1.18|1.19|1.20|1.20|1.19|1.34|

Ratios calculated using data found in Mitchell, B. R. 1983 "International Historical Statistics: The Americas and Australasia".

# **Appendix** I.A.8 **Historical Demographic Data, Colombia** 1918-1973

L-n

|**Year**|1918|1938|1951|1964|1973|
|---|---|---|---|---|---|
|Population aged 15 to 64/<br>populationover65yearsold|16.32|19.18|17.37|16.80|17.23|
|Population aged 20 to 59/<br>population over 60 years old|7.75|8.53|8.22|7.75|8.22|
|Percentage<br>of population<br>over60yearsold|5.57|5.01|5.12|4.94|4.75|
|Percentage<br>of population<br>under 15 years old|39.96|42.00|42.56|46.65|44.54|
|Women over 60 years old/||||||
|Men over 60 years old|1.15|1.16|1.15|1.14|1.09|

Ratios calculated using data found in Mitchell, B. R. 1983 "Intemational Historical Statistics: The Americas and Australasia".
of total trade costs with respect to each trade cost variable. Thus, a smaller coefficient on distance for online flows could simply signal that the bundle of online products has a lower elasticity of substitution than the offline bundle. To estimate these elasticities of substitution we assume that trade costs online and offline are gamma distributed with shape parameter _k_<sup>_f_</sup> , where _f_ is the type of flow, but an identical scale parameter. Then using existing estimates of the elasticity of substitution for aggregate trade flows, we can back up consistent estimates of the elasticity of substitution online and offline using the fact that the variance of a gamma distributed variable is proportional to the mean by a factor equal to the scale parameter. For a detailed description of the methodology to estimate the online and offline elasticities of substitution, see section 5.1.

Our results suggest that for an estimate of the aggregate elasticity of substitution of 5 (see Eaton and Kortum 2012), the online elasticity of substitution equals 4.5, whereas the offline elasticity of substitution equals 5.6. This last estimate is quite close to the Broda and Weinstein (2006) median estimate of 5.9 in our bundle of HS-6 digit goods. Moreover, while the online and offline elasticities of substitution are statistically different from zero at the 5 percent level, they are not statistically different from each other. This comforts us in our matching of online and offline products, and suggests that statistical differences in the estimated coefficients of the gravity equation will be due to the contribution of these variables to trade costs, rather than to differences in the elasticity of substitution.

Our eBay data also include data on average bilateral ad-valorem shipping costs. While we do not have an equivalent for bilateral offline flows, in the case of US imports we do have data on freight and insurance costs from USITC. When plotting these costs against distance (see Figure 4) we find that for both online and offline flows, shipping costs are uncorrelated with distance, even though shipping costs seem to be much higher online.<sup>7</sup> This suggests that the introduction of observable shipping costs in the gravity equation, which are often omitted due to lack of data, is not going to explain the importance of distance in the gravity equation. But this is a testable hypothesis at least in the online sample.

Offline trade data and trade cost variables come from the usual sources and are described

> 7Using data on all country pairs online gives a similar picture.

7
Policy Research Working Paper 8228

# **Abstract**

This paper investigates an alternative proxy for individual worker productivity in physical work settings: a direct measure of physical activity using an accelerometer. First, the paper compares worker labor outcomes, such as labor supply and daily productivity obtained from firm personnel data, with physical activity; they are strongly related. Second, the paper investigates the effect of a health intervention on physical activity, using a temporally randomized offer of malaria testing and treatment. Workers who are offered this program reallocate time from lower intensity activities in favor of higher intensity activities when they work.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jfriedman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Figure 10. ECA: Potential output growth: scenario results

A. Potential growth under reform scenarios

# B. Climate change scenarios

<!-- Start of picture text --> Percent Social benefit reform<br>Labor market reforms<br>6<br>Education and health improvements<br>Investment surge<br>5 Baseline<br>4<br>3<br>2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDE ECA<br><!-- End of picture text --> <!-- Start of picture text --> Percent Investment boost (climate change)<br>6 Disasters (climate change)<br>Baseline<br>5<br>4<br>3<br>2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDE ECA<br><!-- End of picture text --> C. Share of firms reporting competition from D. Poland: Ukrainian migrants and forcibly informal firms as a constraint, 2019 displaced people, through June 2022 <!-- Start of picture text --> Percent ECA EU-26 Thousands Millions<br>25 Males<br>400 Females 2.0<br>20 Refugees - protection scheme (RHS)<br>300 1.5<br>15<br>200 1.0<br>10<br>100 0.5<br>5<br>0 0.0<br>0 Migrants, Work permits, Refugees,<br>Manufacturing Services 2020 2021 2022<br>E. Impact on Central European potential F. EBRD assessment of green transition,<br>growth from NGEU reforms and policy 2021<br>targets<br>Percentage points Index, 10 = Frontier<br>1.0 8 Min-max range Median<br>0.8<br>6<br>0.6<br>0.4 4<br>0.2<br>2<br>0.0<br>Ageing Inclusiveness Institutions Digital<br>ECA CE WBK EE SCC CA<br><!-- End of picture text --> F. EBRD assessment of green transition, 2021 Sources: EBRD (2020, 2021); Haver Analytics; IMF; Oxford Economic Model; Penn World Tables; UNHCR; United Nations (2020); UN Population Prospects; World Bank; World Bank Development Indicators database; Enterprise Surveys database.

Note: CA = Central Asia; CE = Central Europe; ECA = Europe and Central Asia; EE = Eastern Europe; RUS = Russian Federation; SCC = South Caucasus; TUR = Türkiye; WBK = Western Balkans. Period averages of real GDP-weighted averages.

A.B. Potential growth estimates based on production function approach. Sample includes 53 EMDEs. of which 9 countries are from ECA.

C. Refugees indicate those registered for national protection schemes. Migrants indicate migrant stock in mid-2020. D. Percent of firms identifying practices of competitors in the informal sector as a major constraint. Data for the EU-26 country grouping and the euro area exclude Germany. Aggregates are calculated as averages.