rafmacalaba/data-use-mentions-extended huggingface dataset:
taller than 80m,<sup>1</sup> we use satellite height data for all buildings within the defined urban extent. We believe our approach provides a more representative measure of heights for the whole city.

Finally, we estimate income and population elasticities of the evolution of city form to understand how the spatial structures of cities change across the different stages of development and across countries and regions. We introduce novel estimates of density using floor area per resident in a city instead of traditional measures that use surface area or built-up area. We find that doubling of a city’s income is associated with a 11% increase in its built-up surface and 18% increase in average building height. The share of infill development is significantly higher for upper-middle-income and high-income countries.

Following this motivational section, the rest of the paper is organized as follows. Section 2 describes the satellite and economic data used in the research. Section 3 provides stylized facts on the three margins of city form evolution -- horizontal spread (extensive margin), infill development (inside a city’s edge), and vertical layering (building taller structures). Section 4 describes the econometric analysis on the proximate drivers of evolution of city form. Section 5 concludes.

# 2. Data

- 2.1 Defining a city

To measure city expansion and infill development, we use the Global Human Settlement Urban Center Database 2015 (GHS-UCDB) developed by the European Commission, Joint Research Centre (JRC). This data set defines urban centers applying the "Degree of Urbanization”, which is a new global definition of cities, urban and rural areas developed by the European Union and combines the previously developed Global Human Settlement Layer Built-up (GHS-BUILT) areas and Population (GHS-POP) grids. A city consists of "contiguous grid cells with a density of at least 1,500 inhabitants per km<sup>2</sup> of permanent land or with a built-up surface share on permanent land greater than 0.5 and has at least
model of Laubach and Williams (2003), provides only very imprecise estimates and even minor modifications of the model or the estimation strategy can result in very different estimates of equilibrium interest rates.<sup>3</sup> In addition, it is not suited for the estimation of equilibrium real rates in EMDEs due to its data requirements and the need for a strong relationship between the variables included in the model. Recent advances have concentrated on the following areas: first, estimation procedures have been extended and improved for the United States;<sup>4</sup> second, they have been applied to more advanced economies;<sup>5</sup> and, third, equilibrium interest rates for some advanced economies have been constructed for much longer time periods.<sup>6</sup> We contribute to this literature by offering an estimation method that allows to extend the estimation of equilibrium rates to many more countries, which to the best of our knowledge is yet missing.<sup>7</sup> Second, we add to the literature on the global interest rate. Current estimates of the global interest rate are mostly based on small sets of countries: King and Low (2014) present a “world” real interest rate based on data from the G7 countries and Del Negro _et al._ (2019) interpret the common trend of a VAR model estimated for the same seven economies as the “global” interest rate. While these countries constitute a large share of the world economy, they still provide only a limited understanding of the global interest rate. Wynne and Zhang (2018) add the BRICS countries to their estimation of a global

> 3 Among others, this is shown in Clark and Kozicki (2005), Kiley (2015), Lewis and Vazquez-Grande (2017), Brand _et al_ . (2018), and Beyer and Wieland (2019).

> 4 Examples include Holston _et al_ . (2017), Del Negro _et al_
# **1. Introduction**

Public Financial Management is a key concern of governments and development practitioners. Institutions governing public finances have a determining impact on the economic and social costs and benefits of revenue collection and expenditure. Some have recently gone so far as to claim that ‘successful “societal evolution” hinges on the systems and procedures societies develop to manage public finance and procurement’ (Porter, Andrews, Turkewitz, and Wescott 2010). Reflecting its importance for the achievement of development outcomes, increasing attention and resources are being devoted to strengthening the public financial management systems of developing countries worldwide (World Bank IEG 2008). Global trends towards increased investment in Public Financial Management reform are now being played out in the Pacific, where donors – dealing with recent and projected increases in aid flows and strong political imperatives to make greater use of country systems while ensuring value for money and fiduciary control – have substantially increased efforts in public financial management reform. Most of the independent Anglophone Pacific countries currently have PFM reform programs of some sort underway, often supported by international technical assistance.

Following similar regional and global analyses, this paper provides the first detailed quantitative analysis of PFM performance in small Pacific Island countries (PICs) using the Public Expenditure and Financial Accountability (PEFA) framework. The PEFA framework allows standardized assessment of PFM systems against “good practice” norms, with performance against 31 indicators, and 73 dimensions of the public financial management system scored using an A-D scale, and “A” scores representing “international good practice”. At the time of writing, the PEFA framework had been applied in 118 countries, including nine PICs. Using crosssectional data from PEFA assessments, we examine the PFM performance of twelve small PICs (Fiji, Kiribati, Nauru, Niue, Tuvalu, Vanuatu, Samoa, Tonga, Cook Islands, Marshall Islands, Palau, and Solomon Islands) in relation to broader global patterns. We approach this analysis in two steps.

Firstly, we examine the performance in PEFA assessments of small
authors such as Beaudry et al. (2006) Bloom et al. (2015) and Bloom et al. (2012b). As these authors argue, these kinds of measures have the advantage of being recorded consistently in all firms and they do not depend on price indexes. Furthermore, as Bloom et al. (2012b) argue, they are highly correlated with other measures such as IT capital stock per worker. In this sense, we analyze computers-per-worker, the share of labor with Internet, and the share of labor with computer which should behave similarly to computers-per-worker and allows us to test the robustness of our main measure.

As our performance indicator considering the information available in ENTIC, we use sales-perworker, which, although it is an imperfect measure of productivity, has been widely used in previous studies.<sup>3</sup> For example Haltiwanger et al. (1999) and more recently Bloom et al. (2012a) use this indicator as their productivity measure. Table 1 shows descriptive statistics for firms in our sample in terms of performance, ICT use and other characteristics.

Finally, in order to analyze whether firms face competition, following Iacovone et al. (2013) we use the natural shock generated by the increasing competition from China in the Mexican market. In this sense, we measure competition as the change in the share of China in total Mexican imports for each HS1996 subheading between 2000 and 2008.<sup>4</sup> Table 1: Descriptive Statistics

|||20|08|||201|2||
|---|---|---|---|---|---|---|---|---|
|Variable|Mean|sd|p10|p90|Mean|sd|p10|p90|
|Computers-per-worker|0.24|0.22|0.06|0.50|0.28|0.23
bootstrapping.

As a robustness test we also present results _controlling_ for the total reports of crimes in that city and period. The idea behind including this control is that we want to estimate the effects of the program on the _crime report by migrants_ , keeping constant the _overall number of crime reports_ . Reassuringly our results remain robust with and without this control.

# **IV. A Addressing potential dynamic endogeneity**

The validity of the main estimates described in equation 1 rests on the parallel dynamic behavior on the outcomes we examine for municipalities that had higher and lower PEP program take-up. Although evidence on the validity of this assumption will be illustrated in the next section, we also estimate a specification that was robust to dynamic individual responses to the program as a robustness test.

For this purpose, we follow our approach in Bahar et al. (2021). In particular, we employ an instrumental variable approach and estimate specification (1) through two-stages least squares (2SLS).<sup>14</sup> Our instrument is the average number of days undocumented Venezuelan migrants had to register for the PEP program in the last quarter of 2018. The number of days was defined randomly at the individual level by the form number of the registration in the RAMV survey. According to the government officials who oversaw this process, the local registration point gave this number to migrants in the order they arrived and registered. More importantly for us, the numbers did not correspond to any particular geographic or socioeconomic characteristics of the municipalities. The officials said they took this approach to scatter the applications to manage the administrative load and distribute it more evenly across locations. Based on the individual numbers for all PEP holders at the time of registration in a given municipality, we estimate the average registration days per municipality as: Reg. Days _c_ = � RAMV registrants assigned to time window j _<u>c</u> ×_ [Days in time window j] (2) _jεK_ Total RAMV registrants _c_

>
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|Control of Corruption (lagged)|Control<br>of<br>Corruption<br>indicator<br>from<br>Worldwide Governance Indicators. Higher<br>values of the variable imply lower corruption.<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years), where the weights used are the<br>proportion of firms covered in the calendar<br>year.<br>Source: Worldwide Governance Indicators,<br>World Bank.|
|---|---|
|Profit Tax Rate|The amount of taxes on profits paid by the<br>business expressed as a percentage of total<br>commercial profits of the business as per the<br>Doing Business methodology, World Bank. To<br>make the data comparable across countries,<br>several<br>assumptions<br>are<br>made<br>about<br>businesses. The main assumptions are that they<br>are limited liability companies, they operate in<br>the country's most populous city, they are<br>domestically owned, they perform general<br>industrial or commercial activities, and they<br>have certain levels of start-up capital,<br>employees, and turnover.<br>The variable is lagged by 2 years from the<br>fiscal year covered by the Enterprise Survey in<br>the country. In cases where the fiscal year<br>spanned more than one calendar year, the<br>figure used is the weighted average over the<br>calendar years covered (and lagged by 2<br>years
**|(0.000341)*|(0.000302)||(0.000308)|(0.000496)|||||
|Share of hhlds with computer||-0.0249|-0.0578|-0.0804||-0.00569|-0.0283|-0.0347|||||
|||(0.0908)|(0.0851)|(0.0918)||(0.0686)|(0.0558)|(0.0543)|||||
|IDN exports per worker *||||-0.000384||||-0.00361|||||
|Share of hhlds with computer||||(0.00155)||||(0.00156)**|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.969|0.969|0.969|0.969|0.815|0.815|0.815|0.814|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All
poverty reduction. Figure 12 summarizes the age composition and average household size by percentiles of per capita consumption using the 2010 NLSS data. Poorer households tend to have a larger household size with more children and fewer working-age adults. On the other hand, wealthier households tend to have fewer members but more working-age adults and fewer children. The contrasting demographic composition of the wealthy and the poor underscores the important role of population dynamics in poverty reduction and economic development.

**Figure 12: Age Composition and Average Household Size by Consumption Percentile**

<!-- Start of picture text --> 100%<br>15 or Younger<br>80%<br>60%<br>40% Working Age (15 -24)<br>20%<br>0% 65 or Older<br>3 7 11 15 19 23 27 31 35 39 43 47 51 55 59 63 67 71 75 79 83 87 91 95 99<br>100%<br>1 to 3 Members<br>80%<br>60%<br>4 to 6 Members<br>40%<br>20%<br>7 to 9 Members<br>0% 10 or More<br>3 7 11 15 19 23 27 31 35 39 43 47 51 55 59 63 67 71 75 79 83 87 91 95 99<br>Consumption Percentiles<br>Percent of Population<br>Percent of Households<br><!-- End of picture text --> Source: Nepal Living Standards Survey 2010 20
# **Appendix B: Decentralization Indicator**

The indicator of decentralization is developed as part of a doctoral dissertation project (Vincent, 2020). The dataset informs on the discretionary power of sub-national and central governments over the tax system. It is built through in-depth reviews of more than two thousand legal and policy documents that inform on the distribution of power over the tax system across layers of government in each given country. The dataset considers the latest information available for each country based on the publication or ratification date of the most recent legal provisions on local taxation or the general tax codes. As such, not all countries listed in Table 10 are included in estimations with the decentralization variable. For instance, Benin is included in all rounds provided that the Afrobarometer was conducted as the primary law on local finance dates back to 1998 (Law No. 98-007). On the other hand, Madagascar's intergovernmental tax system is most recently defined by 2014 on local government finance and is therefore excluded from the analysis using the local government taxing rights variables. The sources of information are summarized as follows:

**Table 11: Sources of information – A summary**

|Legal Provision|Tax Codes, Local Government Acts, Laws and Decrees on Local Public<br>Finance and Taxation, Constitutions|
|---|---|
|Archives and<br>Policy<br>Documents|Archives from the International Bureau of Fiscal Documentation (IBRD,<br>Access: 2015-2017), Decentralisation Policy Document, Territorial and Public<br>Administration Reforms documents, Development Strategies, Public<br>Financial Reports, Regional and Local Council Reports|
|Scientific and<br>Grey Literature|Peer-reviewed publications, edited volumes, working papers and<br>multilateral organization reports (IMF, World Bank, UCLG, UN, ...)|
|Existing<br>Databases|OECD Tax Autonomy, Regional Authority Index, Local Public Finance<br>Datasets (when available)
national average of total joint or outright female ownership of 15.37 percent, 13.98 percent were outright female ownership.

Table 1: Proportion of households who own their home, including disaggregation for female ownership (%), in select Indian states

||**% of households who**<br>**own their house or any**<br>**other house**|**% of households where a**<br>**female member of the**<br>**household owns the**<br>**main dwelling or any**<br>**other dwelling, either**<br>**outright or jointly with**<br>**someone else**|**% of households where a**<br>**female member of the**<br>**household owns the**<br>**main dwelling or any**<br>**other dwelling outright**|
|---|---|---|---|
|Delhi|67.15|27.40|24.50|
|Gujarat|80.40|12.21|11.32|
|Karnataka|68.75|23.14|22.26|
|Maharashtra|78.89|16.54|15.24|
|Odisha|83.66|10.23|9.65|
|India - national|79.85|15.37|13.98|

_Source:_ National Family Health Survey 2015–16, Ministry of Health and Family Welfare (MoHFW).

At the time of writing, raw data for the more recent release of NFHS data (2019–20) was not yet available. However, the fact sheets so far released reveal figures on home and/or land ownership by females (aged 15–
Horizontal lines denote the average effect (and the confidence interval around it). Average expenditure effects are fairly substantial, ranging from 5 percent of baseline income in large urban areas, to 12.5% in rural areas. For Brazil as a whole, the average reduction in welfare due to the expenditure effect of the 2007-2008 food price increases was of the order of 7.5% of initial income. As expected, the expenditure effects are markedly regressive behind these averages: they range from an average of around 7% (12%) in the poorest decile to 2%

> 8 This is strictly true for the 11 large urban areas where prices are collected directly. As noted in Section 3, households living outside the 11 large urban areas are assigned the vector of price changes from the nearest large urban area, as detailed in Table 2.

> 9 The share is likely to be much lower among Brazil‟s truly rich households but, as is often the case, these are seldom well-captured in household surveys. See Korinek et al. (2006).

> 10 The country as a whole consists of rural areas, large urban areas, as well as all other urban areas, which are not shown separately.
.0|..|..|0|54||..|17|
|Nepal|619|46|45.3|..|..|0|..|..|..|1|
|Mozambique|535|71|23.8|..|..|0|..|..|..|3|
|Uganda|487|58|37.7|..|..|0|..|3|..|..|
|Malawi|371|78|33.9|..|..|0|..|..|..|4|

Notes: /a Number of tariff lines in percentage at HS 6-digit level with applied rate at 15% or above in 2011 or latest data available. /b Trade-weighted share of nonoil imports in % subject to antidumping (AD), countervailing (CV), Safeguads (SG) and China-specific transitional safeguards. /c Trade-wgted shares of nonoil imports in subject to AD only in 2011 for Indonesia and India are 0.8 and 5.8% respectively. All others are the same as total AD,CV,SG. /d Number of measures in force by notifications to WTO in antidumping, countervailing duties, and safeguards as of 2011 /e Frequency ratio in percentage of core non-tariff measures (NTMs) in HS 2-digit product categories.

/f Tariff quota (TQ) data is computed in HS 6-digit products due to data available in WTO TPR reports.

/g * Other core NTMS are based on UNCTAD/World Bank NTM database, where figures are computed at HS 6-digit level.

Sources: World Bank WDI database (GDPPC, GDP and trade data), UNCTAD TRAINS database (tariff data), WTO Trade profiles and TPR reports (various issues); and Bown, Chad (2013), "Emerging Economies and the Emergence of South-South Protectionism," Journal of
are lower than China‘s. _The origin of the investors does not matter though it will become increasing critical for regions such as SSA where investment capital is scarce._

# **4.4 Significant potential for relocation of jobs to low wage countries**

Due to the sheer size of its labor market, the number of jobs China‘s ongoing industrialization will potentially create for low-income countries is huge. Several factors will matter.

As employment statistics for manufacturing is extremely sparse and tentative, we cannot provide any estimates of potential job relocation. However, rough calculations are informative and sufficient. Currently, China employs about 85 million workers in its manufacturing sector nationwide. Going forward, rising wages will force China to upgrade to higher value-added, more capital/technological intensive sectors and relocate the jobs in the existing sectors to countries that have lower wage rate than China‘s. India currently employs about 9 million workers and Brazil about 13 million (Table 4.1). These emerging market countries employ about 120 million workers whose jobs could be relocated to other developing countries in the coming years. Notably, India still has significant surplus labor which implies that its labor intensive industries will expand before they contract and relocate jobs. Its younger population will provide more workers for Indian industries in the future but also more potential jobs for lower wage countries.

**Table 4.1: Employment in Manufacturing, Potential Dragons, 2009/10**

||Brazil|China|India|Indonesia|Total|
|---|---|---|---|---|---|
|Employment share in||||||
|Manufacturing (million)|**13.1**|**85**|**8.7**|**12.5**|**119.3**|
|Population (million)|192|1,324|1,140|277
; McGuirk and Burke 2020), as well as the literature on how higher or lower productivity and opportunity costs of conflict attenuate or fuel fighting, respectively (Miguel et al. 2004; Dell et al. 2014; K ̈onig et al. 2017; McGuirk and Burke 2020; Eberle et al. 2020).

Another relevant body of literature studies the impact on conflict of policy interventions that provide cash transfers or other forms of foreign aid (see also the surveys of Findley 2018; Rohner 2022). While earlier studies have focused on the country-year level (see, e.g., De Ree and Nillesen 2009, who found that foreign aid abbreviates conflicts), many recent studies have leveraged within-country variation. In particular, Nunn and Qian (2014) conclude, using a shift-share instrumentation strategy, that in-kind US food aid tends to increase the risk of conflict. Consistent with this, Crost et al. (2014) have found, 4
Table 8: Loan-to-toilet conversion

||(1)<br>HH re|(2)<br>port|(3)<br>Interviewer|(4)<br> observation|
|---|---|---|---|---|
||OLS|IV|OLS|IV|
|_Second stage_|||||
|Sanitation loan uptake|0.1630***|0.4753***|0.1371***|0.5005***|
||(0.0375)|(0.1558)|(0.0349)|(0.1562)|
|Strata FE|Yes|Yes|Yes|Yes|
|Interviewer FE|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|
|Ratio sample clients/GP size|Yes|Yes|Yes|Yes|
|r2|0.412|0.382|0.384|0.344|
|_First stage_|||||
|SL - First stage||0.1816***<br>(0.0356)||0.1816***<br>(0.0356)|
|F-stat||25.732||25.732|
|N|2821|2821|2821|2821|

_Notes_ : SL equals sanitation loan arm. Standard errors clustered at the village level are shown in parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level. Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. Strata and interviewer fixed effects included. Data source: household survey. Sanitation loan uptake comes from administrative data.

The second conclusion we can draw from this table is that on average,