rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1.2 Ground-truthing Landscan**

Here we attempt to ground-truth Landscan measures at the grid square level for Kampala and Nairobi, two cities where we have fine spatial resolution data on population and employment which is unavailable to Oak Ridge National Lab. We further attempt to replicate Landscan’s ambient population measure by grid square using an assignment algorithm on our own data. We will conclude that Landscan measures do well and seem superior to other commonly used measures which smear population into grid squares on the basis of built cover on the ground.

In the upper panels of Figures 1 and 2, we show the population and employment distribution for Kampala and Nairobi in 1 km grid squares. The population data for Nairobi are at the level of 2,213 enumeration units for 2009 contained in the 2015 built area of Nairobi defined in Henderson et al. (2018). For Kampala in 2002, population is at the level of 174 parishes within the administrative unit of Greater Kampala. We assign population levels from these survey units to the 1km grid square level by applying a weighted sum to the survey area numbers, where the weights reflect the share of land mass from each survey area(s) that falls within a 1km grid square. To make Kampala 2002 population comparable to 2011 employment numbers, we blow up the population in each grid square by an overall population growth rate of 3% per annum from 2002 to 2011. For employment, we use the economic census, which covers private and public employment for Kampala for 2011, and provides exact location points of firms across the city. One issue is that total employment in the census is far below known estimates; hence, given the age distribution in Kampala and labor force participation of urban Uganda, we have multiplied each grid squares employment by 2.761 to make up for the employment deficit.<sup>5</sup> The implicit assumptions in allocating growth and under-counting of employment to grid squares are obvious.

For Nairobi in 2009, we can
of the MFA. Similarly, high-skilled wage premiums relative to low-skilled workers decreased during the same time, from 70 percent in 2005 to 52 percent in 2016. These results align with our expectation that positive export shocks have an aggregate and economy-wide effect in Bangladesh labor markets, benefiting females and low-skilled workers across all sectors.

Our paper proceeds as follows. Section 2 outlines our conceptual framework, which includes some simple but illustrative simulation results. Section 3 describes the trade data, trends in trade and production, the labor market data, and relevant labor market characteristics. Section 4 describes the estimation data set, local labor market definition, and empirical methodology. Section 5 presents our results, including the short-run effects on wages and informality and the economywide changes that we observe in the long run. Section 7 concludes.

# 2. Conceptual Framework

Consider a two-sector model in which output, _y,_ is produced using a fixed factor (“capital”) and two types of workers (“males” and “females”) that are indexed by _j_ . Assuming perfect competition and constant returns to scale, decisions of identical atomistic firms within each sector can be represented by the usual profit maximization decision in which firms take wages (determined in aggregate factor markets) and output prices (determined in global markets) as given. These decisions are often represented by cost functions in which the production cost _C_ for sector _i_ at time _t_ is represented by: (1) i i i i ) ii Shephard’s lemma allows us to derive factor demand curves from the cost function. Holding CC = ff(pp yy , ww capital fixed, the demand for males and females are derived as a function of output prices and factor prices. Using φ _itj_ to represent the demand for factor _j_ at time _t_ in industry _i:_ (2) <u>i</u> i i i ) i ∂∂CC ∂∂ww ii = φφ ii(pp , ww ii Note that equation (2)
|Sales change (percent change year-on-year, same<br>month in 2019)|-53.8|-28.2|
|Change in export share of sales (percentage difference<br>from 2019 ES)|-0.7|-1.2|
|Supply of goods and materials has decreased (1=yes)|0.61|0.45|
|Started/increased online or delivery business activities<br>(1=yes)*|0.21|0.17|
|Faced financial issues (1=yes)*|0.81|0.73|
|Received other policy support (1=yes)*|0.19|0.19|
|Received wage subsidies (1=yes)*|0.08|0.07|
|Interaction between whether had export sales in 2018<br>and trade shock|-5.1|-5.1|
|Source: CFUWBES.|||
|Notes: See note to table 5-2.|||

operation, boost sales, and re-absorb workers even under crisis conditions than their Jordanian counterparts. Between waves 1 and 2, they reported an additional 2.3 weeks of COVID-related closures on average and a lower share of workers was working remotely than before (2.9 percent). Moreover, the percentage of firms receiving policy support remained flat. However, there was a

# modest improvement in

the share of firms reporting recent supply disruptions, with a decline from 61 to 45 percent. Crucially, sales rebounded between the two waves of the CFUWBES to a year-on-year decline of 28.2 percent, up from a 53.8 percent drop for wave 1. Whether due to greater ex ante resilience, better input and output market linkages, or more flexible labor markets, Georgian formal firms were better able to bounce back and rehire PFPS workers.<sup>44</sup>

> 44 This is despite the fact
the opportunity to complete a survey. The World Food Programme (WFP) used this approach effectively in Yemen, completing surveys of 2,400 households every month over the entire course of the conflict in that country (WFP, 2018).

# <u>Data requirements and pitfalls</u>

There are two primary drawbacks to the telephone survey approach. First, phone interviews can only accommodate relatively straightforward questions and have to be much shorter than face-to-face interviews. For these reasons, phone interviews cannot produce more traditional welfare metrics, such as poverty. Straightforward indicators of deprivation—such as lack of access to food, school, sanitation, and medical care—are more suitable for phone data collection.

A second drawback to the phone survey approach is difficulty in obtaining a representative sample of phone numbers. Particularly when face-to-face surveys are not possible, surveyors must settle for a survey that only represents the population with access to a phone and covered by a phone network. In the absence of readily available registries of phone numbers (e.g. from past face-to-face surreys), it may be difficult to obtain phone numbers of the phone-using population once a large shock has occurred. Thus, in many cases, surveyors rely on RDD-based surveys, which introduce selection bias for certain outcomes (see for example., Abraham et al., 2009).

In MENA, where phone penetration rates are high, phone surveys can play a very important role in measuring (subjective) well-being. The phone survey approach is particularly useful in conflict-affected countries—such as Iraq, the Syrian Arab Republic, Yemen, and Libya—where it is necessary to rely on remote data collection methods. Phone surveys can also be useful in other MENA countries where high19
now allocated across more new regions (348 instead of 336) reflecting pressures for regional profusion in the system.

6. To illustrate how the final DAU 2002 amounts were generated, we draw on the local government example, although the process of provinces is similar.

- (1). DAUi = PWBAi + LSi + FGAi

7. The lumpsum (LS) is an identical amount for each region. The proportional wage bill allocation (PWBA) for 2002 is based on the actual annualized realized regional wage bill for 2002 (using September 2001 data). It is proportional in that it does not actually cover each Rupiah of regionally expended wages. The LS and PWBA were collectively referred to as the minimum or basic allocation. The DAU 2002 local government allocations are based 10 % on lumpsum, 50% on proportional wages, and 40% on the formula. The provincial allocation was based on a 20% identical lumpsum allocation across all provinces or local governments, 30% realized wage bill, and 50% formula. The fiscal gap formula amount (FGA) combined measures of expenditure needs and fiscal capacity. The formula used regional population, area, poverty gap, and cost index data as proxies for expenditure needs. Fiscal capacity included expected own tax (PAD)

> 26 With decentralization, local governments were allowed to determine their own organization structure (e.g., _dinases, badans,_ and _kantors_ ).  Depending on size and local preferences, regions chose more expansive organizational structures and numbers of management positions.  The number of management positions ( _echelons)_ had a significant bearing on local governments ensuing wage bill.

> 27 Aceh and Papau were not penalized for their additional natural-resource tax “fiscal capacity” under the 2002 DAU balancing formula component.

29
gap.<sup>15</sup> Despite government interventions, FGD participants reported that many governmentsupported credit programs have not reached them at all.

51. Anecdotal evidence from the FGD study shows that managing the loaned money is a daunting task for HEs owing to business risk, household risk, and fungibility problems, which create the potential for delayed repayment or default. In such cases, the response from some of these creditors can be disproportionately adverse. Some FGD participants narrated their ordeal when the credit-lending organization raided the house of one of the defaulters and parted with belongings worth more than the value of the credit. Managing a large lump sum of borrowed money also poses risks to borrowers and creditors alike. As noted by participants in the FGD study: “If you give a loan of TSh 200,000 to an individual who needs only TSh 20,000 for capital, he/she will not be able to use it. He/she will use TSh 150,000 for other purposes unrelated to the business.” _Male participant, Mwanza_ .

“I borrowed TSh 900,000 one year ago from Akiba Commercial Bank (ACB), and one of the group members borrowed TSh 2,000,000. She disappeared. We are paying for her loan and can‟t get any new loans until we pay up her debt. I would rather do _kibati_ (merry-go-round) from the neighborhood than to be part of a group whose fate can be this devastating.” _Youth participant, FGD, Arusha_ .

52. The “soft” loans provided under most microfinance programs are often viewed not to be so. The additional conditions attached to them, including the need for some form of collateral, or a business registration certificate, or reference from a group lending association, work against the expectation of many HEs. Both the quantitative and qualitative analyses revealed that the youth are more vulnerable to this credit constraint because they are under-age to qualify for a loan. Compared to older traders, they are also
WPS3733

**Comments welcome**

# **DOES OPENNESS IMPLY GREATER EXPOSURE?***

**César Calderón Norman Loayza Klaus Schmidt-Hebbel**** The World Bank The World Bank Central Bank of Chile

## Abstract

External exposure can be measured by the sensitivity of first and second moments of economic growth to openness and foreign shocks. This paper provides an empirical evaluation of external exposure using panel data methods for a worldwide sample of countries. Controlling for domestic conditions, the paper examines the growth and volatility effects of outcome measures of trade and financial integration as well as four types of foreign shocks: terms of trade changes, trading partners' growth rates, international real interest rate changes, and net regional capital inflows. The paper analyzes the possibility of non-linearities by allowing the growth and volatility effects of openness to vary with the general level of economic development and by letting the effects of foreign shocks depend on the degree of trade and financial integration. The findings point toward strong non-monotonic effects of openness and external shocks on growth and volatility. Moreover, all in all, the results contradict the view that international integration increases external vulnerability by hurting growth and increasing volatility or by amplifying the adverse effect of external shocks.

**Key Words:** External Shocks, Openness, Volatility, Growth, Non-linearities **JEL Classification:** F36, F41, F43

## World Bank Policy Research Working Paper 3733, October 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http:/
16

# **The Basic Public Extension Service (ATPb)**

Although not directly the focus of this paper, ATPb is an extension program that will play an important role in the political agricultural arena. Bearing in mind that of the total population of more than 200,000 agricultural producers, only 40,000 are approached by the three ATP programs, and only 25,000-30,000 will be contacted through ATP1 and ATP2 by 1999. In order to reach the big producer population that is not approached through ATP 1 and ATP2, policy makers will need to enhance, and diversify the coverage of ATPb.

Data on ATPb in Nicaragua became available from a study by ESECA (1997) based on 270 representative sample farms. Some of the information in that study can be used in conjunction with the performance indicators suggested in the previous section. Figure 5 shows participation trends of producers in ATPb between 1993 and 1996.

Figure 5: Participation in ATPb in the sample of the study by ESECA (1997) <!-- Start of picture text --> 300<br>250<br>e 200<br>X 150<br>X100<br>50<br>1993 1994 1995 1996<br>Year<br><!-- End of picture text --> Source: Based on data from ESECA (1997a)

# **Measuring Paid-Extension Costs and Performances in Nicaragua**

In this section we will apply the analytical procedures that were presented earlier. First we will use some of the findings of ESECA (1997) to derive several performance measures. Without distinction between extension programs (see footnote) in the sample of the study by ESECA (1997),
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banking in Africa. Section 5 concludes and looks forward.

# **2. STOCK-TAKING: WHERE DOES AFRICA STAND?**

Global data collection efforts on the depth, outreach, stability, and efficiency of financial systems have been successful in collecting data on African financial systems for a wide sample of countries,<sup>2</sup> much wider than those used in the earliest studies of African financial development (see, e.g., Honohan and Beck, 2007). Those data are now routinely collected by the World Bank and housed in the Global Financial Development Database (GFDD), which was last updated in 2022.<sup>3</sup> In addition, aggregate data have been complemented with a number of surveys of enterprises, both informal and formally registered with an authority, and surveys for some countries now have a panel dimension (i.e., firms having been surveyed at several points in time).<sup>4</sup> Similarly, household surveys focused on financial services, such as the Finscope and Finaccess surveys in several African countries, have provided important insights into individual and household access to and use of formal and informal financial services, while the Global Findex dataset includes most countries in Sub-Saharan Africa.<sup>5</sup> The following analysis therefore uses an array of databases and other sources to document the development and structure of banking systems across the region.

> 2 Beck, Demirgüç-Kunt, and Levine (2000, 2010), Demirgüç-Kunt and Klapper (2012), Laeven and Valencia (2013). 3 See Cihák, Demirgüç-Kunt, Feyen, and Levine (2012) for description of the GFDD. The GFDD can be accessed at: <u>https://www.worldbank.org/en/publication/gfdr/data/global-financial-development-database.</u>

> 4 For example, <mark>the World Bank Enterprise Survey is a firm-level survey covering a broad range of business environment topics.
At this point a word is in order about the independent variables that we use. Most are derived from the 2010 VF survey, but we are able to match the areas covered in that survey with household data from the 2009 Socio-economic Survey, and, for rural areas, the 2009 Community Survey. Some of the variables reflect the supply side, including measures of initial injections of capital into the Village Fund, as well as whether the VF encourages, or requires, borrowers to first make deposits; Table 11 provides some summary statistics. The supply of VF credit may also be affected by the characteristics of the VF committee – the educational attainment and professional experience of committee members, the extent to which they feel a need for further training, the method by which officers are chosen, the degree of commitment to equity in lending, and the willingness to borrow from banks to extend VF lending.

The remaining variables pick up demand-side influences, including the size and remoteness of the village or ward, and characteristics of households, including their income, education, gender mix, and propensity to borrow. The complete list of variables that we use is given in Table 11. This is a subset of a much larger list, which we winnowed down by excluding variables that consistently had little or no explanatory power, or had many missing values, or were closely correlated with one of the included variables.

# _Financial or Social Intermediaries?_

Before examining the determinants of VF performance, we first need to ask whether VFs act as proto-banks, or merely as conduits for allocating government funds to members of the public.

Our theoretical conclusions were predicated on the assumption that VFs have a fixed initial quantity of funds ( ) available to lend. In practice, a soundly-run Village Fund can easily borrow a million baht from the BAAC or GSP, at a fairly modest interest rate (3.8%), effectively doubling the amount of funds that they can lend to villagers. In 2010, 24% of VFs undertook such borrowing for
every household in a village, checking for individuals with symptoms. Compared to villages that did not receive ProACT, the odds of symptomatic malaria were 30 times lower in the intervention villages (Linn et al. 2015). This type of policy could be applied to areas that receive travelers during the weeks when the most expected infections enter.

Finally, just as mobile phones can be used to measure aggregate movement patterns to improve targeting, they could also be used more directly for the targeting. Mobile phones are always linked to a tower if they are turned on; therefore, the provider knows as soon as an individual changes towers. An innocuous policy could be to allow users to opt into an information program that sends a targeted text message as soon as someone that has opted into the program changes location from a high malaria tower to a low malaria tower. The message could recommend and provide an incentive to get tested at the closest clinic for free. Text messaging has been used by the Ministry of Health in Senegal for providing information on diabetes, and other studies have found that SMS technology can be eective in changing behavior (Senegal Ministry of Health 2016, Aker and Ksoll 2019, Ksoll et al. 2014, Fischer et al. 2016). Mobile phones could be used for more controversial policies such as targeting mandatory quarantine. There are important ethical concerns in how new technology is used, and the severity and risk of the disease may aect the strategies that are considered.

# 5.2 Targeting Simulations

The policies in the previous section serve as examples, but I focus the simulations on how a policy should be targeted, rather than the particular policy. If there were no targeting and instead the policy were to test every single traveler coming into the 5 pre-elimination districts that are the focus here, it would mean testing 6,956,197 travelers in 2013 based on the scaled mobile phone data. If each traveler were successfully tested so that all travel cases of malaria were found, treated
characterized by increasing returns to scale and knowledge externalities, so that _yi_ is increasing in _si_ . Alternatively, too much congestion in a sector may lower the sectoral productivity, so that _yi_ is decreasing in _si_ (Fujita and Thisse, 2002). As a descriptive starting point, this could be examined by allowing the rate of poverty reduction to depend only on the share weighted change rates in the share of the middle and the metropoles, i.e. by estimating a reduced version of equation (2): ^ ^ In this equation, β _U_ and β _N_ capture both the direct effects of the sectoral transformation on poverty through changes in the income distribution and the indirect effects through changes in the income levels, each time controlling for the relative size of the sector. By comparing the share weighted change rate of the metropolitan share of the population and the share weighted change rate of the share of those living in the intermediate space with and without controlling for income growth, it can be explored whether and how the patterns of spatial and occupational transformation matter for poverty reduction.

# **4 Poverty, occupational, and spatial transformation in 1980-2004 – The information base**

The World Bank’s POVCAL data are used to construct the poverty spells and the rate of poverty reduction.<sup>20</sup> The $1-day and $2-day poverty headcount ratios are taken as measure of poverty, _P_ . Real GDP per worker (in thousand PPP US dollars) is taken from WDI. The annual change rate of each variable _x_ , _dx_ / _x_ , is given by (ln _xt_ − ln _xt_ −t )/ t , where _t-τ_ and _t_ are the initial and the final year of the period, respectively.

The metropolitan share of the population, _sU_ , is represented by the share (in %) of the

> 20 <u>http://iresearch.worldbank.org/PovcalNet/ April, 2008 (i.e. before the latest revisions of the poverty