rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

At the heart of urban economics are the sources and nature of agglomeration economies, which drive the existence and extent of cities. Agglomeration economies are also central to structural transformation in developing countries: why people urbanize. The literature is replete with studies attempting to estimate the productivity or wage premium from being in cities compared to rural areas or from being in bigger versus smaller cities (e.g. Ciccone and Hall (1996) and Glaeser and Mare (2001), with reviews of the literature in Combes and Gobillon (2015) and Rosenthal and Strange (2004)). These studies adopt specific, simple measures of agglomeration such as indicator variables for settlement type (e.g. urban versus rural), a continuous total population measure, or at best, a basic population density measure, where the denominator (area) is often inconsistently defined across cities. They also adopt a specific definition of cities and urbanized areas, often chosen by national statistics bureaus based on qualitative aspects of land use and the built environment, degree of centrality of activity, and other often less than precise measures. In some countries, population density plays a role in the definition but usually not a central one.

In general, these studies do not provide any statistical rationale for their choices. For example, city definitions are driven by the convenience of adopting some official definition. Measures of agglomeration are typically chosen with little analysis as to why, with no quantitative evaluation of what measure(s) would be most relevant. This paper will focus on an evaluation of different agglomeration measures which we characterize as economic density measures. We first consider traditional options: urban versus rural, a continuous total population measure, and a continuous measure of overall population density. However, these measures do not capture, for a given population and overall density, the degree to which economic activity is clustered within a city. Two cities with the same density and population may have very different levels of clustering of economic activity within the city, which can be captured by other measures reflecting variation in population densities within a city. We derive and utilize several measures. This issue is important given the increasing use of the De La Roca and Puga (2017) measure (for example, Collier et al. (2018)), which reflects to some extent the degree of clustering. Does simple population density suffice or do we learn more by using more nuanced measures capturing aspects of clustering?

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Table A4: Descriptive statistics: LSMS Sample

|**LSM**|**S**|||||
|---|---|---|---|---|---|
|Variables|Observation|Mean|Std.Dev|Min|Max|
|Submarine has arrived|1,577,278|.9095562|.2868166|0|1|
|Proximity to road (KM)|1,577,197|-15.19746|22.41966|-192.6164|-.0035672|
|Submarine (2 km) * proximity to road (KM)|1,577,197|-.0561241|1.235172|-81.78571|0|
|Proximity to Hypothetical road (KM)|1,304,065|-36.49485|41.12254|-988.8704|-.0205457|
|Submarine (2 km) * proximity to hypothetical road (KM)|1,304,065|-2.393068|15.42633|-942.7307|0|
|lightening|1,577,263|.4222894|.2858886|.0003547|2.120674|
|Terrain elevation|1,571,687|1008.529|739.0523|0|3451|
|slope|1,471,186|6.73157|10.05156|0|110.6|
|Internet connected (0.5 m)|1,577,278|.040698|.1975896|0|1|
|Internet connected (1 km)|1,577,278|.0389519|.1934805|0|1|
|Internet connected (1.5 km)|1,577,278|.0455462|.2084988|0|1|
|Internet connected (2 km)|1,577,278|.0575821|.2329516|0|1|
|Submarine Internet (0.5 km)|1,577,278|.0019369|.0439674|0|1|
|Submarine Internet (1 km)|1,577,278|0.038409|0.192182|0|1|
|Submarine Internet (1.5 km)|1,577,278|.0447321|.2067152|0|1|
|Submarine Internet (2 km)|1,577,278|.0555742|.2290977|0|1|
|age|1,577,278|35.80227|12.12185|20|65|
|Female individual|1,577,278|.5190537|.499637|0|1|
|Rural residence|1,187,700|.8615879|.3453321|0|1|
|No Education|1,577,276|.1970784|.3977921|0|1|
|Primary Education|1,516,855|.1375227|.3443984|0|1|
|Secondary Education|1,522,283|.2634103|.4404832|0|1|
|Higher Education|1,522,284|.5259735|.4993251|0|1|
|Hours worked in the last seven days|590,010|2.946079|11.94734|0|120|
|Is a government employee|125,047|.2060025|.4044339|0|1|
|Owns a business|978,380|.286588|.4521676|0|1|
|Plans to open non-agricultural business in the next 12 months|772,956|.1417183|.3487612|0|1|
|Survey years|1,577,278|||2005|2019|

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|_T_ = 0_, X_)<br>(5)<br>IV<br>0.018<br>(0.046)<br>0.032<br>(0.224)<br>0.013<br>(0.099)<br>0.023<br>(0.034)<br>-0.026<br>(0.049)<br>0.012<br>(0.043)<br>Yes<br>-|ted to control<br>l groups using<br>ing campaign<br>l3). Standard<br>he respondent,<br>characteristics<br>dicator on the<br>variables. The|
|---|---|
|_, X_)_−E_(_Y |_<br>(4)<br>OLS<br>0.011<br>(0.035)<br>0.020<br>(0.172)<br>0.013<br>(0.067)<br>0.017<br>(0.025)<br>-0.017<br>(0.034)<br>0.014<br>(0.027)<br>Yes<br>0.5549|le means restric<br>ment and contro<br>eived the spray<br>ndicator (mode<br>iage status of t<br>ies and village<br>the treatment in<br>n the selected|
|_E_(_Y |T_ = 1<br>(3)<br>OLS<br>0.040<br>(0.044)<br>0.013<br>(0.181)<br>0.066<br>(0.077)<br>0.042<br>(0.031)<br>-0.004<br>(0.038)<br>0.029<br>(0.025)<br>No<br>0.3683|2) report samp<br>between treat<br>port to have rec<br>eatment group i<br>cation and marr<br>sub-zone dumm<br>it regression of<br>e coeffcients o|
|(2)<br>Treatment<br>0.322<br>[0.468]<br>0.632<br>[2.774]<br>0.456<br>[1.007]<br>0.167<br>[0.462]<br>0.215<br>[0.470]<br>0.075<br>[0.467]<br>p-values =|lumns (1) and (<br>rt the difference<br>seholds who re<br>tion with the tr<br>ender, age, edu<br>characteristics,<br>e, we run a prob<br>ignifcance of th|
|(1)<br>Control<br>0.282<br>[0.450]<br>0.618<br>[1.978]<br>0.390<br>[0.898]<br>0.125<br>[0.399]<br>0.219<br>[0.483]<br>0.046<br>[0.372]<br>1,2,3b-3d|ousehold. Co<br>) and (4) repo<br> between hou<br>ram participa<br>trols include g<br>ter, dwelling<br>nt signifcanc<br>for the joint s<br>nd 3d.|
|1. Respondent participated in LHM in the last 6 months<br>2. Days spent by household in LHM in the last month<br>Participated in LHM in the last month<br>3a. All members<br>3b. Male members_>_15 y.o.<br>3c. Female members_>_15 y.o.<br>3d. Members_<_15 years old<br>Controls<br>Joint tests on variables (with comparable sample size):|Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per h<br>and treatment group, standard deviations in brackets. Columns (3<br>OLS regression (model 2). Column (5) estimates the difference<br>in the last 5 months and those who didn’t by instrumenting prog<br>errors clustered at village level are reported in parentheses. Con<br>household size, tribe and religion, information about access to wa<br>(size and share of women living in the village). To control for joi<br>selected groups of variables and we report p-values of an F-test<br>joint test omits the variable 3a since it is just the sum of 3b, 3c a|

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reflect similar interpretation of publicly available information by the rating agencies and by market participants.

# **3.3 Sub-sovereign Defaults**

Compared with corporate bonds, subnational defaults are less frequent in the United States (Moody’s 2002). Outside the United States, the overall size of the sub-sovereign sample is quite small, and sub-sovereign defaults have tended to be clustered around sovereign crisis (Moody’s 2008b). Based on a sample covering 1983-2007, Moody’s study (2008b) indicates that non-U.S. sub-sovereign ratings have been concentrated in the investment grade category. Rating changes for investment-grade sub-sovereign issuers have been on average less frequent than for corporate issuers and somewhat more frequent than for sovereign issuers. In contrast, Ba and B subsovereign ratings have been less stable than both speculative-grade sovereign and corporate ratings. However, due to the small size of the sample, conclusive evidence is absent.

According to Moody’s research (2008c), historically sovereign ratings have been more stable at higher rating levels and modestly more stable than their corporate counterparts. Sovereign default rates have been generally been lower than corporate default rates. However, the differences are not likely significant as the overall size of the sovereign sample is small. As Bhatia (2002) notes, “Sovereign default probabilities will remain statistically not meaningful until more default experience has accumulated” (p. 37).

Nonetheless, Annex Table 2, which provides Moody’s average cumulative default rates for corporate long-term debt instruments based on empirical evidence spanning over 85 years, illustrates what the different categories of ratings mean in practice. As we can see, at the time of issuance, the probabilities of default for speculative-grade ratings are a lot higher than those for investment-grade ratings. For example, according to Moody’s, an Aaa rating carries with it, on average, only a 0.16 percent probability of default 5 years after issuance. In contrast, a B rating implies a 21.4 percent probability of default for the same duration of time.

# **3.4  Rating Failures**

There are limited data detailing failures of sub-sovereign ratings. But factors contributing to the failure are broadly the same whether it is sovereign or sub-sovereign. The failures of all major rating agencies in anticipating the East Asia crisis are particularly illustrating.

Bhatia (2002) defines rating agencies’ failure to predict a sovereign’s rating accordingly if they downgrade or upgrade long term foreign currency ratings by three or more notches in aggregate during any rolling 12-month period. In the case of Thailand, S&P downgraded the sovereign’s foreign currency long term rating by four notches within the four month period. This, according to Bhatia, signals inability of the credit rating agency to assign correctly a credit rating in the times of financial crises. The two tables below compare ratings before and during the crisis for sovereign foreign currency debt and local currency debt.

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**35**

demarcation process.

We find evidence in support of the first hypothesis. Female-headed households have smaller landholdings than male-headed households in Benin, and this finding holds across a range of contexts in Africa (Doss et al., 2015). This fact has implications for land size measurement, given the tendency of households to systematically over-estimate the size of small tracts of land and – to a lesser extent – over-estimate the size of larger tracts (Carletto et al., 2015). When computing the size of agricultural plots (as well as measures of yield and input intensity), we rely primarily on precise GPS measurements of land area.<sup>40</sup> However, for the comparatively larger parcels, which can be used for a range of purposes (including agricultural), we rely instead on self-reported land size.

To test for a gender difference in measurement bias, we exploit the fact that, during the demarcation process, households learn the true size of their landholdings within the village. We would thus expect treated female-headed households to revise their self-reported parcel size downwards following demarcation activities. Male-headed households, meanwhile, would be less likely to fall prey to this over-reporting bias since they hold larger parcels of land on average. If this bias is indeed driving our result, we should expect no significant gender difference in treatment when comparing similarly-sized parcels located in the village. Our results confirm this conjecture. When comparing parcels under 0.5 ha within in the village, we detect no significant difference in impact on land size across gender of the household head (Table A-11, Panel B).<sup>41</sup> Similarly, we find no gender difference in treatment when restricting the sample to parcels larger than 4 hectares in size (Table A-11, Panel C). These findings suggest that demarcation reduces the systematic bias in self-reported land size in treated villages and this dampening effect is particularly salient for smaller landholders—a group in which women are disproportionately represented.

# **5 Discussion**

We exploit the first randomized controlled trial of a land certification program to investigate changes in household investments following the first key step of land rights formalization– demarcation. We follow Besley and Ghatak (2010) and provide a theoretical framework to

> 40Our findings on agricultural yields, for example, are robust to the restriction of the sample to those parcels for which we have GPS-measured agricultural plot sizes (results available upon request).

> 41Half a hectare is the median parcel size for female-headed households and the 26th percentile for male-headed households. Carletto et al. (2015) find that the over-reporting bias is concentrated in parcels under 0.5 ha.
Distributional Impact Analysis: Toolkit and Illustrations : Policy Evaluation, Distributional Impact Analysis, Heterogeneous Treatment Effects, Impacts on Outcome Distributions, Distribution of Treatment Effects, Random Control Trials. David Evans, and Caio Piza for making this toolkit possible. We are grateful to Moussa P. Blimpo and participants at the DIME seminar and the Course on Distributional Impact Analysis held at the World Bank. We also benefited from the work and data from “The Impact of High School Financial Education: Experimental Evidence from Brazil” and “Parental Human Capital and Effective School Management: Evidence from the Gambia” and from conversations with members of both teams. We thank Kristoffer Bjarkefur for assistance with programpact Evaluation to Development Impact (i2i) Trust Funds.
<!-- Start of picture text --> HOSPITALIZATION RATE FOR ALL PATIENTS<br>Ey DISTAN. PE 1000 PPU..ATI0N<br>so -<br>50<br>40<br>c 30-<br>20-<br>10<br>-10<br>0 20 49 6'1o<br>DISTANCE IN 1)J<br>+ rarAL ADMISSIONS v INSLwM WissIONS I<br><!-- End of picture text --> Figure A.2 The study team examined a number of other indicators of quality for differences between iniured and uninsured persons:

- Proportion of high risk pregnancies delivered in hospital

- - Proportion of all deliveries in hospitals

- Age adjusted overall death rate overall

- Childhood death rate Proportion of pregnant women enrolled in ante-natal care

- - Birth rate

- Length of hospital stay.

- - Delay in starting delivery.

The study team tested most of these indicators with a visit to Isabe health center, one of the health centers served by Bwamanda Hospital. In theory, the data to calculate most of these indicators should have been available at the health center through the family registration cards; the remaining data should have been available at the hospital. In practice, the data proved inadequate because family cards did not necessarily record every birth and death, particularly if they occurred at home or in the hospital. For example the Isable household cards in Isable showed 876 people. The cards reported only 21 births (about half the expected number) and 8 deaths (also fewer than expected).

Table A.10 presents comments of insured and uninsured patients towards possible changes in 26
has been done to date.<sup>3</sup> Ideally, we would expect to understand the degree to which a metric tracks the underlying dimensions of food access that are intended to be captured. The FIES was initially implemented globally in the Gallup World Poll (GWP), and unfortunately the GWP cannot completely validate the FIES in this way given that there is no information on actual household food consumption, on other food access metrics, or on other traditional welfare metrics (e.g., Gallup World Poll 2019). Rather, much of the validation of the FIES has been done by correlating the FIES to other subjective welfare metrics and the limited household characteristics captured in the GWP (e.g., Smith et al. 2017; Wambogo et al. 2018; etc.). However, given that a number of other closely related experiential measures of food access align poorly with other food access metrics (e.g., Headey and Ecker 2013; Maxwell et al. 2014; Broussard and Tandon 2016; etc.), it is important to more thoroughly validate the FIES and understand what aspects of poor food access are being captured.

We perform a more thorough validation of the FIES in Nigeria using data from a nationally representative multipurpose survey that captures a number of food access and welfare indicators from each household. Nigeria is important for regional and global measurement of poor food access. By official global measures, Africa is the continent with the highest prevalence of both undernourishment and the inability to afford a diet sufficient to maintain an active and healthy lifestyle (e.g., FAO 2021), and Nigeria alone accounts for approximately 20 percent of Africa’s entire population.

In this important setting, we compare estimates of food access using the FIES to monetary poverty. Although monetary poverty and food access are distinct concepts, we illustrate there exist empirically supported assumptions under which models of consumer theory predict that many key manifestations of poor food access —undernourishment and undernutrition — are more prevalent in poorer households (e.g., Jensen and Miller 2010).<sup>4</sup> Importantly, given this predicted relationship, and given that both undernourishment and

> 3For a comprehensive list of research articles performing this type of analysis, see ”https://www.fao.org/in-action/voices-of-the-hungry/resources/research/en/”.

> 4This is distinct from the more dynamic relationship with how households become food insecure and poor, where poor human development outcomes can lead to and are also potentially a result of poor earnings (e.g., Kremer and Miguel 2004; Vermeersch and Kremer 2004; Glewwe and Miguel 2008; etc.)

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1 Introduction Preferential trading arrangements have proliferated both in number and importance in recent years. Among these, reciprocal agreements, such as free trade areas, receive more attention in academic studies and policy discussions compared with unilaterally granted ones. However, enhancing unilateral market access became one of the centerpieces of the developing countries’ agenda in the recent trade negotiations. Furthermore, certain unilateral programs allow eligible developing countries to have access to developed countries’ markets in highly protected sectors such as agriculture and apparel. Thus, they have rather significant effects on both beneficiary and excluded countries. This paper analyzes the impact of unilateral preferences on the prices received by the apparel exporters under the Caribbean Basin Initiative (CBI) of the United States, which consists of a series of programs first initiated in 1983. The most important feature of the CBI is the duty and quota-free market access granted to the exports of the 24 eligible countries in Central America and the Caribbean. Apparel preferences are the most valuable and heavily used preferences due to high trade barriers imposed by the United .States against the exports from the rest of the world. The effects of discriminatory arrangements have been widely discussed and studied in the empirical literature, again, mostly in the context of reciprocal agreements. The focus has generally been on the total value of trade, even tough, theoretical literature emphasizes that the prices are the more appropriate instruments to evaluate trade policies, especially preferential arrangements!. The key reason is the data limitations; quantity and price data are either not widely collected or not made available by most countries. Furthermore, even if these data were available, it is not easy to compare unit prices when differentiated products (such as machinery) are aggregated in the same category. In short, the availability of very detailed and disaggregated data, together with the high utilization of the preferences, makes apparel an ideal sector to study price effects of preferential arrangements.

In this paper, we answer two interrelated questions: (i) How much do the prices received by the CBI exporters increase due to preferences? (ii) What portion of the preference margin is captured by the exporters through higher prices? The prices received by the CBI exporters <u>~ 'See Winters [1997] for a convincing argument</u> to this effect.

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and stock markets for output growth (e.g., Demirg ̈u ̧c–Kunt and Maksimovic 1998; Levine and Zervos 1998; Beck and Levine 2004; Shen and Lee 2006), the economic and financial consequences of having a bank-based or a market-based financial system (e.g., Beck and Levine 2002; Demirg ̈u ̧c–Kunt and Maksimovic 2002; Levine 2002; Langfield and Pagano 2016; Bats and Houben 2020), as well as the evolving roles of banks and stock markets during the process of economic development (e.g., Tadesse 2002; Song and Thakor 2010; Demirg ̈u ̧c–Kunt, Feyen, and Levine 2013). We focus on specific transmission channels from banks and stock markets, utilizing disaggregated data on international trade that are available and directly comparable across different countries. One of the channels we examine also relates our work to the recent literature on banks and intangible capital (Dell’Ariccia et al. 2020; Beck et al. 2021) while our findings on large versus active stock markets complement the recent research on different characteristics of stock markets (Bae, Bailey, and Kang 2021).

Second, the paper connects to the strand of literature looking at the role of finance in international trade (e.g., Kletzer and Bardhan 1987; Beck 2002; Greenaway, Guariglia, and Kneller 2007; Manova 2008; Berman and H ́ericourt 2010; Amiti and Weinstein 2011; Ju and Wei 2011; Levchenko, Lewis, and Tesar 2011; Minetti and Zhu 2011; Bricongne _et al._ 2012; Chor and Manova 2012; Becker, Chen, and Greenberg 2013; Muˆuls 2015; Paravisini _et al._ 2015). Several papers in this area look at how the financial sector shapes export entry, export exit or export volume alongside some of the transmission channels that we also explore (e.g., Beck 2003; Svaleryd and Vlachos 2005; Hur, Raj, and Riyanto 2006; Manova 2013; Fan, Lai, and Li 2015; Crino and Ogliari 2017).<sup>1</sup> We focus on the relative importance of banks and stock markets while also allowing for distinct roles for active versus large stock markets. In terms of the outcome variables, we explore both short-term and long-term export dynamics by looking at the export survival across

> 1 Rather than investigating these transmission channels, several other papers look at the role of banks and stock markets in international trade from different angles. Cho et al. (2019) develop a model linking bank and bond financing with firm size distribution, gains from trade, and the real exchange rate. Amissah et al. (2021) explore theoretically and empirically the bi-directional relationship between financial infrastructure and comparative advantage of countries.

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