rafmacalaba/datause-encoder-data huggingface dataset:
cell that has buildings on it. A little over 3.8 million cells are needed to cover the entire area of Ghana.<sup>1</sup> The variable _yit_ exist for four separate years: 1975, 1990, 2000, and 2014. Cells for each of these years have been carefully aligned using detailed GPS coordinates so as to form a panel dataset.<sup>2</sup> Variable _yit_ takes values between 0 and 1, with 0 meaning no building detected in the cell, and 1 meaning the cell is entirely occupied by buildings – i.e., as in a city center.<sup>3</sup> As shown in Table 1, most cells (98.5%) in 1975 are predicted to be unbuilt, which is hardly surprising given that Ghana at the time only had a national population density of 43.9 per square km. Builtup area grows over time as population increases to 119.4 per sqkm in 2014 and Ghana becomes more urbanized (urban population share increasing from 30% in 1975 to 53% in 2014). In 2014, 93.8% of cells were unbuilt.

For each cell _i_ in year _t_ we construct two measures of buildup in the cells surrounding _i_ . The first of these measures is denoted as _yit_<sup>_n_1andistheaveragebuilt-upareainthecellsimmediately</sup> adjacent to _i_ . Since the data is divided into a square grid, each cell on that grid in principle has 8 immediate neighbors, which constitute the set _n_ 1. We thus have: where by an abuse of notation _n_ 1 represents both the set of immediate neighbors and their number. Together with _yit_ , this variable captures the proportion of built-up area in an approximate cell 675 meters wide by 825 meters high (approximately 2025 by 2475 feet).<sup>4</sup> We also construct a variable _yit_<sup>_n_2thatcapturestheaveragebuildupincellsimmediatelyadjacentto</sup><sup>_y_</sup> _it_<sup>_n_1.Thereareinprinciple</sup> 16 cells in set _n_ 2, except in the vicinity of borders and large bodies of water where that number gets truncated. The formula for _yit_<sup>_n_2</sup> is that same as equation (1) but with _n_ 1 replaced by _n_ 2. Together, _yit_ , _yit_<sup>_n_1</sup> and _yit_<sup>_n_2</sup> cover an area 1125 meters wide and 1375 meters wide, roughly 1.55 square kilometers.

> 1In practice, in our data each cell measures approximately 225 meters wide by 275 meters high (approximately 675 by 825 feet or 6.2 hectares) due to Ghana’s location close to the equator.

> 2Aligning satellite photographs with each other and over time was achieved projecting each photograph using the same Mollweide projection.

> 3More precisely, _yit_ = 1 when all its area is covered by impervious surfaces, such as roofs, sidewalks, streets, and courtyards.

> 4Cells located on the border or along the sea or large body of water do not have a full complement of 8 adjacent cells located wholly or partly within Ghana. For these cells, the immediate neighborhood is limited to those cells included in the dataset and the average is adjusted accordingly.

6
# **9. Annex 2. Details on Sources of Information for Countries Not Responding to the ASBA-World Bank Survey**

# **A. Costa Rica**

The data from Costa Rica were sourced from the website of the banking supervisor.<sup>27</sup> The data for the distribution of bank loans by purpose and the maturity profile of bank loans are organized by banks, and the information for the system is not readily available.<sup>28</sup> As a result, for Costa Rica, the data are constructed using data from the top four banks.<sup>29</sup> These four banks constitute an average of 60 percent of the banking system between 2005 and 2014. For the data by purpose, the commercial loans include agriculture, fisheries, manufacturing, mining, trade, utilities, hotels and restaurants, transportation, real estate, education, services, and other loans to the private non-financial sector. Consumer loans are the category consumer (category S) and mortgage loans are category F, which includes the construction, buying, and reparation of buildings.

For the maturity data, the classification of the maturity of loans is available in the individual bank’s financial reports. These are PDF files, and the term structure of assets and liabilities is listed in the notes section of the reports. The term structure of loans is shown by the remaining maturity and for each bank. The following categories are available: (a) less than 30 days, (b) between 31 and 60 days, (c) between 61 and 90 days, (d) between 91 and 180 days, (e) between 181 and 365 days, and (f) more than 365 days. As a result, the maturity data for Costa Rica are divided in maturity ‘pairs’ of less than one year and more than one year.

# **B. Dominican Republic**

Data for the Dominican Republic were sourced from the website of the superintendent of banks.<sup>30</sup> For the distribution of loans by purpose, data are available annually from 2006 across 20 sectors. Total gross commercial loans were calculated by adding the total gross loans in the following categories: agriculture, fisheries, mining, manufacturing, utilities, construction

> 27 _Superintendencia General de Entidades Financieras_ . https://www.sugef.fi.cr.

> 28 See https://www.sugef.fi.cr/servicios/reportes/Actividad_Atraso.aspx for the data interface.

> 29 These are Banco de Costa Rica (BCR), Banco Nacional de Costa Rica (BNCR), Banco Popular y de Desarrollo Comunal (BPDC), and Banco BAC (Banco de América Central) San Jose.

> 30 _Superintendencia de Bancos de la República Dominicana_ : http://sb.gob.do/transparencia-estad%C3%ADsticas- <u>institucionales/estad%C3%ADsticas-financieras-auditadas-anuales.</u>

39
created by the EGS. In the first case, we assume no cost recovery, as appears to have been typical of past experience with workfare programs in India; then 90% of the gross EGS cost is available for the hypothetical un-targeted transfer scheme. In the second case, we assume that all of the non-wage cost of EGS can be recovered by charges to (non-poor) beneficiaries, so that only the wage cost of the EGS is available for the transfer scheme. Notice that we are allocating the same budget across the rural population only, so there is a sense in which our counterfactual transfer scheme is targeted, but only to rural areas as a whole, not within.

The striking finding in Table 11 is that a budget-neutral un-targeted allocation has greater impact on poverty at any EGS wage rate (comparing Tables 9 and 11). Indeed, the poverty impacts only come close at low EGS wage rates and with full cost-recovery of non-wage costs; with moderate to high wages, the un-targeted allocation has substantially greater impact on poverty. The uniform transfer dominates over the entire range of the distribution (Figure 5). Using the augmented specification narrows the differential, but uniform transfers still dominate. Note, however, that cost-recovery for the non-wage costs greatly narrows the advantage of uniform transfers across a wide range of poverty lines (as is evident from how close the middle curves are in Figure 5).

- _4.3 Living wage for the lean season only_

So far we have considered an EGS operating all year. We also simulated a scheme that is constrained to operate for only three months, in the lean season, defined as the first quarter as in the NSS, namely June, July and August. We checked whether this was the low wage season in each state, based on the estimated parameters on the interaction terms between the state dummies and the season dummies in the wage regressions in Table 1. The first (NSS) quarter was the low wage season in all except two states. Note, however, that this is also the monsoon period, which 21
Among institutional factors explaining gender differences in access to credit might be property right restrictions for women. Such restrictions might include requirements for married women to obtain their husband‟s signature and approval for all banking transactions.<sup>1</sup> Women can also be affected by a husband‟s adverse credit history, which might require his wife to repay the debt or be denied credit (Naidoo and Hilton, 2006).

The observation of a gender gap has led many NGO supported microcredit institutions to focus on women rather than men. Given the limitations of microcredit, both in volume and in outreach, however, it is important to understand differences in the use of formal banking services (Honohan, 2004). In addition to formal financial services, many individuals and enterprises across the developing world use informal financial services, ranging from money lenders to ROSCAS. In our empirical work, we will therefore also consider the use of informal financial services by households.

The empirical findings of the enterprise analyses show that firms with female ownership participation do not seem to have worse access to credit than firms with purely male ownership, neither within Sub-Saharan Africa nor across the world. However, we do find that African enterprises are more financially constrained and that larger firms have more access to financial services. We argue that the apparently surprising results of lack of gender difference in access to finance can be actually explained by the existence of a selection process. First, female entrepreneurs are less likely to own sole proprietorships than men and face higher regulatory burden than men, especially in Africa. Second, enterprises with female ownership are smaller and therefore have less access to the financial markets, and women are less likely to be entrepreneurs. Third, female owned enterprises are more likely to innovate what could be explained by the fact that female entrepreneurs need to be especially capable in order to be able to enter the formal sector and in fact have characteristics that make them

> 1 See Women, Business and the Law; “Improving the Legal Investment Climate for Women in Africa” (HallwardDriemeier, 2011`) for more detailed information and broader coverage of countries in Sub-Saharan Africa.

3
more heavily populated states in the US are more heavily regulated, we believe that population has a direct effect on institutions. Institutions being costly to set up, more populated countries would tend to be more heavily regulated or have more complex institutions.<sup>9</sup> We run both ordinary least squares (OLS) and instrumental variables (IV) regressions to instrument for aid. We used two sets of instruments. The first instrument set considers only lags of the log of gnp per capita and the log of population. For instance, to insure exogeneity we consider lags 7 and 11 (depending on the data used), the first, to instrument for the regressions using the ICRG data and the second for those using Fraser data and the CIM indicator. Although not reported, we checked for the first stage F-statistics of the excluded instruments (in order to see if the instruments are weakly correlated with the endogenous regressors, (see Stock Wrigth and Yogo [2004]). All the first stage regressions report an F-test greater than 10.

The second instrument set is larger and it is composed of the first instrument set and a series of dummies for ex-colonies, and for Sub-Saharan Africa, Europe, Central America, and Egypt, as in Rajan and Subramanian (2005). In all the IV regressions, since we have more instruments than endogenous regressors, we can test for the validity of the instruments using the Hausman tests. In all the specifications we do not reject the null hypothesis of instrument validity. Although ten lags seem long enough, it is clearly possible that countries’ economic/ institutional situation of ten years ago could affect the level of economic institutions today. It is hard to find truly exogenous variations in aid particularly since donors do not give aid randomly.<sup>10</sup> Tables 3 and 4, report summary statistics.

> 9 Regressions not presented here are available from the authors upon request.

> 10 Given that the specification we consider includes the initial level of institutions, we believe that considering only aid as endogenous can lead to severe inconsistency in the estimation of the effect of aid on institutions in the cross section estimates. The initial level of institutions is itself correlated with the unobservable country effect and the initial level of institutions is itself endogenous.

14
# **Table 2:** **<u>Correlates of female household headship</u>**

||Household is f|emale headed|
|---|---|---|
|Variables|rural|urban|
|Age of household head|-0.001**|-0.0005|
||[-2.01]|[-0.84]|
|Head married monogamous|-0.077<br>|-0.176<br>|
||[-1.21]|[-1.57]|
|Head married polygamous|-0.036|-0.142|
||[-0.57]|[-1.25]|
|Head is divorced/separated|0.359*|0.531***|
||[1.90]|[3.21]|
|Head is widowed|0.570***|0.698***|
||[7.82]|[5.86]|
|Head chronically ill in past year|-0.003|-0.024*|
||[-0.39]|[-1.97]|
|Share of other members > 15 yrs that were|0.03|-0.019|
|chronically ill|[0.98]|[-0.43]|
|Years of schooling of head|-0.003*|-0.002**|
||[-1.69]|[-2.61]|
|Log household size|-0.045***|-0.039*|
||[-4.13]|[-1.87]|
|Share of members women 56+|-0.133**|-0.193*|
||[-2.49]|[-1.92]|
|Share of members men 56+|-0.04|-0.124|
||[-0.22]|[-0.58]|
|Share of members women 15-55|-0.166***|-0.146***|
||[-4.02]|[-2.87]|
|Share of members men 15-55|0.052|0.032|
||[1.57]|[0.72]|
|Share of members girls 0-6|-0.007|0.043|
||[-0.22]|[0.76]|
|Share of members boys 0-6|0.011|0.012|
||[0.34]|[0.31]|
|Non-agr. landownings (100s m<sup>2</sup>)|-0.0001|-0.001|
||[-1.31]|[-1.31]|
|Agr. landownings (hectares)|0.0001|-0.002**|
||[0.54]|[-2.18]|
|No. large livestock owned|0.00004|0.0001|
||[0.21]|[0.28]|
|No. small livestock owned|-0.0001|-0.0001|
||[-0.35]|[-0.54]|
|House exterior made of cement or tile|0.015|0.002|
||[1.12]|[0.16]|
|Log consumption per capita|-0.007|0.002|
||[-0.78]|[0.20]|
|Constant|<br>0.273**|<br>0.337*|
||[2.08]|[1.71]|
|Observations|2,782|1,549|
|R-squared|0.57|0.68|

Note: The dependent variable is a dummy for whether a household is female headed. A linear probability model is used. Robust t-statistics in brackets, clustered at the community level. *** p<0.01, ** p<0.05, * p<0.1. The regressions also control for location fixed effects and interview months.

Source: 2006 ELIM.

32
Table 2 shows that the available data comprise an unbalanced panel: Among the 20 municipalities sampled, 4 have observations for 4 years, while 9 have observations for only 1 year.<sup>9</sup> Yearly representation is also uneven, with 10 observations recorded for 2013, no observations for 2010 and 2011, and 4 for 2009.

**Table 2:  Panel statistics for Bangladesh municipality data**

|Year<br>2007|Count<br>7|Pct<br>17.07|Cum. Pct<br>17.07|Municipalities:<br>Years Recorded|Count|Pct|Cum.<br>Pct|
|---|---|---|---|---|---|---|---|
|2008|8|19.51|36.59|1|9|45|45|
|2009|4|9.76|46.34|2|5|25|70|
|2012|12|29.27|75.61|3|2|10|80|
|2013|10|24.39|100|4|4|20|100|
|Total|41|100||Total|20|100||

# **5.  Road Maintenance Regression Model**

# **5.1  Specification**

Following equation (7) above, our regression model relates road maintenance expenditure to groundwater salinity, municipal income, road network length, and the network share of _pucca_ roads. We specify the model in log-log form, so that estimated coefficients can be interpreted as response elasticities.

> 9 Prior to estimation, we removed two problematic observations from the data provided by the 20 municipalities. The first, a single annual observation for Paikgachha municipality, Khulna, has an outlier measure for salinity so extreme that it would dominate all other observations in the regressions.  The second, for Satkhira municipality, Khulna, has reported road maintenance expenditure for 2008 that differs by more than an order or magnitude from reported expenditures for 2012 and 2013.  Believing the 2008 report to be a transcription error, we have removed it while retaining the other two observations for Satkhira..

11
stock-by-stock or single-panel regressions (which we show in the last two columns of the table).

In Table 2, we test the robustness of these results to the inclusion of alternative crisis measures (for which we use our SM crisis dummy and the EMBI sovereign bond spread index compiled by J.P. Morgan, a broader country-specific proxy of financial distress), and the evolution of the local stock market index, to control for systemic factors. For the sake of conciseness, we report only the results corresponding to a single panel regression including all stocks.<sup>20</sup> The results are reassuring: the coefficients of the new variables, when significant, are of the expected sign, and the ones of the baseline variables largely retain their explanatory power. The only exception is the early crisis dummy, which when measured using SM crises is no longer positively correlated with volume, although it shows an early widening of the bid-ask spread, possibly reflecting differences in the way it dates the start of crises. In addition, our findings are not altered by a change in data frequency, as can be seen from the results based on a monthly sample reported in columns (4), (8), and (12).<sup>21</sup>

# **4. Conclusions**

Our tests reveal two types of stylized facts. First, they document for emerging markets the asymmetric response to price fluctuations previously reported for developed markets: market downturns are positively correlated with volume traded and negatively correlated with trading costs. Second, they highlight a strong link between crisis episodes and liquidity measures. Specifically, we find no evidence of market “paralysis” at the

> 20 Comparable country-panel and stock-by-stock regression results are available upon request.

> 21 As another robustness test, regressions that control for market capitalization gave similar results.

11
# **Open Skies over the Middle East**<sup>*****∗</sup>

Anca Cristea Russell Hillberry Aaditya Mattoo<sup>†</sup> Dept of Economics DECTI DECTI U of Oregon World Bank World Bank Keywords: air transport; air service agreements; services liberalization; plurilateral agreements JEL: F13; L93; R48 Sector Board: Economic Policy (EPOL)

> _*_ We thank Sibel Kulaksiz for encouraging to us undertake this work. We also thank Antonia Carzaniga for providing us with the policy data, and Guoliang Feng for excellent research assistance. Any remaining errors are our own.

> † _Contact_ : Cristea: Department of Economics, University of Oregon, 1285 University of Oregon, Eugene, OR 97405; E-mail: cristea@uoregon.edu. Russell Hillberry: Development Research Group, World Bank, 1818 H Street NW, Washington DC, 20433, United States; E-mail:  rhillberry@worldbank.org. Aaditya Mattoo: Development Research Group, World Bank, 1818 H Street NW, Washington DC, 20433, United States; Email: <u>amattoo@worldbank.org.</u>
# **TABLES BASED ON THE RICS-AMHARA DATA**

# **Table 11: Share of the Population in Different Occupations in Amhara**

||**Self**<br>**employed,**<br>**agriculture**|**Self**<br>**employed,**<br>**nonfarm**<br>**enterprise**|**Wage**<br>**employed**|**Other (public**<br>**works and**<br>**casual labor)**|**Total**|
|---|---|---|---|---|---|
|**_Primary Employment_**||||||
|Proportion of working<br>population|0.91|0.04|0.04|0.01|1.00|
|Head count|3,913,554|165,108|164,452|54,149|4,297,262|
|**_Secondary Employment_**||||||
|Proportion of all<br>secondary employment|0.15|0.30|0.04|0.50|1.00|
|Head count|55,992|112,053|15,605|18,4635|368,286|

_Note_ : The proportions in these tables are calculated from the information on primary and secondary occupation in the household questionnaire, using household weights to account for differential probabilities of being included in the sample. The head count numbers are derived from the estimated proportions combined with population data.

**Table 12: Profits in Nonfarm Enterprises and Wage Rates in Agriculture**

||**Mean**|**Sd**|**Median**|**N**|
|---|---|---|---|---|
|Log of Profit per day in NFE, male headed|1.77|2.05|1.56|315|
|Log of Profit per day in NFE, female headed|0.78|1.95|0.86|282|
|Casual male agricultural worker:|||||
|- Daily wage|9.00|4.11|8|175|
|- Percentage increase in wage 2003-06|75.5%|72.0%|60%|175|
|Casual female agricultural worker:|||||
|- Daily wage|7.35|3.46|7|160|
|- Percentage increase in wage 2003-06|68.0%|67.2%|60%|158|
|Observations|||||

_Note:_ Means and standard deviation are calculated using sampling weights, medians are not. About 17% of the observations on profits in are negative, and are not used in the computations above. The information on the daily wage of casual laborers in agriculture was taken from the community questionnaire.

-31-
Schiff (1999) argues that the volume of trade does not necessarily provide an objective measure of the extent to which trading partners are “natural.” The reason for this is that the volume of trade is itself affected by trade policy. Schiff (1999) introduces a version of the natural trade hypothesis that is independent of trade policy. He proposes a definition of natural trading partners as a situation characterized by _complementarity_ rather than _substitutability_ . If a country imports what its trading partner exports, Schiff concludes that the hypothesis of “natural trading partner” is likely to hold.

Proponents who utilize statistical measures such as the _complementarity index_ argue that the higher the observed values of the index between partners, the more likely is it that a proposed regional trade agreement will succeed (Michaely, 1996). Furthermore, a substantial empirical literature refers to the existence of complementarity rather than competitive trade as a precondition needed to enhance the probability of a net trade-creating, rather than net tradediverting, regional trade arrangement.<sup>6</sup> Countries with different comparative advantage profiles should, in principle, have more opportunities to trade with each other compared with those with similar comparative advantage profiles.

Lastly, the empirical literature refers to the impact of concentration and diversification of exports on regional trade agreements. Yeats (1998) states that studies have shown that countries with highly concentrated exports may experience a relatively high degree of instability in export earnings that could reduce a country’s ability to consistently maintain financial commitments required by regional agreements.

The objective of this paper is, then, to evaluate whether South Asia’s trade patterns meet the conceptual criteria outlined above, which are deemed to be necessary prerequisites for a successful FTA. The approach is based on an examination of trends and characteristics of recent

> 6 Ng and Yeats (2003), Yeats (2001).

4