rafmacalaba/datause-encoder-data huggingface dataset:
|(13)|16.40**<br>(6.992)<br>19.73<br>(15.57)|13,736|5.041**<br>(2.060)<br>18.23***<br>(5.794)<br>12,820|
|---|---|---|---|
|(12)|16.40**<br>(7.077)<br>19.73<br>(22.64)|15.009<br>13,736|5.041**<br>(2.270)<br>18.23**<br>8.421)<br>7.307<br>12,820|
|(11)|15.20***<br>(4.963)<br>25.20<br>(15.46)|14,805|5.391***<br>(1.412)<br>17.43***<br>(4.989)<br>13,757|
|(10)|15.20***<br>(5.814)<br>25.20<br>(22.29)|12.851<br>14,805|5.391***<br>(1.527)<br>17.43**<br>(7.606)<br>8.838<br>13,757|
|(9)|20.97**<br>(9.406)<br>16.72<br>(20.89)|14,805|**cts**<br>4.784<br>(4.181)<br>27.96*<br>(14.58)<br>13,757<br>ates with<br>ctor-size,<br> potential<br>rentheses.|
|(8)|**n**<br>20.97**<br>(9.847)<br>16.72<br>(29.40)|6.943<br>14,805|**nt contra**<br>4.895<br>(4.483)<br>26.10<br>(19.44)<br>2.390<br>13,757<br>port estim<br>ncluding se<br>h tests for <br>evel in pa|
|(7)|**: Tax evasio**<br>16.91**<br>(7.474)<br>11.02<br>(15.08)|14,805|**r governme**<br>3.815*<br>(2.176)<br>16.87***<br>(5.740)<br>13,757<br>) and (2) re<br>s estimates i<br>t results wit<br> the country l|
|(6)|**Panel A**<br>16.91**<br>(7.809)<br>11.02<br>(22.71)|13.095<br>14,805|**Bribing fo**<br>3.815*<br>(2.267)<br>16.87**<br>(7.477)<br>6.796<br>13,757<br>Columns (1<br> (11) report<br> (13) repor<br>lustered at|
|(5)|16.92<br>(20.96)<br>12.74<br>(11.95)|14.017<br>14,736|**Panel B: **<br>-4.867<br>(3.839)<br>-3.123*<br>(1.703)<br>14.220<br>13,696<br>teristics.<br>ns (6) to <br>s (12) and<br>rd errors c|
|(4)|10.67<br>(10.85)|66.170<br>14,768|-2.750*<br>(1.559)<br>60.736<br>13,723<br>dual charac<br>ests. Colum<br>y.<br>Column<br>ust standa|
|(3)|4.186<br>(19.87)|31.640<br>14,795|-1.944<br>(3.039)<br>30.679<br>13,748<br>rms’ indivi<br>lsifcation t<br> respectivel<br> efects. Rob|
|(2)|16.00**<br>(7.151)<br>-1.214<br>(9.984)<br>14.92<br>(15.52)|14,730|3.448*<br>(2.000)<br>15.57***<br>(5.046)<br>-4.071<br>(4.651)<br>13,693<br>d include f<br>ults from fa<br>xed efects, <br> year fxed|
|(1)|16.29**<br>(7.406)<br>9.941<br>(21.29)<br>4.442<br>(20.53)|8.841<br>14,730|3.531*<br>(2.049)<br>15.70**<br>(6.523)<br>-2.992<br>(4.965)<br>5.190<br>13,693<br> models an<br> present res<br>e clusters f<br> sector, and|
|||tatistics)|tatistics)<br> and 3SLS<br>(3) to (5) <br>ountry-siz<br>e country, <br>_<_ 0.01.|
||nd side of corruption<br>y side of corruption<br>ruptibles<br>ribe payer|identifcation test (F s<br>vations|nd side of corruption<br>y side of corruption<br>ruptibles<br>ribe payer<br> identifcation test (F s<br>vations<br>All regressions use IV <br>ptible frms. Columns <br>-sector-country, and c<br>. All regressions includ<br>.1; ** _p <_ 0.05; *** _p _|
||Dema<br>Suppl<br>Incor<br>Non b|Weak <br>Obser|Dema<br>Suppl<br>Incor<br>Non b<br>Weak <br>Obser<br>Notes.<br>incorru<br>location<br>outliers<br>* _p <_ 0|

22
# **I. Introduction**

With the on-set of the financial crisis of 2007/8, the financial sector is yet again at the top of the agenda of policy makers. While a large literature has established the importance of financial sector development for growth and equity of economies, an overleveraged and fragile financial system can also bring about major crises as economies across the globe are currently experiencing.<sup>1</sup> As proper measurement is at the core of any proper analysis of causes and design of solutions, indicators measuring the size, activity, efficiency and stability of the financial system are important for analysts, researchers and policy makers alike.

This paper introduces the updated and expanded version of the Financial Development and Structure Database and presents recent trends in structure and development of financial institutions and markets across countries. This database provides statistics on the size, activity, efficiency and stability of banks, nonbanks, equity markets, and bond markets across a broad spectrum of countries and through time. It also contains several indicators of financial globalization, including statistics on international bond issues, international loans, off-shore deposits and remittance flows. The database draws on a wide array of primary sources to cover different dimensions of the financial system. The database and further details on its construction are available at the following web-site: <u>http://econ.worldbank.org/programs/finance</u> This paper also presents financial system trends across the globe over the past decades. We show that financial systems across the world deepened over the past decades along many dimensions; standard indicators of financial intermediary and market development have increased over the past decades. However, progress has been uneven across income groups and regions. Specifically, the deepening has been concentrated in high-income, while there has been no significant deepening in middle- and low-income countries. While our data end in 2007 and can thus not capture the recent crisis fully, indicators of banking efficiency, profitability and stability match the trends in the boom period leading up to the recent global financial crisis, especially in high-income countries. Specifically, while net interest margins decreased – pushing banks to look for other income sources; profitability, especially return on equity, went up and stability down, as measured by the z-score, with

> 1 For an overview over the extensive literature on the finance and growth relationship, see Levine (2005) and Beck (2009).  For more recent evidence on the relationship between finance, income equity and poverty reduction, see Beck, Demirguc-Kunt and Levine (2007) and World Bank (2007).
# 4. Methods

The key survey data we use is the 2014 MCS-ENIGH. Although we are interested in estimating poverty for all municipalities in Mexico, the survey is not designed to estimate poverty at the municipality level, but rather for urban and rural areas within states. Therefore, the direct estimates from the survey will not consistently estimate poverty accurately for municipalities.

The rest of this section describes how we construct the direct estimates and the household-model estimates, as well as two robustness checks using sub-area and area models.

## 4.1 Direct estimates

The direct survey estimates for each municipality are the weighted mean, across sample households, of a dummy variable indicating whether the household’s per capita income falls below the official poverty line, which is defined separately for urban and rural areas. These are taken for each municipality, using the product of the household sample weight and household size as weights. Estimating uncertainty properly is a bit more complicated. This is because standard methods assume that per capita income is independent across primary sampling units, which can significantly underestimate the uncertainty associated with municipal poverty estimates. We therefore use two methods for estimating uncertainty: the Horvitz-Thompson approximation (Horvitz and Thompson, 1952) and the standard method of clustered standard errors, using AGEBs as the clustering unit (Huber, 1967). As shown below, the Horvitz-Thompson approach gives much more accurate estimates of uncertainty and is therefore preferred.

## 4.2 Household model estimates

The baseline method for the household model is similar to the method used to predict nonmonetary poverty rates in Masaki et al. (2022), in that it relies on a conditional random effect model, which is also often referred to as a mixed model. Many previous iterations of householdlevel models rely on detailed census data (e.g., Elbers, Lanjouw, and Lanjouw (2003) and Tarozzi and Deaton (2009)). However, such data is usually not available for many developing countries, while satellite imagery is. As such, instead of using household-level covariates to predict poverty, 15
sectors and time as this constitutes our preferred econometric specification. For now, the results in Table 2 reveal that, all else equal, international borders reduce services trade substantially. Ten of the twelve possible estimates on _SMCTRY_ are positive and six of them are large and highly significant. The border effects in services vary widely across sectors. This motivates our preferred specification that allows for _country-year_ -specific estimates of the border effects _βit_<sup>_k_foreachsectorinoursampleaccordingto:</sup>

# **4.2 Results on Standard Gravity Variables and Services Trade**

Estimates of the effects of standard gravity variables on services trade are reported in Table 3. We note first of all that the effects that we capture in Table 3 and the corresponding numbers in Table 2 are qualitatively similar. At the same time, there are also differences in terms of statistical significance and economic magnitude which underscore the importance of a flexible specification of border barriers. The price of this flexibility is that the associated coefficients are ‘estimated’ with zero degrees of freedom. Yet an auxiliary regression finds meaningful patterns in the flexible fixed effects estimates.

Distance is a significant impediment to trade in services, though its effect varies widely across sectors and depends on the distance interval. In many sectors such as Transportation, Travel, Communication, Construction, Merchanting and Audiovisual services, distance effects are large and highly significant only over short intervals. It appears intuitive that Travel services exhibit the largest distance effect. In Financial and Computer services, respectively, we obtain insignificant short-distance effects but negative and significant (though small) effects over long distances. Finally, distance does not exert any trade-impeding effect in Insurance services, Operational Leasing, Business services, and Research and Development.

13
|**EXAMPLES**<br>- Behrens et al. (2018)<br>- Allen et al (2021)<br>- Teravaninthorn<br>and Raballand (2009)<br>- Herrera Dappe<br>et al. (2019)<br>- Combes and<br>Lafourcade (2005)<br>- Zofío et al. (2014)<br>- Duranton (2015)<br>- Coşar et al. (2021)<br>- Atkin and<br>Donaldson (2015)<br>- Chatterjee (2020)|
|---|
|**CHALLENGES**<br>n<br>Obtaining micro<br>data from ofcial<br>agencies<br>- Expensive data<br>collection<br>- Sampling from<br>carrier or frm<br>registries<br>- Obtaining cost<br>data to be used<br>as inputs<br>in computations<br>- Complex GIS<br>analysis<br>Estimation is<br>standard but<br>applications<br>require a<br>structural<br>gravity model<br>Obtaining micro<br>data from ofcial<br>agencies|
|**OUTCOMES/TYPE OF**<br>**TRANSPORT COST INFERRED**<br>- Monetary costs in levels<br>- May require computation of unit (ton-km)<br>costs by dividing over road distances betwee<br>reported origin and destination<br>- Monetary costs in levels<br>- Monetary costs in levels<br>- Enables simulation of per ton cost<br>for any route<br>- Trade costs including transportation<br>and other frictions<br>- Relative costs by distance<br>- Can be commodity specifc if trade fows<br>reported by commodity<br>-Enables simulation of relative costs<br>between routes<br>- Trade costs including transportation<br>and other frictions<br>- Relative costs by distance<br>- Commodity specifc by design|
|**DATA**<br>**REQUIREMENTS**<br>Availability from national<br>statistical agencies<br>Sampling from frm and<br>carrier registries. Carrier<br>surveyed on the cost of<br>per ton-km shipment with<br>a standard truck. Shipper<br>surveyed on the price paid<br>for cargo with its origin,<br>destination, weight, value<br>Two types of data needed.<br>Per ton-mile and time<br>costs: vehicle operation<br>costs, driver wages, tolls,<br>fuel price. For routing<br>decisions: GIS analysis on<br>a digitized transport<br>network with slope and<br>congestion & speed of<br>segments<br>Domestic trade fows<br>(value, weight) between<br>regions. Could be exports<br>or imports between ports<br>and regions. Road<br>distances from GIS based<br>routing analysis or from<br>an online map API<br>Prices of the same goods<br>in various locations in the<br>country. Road distances<br>as above.|
|**METHODOLOGY**<br>Using data from an<br>ofcial survey<br>Collecting data<br>through a special<br>survey<br>Imputation<br>Estimation from<br>trade fows<br>Estimation from<br>price gaps|
Table A34: Robustness of impact on nutrition index to differential attrition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1 (Safe Spaces)|-0.40**|-0.36**|-0.43***|-0.41**|-0.41**|-0.40**|-0.40**|
||(0.16)|(0.16)|(0.16)|(0.16)|(0.16)|(0.16)|(0.16)|
|T1+Leaders|-0.24*|-0.01|-0.39***|-0.27*|-0.26*|-0.24*|-0.24|
||(0.14)|(0.12)|(0.14)|(0.14)|(0.14)|(0.14)|(0.14)|
|T2 (Safe Spaces + IGA)|-0.36**|-0.22|-0.47***|-0.38**|-0.38**|-0.35**|-0.35**|
||(0.16)|(0.14)|(0.14)|(0.15)|(0.15)|(0.15)|(0.15)|
|T2+Leaders|-0.15|0.14|-0.32*|-0.20|-0.20|-0.15|-0.15|
||(0.15)|(0.14)|(0.16)|(0.15)|(0.15)|(0.15)|(0.15)|
|T3 (Safe Spaces + Husbands Clubs)|-0.68***<br>(0.20)|-0.48***<br>(0.16)|-0.89***<br>(0.18)|-0.74***<br>(0.20)|-0.69***<br>(0.20)|-0.67***<br>(0.20)|-0.68***<br>(0.20)|
|T3+Leaders|-0.05|0.02|-0.09|-0.06|-0.06|-0.05|-0.05|
||(0.14)|(0.13)|(0.14)|(0.14)|(0.14)|(0.14)|(0.14)|
|T4 (control spillover)|-0.25**|-0.24*|-0.25**|-0.25**|-0.25**|-0.25**|-0.25**|
||(0.13)|(0.13)|(0.13)|(0.13)|(0.13)|(0.13)|(0.12)|
|N|3003|3002|3002|3004|3004|3004|3003|
|Control Mean|0|0|0|.001|.001|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

66
governance in Tamil Nadu.<sup>13</sup> During the peak of the Chola Dynasty (roughly 900–1200 AD), villages were strong, self-governing units. Local governance was also reasonably strong during the colonial period, though it weakened in the final years before independence. Through the Madras Panchayat Act of 1958, local governance strengthened, and Tamil Nadu was a panchayat success story. However, from the 1970s to the mid-1980, things were in flux. After the passage of India’s 73rd Constitutional Amendment, Tamil Nadu created the 1994 Tamil Nadu Panchayats Act, which included women’s reservations. Its first post-Act panchayat elections were held in 1996 and have since been held every five years.

The 73<sup>rd</sup> Amendment outlines 29 areas that can be allocated to the GP; each state then chooses which areas it will devolve. The Amendment also sets requirements on the reservation of seats, but each state can implement this in its own way. In Tamil Nadu, the _Rural Development and Panchayati Raj Department_ of the Government of Tamil Nadu is responsible for the Tamil Nadu Panchayats Acts of 1994 as well as many other documents and government orders outlining the rules of the panchayat system and what is devolved to the GP.<sup>14</sup> GP reservations in Tamil Nadu are held for 10 years (two five-year election terms), and the selection of GPs for reservation—for women, SC, and ST persons—is performed at the block level. Tamil Nadu, similar to many Indian states, does not randomly select GPs for women, SC, and ST reservations. For the 1996 elections (the first elections following the Tamil Nadu Panchayats Act), one-third of GPs in each block were reserved for women, and 20 percent were reserved for SC and ST presidents. Reserved GPs are chosen as follows:

> 13 All historical information about Tamil Nadu’s governance in this paragraph comes from Aram and Palanithurai (2000).

> 14 Some documents are available on the _Rural Development and Panchayati Raj Department_ website: http://www.tnrd.gov.in/acts_go.html.  Many, however, are not.  We obtained some documents from our field contacts to help in understanding the devolution of power and responsibility to GP presidents in Tamil Nadu.

8
|**[8]**<br>11.071***<br>(2.647)<br>-0.014<br>(0.052)<br>0.086<br>(0.063)<br>0.017<br>(0.011)<br>0.442<br>(2.72)<br>**or - ∆%CPI**<br>**average)**|454 / 83<br>0.29<br>-0.016<br>3.057|
|---|---|
|**[7]**<br>2.248**<br>(1.032)<br>-0.031<br>(0.052)<br>0.110*<br>(0.065)<br>0.018*<br>(0.010)<br>0.747<br>(2.68)<br>**∆%Deflat**<br>**(3-year**|409 / 82<br>0.305<br>-0.065<br>2.775|
|**[6]**<br>3.109**<br>(1.520)<br>0.031<br>(0.034)<br>-0.013<br>(0.035)<br>-0.006<br>(0.011)<br>-2.051<br>(2.08)<br>**or - ∆%CPI**|1232 / 84<br>0.159<br>-0.033<br>4.163|
|**[5]**<br>1.085**<br>(0.425)<br>0.024<br>(0.035)<br>-0.01<br>(0.035)<br>-0.015<br>(0.010)<br>-0.683<br>(2.12)<br>**)**<br>**∆%Deflat**|1200 / 82<br>0.162<br>-0.028<br>4.118|
|**[4]**<br>14.267***<br>(3.599)<br>0.168***<br>(0.024)<br>-0.174***<br>(0.064)<br>0.229***<br>(0.052)<br>4.2385***<br>(4.95)<br>**( t**<br>**flator**|1429 / 90<br>0.714<br>10.78<br>13.851<br>measured.|
|_nd GDP Deflator_<br>**[3]**<br>1.408<br>(0.891)<br>0.167***<br>(0.032)<br>-0.129**<br>(0.064)<br>0.183***<br>(0.056)<br>4.6855***<br>(4.98)<br>**∆%De**|1362 / 86<br>0.709<br>10.638<br>13.61<br>.<br>dependent variable is<br>* significant at 1%|
|_umer Price Index a_<br>**[2]**<br>1.972<br>(2.978)<br>0.138***<br>(0.028)<br>-0.129**<br>(0.058)<br>0.124***<br>(0.038)<br>3.9947***<br>(4.82)<br>**CPI**|1282 / 85<br>0.722<br>9.738<br>11.327<br>o t+s of the variable_y_ <br>eriod over which the<br>ies<br>** significant at 5%; **|
|**Table 2**<br>_Sample of 1974-2004_<br>_Dependent Variable: Percentageof Change of Cons_<br>**Variables**<br>**[1]**<br>int1. Index (t)<br>0.325<br>(0.753)<br>int2. Index (t)<br>_Control Variables_<br>Lagged dep. var.<br>0.154***<br>(0.032)<br>∆%GDP<br>-0.122**<br>(0.058)<br>∆%M2<br>0.101***<br>(0.039)<br>Interest Rate<br>3.9024***<br>(4.99)<br>**∆%**|Observations / Countries<br>1255 / 83<br>R-squared<br>0.722<br>Mean Dep. var.<br>9.592<br>St Dev Dep. var.<br>11.17<br>Note:_y_(t, t+s) corresponds to the average over the period t t<br>Except otherwise indicated, all controls are averages for the p<br>All regressions included country fixed effects and Time dumm<br>Robust standard errors in parentheses / * significant at 10%;|
scale for certain types of purchases), the way durables are depreciated and how their flow value is calculated, as well as a series of judgments about how to deflate values across space and time.

For five of the surveys analyzed here, we use the expenditure aggregates described in Deaton and Zaidi (2002)<sup>12</sup> , and for the remaining six surveys we use the expenditure aggregate that was constructed for the main poverty report that resulted from the survey.<sup>13</sup> All expenditure aggregates are spatially deflated to adjust for regional price differences within countries.

The first asset index we consider is based on **predicted per capita household expenditures** . The weights for this index are derived from an OLS regression of per capita expenditures on the asset and housing indicators, that is, the estimates of the βs in: Yi = β1·a1i + β2·a2i + ... + βk·aki + εi (2) These estimates of β1 to βk are then substituted for b1 to bk in equation 1. While asset indices are typically used in situations where expenditures are unavailable, predicted expenditures is an interesting variable for our analysis. First, it represents the linear combination of these assets and housing characteristics that best predicts per capita expenditures. No other linear aggregation of the same indicators will come as close to giving the same ranking of households and, therefore, come as close to giving similar results to per capita expenditures. Second, this regression-based prediction is consistent with what is sometimes used in proxymeans tested approaches to targeting populations for social programs. As discussed above, principal components analysis has sometimes been used for this purpose but using predicted expenditures is probably more common (Coady, Grosh and Hoddinott 2004). In addition, predicted expenditure mimics the “best possible” linear prediction for situations where indicators are available in one dataset, but they can only be related to expenditures in another.<sup>14</sup> It is therefore the best possible “economic” asset index in the terminology of Stifel and Christiaensen (2007) that can be constructed from this set of assets. Third, under some interpretations, predicted expenditures capture a stable component of expenditures. This argument has two variants. If expenditures are measured with substantial error, then using the prediction is a way

> 12 These aggregates were graciously made available by those authors for Brazil 1996-97; Nepal 1996; Panama 1997; South Africa 1993; Vietnam 1992-93.  The other datasets we use were not available to those authors at the time of their analysis .

> 13 Albania 2002, Ghana 1991/92, Nicaragua 2001, PNG 1996 are available at <u>http://www.worldbank.org/lsms.</u> Uganda 2000 and  Zambia 2004 were made available by the agencies responsible for data collection or analysis. 14 Note that this is consistent with the way this is often carried out (such as in Stifel and Christiansen 2007)— although not with the “Poverty Mapping” approach which includes area-level aggregates (in Elbers, Lanouw and Lanjouw 2002, 2003 and Alderman, Babita, Demombynes, Makhatha and Özler 2002).

13
|166|Inter-village|Committee other|Continuous (0-9). Existing committees that work together with other villages: 1) water/<br>sanitation, 2) roads and erosions, 3) health (CODESA), 4) education/ school (COPA),<br>5) farming or agriculture, 6) protection or security, 7) conflict resolution, 8)<br>developmentgeneral, and 9)other. Simple summation.|Q182|1.36|1.32|0|7|2405|
|---|---|---|---|---|---|---|---|---|---|
|167|Inter-village|Projects other|Continuous (0-6). Voluntary projects undertaken with other villagers: 1) construction<br>and maintenance of primary schools or health infrastructure, 2) construction or<br>maintenance of roads, 3) construction or maintenance of wells, 4) organization of<br>security patrols, 5) maintenance of a church or mosque, 6) construction of a market.<br>Simple average.|Q195|0.45|0.46|0|1|1385|
|168|Inter-village|Associations other|Continuous (0-11). Existing associations that undertake activities with other villages: 1)<br>an association affiliated to the church/ mosque, 2) a peasant association, 3) an<br>association of the elderly, 4) an association of women, 5) a youth organization, 6) an<br>association of former combatants / militia 7) an association for savings and credit, 8) an<br>association to support a certain politician or political party, 9) a human rights<br>association, 10)a cultural association / ethnic, and 11)other. Simple average.|EC105g-<br>EC120g|0.66|0.37|0|1|628|
|169|Inter-village|Resources other|Continuous (0-8). Community resources held jointly with the other villages in this<br>territory: 1) arable land, 2) community forest, 3) pasture, 4) water (lake, rivers), 5)<br>mineral mine, 6) quarry for stone/ sand, 7) hunting reserve, and 8) other. Simple<br>average.|EC121j-<br>EC128j|0.52|0.45|0|1|457|
|170|Inter-village|Managed conflict|Binary. In the last three months, chief has managed conflict between his/her village and<br>a neighboringvillage.|EC179|0.24|0.43|0|1|655|
|171|Inter-village|Hosted other|Binary. In the last three months, chief has hosted the chief of a neighboringvillage.|EC179|0.16|0.36|0|1|655|

Note: Variable definitions and summary information.

8