rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 5: Average ROA 2008-2010 for High and Low Capitalized Banks.**

<!-- Start of picture text --> 3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>-0.50 ROW EU CE WB SC CA Russia Turkey EE<br>-1.00<br>-1.50<br>-2.00<br>Low Capital High Capital<br><!-- End of picture text --> Source: Archived data from Bureau van Dijk’s Bankscope and BankFocus.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.** Capital is calculated using simple leverage (Equity/Total Assets). High (Low) capital banks are those that have capital ratios above (below) the median in a given country in 2007.

In discussions of bank capital, it is important to distinguish between economic capital and regulatory capital. Bank economic capital can be defined as the value of the equity of a bank that can withstand losses. It has the lowest priority if the bank liquidates. Although there are several types of equity instruments, equity consists mainly of common equity and profits retained by a bank or obtained from selling shares to investors.<sup>6</sup> Regulatory capital is the amount of capital that banks are required to hold by domestic supervisors and regulators. Regulatory capital can include financial instruments other than common equity and is typically divided up into tiers. Tier 1 consists of higher quality capital that is made mostly of common stock held by a bank. Tier 2 capital is designated as supplementary capital and includes items such as revaluation reserves, undisclosed reserves, hybrid instruments, and subordinated debt. Items (other than common equity) that can be included as part of Tier 1 capital varies across countries. Regulatory capital ratio is calculated as the sum of Tier 1 and Tier 2 capital divided by risk-weighted assets (RWA). To calculate RWA, the assets on a bank’s balance sheet are assigned weights based on their risk level. Domestic sovereign bonds, for instance, typically receive a weight of zero.<sup>7</sup> The notion of risk-

> 6 Measuring equity is not simple because its value depends on how all financial instruments and on– and off–balance sheet assets of banks are valued (Berger et al., 1995).

> 7 The regulatory treatment of sovereign debt has been subject to heated debate especially in Europe following the 2012 sovereign debt crisis (BCBS 2017).  Sovereign bonds of fiscally distressed countries such as Greece receive a risk-weight of zero for the purposes of regulatory capital calculation.

6
Table S1.6: EFFECTS ON MATCHING, HETEROGENEITY BY BASELINE CONNECTIONS

|Reported by:|U|ltra-Poor|Househol|ds|Non-Ul|tra-Poor H|ouseholds|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|_Panel A:_|_Sam_<br>ultra<br>poor|_ple of Hou_<br>other<br>poor|_seholds Gi_<br>middle<br>class|_ving Transf_<br>upper<br>class|_ers to Ultr_<br>other<br>poor|_a-Poor at Ba_<br>middle<br>class|_seline_<br>upper<br>class|
|Treatment|0.107***|0.097***|0.079***|0.114***|0.121***|0.089***|0.074***|
||(0.026)|(0.024)|(0.023)|(0.027)|(0.031)|(0.023)|(0.025)|
|Mean in control|0.497|0.509|0.501|0.241|0.558|0.384|0.126|
|Adj. R-squared|0.232|0.215|0.241|0.198|0.334|0.427|0.223|
|Observations|2370|4386|8828|2872|1296|978|510|
|_Panel B:_|_Samp_<br>ultra<br>poor|_le of House_<br>other<br>poor|_holds Not_<br>middle<br>class|_Giving Tran_<br>upper<br>class|_sfers to Ul_<br>other<br>poor|_tra-Poor at_<br>middle<br>class|_Baseline_<br>upper<br>class|
|Treatment|0.026***|0.007|0.024|-0.001|0.028***|0.020***|0.004|
||(0.006)|(0.011)|(0.019)|(0.003)|(0.006)|(0.004)|(0.003)|
|Mean in control|0.059|0.117|0.164|0.024|0.048|0.026|0.005|
|Adj. R-squared|0.102|0.104|0.058|0.019|0.269|0.130|0.080|
|Observations|11094|9078|4636|10592|13384|12506|3920|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. In columns 1-4, the sample includes observations from ultra-poor households; while in columns 5-7 non-ultra-poor households. In particular, the sample in column 5 includes other poor, in column 6 middle class and in column 7 upper class households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively; in columns 4, 5 and 6 (where the sample is limited to non-ultra-poor households) the dependent variables are dummies equal to 1 if the respondent’s household received any transfers from any ultra poor households (within her community). In Panel A, the sample is restricted to households who had at baseline reported giving transfers to at least one ultra-poor household within their community; in Panel B the sample includes households who did not report any ultra-poor household as a recipient of transfers.
The standard approach used by economists to adjust for differences in cost-of-living is to use purchasing power parity exchange rates, and to use regional price deflators. These help to adjust for differences in the price of a set basket of items in different locations, and then adjusting for living costs becomes largely a measurement issue. However, migration does induce a couple of additional issues into this adjustment. The first is that temporary migrants might earn income at destination with the intention of spending it at home, so that migrants may not be spending all of their income on the more expensive goods at destination (Dustmann et al, 2021), and so cost-ofliving adjustments may understate the gains from migration. However, conversely, Atkin (2013) shows that inter-state migrants in India carry their food tastes with them, paying more for food as they buy products from their origin state that are more expensive than the local food bundle. Costof-living adjustments may then undercount the costs of migrants being able to obtain the same utility from their consumption expenditure as at home. Nevertheless, in many cases these cost-ofliving adjustments will be small compared to the large income gains from migration.

Addressing the issue of differences in productivity between migrants and natives requires measuring what the change in income is for those individuals who actually migrate. One approximation of this is carried out by Clemens et al. (2019), who use survey data to compare the incomes earned by migrants in the U.S. to the (purchasing power parity adjusted) incomes of individuals of the same age, gender, and education in their home countries. They find a wage gap of PPP$13,710 per year, with U.S. incomes more than 5 times that in the average sending country. Gibson and McKenzie (2011, 2012), consider high academic achievers from Ghana, Micronesia, New Zealand, Papua New Guinea and Tonga, and compare the (cost-of-living adjusted) incomes of migrants to those with similar skill levels who did not move, finding income gains of $40,000– $75,000 per year. Gibson and McKenzie (2014) track households in Vanuatu and Tonga over two years, and measure impacts of temporary migration in a new seasonal worker program, comparing income gains to both the incomes they earned before the program, and to what similar households who applied for the program and that were not chosen earned while the migrants were away. They find per capita income of participating households rises by over 30 percent.

These studies suggest that the income gain from migrating is large for a wide variety of skill levels and types of migration. However, the concern may still be that those who choose to migrate may differ in unobserved ways from those that remain behind, and that the marginal migrant may have 6
13

|**Table 2. Differ**|**ence (Arella**|**no-Bond) an**|**d System (B**|**lundell-Bon**|**d) GMM Reg**|**ressions 199**|**5-2011**||
|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|
||Gini|Gini|Gini|Gini|Gini|Gini|Gini|Gini|
|Lagged Dependent Variable|0.595***|0.670***|0.648***|0.654***|0.543***|0.624***|0.565***|0.593***|
||[0.096]|[0.093]|[0.081]|[0.077]|[0.107]|[0.094]|[0.122]|[0.120]|
|∆(Cyclically Adj. Primary Surplus/GDP)t-1|-0.512***|-0.390***|-0.442***|-0.392***|-0.256**|-0.309**|-0.257**|-0.292**|
||[0.098]|[0.117]|[0.100]|[0.110]|[0.103]|[0.111]|[0.119]|[0.116]|
|(GDP Growth per capita)t-1||0.015|0.033|-0.086|-0.119|-0.099|-0.072|-0.065|
|||[0.107]|[0.118]|[0.096]|[0.081]|[0.090]|[0.095]|[0.090]|
|(Employment rate)t-1|||-0.329**|-0.283*|-0.206**|-0.316***|-0.294***|-0.288***|
||||[0.155]|[0.149]|[0.091]|[0.085]|[0.071]|[0.078]|
|(Inflation)t-1||||0.021|0.018|0.020|0.012|0.027|
|||||[0.058]|[0.061]|[0.064]|[0.057]|[0.058]|
|(Sub-national Social Expenditure)t-1|||||0.286**|0.337**|0.368**|0.377***|
||||||[0.125]|[0.136]|[0.140]|[0.135]|
|(Federal Social Transfers/GDP)t-1||||||-0.761**|-1.108**|-1.109***|
|||||||[0.358]|[0.403]|[0.381]|
|(Years of education)t-1|||||||-0.001|-0.008|
||||||||[0.029]|[0.029]|
|(Returns to education)t-1||||||||0.016|
|||||||||[0.057]|
|Constant|-5.302|-5.172|-5.924|12.538|25.931**|19.519|18.859|13.879|
||[8.187]|[15.363]|[16.386]|[14.812]|[11.323]|[12.777]|[14.220]|[17.910]|
|Time Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Pesaran (2004) cross-sectional<br>dependence test|-1.99**|-1.90*|-2.00**|-1.91*|-2.08**|-2.08**|-2.16**|-2.16**|
|<br>Sargan test|98.24***|153.3***|159.9***|171.8***|165.1***|181.7***|173.1***|177.0***|
|<br>Hansen test|10.70|12.33|16.41|14.73|8.519|2.873|2.142|1.627|
|Arellano-Bond AR(2) test|0.354|0.563|0.547|0.837|0.657|0.603|0.499|0.515|
|Observations|405|405|405|405|405|405|405|405|
|Number of groups|27|27|27|27|27|27|27|27|
|<br>HAC robust standard errors in brackets. *** p|<0.01, ** p<0.05, * p|<0.1. Null hypoth|esis of Pesaran (20|04) test for regres|sion residuals is cro|ss-sectional indep|endece. Null of Ar|ellano-Bond test|
|is that first-differenced errors exhibit no 2nd o|rder serial correlatio|n. Sargan and Ha|nsen tests of tests|of the validity of ov|eridentifying restrict|ions.|||
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 47 interest and of the auxiliary variables for all areas, _{_ ( _zdi,_ **x** _di_ ); _i ∈ sd, d_ = 1 _, . . . , D}_ , as well as a census with the values of the same auxiliary variables for all the population units, _{_ **x** _di_ ; _i_ = 1 _, . . . , Nd, d_ = 1 _, . . . , D}_ . The original EB estimator proposed by Molina and Rao (2010) additionally requires identifying the units in the census that are also in the sample for each area, a process that was not needed in the ELL approach. Linking the survey and census data files for each area is not always feasible in practice. Actually, the original EB method assumes that, for each area _d_ , the survey sample _sd_ is contained in the set of census units, which is regarded as _Ud_ . In practice, these two data files might come from different time instants, and the census set of units does not necessarily include the set of survey units. Hence, **y** _ds_ is not necessarily a sub-vector of **y** _d_ , _d_ = 1 _. . . , D_ .

Correa, Molina, and Rao (2012) proposed the _Census_ EB (CEB) predictor, which does not require identifying the survey sample units in the census file and delivers practically the same point estimate as EB, if the sampling fraction _nd/Nd_ is small. The CEB predictor of _δd_ = _hd_ ( **y** _d_ ) is the EB predictor of _δd_ = _hd_ ( **y** _d_ ), assuming that the augmented vectors **y** _da_ = ( **y** _ds,_ **y** _d_ )<sup>_′_</sup> , composed by the survey and the census vectors of model responses, _d_ = 1 _, . . . , D_ , follow the nested error model (5.1). Hence, the CEB predictor of _δd_ is the EB predictor _δ_<sup>ˆ</sup> _d_<sup>_CEB_</sup> = _E_ **y** _d_ [ _δd|_ **y** _ds_ ; _θ_<sup>ˆ</sup> ] obtained under the nested error model for **y** _da_ , _d_ = 1 _, . . . , D_ . A general MC simulation procedure that approximates the CEB predictors is then adapted from that for EB predictors. This procedure is described next.

# **Monte Carlo simulation procedure for Census EB estimator:**

1. Fit the model (5.1) to the sample data ( **y** _s,_ **X** _s_ ), obtaining an estimator **_θ_**<sup>ˆ</sup> = ( **_β_**<sup>ˆ</sup><sup>_′_</sup> _,_ ˆ _σu_<sup>2</sup><sup>_,_ˆ</sup><sup>_σ_</sup> _e_<sup>2)</sup><sup>_′_of</sup><sup>**_θ_**= (</sup><sup>**_β_**</sup><sup>_′, σ_</sup> _u_<sup>2</sup><sup>_, σ_</sup> _e_<sup>2)</sup><sup>_′_.</sup>

2. Generate, for _l_ = 1 _, . . . , L_ , a census vector **y** _d_<sup>(</sup><sup>_l_), whose elements</sup><sup>_y_</sup> _di_<sup>(</sup><sup>_l_),</sup> are generated as follows
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analyze the pattern of stop locations by Gramin Sewa operations to examine the role of informal public transport in New Delhi. According to Tiglao et al. (2020), in Metro Manila, informal public transport modes, such as Jeepneys, still tend to dominate public transportation because of operational speed, customer care and other key factors.

The current paper is focused on one of the fast-growing cities in Africa, Antananarivo, Madagascar, aimed at deepening our understanding of informal public transport in the region. It will contribute to filling the gap in the literature, providing useful policy recommendations in the region where paratransit services have been expanding. The paper casts particular light on demand behavior in the market. The existing literature is relatively rich on the supply side, describing physical or operational characteristics of paratransit services (e.g., Shimazaki and Rahman, 1996; CDIA, 2011; Tichagwa, 2016; Permana et al. 2018). However, the demand-side analysis has been carried out in a relatively limited number of studies thus far.

One popular approach to examine the paratransit demand is based on case studies and qualitative methods. For instance, Tichagwa (2016) carried out user interviews with a relatively limited sample of 43 paratransit users in Harare and Masvingo, Zimbabwe, to assesses the difference in their sociodemographic profiles among different routes. Based on user survey data at 48 informal public transport stops in New Delhi, Behal et al. (2020) show that paratransit users are concerned about safety, while formal bus users are more concerned about reliability. Similarly, based on interview data from users, Tiglao et al. (2020) found five key determinants of paratransit use: vehicle condition, service availability, reliability and convenience, and access to information in the Philippines.

There are only a few studies which adopt more rigorous quantitative methods to analyze the demand behavior for informal public transportation. Applying the discrete choice model, Golub et al. (2009) estimate the welfare impacts of different policy interventions in Rio de Janeiro. It is shown that the demand for “vans,” an informal transport service using 10-15 passenger minibuses, as opposed to train and bus transport, is elastic to household income and travel time. Gadepalli et al. (2020) examine socioeconomic characteristics of paratransit
_Africa’s Macroeconomic Story_

**Figure 1: Real GDP per Capita Growth in Sub-Saharan Africa Region**

# _Percent_

<!-- Start of picture text --> 4<br>2<br>0<br>-2<br>-4<br>1962 1972 1982 1992 2002 2012<br>Trend Actual<br><!-- End of picture text --> _Source:_ World Bank _Global Economic Prospects_ January 2013. _Note:_ Trend generated by Hodrick-Prescott (HP) filter with smoothing parameter set to 100.

# _Cross-Regional Perspective_

Africa’s “U-shaped” growth history was shared by most other developing regions. The East Asia and Pacific region is an exception. Growth increased over time largely due to a sustained growth surge, initially in China beginning in the early 1970s and then in India beginning in the early 1990s. The differential between GDP growth per capita across all developing countries and that in Africa widened from about one percentage point over the first sub-period 1961-73 to about two percentage points over the two subsequent sub-periods (Table 1). A different picture emerges, however, when China and India are excluded from the calculations. The differential begins to narrow in the 1970s and almost disappears by the early 1990s (Figure 2). Since the early 1990s, trend growth in Africa has been very similar to that in all developing countries if one excludes China and India.

10
9 Table 2 takes a more disaggregate look at stru( u.al trade chanqes by analyzing similar statistics for individual comn,odity chains. The results are remarkably consistent throughout the agricultural materials group (and to a lesser extent for ores and metals) where ma,or shifts occurred in the relative importance of processed stage commodities. In cases the shift was particularly dramatic--the share of hides and skins in the leather chain fell from 60.5 to 3 percent, the share of raw cork fell from 72 to about 9 percent--althoug.i these changes often were accompanied by sizable declines in developing counltries market shares for the primary stage products (exceptions occur for pulpwood, silk and hard fibers) . The same general pattern--that of a shift toward processed conmodities ar;d a loss of primary stages market share--occurred in most of the individu,il ore, minerals and metals chains except in the case of nickel where devaloping countrie._: managed both to ai'ieve a sizable increase in both their maacket share for nickel ore (largely due to Indonesia's 1987 exports of US$125 m.li.on) as well as the share of fabricated nickel products in total exports.

For the food commodities a less-consistent picture emerges although the overall share of primary stage items did 'all by almost two and one-half points (to 75.8 percent). ° However, for almost 40 percent of the 19 food proces-.ing chains the share of primary stage commodities increased, and irn two of 'he remaining chains the decline in the relative irrportance of the primary stage l°Several of the tropical product commodities appear ^- have improperly recorded trade, or to have undergone some processing, which explains why imports are reported from developed countries. For example, in 1987 developed countries reported imports of USS355 million in green and roasted coffee beans (071.1) from tile Federal Republic of Germany and US$126 million from Belgium. From the UN data there is no way to determine if this reflects improper recording of entrepot trade or processincg (roasting) of beans in these countries. Similarly, developed countries reported US$102 million of raw or roasted cocoa bean impr,rts from the Netherlands, US$89 million in tea from the United Kingdom, US$7 million in palm nuts from the Netherlands, and US$1.6 million in raw silk from Switzerland.
Table 7. Long-run equilibrium implications of policy changes (values in dollars)

||Baseline|No preferences|Higher doc. costs<br>No home yarn req.|Costless pref.|
|---|---|---|---|---|
|Tari¤ in EU (tBD;EU)|12%|12%|12%<br>12%|0%|
|Tari¤ in EU, ROO (t<sup>ROO</sup><br>BD;EU<sup>)</sup>|0%|12%|0%<br>0%|0%|
|Tari¤ in US (tBD;US)|20%|20%|20%<br>20%|20%|
|Cost disadvantage (�)|0.85|1.00|0.85<br>1.00|1.00|
|Documentation costs (d<sup>EU</sup>=f)|0.66|0.00|1.32<br>0.66|0.00|
||||Endogenous quota price case||
||||Change in quota price in US, %||
|Change in quota price in US|0.070|-100%|-5.7%<br>+43.4%|+49.3%|
||||Change in Bangladeshi exports, %||
|EU imports from Bangladesh|482.3m|-31.7%|-1.5%<br>+17.1%|+19.0%|
|US imports from Bangladesh|233.6m|-11.9%|-0.1%<br>+1.1%|+1.2%|
||||Change in mass of entrants, %||
|Implied mass of entrants|4,712|-22.3%|-0.7%<br>+5.8%|+6.6%|
||||Change in productivity cuto¤s, %||
|Productivity cuto¤ in EU|0.8508|+15.9%|+0.91%<br>-6.06%|-7.42%|
|Productivity cuto¤ in US|1.0355|-6.8%|-0.34%<br>+2.61%|+2.97%|
||||Change in demand shock cuto¤s, %||
|Demand shock cuto¤ in EU|0.1866|-10.24%|-0.59%<br>+5.55%|-10.24%|
|Demand shock cuto¤ in US|6.9570|0.00|0.00<br>0.00|0.00|
||||Share of ...rms invoking ROO, %||
|Share of ROO ...rms (model)|70.2%|0%|57.0%<br>77.7%|100%|
||||Change in EU and US price indices, %||
|Price index in EU|100%|+19.1%|+0.87%<br>-9.38%|-10.41%|
|Price index in US|100%|+1.1%|+0.01%<br>-0.10%|-0.11%|
||||Change in tari¤ revenues, %||
|Tari¤ revenue in EU|447k|+8,742%|+125.9%<br>-34.2%|-100%|
|Tari¤ revenue in US|46,728k|-11.9%|-0.1%<br>+1.1%|+1.2%|
||||Change in welfare, $||
|Change in welfare in EU ($)|—|-480,935k|-25,208k<br>293,418k|327,162k|
|Change in welfare in US ($)|—|-68,538k|-709k<br>6,191k|6,964k|
||||Exogenous quota price case||
||||Change in Bangladeshi exports, %||
|EU imports from Bangladesh|482.3m|-45.5%|-2.24%<br>+22.7%|+25.5%|
|US imports from Bangladesh|233.6m|-41.6%|-1.94%<br>+14.3%|+16.1%|
||||Change in EU and US price indices, %||
|Price index in EU|100%|+28.15%|+1.28%<br>-12.39%|-13.8%|
|Price index in US|100%|+3.84%|+0.18%<br>-1.30%|-1.5%|
||||Change in mass of entrants, %||
|Implied mass of entrants|4,712|-45.5%|-2.3%<br>+16.9%|+19.0%|
||||Change in welfare, $||
|Change in welfare in EU ($)|—|-707,594k|-37,343k<br>393,918k|441,279k|
|Change in welfare in US ($)|—|-238,328k|-11,193k<br>82,661k|92,933k|

32
Table 6s Effect of hom background, parental support, Student educational expectations, student attitudes, **and** student offort on Grade 8 mathematics pretest **achievement in** urban **and** rural schools, **Thailand,** 1981-82

|Ind-pondent<br>Variables|School L<br>Urban|ocation<br>Rural|
|---|---|---|
|Sex (2- boy)|-1.22***<br>(3.43)|1.03**<br>(2.94)|
|Age|.19|.46|
||(.59)|(1 00)|
|Age**squared**|.00|.00|
||(.92)|(1.18)|
|Father's occupation|.84***|.36|
||(4.47)|(1.62)|
|Mothers education|.77***|98***|
||(3.52)|(3.65)|
|Calculator at home|.39|70|
||(1.03)|(1.62)|
|District PCI(1000bh)|.00|27*|
||(.05)|(2.23)|
|Lang.ofinst. at home|-3.07|.68|
||(1.64)|(.41)|
|Dist. PCI x Langofinst.|.15|.27|
||(1.34)|(.04)|
|Constant|.55|-32.57|
||(.02)|(.81)|
|Adjusted R-squared|.06|.05|
|_N_|1995|1646|

*** p < .001, ** p < .01, * p <.05.

Note: Unstandardized regression coefficients; t statistics in parentheses.

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