rafmacalaba/datause-encoder-data huggingface dataset:
potential weakness of this research, however, is the reliance on bank-level data that does not provide sufficient granularity on funding structures, including the levels of foreign funding. The exposure to cross-border funding—arguably one of the main transmission channels of international financial crises—is generally proxied by the ratio of deposits to assets. The fact that recent research work has found that this variable is significant is reassuring, confirming the importance of funding structures, but the lack of a variable measuring more accurately the exposure to foreign funding raises questions about the relevance of these results for the regulatory debate on cross-border banking.

Our paper contributes to the literature by examining the relative importance of foreign ownership and foreign funding from a different angle. First, we use country-level data instead of bank-level data, which allows us to explicitly measure the reliance on foreign funding. Second, we follow a different methodology—panel vector autoregressions (PVARs)—to examine the relationship between funding structures and credit growth.

There are several advantages in using the PVAR methodology to study the importance of different factors in explaining credit growth. First, in VARs, all variables are treated as endogenous and interdependent so all the feedback effects are explicitly included in the model. Thus, VARs are designed to explicitly address the endogeneity problem, which is a serious challenge in studying the empirical relationships between credit growth, economic growth, and funding sources. Second, unlike single country VARs that need long time series for efficiency, PVARs can be used with relatively short time-series. This is important for our study since the quarterly data on foreign liabilities are only available for about the past 10 years. Third, the PVAR methodology can distinguish between the short-term impacts of each of the factors based on the impulse-response functions and the long-term cumulative impacts of shocks based on variance decompositions. In addition, the impulse-response functions based on PVARs can register any delayed impacts on (and of) the variables under consideration. Fourth, as in any panel-based method, PVARs allow us to control for country- and year-fixed effects. Countryfixed effects will capture time-invariant country characteristics that can explain credit growth, such as institutions, rule of law, credit information and other relatively static features of the 4
**Table 8: Boys' Enrollment - Grades 5 to 8**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Class 5|Class 6|Class 7|Class 8|
|Stipend District|||||
|2004|0.246|0.333|1.143*|0.626|
||(0.268)|(0.484)|(0.449)|(0.455)|
|2005|0.445|3.940***|3.030***|2.051***|
||(0.274)|(0.505)|(0.468)|(0.470)|
|2006|-1.426***|5.126***|4.029***|2.354***|
||(0.140)|(0.330)|(0.287)|(0.276)|
|2007|-1.547***|1.895***|3.944***|1.723***|
||(0.137)|(0.304)|(0.281)|(0.270)|
|2008|-1.131***|-0.811**|2.139***|2.295***|
||(0.138)|(0.301)|(0.259)|(0.255)|
|Stipend District * 2004|0.543**|1.847***|-0.187|0.563*|
||(0.184)|(0.355)|(0.290)|(0.270)|
|Stipend District * 2005|0.0652|3.070***|1.144**|0.620*|
||(0.194)|(0.411)|(0.348)|(0.299)|
|Stipend District * 2006|1.071***|2.705***|2.549***|2.009***|
||(0.201)|(0.462)|(0.407)|(0.363)|
|Stipend District * 2007|1.218***|3.615***|2.329***|3.921***|
||(0.197)|(0.413)|(0.388)|(0.358)|
|Stipend District * 2008|1.675***|4.926***|2.836***|3.686***|
||(0.202)|(0.409)|(0.367)|(0.348)|
|Constant|15.16***|32.48***|27.30***|24.14***|
||(0.250)|(0.449)|(0.424)|(0.432)|
|Observations|69047|70074|70065|70058|
|Number of Schools|12170|12165|12165|12165|
|Adjusted R-squared|0.011|0.029|0.026|0.020|
|R-square within|0.0108|0.0291|0.0265|0.0197|
|ControlsIncluded|Y|Y|Y|Y|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust and clustered at the school level. Schoollevel fixed effects regressions reported. Dependent variable is boys’ enrollment in grades 5 to 8. Controls include a series of indicators for how many of the following three facilities a school has: toilets, drinking water and electricity. The excluded group is no facilities. The excluded year is 2003. Each column excludes mosque and primary schools and two districts whose treatment status was interchanged once the program began: Khanewal and Multan. The average school in a treatment district at baseline has 7, 30, 25 and 22 students in grades 5, 6, 7 and 8 respectively.

46
22

# Table 5: Initation of Antidm ug Invetlgtions, 1985-fl

|Country_or_petitioner<br>_No. of cases_|Country or respondent|No. of case|
|---|---|---|
|European Union<br>242|European Union or Member|209|
||States||
|Japan<br>3<br> <br>|Japan<br>|_105_<br>|
|United Stares<br>300|United States<br>|100<br>|
|Korea<br>9|Republic of Korea|78|
||China|69|
||China. Taiwan|68|
|Brazil<br>13|Brazil|_54_|
||Yugoslavia (former)|31|
|Canada<br>129|Canada|25|
|Poland<br>24|Poland|24|
||Czech Republic & Slovakia|23|
||Romania|23|
||Hong_Kong_|22|
|Mexico<br>84|Mexico|22|
||Turkey|22|
||<br>Thailand|_19_|
||Singapore<br>|I8<br>|
||Argentina|17|
|India<br>_5_|India<br>|17<br>|
||USSR (fonner)|16|
|Sweden<br>11|Sweden<br>|_15_<br>|
||Venezuela|14|
||Hungary|12|
||Malaysia|12|
|Austria<br>4|Austria|11|
||Indonesia|9|
||Israel|B|
||Norway|6|
||South Africa|6|
||Colombia|5|
||Philippines|_5_|
||<br>Saudi Arabia|_5_|
||Bulgaria|4|
|Finland<br>13|Finland|4|
|New Zealand<br>29|New Zealand|4|
||Bangladesh<br>|3<br>|
||Egypt<br>|3<br>|
||Chile|3|
|Australia<br>282|Austalia|2|
||Kazakhstan|2|
||Russian Federation|2|
||Trinidad and Tobago<br>|2|
||Ukraine|2|
||_l Others_|47|
|TOTAL<br>1148|TOTAL|1141|

Source: GATT (1993) International Trade and the Trading System: Report by the Director General 1992-1993 (Geneva: GATI).
lower scores at baseline. The OLS results show that program communities managed to bridge the initial disadvantages in underweight: the estimated effect is of about 7.6 pct points in moderate underweight. It is interesting to note that over the seven years between the baseline and follow-up surveys, the estimates confirm 2.5 pct point positive trend for stunting and a 1.9 pct negative trend for underweight. The differences in the underlying trends for underweight and stunting are important. Underweight is a combination of short and long term nutritional outcomes. The indicator is nonetheless sensitive to sudden weight losses by children: these changes are more visible to mothers, as children who have recently faced a decrease in food intakes or a serious illness. On the other hand, stunting typically reflect a poor diet quality and chronic illnesses (Frongillo et al 1997). Improvements in underweight might not be mirrored by improvements in stunting if the former are not accompanied by cumulative improvements in income and diet quality and consumption. We conjecture that the negative trend could be accounted for, by lack of significant economic growth throughout the period coupled with a sequence of severe weather shocks (in 2000 and 2004) and a political crisis in 2002. The positive and significant effects on both stunting and height/age z-scores for Seecaline communities simply compensated in size for this negative trend that would have occurred in the absence of the program. In short, the availability of the nutrition program helped improve short term nutritional outcomes and helped protect participating communities from worsening their long term outcomes.

Our preferred results based on community fixed-effects regressions with weighting on the inverse of a propensity score confirm the basic OLS results (Table 2). Overall we find significant effect of the program on all nutritional outcomes. When we compare the OLS estimates with the DD we find that weighting reduces the program effect on weight-for-age by 0.069 SD (32% less than the OLS effect), while increasing the magnitude of the trend. There is virtually no change in height-for-age and stunting due to weighting. This is indicative that our weighting strategy improves the selection of comparable control villages. Change over time in the weight-for-age is more susceptible to higher initial malnutrition rates, which can recover partially even in the absence of the program. Our interpretation is that as a cumulative stock variable height is less susceptible to such mean reverting fluctuations and therefore less sensitive to the propensity score weighting.

All our estimation specifications condition on covariates that are present in both the baseline and follow-up surveys, namely age, gender, birth order of the child, education of the 12
Table B12: Heterogeneous treatment effects on net ownership (Part A)

||Depend|ent variable: Nu|mber of bed nets o|wned|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||OLS|OLS|OLS|OLS|
|Treatment|0.286***|0.206**|0.312**|0.238**|
||(0.107)|(0.088)|(0.149)|(0.103)|
|Treatment x unemployed|-0.007||||
||(0.133)||||
||[0.012]||||
|Unemployed|-0.042<br>(0.099)||||
|Treatment x Literate||0.233|||
|||(0.142)|||
|||[0.003]|||
|Literate||0.233|||
|||(0.142)|||
|Treatment x muslim|||-0.074||
||||(0.182)||
||||[0.020]||
|Muslim|||-0.211||
||||(0.161)||
|Treatment x tigre tribe||||0.040<br>(0.160)|
|||||[0.034]|
|Tigre|-0.147|-0.102|-0.105|-0.132|
||(0.104)|(0.100)|(0.102)|(0.137)|
|Observations|1408|1496|1496|1496|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. One observation per household. The dependent variable is the number of bed nets owned by the household. Columns (1)-(4) report the difference between treatment and control groups using OLS regression (model 2) and the coefficients on interactions between the treatment status and unemployment status (Column 1), literacy status (Column 2), religion (Column 3) and tribe (Column 4). Standard errors clustered at village level are reported in parentheses. All specifications include controls for gender, age, education and marriage status of the respondent, household size, tribe and religion, information about access to water, dwelling characteristics, sub-zone dummies and village characteristics (size and share of women living in the village). The p-value for the F-test of _treatment_ + _tinteraction_ = 0 is reported in square brackets.

28
_Figure 3. Female and male poverty rates by age group_ <!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>0‐4 5‐9 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65‐69 70‐74<br>Women Men<br><!-- End of picture text --> Note: Total sample 89 countries Source: WB Staff’s calculations based on GMD.

More importantly, introducing age as an element when measuring poverty starts to reveal important gender differences. Poverty decreases with age, especially between childhood and adulthood but it decreases at different ages for women and men. Poverty rates decrease sharply for women and men as they reach adulthood and tend to stabilize after 50 years of age. By age 25 (and up to age 34), women are two percentage points poorer than men, a significant and sizeable difference. In this age group, there are, on average, 120 women living in poor households for every 100 men. This gap coincides with the peak productive and reproductive ages of men and women, and can be related to factors such as household formation and income generation for both men and women, and the implications of such processes on their welfare. For example, it has been well documented that women’s labor force participation goes down during their reproductive years, particularly when they have young children (see among others Aguero and Marks 2008, Goldin and Katz 2002). This pattern is consistent across regions, but gender differences in poverty rates vary widely. For the 20‐34 age group, the average gender poverty gap ranges between 0.12 percentage points in Europe and Central Asia to 7.1 percentage points in Sub‐Saharan Africa (see annex for graphs per region).

If we are able to see differences at the regional level, does the variation increase further when looking within the regions to the country‐level differences? Not much. As in the larger groupings, female and male poverty rates are similar in most countries for which we have data available. Figure 4a shows that gender differences in poverty rates are rather small when all ages are averaged and poverty rates are disaggregated only by the sex of household members. More interestingly, consistent with overall and regional patterns, gender differences in poverty rates at the country level are more evident when focusing on individuals aged 25‐34 (Figure 4b). Women in this age group are at least one percentage point poorer than men in 65 of the 89 countries included in the GMD sample.

The gap in poverty rates between men and women aged 25‐34 is wider in the poorest countries. Female poverty rates are between 5 and 20 percentage points higher than male poverty rates in 17 of the countries included in the database. These are also the countries with overall poverty rates above 35 percent. Except for Haiti, all these countries are in Sub‐Saharan Africa.

12
|**Dependent Variable**|**Coefficient**|**z-statistic**|
|---|---|---|
|**Migration out of the SISBEN sample**|||
|**_municipality level variables_**|||
|no of guerrilleros<br>-|0.00002<br>|*<br>1.49<br>-|
|no of guerrilleros2|2.24E-11|0.30|
|landmine incidents<br>-|0.562<br>|0.45<br>-|
|landmine incidents2|0.831|0.95|
|kidnappings|0.107|1.05|
|kidnappings2<br>-|0.009<br>|*<br>1.53<br>-|
|homicide rate|0.055|*<br>1.48|
|homicide rate2<br>-|0.001<br>|1.28<br>-|
|capitalisation/head|-2.58E-06|1.34<br>-|
|capitalisation/head2|4.31E-12|*<br>1.73|
|constant<br>-|0.581<br>|***<br>3.29<br>-|
|F-test violence var (p value)|0.01||
|Pseudo R2|0.17||
|Log pseudo likelihood<br>-|6,208.72<br>||
|Number of obs|11,380||

# Results-Table 1: Estimation of the determinants of drop-out out of the SISBEN sample<sup>24</sup>

_Note: The regression presents the Probit coefficients of available past violence, and welfare indicators on the probability to leave the SISBEN sample. Stars mark significance at the 10% (*), 5% (**) and 1% or lower (***) levels. Regressions are with robust standard errors, adjusted for clustering on municipality. The F-test examines joint significance of the violence variables._ A higher number of _guerrilleros_ appears to reduce emigration, while kidnappings and homicides encourage it until a certain level. People from very poor, as well as from very wealthy municipalities, as measured by the bank assets per head, seem to emigrate more.

When interpreting the results, we need to remember that various motives can prompt emigration. Displacement through violence is only one of them; improvements in wealth, ‘upgrading’ from one neighborhood to the next is also common. To the extent that our limited data reflects both motivations, we observe out-migration rising with kidnappings

> 24 The drop in observations is due to an imperfect merge with the _Familias_ dataset. We merge with the National ID number of the household head, which is not reported for all households.

25
**Table A.3: Randomization balance for dual sample providers' assignment of Unstable Angina cases**

||**(1)**|**(2)**|**(3)**<br>**Ast**|**(4)**<br>**hma outcom**|**(5)**<br>**es**|**(7)**|**(8)**|**(9)**<br>**Dysentery**|**(10)**<br>**outcomes**|
|---|---|---|---|---|---|---|---|---|---|
|||Percent|||||||Percent|
||Time spent<br>(mins)|<br>checklist<br>completed|Gave<br>diagnosis|Correct<br>diagnosis|Correct<br>treatment|Palliative<br>treatment|Unnecessary<br>treatment|Time spent<br>(mins)|<br> <br>checklist<br>completed|
|Is private|1.497***|13.190***|0.181|0.077|0.131|-0.230**|-0.017|0.302|9.109**|
||(0.483)|(3.292)|(0.118)|(0.099)|(0.113)|(0.117)|(0.075)|(0.241)|(4.119)|
|Received Unstable Angina in private|0.433|5.441|0.100|0.075|-0.194|-0.079|0.094|0.205|-0.862|
||(0.518)|(3.534)|(0.127)|(0.106)|(0.121)|(0.126)|(0.080)|(0.255)|(4.356)|
|(Is private) x<br>(Received Unstable Angina in private)|0.143|-2.996|-0.214|-0.094|0.044|0.131|-0.051|0.268|-0.604|
||(0.719)|(4.898)|(0.176)|(0.147)|(0.168)|(0.174)|(0.111)|(0.354)|(6.053)|
|Constant|1.644***|13.687***|0.307***|0.150**|0.639***|0.487***|0.873***|0.783***|17.088***|
||(0.347)|(2.367)|(0.085)|(0.071)|(0.081)|(0.084)|(0.054)|(0.172)|(2.941)|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors are in parenthesis. All regressions include district fixed effects.
different datasets: a dummy to proxy whether or not the data is at establishment (vs. firm) level and the midpoint year of the country’s sample. However, neither of these two variables returns a statistically significant coefficient.

In column (3) we include a host of other country characteristics that may potentially explain differences in export premia. These include the size of the economy, proxied by the GDP, its level of development, proxied by GDP per capita, the overall openness of the economy, and institutional characteristics. The latter are proxied using indices on the ease of doing business, the effectiveness of government and regulatory quality. We can justify the inclusion of these variables based on recent models of firm heterogeneity (Melitz 2003), where the existence of trade costs explains why only some firm export. We expect more open economies and those with better institutional characteristics to have lower trade costs. In such a case, the productivity differential between exporters and non-exporter would tend to be lower.

Including these variables in the regression shows that, once we control for a number of observable country characteristics and other attributes of the data and estimation technique, estimates from establishment level data are systematically higher than those from firm level data. The meta-regression analysis verifies the lack of a statistically significant relationship between export premia and GDP per capita reported in Figure 1. However, we find that other country-specific variables matter. For instance, we find that country size, proxied by the country’s GDP, is positively related to the export premia, although the effect is small. Furthermore, we find that in more open economies exporting firms display larger differentials vis-à-vis non exporters. As for the indicators on institutional quality we find that countries with a more business friendly environment, and countries with more effective government also show, ceteris paribus, higher exporting premia.

> 16 The baseline category is large firms with more than 500 employees.  Note that we do not include as dependent variables in the meta-analysis regressions the coefficients obtained based on all firms, we only include the coefficients obtained by size class.

12
# _3.3 Welfare Effects_

After estimating the price effects of trade controls, the next logical step is assessing the welfare implications of the price impact (if any) on farmers and urban consumers. We employ the classical static welfare analysis framework that uses measures of compensating variation or equivalent variation to estimate changes in household welfare due to price changes based on Deaton (1989): C � nn i (11) 0 ii0 ıı where CO is the compensating variation for a proportional change in price of good = �(ss −qq ) EE ( �); 0 is the initial YY ii=1 <u>Qi pi i0 i0</u> income; the budget share of consumption good, i.e. = 0 ; and = 0 iiis the value of output EEıı YY SS PP as a share of income. Note that ii is the household demand for good ii , ii is the output supply produced qq qq YY ss YY for by the household. The parameter i ) is often referred to as the net benefit ratio/NBR. It is also ii ii ii known as the net consumption ratio/NCR, which is the ratio of the net sales of commodity QQ ii SS to the household ii0 ii (ss −qq income (Minot and Dewina 2013). ii A caveat to be noted is that the above measure of welfare captures a static view of the welfare implications of price increases. Past research has found that the NBR measure can provide a reasonable approximation of the welfare effects in the short run. However, it fails to account for dynamic adjustments that a household might undertake in response to price increases over the long run. Such adjustments include changes in demand through substitution, income effects through changes in the labor market, or supply responses to the maize price changes, such as switching to other crops or investing less on agriculture inputs.

# **4. Data and Descriptive Statistics**

We secured and organized monthly price data from more than 150 markets across Malawi and its neighboring countries of Zambia, Tanzania, and Mozambique from government sources as well as from FEWS NET. The prices are at the retail level, except for Tanzania, wherein we have data only on wholesale prices. The starting time for the price data varies by country, but we have consistent data for all four 10
# FIGURE S: THE NORMATIVE AND REQUIREMENTS MODEL

<!-- Start of picture text --> Fix Targets: g, W, q, res.<br>Goods Balance of money<br>Marle Payments Market<br>Cb AFt H<br>Foreign-Asset Central Bank<br>Market Balance Sheet<br>f ---- F*g{ I CRt <<br>Public Sector<br>Budget S<br>[It] | _ CR_ Central<br>,c-CRg< Bank <- CRd < Banking System<br>Credit Balance Sheet<br>residual CRO Market<br>t<br>B9<br>|estic Bond Bp <C<br>Market Secto;r-3<br><!-- End of picture text -->

<!-- Start of picture text --> 30<br><!-- End of picture text -->
**<u>Table 5: Determinants of treatment for preventable diseases in generation III</u>**

||<br>**Entire**|<br>**Dummy:**<br>**sample**|<br>**Treatment f**<br>**< 6 y**|**or preventab**<br>**ears**|**le disease**<br>**> 7**|**years**|
|---|---|---|---|---|---|---|
|Female|0.203|0.074|0.178|-0.370|0.445***|0.496**|
||(0.095)|(0.180)|(0.423)|(0.257)|(0.044)|(0.082)|
|Female*Mother’s father died after 1994 or alive|-0.041**|-0.050**|0.265|0.769|-0.106|-0.128|
||(0.007)|(0.009)|(0.132)|(1.047)|(0.044)|(0.071)|
|Female*Education gap mother-father||0.012*||-0.027||0.011|
|||(0.004)||(0.009)||(0.004)|
|Female*Share of mother’s gifts at marriage||0.197||0.769||0.456|
|||(0.343)||(2.211)||(0.594)|
|Reform*Female|-0.646**|-0.617*|-0.617|-0.148|-0.179*|-0.221|
||(0.095)|(0.180)|(0.423)|(0.257)|(0.044)|(0.082)|
|Reform*Female*Mother’s father died after 1994/|-0.236***|-0.221***|-1.047**|-1.407|-0.060|-0.074|
|alive|||||||
||(0.007)|(0.009)|(0.132)|(1.047)|(0.044)|(0.071)|
|Reform*Female*Education gap mother-father||-0.015*||-0.054**||-0.020**|
|||(0.004)||(0.009)||(0.004)|
|Reform*Female*Share of mother’s gifts at||0.048||0.577||-0.814|
|marriage|||||||
|||(0.343)||(2.211)||(0.594)|
|Observations|1,399|1,399|275|275|1,124|1,124|
|R-squared|0.651|0.654|0.882|0.889|0.680|0.683|

_Note:_ All regressions include household fixed effects as well gender and state specific year of birth fixed effects. Figures in brackets are robust standard errors clustered by state. * significant at 10%; ** significant at 5%; *** significant at 1%.

20