rafmacalaba/datause-encoder-data huggingface dataset:
TABLE A11: Impact of text messages on individual intermediary outcomes: preventive health

||**ITT**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Health_**||||||
|**Health index**|0,07|( 0,04)|0,07|0,00|2.512|
|Insist on brushing teeth|0,01|( 0,01)|0,41|0,02|2.512|
|Ensure a healthy diet|0,03|( 0,01)|0,02|0,06|2.512|
|Keep child away from stove|-0,00|( 0,00)|0,93|0,00|2.512|
|Ensure child’s good hygiene|-0,01|( 0,02)|0,46|0,21|2.512|
|Wash child’s clothes properly|0,01|( 0,02)|0,60|0,12|2.512|
|Avoid giving coffee|-0,00|( 0,00)|0,32|0,00|2.512|
|Make child wash hands|0,00|( 0,02)|0,86|0,27|2.512|
|Smoke at a distance from child|-0,00|( 0,00)|0,74|0,00|2.512|
|Do not give child unhealthy snacks|0,00|( 0,01)|0,49|0,01|2.512|
|Cover fresh food|0,00|( 0,02)|0,78|0,13|2.512|
|Teach child to chew slowly|-0,00|( 0,00)|0,61|0,00|2.512|
|Prevent child from catching the fu|0,00|( 0,01)|0,80|0,06|2.512|
|Boil or chlorinate the water|0,01|( 0,01)|0,13|0,02|2.512|
|Ensure that child vaccines are up-to-date|0,02|( 0,01)|0,23|0,07|2.512|
|Use a mosquito net|-0,00|( 0,01)|0,71|0,01|2.512|
|Insist child wears shoes|0,01|( 0,01)|0,56|0,03|2.512|
|Make sure child eats regularly|0,00|( 0,00)|0,25|0,00|2.512|
|Keep child away from dangerous products|0,00|( 0,00)|0,75|0,00|2.512|
|Show child affection|-0,01|( 0,00)|0,09|0,01|2.512|
|Other|-0,00|( 0,02)|0,97|0,17|2.512|

Every row corresponds to a separate estimation (equation 1) and shows the ITT estimates on the aggregate health index or on individual questions measuring whether the caregiver reported a given practice when asked how to avoid their young child getting sick. The health index is calculated based on the number of different preventive health practices. The index is standardized using the mean and standard deviation of the control households. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variable for a set of households surveyed in 2016, as well as village fixed effects. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01.

59
- utilizing alternative samples, such as a balanced panel to avoid attrition (see Appendix 2), or an unbalanced panel that includes observations from years 2000 and 2001.

# **V. CONCLUSIONS**

Utilizing Croatia’s corporate tax reform in 2001 as a natural experiment, this study suggests a significant effect of corporate taxation on capital structure by demonstrating that lower taxes in Croatia resulted in lower firm leverage. Overall, the corporate tax reform in Croatia provides strong evidence for a tax effect on firms’ financing policy. These findings are consistent with the trade-off theory of capital structure, which suggests that lower taxes decrease the incentive to hold debt due to decreasing interest tax deductibility. Notably, the current study focuses on the sign and the significance of the effect from tax reform without making any further claims on the magnitude of the change in debt/equity ratios, because the firm- and time-varying adjustment speed of capital structures complicates the exact identification and economic interpretation of a magnitude (Nivorozhkin, 2005).

From a policy perspective, it could be argued that the reduction in corporate debt after the corporate tax reform could have impacted firms’ access to finance. Just like other transition economies, Croatia has a bank-based financial system where low levels of debt financing are typically a supply-side phenomenon given the preferential bank financing towards state-owned enterprises and large firms. By reducing the optimal debt level, the tax reform may have - indirectly- enhanced SMEs’ access to finance. This was important since access to finance was one of the major obstacles for Croatian firms. More specifically, based on surveys from the European Bank of Reconstruction and Development (EBRD), the portion of private SMEs in Croatia that reported obtaining financing from banks as a major obstacle in their operations was reduced from 48% in 1999 to 28% in 2005.<sup>16</sup> Besides access to finance, the observed postreform reduction in the average of debt loading for the average SME may also have contributed to lowering the probability for default on its debt. In fact, from 2000 to 2004, the bank ‘nonperforming loans to total loans’ ratio in Croatia’s banking sector was steadily reduced from 9.5% to 4.6%.17

> 16 Source: EBRD - Business Environment and Enterprise Performance Survey (BEEPS) for 1999 and 2005.

> 17 Source: IMF - Global Financial Stability Report (2005).

10
**Table 15.** Effects on Children. IFLS 2007.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Weight<br>for height<br>z-score|<br>Height<br>for age<br>z-score|Weight<br>for age<br>z-score|Hours at<br>school<br>weekly|Cognitive<br>test score|Hours<br>work for<br>wage<br>weekly|Hours<br>work<br>family<br>business<br>weekly|Hours<br>work<br>househol<br>d weekly|
|**OLS**|||||||||
|Migrant mother|-0.008|0.057|0.017|-3.699**|-0.014|0.063|0.382|0.144|
||(0.034)|(0.113)|(0.108)|(1.468)|(0.023)|(0.437)|(0.655)|(0.730)|
|Age|0.083***|-0.018*|-0.020**|1.260***|0.022***|0.142|0.222***|0.462***|
||(0.003)|(0.010)|(0.009)|(0.252)|(0.003)|(0.094)|(0.069)|(0.108)|
|Female|0.035|-0.307***|<br>-0.038|0.939|0.032*|0.222|0.640|1.616***|
||(0.032)|(0.098)|(0.087)|(1.225)|(0.019)|(0.433)|(0.662)|(0.543)|
|Controls|y|y|y|y|y|y|y|y|
|Observations|610|611|610|348|439|404|404|404|
|R<sup>2</sup>|0.546|0.042|0.033|0.110|0.173|0.013|0.049|0.082|
|**TSLS**|||||||||
|Migrant mother|-0.139|0.511|-0.070|-15.505**|<br>0.010|0.605|0.203|6.406**|
||(0.106)|(0.373)|(0.370)|(6.409)|(0.078)|(1.199)|(1.932)|(3.177)|
|Age|0.084***|-0.019*|-0.014|1.174***|0.022***|0.151|0.226***|0.474***|
||(0.003)|(0.012)|(0.010)|(0.279)|(0.003)|(0.100)|(0.072)|(0.121)|
|Female|0.027|-0.327***|<br>-0.079|1.789|0.036*|0.183|0.713|1.367**|
||(0.034)|(0.104)|(0.087)|(1.370)|(0.020)|(0.517)|(0.649)|(0.589)|
|Controls|y|y|y|y|y|y|y|y|
|Observations|566|567|566|324|404|378|378|378|
|R-squared|0.537|0.009|0.027|-0.081|0.166|0.009|0.049|-0.202|
|Uncentered R<sup>2</sup>|0.769|0.0334|0.0595|0.710|0.923|0.0200|0.0741|0.00585|
|_F_statistic|22.57|22.59|22.57|10.62|21.23|11.49|11.49|11.49|

**Notes:** Controls are size of household, rural household, mother’s years of education, log total household expenditure. Standard errors robust to household level clustering in parenthesis. *** p <0.01, ** p <0.05, * p <0.1.

29
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39
Table 5: Subgroup Peer Effects from Random Assignment to Dormitories

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Own HS graduation|0.327|0.327|0.369|0.322|
|test score|(0.016)|(0.016)|(0.017)|(0.017)|
|Mean dorm HS graduation test|0.203|0.162|||
|score for own race|(0.059)|(0.083)|||
|Mean dorm HS graduation test|-0.007|-0.035|||
|score for other races|(0.055)|(0.091)|||
|Mean dorm HS graduation test|||0.050|0.099|
|score for own faculty|||(0.045)|(0.048)|
|Mean dorm HS graduation test|||0.198|0.190|
|score for other faculties|||(0.062)|(0.083)|
|Adjusted _R_<sup>2</sup>|0.219|0.243|0.214|0.249|
|# students|3068|3068|3068|3068|
|# dormitory-year clusters|30|30|30|30|

_Notes:_ Table 5 reports results from estimating equation 3 using race subgroups (columns 1-2) and faculty subgroups (columns 3- 4). “Faculty” refers to colleges/schools within the university such as commerce and science. Columns 2 and 4 include dormitory fixed effects and control for students’ gender, language, nationality and race. The sample is all dormitory students in the random assignment period with non-missing high school graduation test scores. Standard errors in parentheses are from 1000 bootstrap replications clustering at the dormitory-year level.

The linear-in-means model can be augmented to allow the effect of residential peers to vary within and across sub-dormitory groups. Specifically, I explore within- and across-race peer effects by estimating: For student _i_ of race _r_ in dormitory _d_ , _HSrd_ and _HS−rd_ denote the mean high school graduation test scores for other students in dormitory _d_ of, respectively, race _r_ and all other race ˆ ˆ groups. _β_ 2 and _β_ 3 equal 0 _._ 16 and _−_ 0 _._ 04 respectively (table 5, column 2). The difference strongly suggests that peer effects operate primarily within race groups but it is quite imprecisely estimated (bootstrap _p_ -value 0.110). I interpret this as evidence that spatial proximity does not automatically generate peer effects. Instead, peer groups are formed through a combination of spatial proximity and proximity along other dimensions such as race, which remains highly salient in South Africa.<sup>33</sup> This indicates that interaction patterns by students may mediate residential peer effects, meaning that estimates are not policy-invariant.

> 0.186 using dormitory clusters.

> 33I find a similar result using language instead of race to define subgroups. This pattern could also arise if students sort into racially homogeneous geographic units by choosing rooms within their assigned dormitories. As I do not observe roommate assignments, I cannot test this mechanism.

23
international trade costs, changes in the geographic concentration of industries can go either way, depending on the relative magnitude of changes in these costs. Since domestic transport and international trade costs are partly determined by the same underlying forces – international trade still requires domestic transportation at some point – any analysis of the changes in transport costs on the geographic concentration of industries needs to control for changes in international trade costs broadly defined. We will control for these costs using measures of trade exposure – both for imports and exports and for different groups of countries – though these are only proxies for international trade costs since they are an amalgam of transport costs, foreign productivity, and other forces.

Second, changes in the trading environment of one sector, intermediates in this case, have a direct impact on the geographic structure of the other sector, final in this case. As the mass of imported intermediates increases, holding trade and transport costs fixed, the final sector disperses more although it is not directly but only indirectly affected (through the input-output links). This suggests that we need to control for general equilibrium effects that are channeled through the impacts of changes in the geographic structure of some sectors on the other sectors. We will control for these effects novel measures equilibrium by constructing microgeographic that proxy access to potential local inputs and outputs.

Last, a large set of parameters that are fairly stable over time influence the degree of geographic concentration across industries in any cross section. To control for these factors, we will use panel data and focus on changes and not on levels. Doing so obviously does not solve the problem of industry-specific time-varying unobservables, but it is a step forward compared to purely cross-sectional analyses of the determinants of geographic concentration that most of the literature (e.g., Rosenthal and Strange, 2001; Ellison _et al._ , 2010) has used until now (yet, see Storeygard, 2016, for an approach using time-varying proxies for transport costs).

# **3 Measurement and descriptive evidence**

Our analysis requires two key pieces of information: (i) a measure of geographic concentration of industries; and (ii) a measure of transport costs of industries. To control for unobserved factors that drive cross-sectional differences in geographic concentration and transport costs, we also require a panel dimension. We now discuss the data and the procedures that allow us to construct these key empirical measures. We also take a first descriptive look at the data.

## **.1 3 Geographic concentration**

We draw on Statistics Canada’s Annual Survey of Manufacturers (asm) Longitudinal Microdata file from 1990 to 2009. This file contains between 32,000 and 53,000 manufacturing plants per year, covering 257 naics 6-digit industries. For every plant, we have information about: its 6
36

|**Secondary**<br>**Outcome**|**Handwashing with soap/ash**|Share of adult household members who<br>were observed washing their hands with<br>soap/ash at any juncture, measured via<br>structured observations.<br>The considered junctures are the<br>following:<br>Before obtaining water from a wide-<br>mouthed storage container;<br>Before cutting or preparing food;<br>Before serving food;<br>Before eating;<br>Before feeding child under 5;<br>Before breastfeeding child;<br>After defecation;<br>After toileting;<br>After cleaningchildpost- toileting|
|---|---|---|
|**Secondary**<br>**Outcome**|**School attendance**|Number of days child aged 6 to 18 years<br>old attended school in past week, based<br>on responses to “How many days has this<br>child attended school in the past week?”<br>Children who were not enrolled in school<br>were coded as zero.|
|**Secondary**<br>**Outcome**|**Water point has water**|1 if water point provides water<br>0 Otherwise<br>(Locked water points during survey are<br>not considered)|
|**Secondary**<br>**Outcome**|**WP Coliforms MPN/100mL**|Water quality at point of collection (village<br>water source) is Most Probable Number<br>(MPN) in 100 mL as defined for<br>Aquagenx CBT EC+TC MPN water<br>quality testing kits and follow WHO<br>standards. For nondetects, we substitute<br>half the lower detection limit.|
|**Secondary**<br>**Outcome**|**HH Coliforms MPN/100mL**|Water quality at point of use (Drinking<br>water stored in the household) is Most<br>Probable Number (MPN) in 100 mL as<br>defined forAquagenx CBT EC+TC MPN<br>water quality testing kits and follows WHO<br>standards. For nondetects, we substitute<br>half the lower detection limit.|
|**Secondary**<br>**Outcome**|**Weight for age**|Weight for age z score for children under<br>5.|
# Inflation and Exchange Rate Pass-Through

Jongrim Ha, M. Marc Stocker, and Hakan Yilmazkuday<sup>*</sup>

## JEL Classification: E31; E42; E52; F31

Keywords: Inflation; foreign exchange; monetary policy; exchange rate pass through.

> * Ha: World Bank, Development Prospects Group; jongrimha@worldbank.org. Stocker: World Bank, Development Prospects Group; mstocker1@worldbank.org. Yilmazkuday: Florida International University; skuday2@gmail.com. We thank Carlos Arteta, Menzie Chinn, Zolt Darvas, Alistair Dieppe, Graham Hacche, Gene Kindberg-Hanlon, Ergys Islamaj, Sergiy Kasyanenko Ayhan Kose, Franziska Ohnsorge, Christopher Towe, and seminar participants at the World Bank for their comments. Heqing Zhao and Xingyue Wang provided excellent research assistance. We gratefully acknowledge financial support from the PHRD fund. The findings, interpretations and conclusions expressed in this paper will be entirely those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
||obtaining<br>construction<br>permit,<br>obtaining<br>import license, obtaining operating license,<br>and inspections or meetings with tax officials.<br>Based on this information, the ES compiles a<br>measure of the incidence of petty corruption<br>which is dummy variable equal to 1 if a firm<br>experienced a bribe payment or request in one<br>or more of the six transactions listed above and<br>0 otherwise. This incidence of petty corruption<br>measure is available for only those firms that<br>solicited the public services listed above or<br>were inspected by tax officials.<br>We use average value of the dummy variable<br>obtained above, where the average is taken<br>over all firms in the country-industry cell other<br>than the firm in questions. Industry grouping is<br>defined at the 2-digit ISIC Rev. 3.1 level.<br>There are 7 industries in our sample.<br>Source: Enterprise Surveys.<br>|
|---|---|
||www.enterprisesurveys.org|
|Petty Corruption Depth (CI avg.)|The ES asked firms if they were requested to<br>pay a bribe or expected to do so in soliciting<br>the following services: obtaining electricity<br>connection,<br>obtaining<br>water<br>connection,<br>obtaining<br>construction<br>permit,<br>obtaining<br>import license, obtaining operating license,<br>and inspections or meetings with tax officials.<br>Based on this information, the ES compiles a<br>measure of the depth of petty corruption which<br>equals the proportion of the 6 transactions<br>listed above for which the firm experienced a<br>bribe payment or request for one. This<br>corruption measure is available for only those<br>firms that solicited the public services listed<br>above or were inspected by tax officials.<br>Following the ES methodology, a refusal to<br>answer a question on whether bribes were<br>requested or expected is considered as an<br>affirmative answer.<br>We use average value of the petty corruption<br>depth variable as defined above, where the<br>average is taken over all firms in the country-<br>industry cell other than the firm in questions.<br>Industry grouping is defined at the 2-digit ISIC|

47
<!-- Start of picture text --> Deviation (%)<br>1.2<br>1.0 DEV growth<br>0.8<br>G4 growth<br>0.6<br>0.4<br>G4 ST rates<br>0.2<br>0.0<br>G4 yield curve<br>-0.2 VIX<br>-0.4<br>-0.6<br>-0.8<br>1 2 3 4 5 6 7 8 9 10<br>Periods<br><!-- End of picture text --> Figure 2: Impulse response function for aggregate developing country gross inflows for a positive one standard deviation shock in the transmission channels and endogenous controls (GDP). Increases in interest rates and the VIX are associated with lower gross inflows, while increases in growth are associated with higher inflows. Shocks appear to die out after around 10 quarters.

# (so the final VAR specification is six-dimensional).

Standard model selection metrics suggest between one and four lags; we adopt two ( _k_ = 2) as a middle-ground compromise.<sup>41</sup> Formal Johansen tests reject the presence of cointegrating relationships in the system, which supports the estimation of the model as an unrestricted VAR. Our Cholesky ordering assumes the following: G4 GDP growth, developing GDP growth, developing capital inflows, the VIX, G4 short-term interest rates, and the yield curve (which potentially responds to all other variables in real time).

To provide some sense of the overall performance of the VAR, Figure 2 presents the impulse response functions for gross inflows (as a share of GDP) for a positive one standard deviation shock in the transmission channels and endogenous controls in (2). The predicted deviations appear to be consistent with what one would intuitively expect; for example, a shock to the yield curve or VIX is associated with declines in inflows, while GDP growth is associated with increases. In general, the shocks appear to die out after about 10 quarters.

Our benchmark is a no-change scenario that maintains the _status quo_ . We use the VAR to generate a scenario where unconventional monetary policy normalizes over the course of the following three years. The paths are detailed in the upper panel of Table 7. Our policy normalization scenario seeks to match growth paths for high-income and developing economies consistent with the World Bank’s _Global Economic Prospects_ forecast for the global economy (World Bank 2014). This involves three assumptions for the 2014–16 period: a gradual start to normalization of long-term

> 41Specifically, Hannan and Quinn Information Criterion and the Bayesian Information Criterion suggest one lag, Final Prediction Error and Likelihood Ratio statistics recommend two, and the Akaike Information Criterion points to four. In the two-lag model, all significant eigenvalues are less than one.

29