rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1: Summary statistics**

## **Panel A: Macro variables**

|**VARIABLES**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|ΔRegulatory Capital/RWA|112|1.516|4.450|-12.570|16.150|
|ΔTier1/RWA|92|1.854|4.570|-10.704|15.300|
|ΔEquity/TA|110|1.116|2.869|-8.775|9.661|
|ΔTier1Stringency|109|-0.174|1.820|-4.000|5.000|
|Banking crisis|159|0.164|0.371|0.000|1.000|
|ln(GDP per capita)|121|8.729|1.521|5.443|11.858|
|Inflation|121|3.977|3.253|-2.425|14.470|
|ln(population)|123|15.823|2.011|10.410|20.986|
|Regulatory Quality|122|0.243|0.890|-2.071|1.877|

## **Panel B: Correlations**

|ΔRegulatory Capital/RWA|1|||||||||
|---|---|---|---|---|---|---|---|---|---|
|Δ Tier 1/RWA|0.646*|1||||||||
|ΔEquity/TA|0.349*|0.220*|1|||||||
|ΔTier1Stringency|0.097|-0.124|0.098|1||||||
|Banking crisis|0.239*|0.405*|0.151|-0.116|1|||||
|ln(GDP per capita)|0.364*|0.447*|-0.019|0.095|0.416*|1||||
|Inflation|-0.109|-0.225*|-0.024|0.251*|-0.123|-0.392*|1|||
|ln(population)|-0.219*|-0.132|-0.119|0.058|0.114|-0.172*|0.292*|1||
|Regulatory Quality|0.373*|0.375*|0.016|0.134|0.388*|0.828*|-0.426*|-0.074|1|

* significance at the 10% level.

## **Panel C: Bank level variables**

|**VARIABLES**|**Obs**|**Mean**|**Std. Dev.**|**p25**|**p50**|**p75**|
|---|---|---|---|---|---|---|
|Z-score [4 year]|153,353|66.650|97.006|14.723|32.502|72.543|
|NPL over Total Loans|145,983|2.113|2.890|0.872|1.408|2.264|
|ln(Assets in 2012 USD)|153,353|6.018|1.839|4.714|5.565|6.860|
|Regulatory Capital/RWA|153,353|17.466|8.877|12.310|15.010|19.400|
|Tier 1/RWA|137,163|15.983|8.694|10.990|13.600|17.900|
|RWA/TA|127,062|66.418|14.265|57.525|67.287|76.471|
|Tier 1/Regulatory capital|134,748|90.502|8.314|89.710|92.331|94.964|
|Equity/TA|153,325|10.675|5.235|7.937|9.585|12.000|
|Loans/TA|153,353|61.505|16.479|52.047|63.636|73.333|
|Short-term funding/TA|153,353|6.037|10.098|0.000|2.075|7.595|
|Liquid assets/TA|153,353|12.365|11.999|4.594|8.451|15.623|
|ROA|153,353|1.025|1.629|0.428|1.099|1.716|
|Tier 1 stringency|81,845|7.077|1.268|6.000|8.000|8.000|

The results from the regressions specified in (1a) and (1b) above are reported in Table 2. In column (1) we provide evidence that banking crisis is weakly related to increases in total regulatory capital ratios.

In column (2) we show that there is a positive and statistically significant association between countries 27
# **Figure 9**

# **Investment Ratio to Base: Permanent Increase in Foreign Aid, with and without Borrowing Constraints**

<!-- Start of picture text -->
1.35<br>1.3<br>1.25<br>1.2<br>1.15<br>1.1<br>AID-P AID-P & G adj AID-P & G adj & myopic AID-P & I adj & myopic<br>1.05<br>1<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>investment ratio<br><!-- End of picture text -->

_Source: Authors’ calculations._

# **Figure 10 Consumption Ratio to Base: Permanent Increase in Foreign Aid, with and without Borrowing Constraints**

<!-- Start of picture text -->
1.2<br>AID-P AID-P & G adj AID-P & G adj & myopic AID-P & I adj & myopic<br>1.15<br>1.1<br>1.05<br>1<br>0.95<br>0.9<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>consumption ratio<br><!-- End of picture text -->

_Source: Authors’ calculations._ If aid all goes to investment so that supply responds directly over time, the real exchange rate appreciation is less at 7.5 percent initially (see figure 8). Relative to the reference path, investment rises by 11.0 percent initially and eventually to 22.5 percent at the end of 40 years (see figure 9). Output and consumption both increase eventually by about 10 percent of the reference level (see figure 10). However, all the levels regarding investment, consumption, and output are below simulations 1 (permanent aid, no borrowing constraint) and 3 (permanent aid, borrowing constraint). The ideal case is simulation 1 when investment is only undertaken strictly on a merit basis (as in cost– benefit calculations) and the current account balance is part of the intertemporal 25
|**Table 13a. Impact of WTO Acce**<br>|**ssion on**<br>Overall<br>_change from_|**Federal Ok**<br>_base year, %_|**rugs**<br>||||||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|2.4|3.7|2.8|2.7|2.6|1.8|2.5|3.7|
|sigma=1|3.8|4.8|3.8|3.8|3.8|2.7|3.1|4.5|
|sigma=4|4.3|5.4|4.2|4.3|4.6|3.6|3.6|4.9|
|sigma=+inf|4.5|5.6|4.4|4.5|4.9|3.9|3.8|5.1|
|ev/C|8.1|10.6|7.0|8.1|9.1|9.6|6.4|10.0|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|3.5|4.7|3.5|2.5|3.3|1.7|1.7|3.6|
|Regional exports (% change)|1.8|2.4|2.4|1.6|1.9|0.6|1.3|2.3|
|Real exchange rate (% change)|2.6|3.3|2.8|2.6|2.7|1.9|1.7|2.9|
|International exports (% change)|8.8|12.3|16.2|10.2|12.3|2.7|8.6|9.6|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|5.1|6.7|5.0|5.0|5.4|4.2|4.2|6.3|
|Skilled labor|5.1|6.8|4.9|5.0|5.7|4.2|4.3|6.3|
|Aggregate capital earnings|3.9|4.6|4.0|3.9|3.9|3.2|3.0|4.1|
|Regional mobile capital|7.6|11.0|9.1|7.3|8.2|4.9|6.4|8.8|
|Energy sector resources|3.0|6.0|15.6|4.9|5.2|1.4|7.7|8.5|
|Specific capital in domestic firms|-25.7|-26.0|-27.7|-27.4|-25.8|-22.7|-22.3|-25.5|
|Specific capital in multinational firms|102.7|66.7|73.7|126.2|147.0|184.7|164.8|121.0|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|2.3|3.0|2.2|1.7|2.6|2.1|2.2|2.4|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|2.7|3.3|2.8|2.1|2.7|2.8|2.5|2.8|

48
For students who remained with the same teacher, there is a large and signi fi cant effect of negative shocks to the teacher on learning. This result is robust across speci fi cations. It is also robust to a number of identi fi cation problems common to estimating the impact of school inputs on learning, which we discuss below. However, the results obtained on the sample of students who remained with the same teacher do not extend to those who switched teachers during the two years of the study. We suggest two reasons for this fi nding. The fi rst is selective matching of students and teachers, and the second is higher precautionary educational spending among households whose children switched teachers. Although we are able to show that the differential impact among the movers and non-movers did not arise from differences in the sample of students, we are unable to distinguish between these two suggested channels.

The remainder of the paper is organized as follows. Section 2 reviews the literature. Section 3 outlines the theory. Section 4 presents the empirical speci fi cation and econometric concerns. Section 5 discusses the data used and section 6 presents the results and robustness tests for the sample of children who stayed with the same teacher. Section 7 focuses on the sample of children who switched teachers and section 8 concludes with some caveats and a discussion of the policy implications.

# 2 Literature Review

Interest in teacher attributes on student learning has recently emerged in the educational production function literature. Papers using analysis of variance techniques have shown that the variation in test scores explained by teachers is substantial. Hanushek, Kain and Rivkin (1998), using data from Tennessee schools, fi nd evidence of signi fi cant teacher effects. Park and Hannum (2002), using student-teacher matched data from China, fi nd that variation due to teacher effects explains about 25 percent of variation in test scores. More traditional regression-based studies also validate this fi nding. Rockoff (2004) using a 12-year panel of teacher-student data from two school districts in New Jersey fi nds signi fi cant teacher fi xed effects. A one standard deviation change in the teacher fi xed effect (unobserved quality) is associated with gains in Mathematics and reading of 0.26 and 0.16 standard deviations respectively. Less is known about the speci fi c attributes of teachers that affect student learning. Limited evidence on teacher experience and training is provided by Rockoff (2004) and Angrist and Lavy (2001), who fi nd that both experience and training have a positive impact on learning achievement.

Closer to the results presented here are the studies by Jacobson (1989) and Ehrenberg and others (1991). Jacobson (1989) describes an interesting policy experiment in which a pot of money 4
2018, World Bank, 2017). In urban areas, women rely on low skilled, low paying service jobs, including cleaning, washing, and childcare (Federal Government of Somalia, 2018).

Women’s voice in society and their ability to exercise leadership are limited. They remained virtually excluded from the political and judicial structures that emerged in different parts of the country after the collapse of the state in 1991 (Gardener, 2007; Mahmood, 2018; Parke et al., 2017). Recent reforms have addressed women’s representation in a number of ways including through the introduction of quotas in national parliamentary elections (United Nations Development Program (UNDP), 2019). Nevertheless, the politicization of clan identity and stigma attached to women entering government and politics remain barriers to women’s political participation and adoption of leadership roles (Parke et al., 2017; UN Women, 2018).

Permeating women’s lives are high levels of gender-based violence (GBV). Conflictrelated violence against women and girls has been extensively documented (Perrin et al., 2019; UN Women, 2018; United Nations, 2020). GBV is particularly prevalent in IDP settlements, among minority clans, and in conflict zones where all parties have reportedly perpetrated sexual abuse and violence (Perrin et al., 2019; United Kingdom: Home Office, 2017). Increasing rates of violence in the home have been linked to changing gender roles as men experience changing dynamics within the household and more women move into breadwinner positions (OCHA, 2019). For women, heightened risk of sexual and gender-based violence is exacerbated by overcrowded makeshift facilities without adequate safety and security measures – from lighting to safe water, and sanitation and hygiene facilities with privacy. Child marriage, another form of GBV is common throughout the country, with 35 percent of women aged 20 to 24 married before the age of 18.<sup>5</sup> An estimated 98 percent of Somali women have undergone female genital mutilation (United Nations General Assembly, 2018).

> 5 World Bank’s Gender Data Portal based on Somalia Demographic and Health Survey (2020).

9
ownership in some sectors which are perceived strategic. The following section justifies the choice of variables describing the business environment.

# _Measuring Indicators of Business Environment_

To quantify market competition faced by enterprises, we will attempt to use Herfindahl index (H-index) based on sales in 2-digit industries of which the dataset has 37. Sleuwaegen and Dehandschutter (1986) show that performance of H-index is superior to that of concentration ratios (share of N largest firms in the market) when sectors/markets with high concentration are common. In addition, the H-index would be a more accurate measure of concentration in cases when a number of large enterprises are left out of the sample. With this in mind, I calculated H-index for 37 sectors based on sales/revenues reported by 7,200 enterprises contained in 1997 sample. Although covering a broad base of enterprises (over 60 percent of enterprises reporting to Lithuanian Department of Statistics) and sectors in Lithuania, this measure is not without problems. First of all, it fails to account for the share of imported goods in the market and thus the competition faced by domestic producers from foreign firns exporting to Lithuanian markets. Not accounting for this effect is bound to overstate the market share of each enterprise and understate its effect on efficiency and performance. This bias will be greater the larger the share of imported goods in total market for that particular good.

Second, market share and hence market concentration can be endogenous to performance. It can be argued, that being determined by central planners decisions, market structure at initial stages of Transition can be treated as exogenous. Yet it would be less accurate to do so at a later stage when market-based incentives and mechanisms worked their way to improved performnance and subsequently to market share of enterprises. However in our case, the extent of potential endogeneity of H-index will be significantly reduced in the final sub-sample of 618 enterprises used in the analysis (see below for the selection criteria) because of the large number of enterprises removed from the initial sample of 7,200.

There are at least two reasons for using lagged (as opposed to contemporaneous) values of subsidies as a dependent variable in the regressions to account for state support. First of all, using lagged value of subsidies is likely to eliminate any potential 24
corresponding elements, which pointed to 27 behaviors. Like the current version of _Teach_ , the behaviors are characterized as low, medium, or high, based on the evidence collected during the observation. These behavior scores are translated into a 5-point scale that quantifies teaching practices as captured in one 20-minute observation (as discussed above). After the 20-minute segment ends, the observer spends 15 minutes scoring the segment. Observations were conducted in person by two trained observers, who scored each segment independently. For each selected school, one grade 4 classroom was randomly selected to be observed during their mathematics or language class. For the analysis of the overall score and areas, we randomly selected one of the observers in the classroom rather than averaging the results from each observer, to obtain integer values.

To conduct the study, local observers were recruited through the RCons survey firm to collect data. None of them reported having had previous experience with classroom observation tools. Observers participated in a four-day training that required them to practice coding using recorded videos, participate in a live field visit, and pass a certification exam. The exam required them to code three 20-minute classroom observation segments in accordance with the manual’s rubric. After watching the 20-minute segment, observers were given 15 minutes to score the video. To pass the exam, they must be accurate within one of the master codes in eight of the 10 elements for each segment. Of the 53 observers who were trained and took the exam, 51 passed the exam. Table 1 displays the agreements between observers and master codes developed by _Teach_ experts. The percentage of agreement within one point is 87%+ for all elements (except Feedback at 75%).

**Table 1** : Observer-expert accuracy for _Teach_ Training

|Element|Exact<br>Agreement|Within±1<br>Agreement|
|---|---|---|
|Supportive Learning Environment|42%|87%|
|Positive Behavioral Expectations|56%|98%|
|Opportunities to Learn|73%|99%|
|Lesson Facilitation|39%|90%|
|Checks for Understanding|55%|92%|
|Feedback|61%|75%|
|Critical Thinking|88%|100%|
|Autonomy|43%|100%|
|Perseverance|58%|98%|
|Social and Collaborative Skills|92%|99%|

Note: The training sample includes observers who passed the _Teach_ certification exam. To pass the exam participants need to be at most within one point of the master code in at least 8 out of the 10 elements in each video.

15
find that daily dietary supplementation for pregnant women in rural Gambia increased birth weight, and Kanade et al. (2008) document in observational data that Indian mothers who ate more fat and micronutrients during pregnancy had larger babies.

Is it possible that historical conditions that restricted Indian mothers’ size, but have now improved, are importantly restricting the fetal growth of their children? This question is difficult to answer in part because there are at least five reasons mothers’ and children’s height would be correlated: (1) mother’s genetics, (2) assortative mating and father’s genetics, (3) correlation of the child’s early-life environment with the mother’s early life environment, (4) endogenous effects of mothers’ early life environments on their adult ability to care for their children (including _in utero_ and through marriage markets), and finally (5) intrauterine growth restriction directly caused by the historically determined aspects of a mother’s size. Although all five would be reflected in a simple regression of child size on mother’s size, only the last mechanism would allow mothers’ stunting to itself cause present-day children’s stunting.

Arguing that “variability in stature among young children is often ascribed to to health and nutrition differences in malnourished populations and to genetic differences in wellnourished populations,” Martorell et al. (1977) hypothesized that parent-child height correlation would be greater in the U.S. than in Guatemala. This is consistent with a model in which height _h_ is the sum of two uncorrelated factors, genetics _g_ and environment _e_ , so _h_ = _g_ + _e._ Then, assuming that child genetics are a weighted sum _ω_ of mother’s and father’s genetics, so _gc_ = _ωgm_ + (1 _− ω_ ) _gf_ , and that environmental shocks are uncorrelated across generations, then the mother-child correlation of height is where _σ_<sup>2</sup> is variance. In rich countries, where there is little relevant variance in environmental conditions, height correlations will be high.

24
Table 7. Foreign bank ownership, income shocks and household consumption

|Countries:|Crisis,mid|dle-income|
|---|---|---|
|Dependent variable:|Staple foods|Other consumption|
|Rural|0.00|-0.0406***|
||[0.0120]|[0.0119]|
|Low income|0.199***|-0.0378**|
||[0.0170]|[0.0167]|
|Income shock|0.221***|0.315***|
||[0.0351]|[0.0346]|
|_Foreign banks_|||
|_* Income shock_|-0.02|0.03|
||[0.0242]|[0.0238]|
|_Credit to GDP_|||
|_* Income shock_|-0.03|-0.04|
||[0.0513]|[0.0506]|
|Observations|6'988|6'988|
|Countries|6|6|
|CountryFE|yes|yes|
|Method|OLS|OLS|
|R2|0.11|0.14|

_Notes: The reported coefficients are estimates from a linear probability model. All regressions include country fixed effects. See the Appendix for a definition of all dependent and explanatory variables._ Table 7 presents our estimates of the impact of foreign-bank ownership. Our methodology is identical to that applied above: We examine whether households which experienced an income shock are less or more likely to have to cut back on consumption if they are located in a country with foreign bank dominance. In both regressions we include household location, household income and country fixed effects as covariates. The interaction term _Foreign banks * Income shock_ captures the differential effect of foreign bank dominance on households which experienced an income shock (versus those that did not). To rule out that our findings are driven by a correlation of foreign bank ownership and financial sector size we include the interaction term _Credit to GDP * Income shock_ as a control variable.

The estimates displayed in Table 7 suggest that there is no relation between the market share of foreign-owned banks and the transmission of the banking crisis to households. The estimated interaction term _Foreign banks * Income shock_ is insignificant suggesting that foreign ownership did 24
# **4.5 Determinants of private transport**

## **4.5.1 Extensive margin: the determinants of vehicle ownership**

In this section, we investigate the empirical determinants of vehicle ownership at the household level. We include cars as well as other vehicles including motorcycles and bicycles. A bicycle is often the first private transport asset poorer households can afford to acquire. Motorcycles can be a cheaper and more available substitute of cars in many countries. For example, less than 5% of households own a car in Mali, Burkina Faso, Togo and Benin, but more than 30% own a motorcycle (World Bank SSAPOV harmonized survey database). While the acquisition of a bicycle does not contribute to the growth of CO2 emissions, motorcycles do but to a lesser extent than cars. This section therefore looks at the determinants of first vehicle acquisition and ensuing substitution between bicycles, motorcycles, and motor cars.

In order to quantify the determinants of vehicle ownership, we estimate the following equation: The ordinal dependent variable _Pr_ ( _ownershipi_ = _k_ ) is the probability for a household to own a vehicle of type k, but not of higher order.

If a household owns more than one type of vehicle, we classify this household in the highest vehicle ownership category. For instance, k=4 if a household owns a bicycle and a car. The explanatory variables are defined as follows: _xi_ is total expenditure of the household i, _ni_ household size, and _zi_ a vector of other household socio-demographic characteristics, including age, gender, and educational level of household head.

We estimate Equation 4.6 using an ordered logistic model whose results are presented in Table B.16 in the Appendix. Belonging to a wealthy, educated, and large household increases the chances of being in a higher vehicle’s ownership category. As households become richer, they progress in the vehicle ownership hierarchy by switching from a low to a higher level. These results are consistent across the two specifications considered in Table B.16 in the Appendix.

Table B.17 in the Appendix provides average marginal effects that can easily be interpreted. The probability of not owning a vehicle decreases with income and education but increases with the household head’s age. Doubling per-capita expenditure decreases the probability of not owning a vehicle by about ten percentage points. The probabilities of owning a vehicle (bicycle, motorcycle, and car) increase with income. However, the effect is more important for 32
**<u>Table 7: Impact of inheritance-related factors on land-related investment by smallholders</u>**

||**Planted**|**fruit trees**|**Built t**|**errace**|**Practices c**|**rop rotation**|
|---|---|---|---|---|---|---|
|Inh. matrilineal and matrilocal/neolocal|0.028**|0.059***|-0.003|-0.003|-0.061***|-0.027|
||(0.013)|(0.017)|(0.012)|(0.015)|(0.015)|(0.017)|
|Inh. matrilineal and matrilocal/neolocal||-0.050***||-0.002||-0.058***|
|*Male resp.||(0.017)||(0.015)||(0.018)|
|Inh. patrilineal and patrilocal/neolocal|0.021|0.016|-0.047***|-0.051***|-0.019|0.005|
||(0.018)|(0.022)|(0.017)|(0.019)|(0.020)|(0.022)|
|Inh. patrilineal and patrilocal/neolocal||0.006||0.003||-0.043**|
|*Male respondent||(0.019)||(0.015)||(0.021)|
|Inh. do not know/other|0.033|0.074**|0.002|0.013|-0.069***|-0.068**|
||(0.021)|(0.029)|(0.026)|(0.031)|(0.023)|(0.028)|
|Inh. do not know/other*Male respondent||-0.069**||-0.011||0.002|
|||(0.034)||(0.025)||(0.034)|
|Observations|17,154|17,154|17,154|17,154|17,154|17,154|
|R-squared|0.066|0.081|0.090|0.097|0.155|0.164|
|**Tests:**Matrilineal+Matrilineal*Male=0||0.34||0.12||22.47***|
|Patrilineal+Patrilineal*Male=0||1.43||7.12***||3.02*|
|Unknown+ Unknown *Male=0||0.03||0.00||5.21**|

_Note:_ Basic controls at plot, household, and village level, district fixed effects, and all variables reported in table 6 are included throughout but not reported. Basic plot controls include land area and land topography. Basic household controls include the number of children, the number of adults, the number of old people, head’s characteristics (gender, age, education, birth place, and main job), ownership of durable goods, housing conditions, the value of livestock and poultry, the value of agricultural assets, and the number of extension activities attended. Basic village controls include access to the all seasonal road. Robust standard errors in parentheses clustered by village. *** p<0.01, ** p<0.05, * p<0.1.

22