rafmacalaba/datause-encoder-data huggingface dataset:
the presence of a bank loan) is positively correlated with the capacity to innovate. However, this correlation could also reflect unmeasured productivity attributes of the firm that are correlated with both its ability to innovate and its decision to receive a loan. Using data for the Republic of Korea over the period 1967-2005, Ang (2010) found a significant long-run relationship between knowledge production and financial liberalization. By analyzing the impact of a self-assessed measure of constraints upon R&D investment, Silva and Carreira (2012) found that, when the endogeneity problem associated with that variable is taken into account, financial constraints significantly decrease the amounts invested in R&D in Portugal. Furthermore, also resorting to the same direct measure, they found that innovation (in a broad sense) is significantly hampered by financial constraints only once they allow for a joint specification of errors of both equations.

Studies by Hirukawa and Ueda (2011), Popov and Roosenboom (2012), and Faria and Barbosa (2014) focus more specifically on the link between venture capital (VC) and innovation. The key question asked by Hirukawa and Ueda (2011) is related to the causal relationship between VC and innovation: does VC investment make the invested firms innovative, or do innovative firms attract VC investment? This in turn rests on the validity of the “VC-first” (VC induces innovation) versus “innovation-first” (the arrival of new technology that spurs the demand for VC) hypotheses. They argue that the earlier literature (reviewed by Sharpe (2009) and Hall and Lerner (2010)) was often viewed as being supportive of the VC-first hypothesis. However, they provide a more careful analysis of the causality between VC and innovation by studying two types of VC investments (the first and follow-on investments) and two innovation indicators (patent counts and TFP growth) in a panel of U.S. manufacturing industries. They found distinct results for each indicator and each type of VC investment. In relation to patent growth in the first VC investment, there is little evidence to support either the innovation-first or the VC-first hypotheses but VC appears to slow down patent growth one year later.

Popov and Roosenboom (2012) provide apparently the first cross-country evidence of the effect of VC investment on patented inventions. Using a panel of 21 European countries and 10 manufacturing industries covering the period 1991-2005, they study 9
of red, where a darker red indicates a higher travel budget, with some residents needing to spend 100% of per capita income to reach opportunities.

While there are clearly higher travel budgets required with increasing fuel price scenarios, the spatial trends associated with each scenario consistently suggest that the fuel price increases would exclude already vulnerable communities. The same areas need higher travel budgets and are located in lower income neighborhoods (such as Maré, Cidade de Deus, Rocinha, etc.). Areas along key transit corridors (Rail, Metro, BRT) with reasonable access to the CBD, however, generally need less than 30% of income, even in the 500% fuel price scenario. However, much of the population located along rail stations further West remain excluded.

The exclusion of vulnerable people is even clearer in the distribution of population in each group within income deciles on the right. Even though the charts show almost no changes between scenarios, there are some increases to the excluded population in the lower income deciles with an increase from 55% to 70% of the poorest decile’s population. Inversely, the percentage of the population that is independent of travel budgets account for over 20% of the top 2 deciles, decreasing to 0% of the lowest income deciles, with no changes to these groups with increasing fuel prices. Wealthier residents along the South coast require the lowest budgets and appear to have almost no changes in travel budget requirements with increasing fuel prices.

These suggest that the fuel price increases will disproportionately increase the required travel budgets of lower income communities to access opportunities in Rio and that there are large portions of the population that are already economically excluded independently of a fuel price change. Fuel price increases, therefore, will further increase the share of Rio’s population (located almost exclusively in the poorest neighborhoods) excluded from transit services.

Similar analysis was conducted for Kinshasa, shown in Figure 12.

26
50

|Dependent variable|Log<br>exp/capita<br>(excl. health<br>expenditure)|Log (asset and<br>consumer<br>durable value)|Disaster<br>relief|Domestic<br>remittance<br>s|Remittance<br>s from<br>outside VN|Herfindahl<br>index|
|---|---|---|---|---|---|---|
||ML|OLS||Tobit||OLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
||parameter/|parameter/|parameter/|parameter/|parameter/|parameter/|
||t‐value|t‐value|t‐value|t‐value|t‐value|t‐value|
||‐37.63|‐23.52|1.26|‐1.33|‐3.51|‐10.36|
|Areas of land owned (squared<br>metres)|0.0883***|0.3802***|‐138*|40|1265**|‐0.0038**|
||38.29|28.21|‐1.93|1.4|2.2|‐2.38|
|**_Community characteristics_**|||||||
|1 if sanitary drinking water in<br>enumeration area|‐0.0723*|0.1658|‐949**|‐124|‐2687|0.0011|
||‐1.74|0.47|‐2.09|‐0.84|‐1.02|0.04|
|1 if sanitary living water in<br>enumeration area|0.1014***|‐0.1143|18|96|8013***|0.014|
||2.51|‐0.34|0.04|0.6|2.67|0.58|
|1 if electricity in enumeration area|0.0018|0.18|76|333***|2023|‐0.0324**|
||0.12|0.96|0.31|3.36|0.87|‐2.09|
|1 if sanitary latrine in enumeration<br>area|0.1002***|0.4157***|‐179|403***|13758***|0.0176***|
||12.88|6.65|‐1.1|6.47|9.02|3.22|
|1 if living on primary or secondary<br>road|0.1080***|‐0.2622|1127|216|‐4525|‐0.0018|
||4.26|‐1.54|1.13|0.57|‐0.79|‐0.11|
|Log of distance (km) to closest<br>primary or secondary road|0.0194***|‐0.0373|117|56|‐432|‐0.0012|
||5.53|‐1.55|0.88|1.11|‐0.53|‐0.52|
|Log (1+ elevation in meters)|0.0008|0.003|94|61|2879**|‐0.0035|
||0.22|0.1|0.44|0.69|2.21|‐1.03|
|Log (1+diff bw highest & lowest<br>elevation in 1 km<br>2)|‐0.0172***|‐0.0773**|‐359|‐161*|‐4553***|‐0.0004|
||‐4.62|‐2.51|‐1.56|‐1.84|‐3.98|‐0.11|
|Log (1+tenths hours to urban area of|‐0.0530***|‐0.1523***|626*|‐199*|‐1339|0|
|Employment rate (emp/hhsize)|0.20|0.55|0.72|
|---|---|---|---|
|Dependency ratio|-0.49|-0.44|-0.31|
|Female headed household|-0.09|-0.03|0.10|
|Household size|-0.07|-0.09|-0.05|
|IDP|-0.01|-0.28|0.08|
|Constant|5.98|5.84|5.56|
|Observations|540|300|660|
|_R_-squared|0.24|0.50|0.28|

_Data source_ : Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

_Table 4. Pseudo-PMT for the SEVAT model_

||(1)|(2)|(3)|
|---|---|---|---|
||**Kurdistan**|**North**|**Center-South**|
|Home: not owned by HH|-0.03|-0.14|-0.04|
|Water: no public network tap|--|0.12|--|
|Household size|-0.07|-0.10|-0.05|
|Employment rate (emp/hhsize)|0.48|0.83|0.93|
|Constant|5.70|5.65|5.40|
|Observations|540|300|660|
|_R_-squared|0.19|0.47|0.27|

_Data source_ : Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

# 3.4 Cross-Eligibility Calculation and Referral Accuracy

This exercise attempts to create a tool to assess the eligibility of the CLCI’s beneficiaries for the MoLSA’s CTP. Given the small set of covariates that overlap between the two eligibility criteria, the pseudo-PMT cannot solely determine qualification into the MoLSA’s CTP.<sup>9</sup> However, it allows to evaluate the eligibility

> 9 Although all pseudo-PMT indicators were included in the set of candidate proxies, the final MoLSA PMT may not reflect some. A Lasso statistical technique was used as a model selection device for the development of the full PMT. Out of numerous candidate proxies, Lasso selects an optimal set of covariates that jointly have the best predictive power. Coefficients of the proxies with lesser predictive power are zeroed-out and not selected. See

10
||Post-Int<br>|ervention<br>Ref<br>|**Tab**<br>Cbange in<br>orms**in**As<br>|**le**3.2<br>M2/GDP,<br>cending G<br>|Regressio<br>roup<br>|n Results<br>|||
|---|---|---|---|---|---|---|---|---|
|Explanatory<br>Variable|**Macro**+<br>Financia<br>Ascendi|<br>l +<br>ng|Macro +<br>Financia<br>+ Privati|<br>l + Trade<br>zation|Macro +<br>Financia<br>+ Re-<br>Capitali|<br>l + Trade<br>zation|Macro +<br>Financia<br>+ Super|<br>l + Trade<br>vision|
||**(9)**|**(10)**|(11)|(12)|(13)|(14)|**_(15)_**|**(16)**|
|Constant|-11.00|-5.22|-14.10|-4.52|-9.76|-6.74|-13.80|-5.27|
||(1.66)||(2.34)|(1.35)|(1.57)|(1.86)|(2.18)|(1.39)|
|Group**1**|7.38||8.94||_5.51_||9.37||
||(1.85)||(2.43)||(1.40)||(2.47)||
|Group 2|2.98||8.57||2.22||5.63||
||(0.66)||(1.95)||(0.53)||(1.40)||
|Group 3<br>DBPC/GDPxInfl|2.44<br>(0.54)<br>.|-.0051|6.75<br>(1.60)|-.0045|1.25<br>(0.29)|-.0040|4.79<br>(1.17)|-.0051|
|||(1.83)||(1.79)||(1.48)||(1.82)|
|IMP/GDP|0.29|0.37|0.43|0.39|0.291|0.36|0.33|0.37|
|Ascending|(1.67)<br>3.99<br>(1.19|(2.44)<br>0.83<br>)(0.25)|(2.74)|(2.83)|(1.86)|(2.60)|(2.09)|(2.42)|
|Privatization|||-4.18<br>(1.82)|-3.27<br>(1.67)|||||
|Re-Capitalization|||||3.20<br>(1.80)|2.80<br>_(1.51)_|||
|Supervision|||||||2.85<br>(1.66)|0.90<br>(0.46)|
|Adj. R-Squared|.398|.278|.482|.411|.480|.391|.460|.287|
|Number of<br>Observations|16|16|16|16|16|16|16|16|

Note: t-statistics in parentheses; group dummies defined in Table 2.2.

Among the three descending reforms (interest rate distortions, non-bank financial regulations, and prudential regulations), only prudential regulation reforms appear to have been substantially more successful than other types of reforms. Relative to other interventions, those that tackled interest rate distortions were somewhat better able to boost M2/GDP, but the disparity was not statistically significant. Similarly, those interventions that dealt **with** non-bank financial regulations performed somewhat better than those that did not, although the result approached significance in Model (21) but not 36
<!-- Start of picture text --> 0-23 months 0-60 months<br>NNS Form 2.1: Anthropometry (with non-missing _ 5,974 16,873<br>height measure & biologically valid HAZ) 0-23m) 0-60m)<br>: . . 5,974 16,873<br>. 5,974 16,873<br>. : 5,974 16,873<br>. :<br>NNS Form 3.1: Maternal care, knowledge & behaviors ee. 5,344 so ga10,430<br>po : . 5,777 16,239<br>- Chi . 5,974 16,873<br>NNS Form 4.3: Infant & young childpractices feeding _ 90(0-23m)5,945 2590 |(0-23m)5,945<br>: . 4,655 13,172<br>Represented in. all modules/datasets 500g4,049 255 ghee4,049<br>Represented with non-missing values in a simple _ 4,732 4,732<br>model (without a variable from FIES) 0-23m) 0-23m)<br>Represented with non-missing values in a simple _ 3,681 3,681<br>model (with a variable from FIES) (0-23m) Q23m)<br>0.00 0.25 0.50 0.75 1.00 1.28.00 025 050 075 1.00 12<br>Percent of sample<br><!-- End of picture text -->

The unweighted stunted rates for each respective sample are shown as red circles with the values labeled
<!-- Start of picture text --> Al<br>-1 men SeSs _—<br>~s “7<br>2 ~see-<br>-3<br>-4<br>a a a |<br>-10 5 0 5 10<br>Periods to Treatment<br>1 99% CI MM 95% Cl —— Median<br>--- DIDw/ocontrols ----- DID w/ controls<br><!-- End of picture text -->

<!-- Start of picture text --> Al<br>-1 i:P = xx =<br>f ‘ “<br>-.2 .% “teense!”<br>-3 YessS SSLceettttee<br>-4 ae .es<br>a a a |<br>-10 5 0 5 10<br>Periods to Treatment<br>1 99% CI MM 95% Cl —— Median<br>--- DIDw/ocontrols ----- DID w/ controls<br><!-- End of picture text -->
- Rodrigues, A., Andelman, S., Bakarr, M., Boitani, L., Brooks, T., Cowling, R., Fishpool, L., da Fonseca, G., Gaston, K., Homann, M. et al.: 2004, Eectiveness of the Global Protected Area Network in Representing Species Diversity, Nature 428(6983), 640643.

- Rose, S. K., Ahammad, H., Eickhout, B., Fisher, B., Kurosawa, A., Rao, S., Riahi, K. and van Vuuren, D. P.: 2012, Land-based Mitigation in Climate Stabilization, Energy economics 34(1), 365380.

- Searchinger, T., Heimlich, R., Houghton, R., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D. and Yu, T.: 2008, Use of US Croplands for Biofuels Increases Greenhouse Gases through Emissions from Land-Use Change, Science 319(5867), 1238.

- Sinn, H.: 2008, Public Policies Against Global Warming: a Supply Side Approach, International Tax and Public Finance 15(4), 360394.

- Sohngen, B. and Mendelsohn, R.: 2003, An Optimal Control Model of Forest Carbon Sequestration, American Journal of Agricultural Economics 85(2), 448457.

- Sohngen, B. and Mendelsohn, R.: 2007, A Sensitivity Analysis of Forest Carbon Sequestration, in M. Schlesinger (ed.), Human-Induced Climate Change: An Interdisciplinary Assessment, Cambridge University Press, pp. 227237.

- Sorrell, S. and Dimitropoulos, J.: 2008, The Rebound Eect: Microeconomic Denitions, Limitations and Extensions, Ecological Economics 65(3), 636 649.

- Stavins, R.: 1999, The Costs of Carbon Sequestration: A Revealed-preference Approach, The American Economic Review 89(4), 9941009.

- Steinbuks, J. and Timilsina, G. R.: 2014, Land-use change and food supply, in G. Timilsina and D. Zilberman (eds), The Impacts of Biofuels on the Economy, Environment, and Poverty, pp. 91102.

- USGAO: 2003, Natural Gas: Domestic Nitrogen Fertilizer Production Depends on Natural Gas Availability and Prices, Report to the Ranking Democratic Member, Committee on Agriculture, Nutrition and Forestry, U.S. Senate GAO-03-1148, United States General Accounting Oce.

44
We find that living within a short distance to a weather station that received drought compensation increased the share of votes for the presidential incumbent party. The result is robust to including controls at the state, municipality and electoral section levels as well as fourth-order polynomial terms for the forcing variable and narrowing the window of analysis around the threshold. Consistent with previous findings for the case of Germany, our estimates indicate that receiving drought compensation within six to nine months prior to the election had an effect of approximately 7.6 additional percentage points in the share of votes for the incumbent party. Results of our analysis suggest that recipients of disaster aid reward the incumbent party and non-recipients punish it voting in higher proportion for contestant parties.

Analyzing the case of a WII scheme not only provides an exceptional framework for the econometric identification of the effect, but also reveals an important interpretation of our results: Voters reward the incumbent party for disaster relief transfers under an insurance design. We are not able to identify whether voters reward the incumbent for insurance enrollment itself; however, the results in this paper imply that politicians may find it attractive to implement insurance programs that are more efficient than relief spending funded from fiscal resources.

# 6. **References**

Achen, C. H. and L. M. Bartels. 2002. “Blind Retrospection: Electoral Responses to Drought, Flu and Shark Attacks”. Paper presented at the annual meeting of the American Political Science Association, Boston.

Agroasemex. 2006. “La Experiencia Mexicana en el Desarrollo y Operación de Seguros Paramétricos Orientados a la Agricultura.” Working Paper, April 19, 2006. http://www.agroasemex.gob.mx Bechtel, M. and J. Hainmueller. 2011. “How Lasting is voter Gratitude: An Analysis of the Short-term and Long-term Electoral Returns to Beneficial Policy”. American Journal of Political Page **16** of **29**
Uruguay Round. The third panel in Table 2 reports estimates from threat NTM models for the 3 years. Whereas in 1993 lobbying was associated with THREAT, there was no association in the other years. Apparently, THREAT was lowered considerably after the inception of the Uruguay Round.

CVD and QUAL, however, did not reduce in frequency and intensity after the inception of the Uruguay Round. This message is evident from the results in Table 3. The top panel of Table 3 indicates that %PACEXP is strongly associated with the imposition of CVDs in all the three years. However, the coefficient on RANKPACEXP is statistically insignificant indicating the possibility of influential %PACEXP values. The bottom panel of Table 3 indicates that QUAL is significantly associated with lobbying spending and is robust across the two measures of lobbying. The size of the coefficients on %PACEXP and RANKPACEXP also indicate that QUAL does not appear to have declined after the Uruaguay Round.

**−** **<u>Table 3: Agricultural Trade Protection Regressions NTM II</u>**

|**Year Dep. Va**|**r.**|**Constant**|**M/Y **|**X/Y **|**%PACEXP **|**RANKPACEXP**|_N_|_R_<sup>2</sup>|Adj. _R_<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|---|
|1993 CVD|est<br>_t_|−0.038<br>−0.691|−0.007<br>−0.987|0.191<br>0.997|1.997<br>**4.026**||31|0.378|0.309|
|1993 CVD|est<br>_t_|−0.048<br>−0.451|−0.003<br>−0.341|0.080<br>0.341||0.183<br>1.180|31|0.054|−0.052|
|1996 CVD|est|−0.028|−0.008|0.117|2.155||33|0.395|0.333|
||_t_|−0.605|−0.881|0.885|**4.331**|||||
|1996 CVD|est|−0.050|−0.002|0.031||0.201|33|0.064|−0.033|
||_t_|−0.530|−0.200|0.193||1.360||||
|1999 CVD|est|−0.064|−0.015|0.320|2.778||38|0.288|0.225|
||_t_|−1.248|−1.352|1.362|**3.634**|||||
|1999 CVD|est<br>_t_|−0.040<br>−0.481|−0.007<br>−0.582|0.154<br>0.577||0.140<br>1.096|38|0.045|−0.039|
|1993 QUAL|est|0.064|0.008|−0.225|2.728||31|0.514|0.460|
||_t_|1.049|0.992|−1.054|**4.930**|||||
|1993 QUAL|est|−0.004|0.014|−0.362||0.348|31|0.187|0.096|
||_t_|−0.033|1.296|−1.326||**1.917**||||
|1996 QUAL|est|−0.006|0.000|−0.004|3.231||33|0.599|0.558|
||_t_|−0.123|0.018|−0.029|**6.490**|||||
|1996 QUAL|est|−0.136|0.009|−0.135||0.489|33|0.252|0.175|
||_t_|−1.321|0.761|−0.756||**3.020**||||
|1999 QUAL|est|−0.022|−0.006|0.122|4.294||38|0.340|0.282|
||_t_|−0.318|−0.386|0.383|**4.141**|||||
|1999 QUAL|est|−0.132|0.006|−0.135||0.498|38|0.222|0.153|
||_t_|−1.233|0.400|−0.397||**3.062**||||

Notes: 1. See note to Table 2. 2. CVD=Countervailing Duties, QUAL=Quality Monitoring.

50
# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Foundations for Country Fund Pricing|5|
||A. Market Setting for Trading of Country Funds and Their<br>Underlying Securities<br>|6<br>|
||B. Technology|7<br>|
||C. Portfolio and Market Equilibria|11|
|HI.|The Efficiency Role of Country Funds<sup>for Emerging Markets</sup>|14|
||A. ModelI<br>|17<br>|
||B. Model II<br>|19<br>|
||C. Model III|23|
|IV.|Extensions|29|
||A. Interest Rate Differential<br>|29<br>|
||B. fax and Regulatory Factors<br>|31<br>|
||C. Optimality of Country Fund Size|32|
|V.|Condusions and Policy Implications|32|
||1. Pricing Efficiency and Resource Mobilization.<br>|33|
||2. Efficient Promotion of Country Funds and a<sup>Case for</sup><br>Price Stabilization<br>|35|
||3. Fund Premium or Discount and a Case for Initial<br>Discount Subsidization|36|
|Ref|erences|38|
|App|endices|41|
agricultural trade policy that encourages private investment, and investments in public goods that encourage productivity growth (World Bank, 2019).

**Figure 2: Poverty among agricultural households in 2014**

<!-- Start of picture text --> 70.0<br>60.0<br>50.0 24.0<br>36.7<br>40.0<br>22.0<br>30.0<br>20.0<br>37.6<br>24.5 22.9<br>10.0<br>0.0<br>East Center West<br>Poor Non-poor<br>% of agriculturral households<br><!-- End of picture text --> _Source:_ 2014 Timor-Leste Survey of Living Standards Notwithstanding the role that agriculture can potentially play in reducing poverty in Timor-Leste, the country has one of the least developed agricultural production systems in the world. All subsectors are dominated by subsistence systems and by farmers who are very risk averse (Denning 2013; World Bank 2017). Only 30 percent of the country’s arable land is currently under crop production and grazing, and the sector’s productivity has been very low. Agricultural productivity as measured by the value of crop output per hectare in Timor-Leste is well below that of other small island developing states and has fallen below the average for other low-income, food-deficit countries in recent years (Figure 3a). Also, agricultural value added per worker has been falling since 2012 (Figure 3b). The estimated 64,000 metric tons of maize and 65,000 metric tons of rice produced in 2015 are 30 percent below the five-year average and well below the country’s estimated requirements (245,000 metric tons) of these staples (FAO statistics). The country’s cereal yield is also among the lowest in the East Asia and Pacific region (UN Women and World Bank 2018). The agriculture sector is therefore underperforming in terms of its contribution to food security, sufficient livelihood, and overall economic growth.

3
|Math raw score 2011|17.08|20.23|22.29|23.90|28.94|-5.21***|-6.65***|-11.86***|
|---|---|---|---|---|---|---|---|---|
|if in school|(7.87)|(8.15)|(7.76)|(8.69)|(8.07)|(0.84)|(0.70)|(0.85)|
|Math z-score 2011|-0.674|-0.276|-0.072|0.231|0.826|-0.894***|-0.606***|-1.500***|
||(0.816)|(0.876)|(0.871)|(0.906)|(0.818)|(0.078)|(0.076)|(0.075)|

Note: The last three columns (Q5-Q3, Q3-Q1, Q5-Q1) provide the mean and the standard error of the difference in means across two quantile groups: *,**,*** denote significance of at 10%, 5% and 1% levels.

Although the mathematics test is well distributed, the literacy test is positively skewed. The bimodal distribution of scores for the literacy test is consistent with responding correctly to the comprehension questions administered after listening to a short story; any additional points for literacy required some knowledge of letters. In the analysis, the literacy test is defined to be censored at zero and the mathematics test to be censored at a raw score of five, representing about 12% of our non-missing responses in both cases.

# _Magnitude of the Socioeconomic Gradients_

Table 3 presents the estimated wealth coefficients across four specifications. For parsimony, table 3 reports only coefficients for the top wealth quintile relative to the first quintile, but the full set of coefficients on all wealth quintiles and other regressors are presented in table A1 in the appendix. The four specifications are expressed in the different columns. The base model includes demographics and wealth (column base). The following group of regressors are added consecutively to test for robustness: (i) lagged test values and early child conditions; (ii) maternal education and endowments; and (iii) home environment.

Table <u>3. Magnitudes of SES gradient (Q5 v Q1), whole sample</u>

||_(base)_|_(i)_|_(ii)_|_(iii)_|
|---|---|---|---|---|
|Receptive vocabulary|1.060***|0.892***|0.672***|0.532***|
||(0.085)|(0.094)|(0.108)|(0.100)|
|Cognition composite|0.893***|0.601***|0.383***|0.288***|
||(0.079)|(0.098)|(0.112)|(0.109)|
|Sustained attention|0.810***|0.646***|0.483***|0.400***|
||(0.085)|(0.109)|(0.113)|(0.107)|
|Memory of phrases|0.715***|0.583***|0.423***|0.308***|
||(0.079)|(0.109)|(0.117)|(0.111)|
|Demographics|Yes|Yes|Yes|Yes|
|Lagged outcomes & early conditions|No|Yes|Yes|Yes|
|Maternal education & endowments|No|No|Yes|Yes|
|Home environment|No|No|No|Yes|
# **The Influence of Non-Cognitive Skills on Wages within and between Firms: Evidence from Bangladesh’s Formal Sector**<sup>**1**</sup>

Shinsaku Nomura* and Samik Adhikari The World Bank JEL Classification: I26, J24, J31 Key words: personality traits, wages, occupations, firm-fixed effects

> 1 This paper benefited from contributions by Seo Yeon Hong at an early stage of data analysis. We would like to thank Futoshi Yamauchi, Norihiko Matsuda, Keiko Miwa and Iffath Sharif for their feedback. The findings, interpretations, and conclusions in this paper are entirely those of the authors and should not be attributed to the World Bank or its affiliated organizations.

> * Corresponding author: The World Bank, Education Global Practice. Washington, D.C.: <u>snomura@worldbank.org</u>