rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Import Uncertainty and Export Dynamics: Core Results

||Firm Entry<br>Rate<br>(1)|Firm Exit<br>Rate<br>(2)|First yr.<br>Survival<br>(3)|Second yr.<br>Survival<br>(4)|Third yr.<br>Survival<br>(5)|
|---|---|---|---|---|---|
|Log import uncertainty|0.000<br>|0.000<br>|-0.040***<br>|-0.059***<br>|-0.072**<br>|
||(0.006)|(0.007)|(0.014)|(0.021)|(0.036)|
|Log direct imports (%)|-0.014|-0.004|-0.026|-0.020|0.061|
||(0.011)|(0.015)|(0.031)|(0.053)|(0.093)|
|Log direct exports (% sales)|-0.027***|-0.032***|0.105***|0.113***|0.088|
||(0.005)|(0.013)|(0.028)|(0.040)|(0.062)|
|Log experience|0.001|-0.004|0.115***|0.157***|0.126|
||(0.013)|(0.016)|(0.035)|(0.058)|(0.104)|
|Log nb. of employees|-0.020*|-0.027***|0.081***|0.117***|0.132**|
||(0.010)|(0.009)|(0.019)|(0.029)|(0.057)|
|Log TFP|0.043*|-0.045**|0.175***|0.308***|0.269***|
||(0.024)|(0.019)|(0.039)|(0.063)|(0.100)|
|Common border|-0.033|-0.040|0.065*|0.068|-0.020|
||(0.022)|(0.027)|(0.039)|(0.059)|(0.089)|
|Colony|-0.079*|-0.019|0.148**|0.219*|0.080|
||(0.041)|(0.041)|(0.073)|(0.112)|(0.283)|
|Log distance|0.110***|0.037***|-0.071***|-0.109***|-0.150***|
||(0.009)|(0.010)|(0.018)|(0.029)|(0.051)|
|Trade agreement|-0.063***|-0.035**|0.098***|0.162***|0.194**|
||(0.016)|(0.018)|(0.033)|(0.051)|(0.082)|
|Log tarifs|0.008|0.012*|-0.027**|-0.028*|-0.030|
||(0.005)|(0.006)|(0.011)|(0.017)|(0.027)|
|Log exporter GDP cap.|-0.075|0.056|-0.042|-0.182|2.706*|
||(0.097)|(0.117)|(0.181)|(0.260)|(1.640)|
|Log importer GDP cap.|0.061|0.004|0.122|0.136|0.264|
||(0.041)|(0.049)|(0.098)|(0.152)|(0.229)|
|Log exporter entry cost (% GNI)|-0.021|-0.078*|-0.141*|-0.327***|0.155|
||(0.036)|(0.043)|(0.084)|(0.121)|(0.400)|
|Log importer entry cost (% GNI)|0.013|-0.013|-0.011|-0.021|-0.115|
||(0.014)|(0.018)|(0.035)|(0.054)|(0.083)|
|Constant|-1.036|-1.151|-1.232|-0.556|-25.937*|
||(0.863)|(1.032)|(1.642)|(2.424)|(14.823)|
|Observations|14,476|12,001|11,834|10,755|5,680|
|R<sup>2</sup>|0.182|0.183|0.106|0.100|0.123|
|Country Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Destination Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Sector Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Log direct imports (%), Log direct exports (% sales), Log experience, Log nb. of employees and Log TFP computed over the sample of exporting firms, except in Column 1. The description and source of variables are provided in Table A3 in the Appendix.

15
# **Non-Tariff Measures and the World Trading System**<sup>**1**</sup>

# **Josh Ederington University of Kentucky**

**Michele Ruta World Bank**

**JEL:** F13, F55

**Keywords:** Non-Tariff Measures, Trade Effects, Trade Agreements, Deep Integration

> 1 This paper will appear as Chapter 13 of the Handbook of Commercial Policy edited by Kyle Bagwell and Robert Staiger. We thank our discussants Peter Neary and Marcelo Olarreaga for detailed coments on an earlier draft, and Kyle Bagwell, Chad Bown, Robert Staiger and participants at the Dartmouth Handbook of Commercial Policy conference on June 4-6 2015 for helpful discussions and comments. Alberto Osnago provided outstanding research assistance. The remaining errors are our repsonsibility only. The views expressed in this article are those of the authors and they do not necessarily represent the views of the World Bank Group.
Cross country studies show that ICT production and usage increase women’s access to economic opportunities by increasing access to information, reducing transaction costs, providing flexible arrangements, and overcoming distance barriers, especially in conservative cultures where women face mobility restrictions. A study using panel data on 209 countries for the years 1960 through 2002 found that an increase in ICT infrastructure and the use of ICT would reduce the gender gap in education and employment (Chen, 2004). The results confirm the positive role played by ICT in improving gender quality in employment, which in turn enhances economic development. A study in Africa used a panel of 48 countries for the period 1990 to 2014 (Efobi et al., 2018). It found that increasing mobile phone penetration, internet penetration, and fixed broadband penetration increased women’s participation in the labor market, while controlling for other factors such as economic level, openness, and democracy level of the economy. Similarly, Valberg (2020) found that ICT, measured by internet users (per 100 inhabitants) and mobile subscriptions (per 100 inhabitants), has a positive effect on female labor force participation using panel data for 156 developed and developing countries from 1991 to 2014. However, this positive effect differs with the country’s economic level. The results show that ICT is not the “ _leapfrogging_ ” strategy for a more gender-equal labor market in developing countries.

Research to date has always looked at access to the internet and its impact on the gender gap in the labor market. However, access to the internet is a necessary but not sufficient condition to ensure that women benefit, as they must consider using the internet to be safe and relevant to their lives and to have the skills and resources—such as a device with which to access it— to use it. An inclusive internet environment is one that offers availability, affordability, security, relevance, and inclusive policies (The Economist Intelligence Unit, 2020b).

This paper aims to fill the gap in the existing literature in two ways. First, it tests the hypothesis that an inclusive internet environment might reduce the gender gap in labor force participation. Second, in addition to economic factors, the paper controls for formal and informal institutions that shape gender roles in society. Following Chen (2004) and Valberg (2020), the paper uses a cross-country approach to allow for comparability. It considers MENA countries because of the dire economic gender gap that exists in the region.

# **2. Economic Gender Gap and Digital Inclusion in MENA Region**

The gender economic gap measured by the ratio of female-to-male labor participation rate in the MENA region is considered the lowest worldwide and has been stagnant for years. In 2021, the FLFP rate in the MENA region varied between 21% of male labor force participation in the Islamic Republic of Iran to 60% in Qatar (Figure 1). Saudi Arabia is the only country that achieved progress between 2018 and 2021, where the female-to-male labor participation rate increased from 28% to 39%.

5
- Frondel, M.: 2004, Empirical Assessment of Energy-Price Policies: the Case for Cross-Price Elasticities, _Energy Policy_ **32** (8), 989–1000.

- Fuss, M.: 1977, The Structure of Technology over Time: A Model for Testing the ’Putty-Clay’ Hypothesis, _Econometrica_ **45** (8), 1797–1821.

- Gately, D. and Huntington, H.: 2002, The Asymmetric Effects of Changes in Price and Income on Energy and Oil Demand, _Energy Journal_ **23** (1), 19–56.

- Greenwood, J., Hercowitz, Z. and Krusell, P.: 1997, Long-run Implications of Investment-specific Technological Change, _The American Economic Review_ **87** (3), 342–362.

- Griffin, J. and Gregory, P.: 1976, An Intercountry Translog Model of Energy Substitution Responses, _American Economic Review_ **66** (5), 845–857.

- Griffin, J. and Schulman, C.: 2005, Price Asymmetry: A Proxy for Energy Saving Technical Change?, _The Energy Journal_ **26** (2), 1–21.

- Haas, R. and Schipper, L.: 1998, Residential Energy Demand in OECDcountries and the Role of Irreversible Efficiency Improvements, _Energy Economics_ **20** (4), 421–442.

- Hart, R.: 2004, Growth, Environment and Innovation - A Model with Production Vintages and Environmentally Oriented Research, _Journal of Environmental Economics and Management_ **48** (3), 1078–1098.

- Howarth, R. and Andersson, B.: 1993, Market Barriers to Energy Efficiency, _Energy Economics_ **15** (4), 262–272.

- International Energy Agency, I.: 2008, _Energy Prices and Taxes, 4th Quarter 2008_ , OECD/IEA, Paris, France.

- Jacoby, H. D. and Sue Wing, I.: 1999, Adjustment Time, Capital Malleability and Policy Cost, _Energy Journal_ **20** (S), 73–92.

- Johansen, L.: 1959, Substitution versus Fixed Production Coefficients in the Theory of Economic Growth: A Synthesis, _Econometrica_ **27** (2), 157–176.

- Jorgenson, D.: 1980, Accounting for capital, _in_ G. von Furstenberg (ed.), _Capital Efficiency and Growth_ , Ballinger: Cambridge, MA, pp. 251–319.

33
**Table E6: Relationship between food security measures and height-for-age z-scores (Bangladesh): Expanded Controls Version**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES||Height-for-|age z-score||
|HFIAS Score|-0.032**||||
||(0.012)||||
|Child DDS||0.036**|||
|||(0.015)|||
|FCS|||0.043***||
||||(0.013)||
|MDDS||||0.038***|
|||||(0.011)|
|Age (in month)|-0.041***|-0.046***|-0.041***|-0.042***|
||(0.002)|(0.003)|(0.002)|(0.002)|
|Age squared|0.000***|0.001***|0.000***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Female|-0.046**|-0.046**|-0.046**|-0.046**|
||(0.020)|(0.020)|(0.020)|(0.020)|
|Household size|-0.018***|-0.019***|-0.020***|-0.019***|
||(0.006)|(0.006)|(0.006)|(0.006)|
|Share of kids under 5|-0.364***|-0.359***|-0.347***|-0.357***|
||(0.117)|(0.117)|(0.117)|(0.117)|
|_Household head education_|||||
|Functional education and/or < 5 yrs of education|0.066**|0.066**|0.068**|0.067**|
||(0.031)|(0.031)|(0.032)|(0.031)|
|[5-7 years of education]|0.059**|0.062**|0.061**|0.062**|
||(0.029)|(0.029)|(0.029)|(0.029)|
|[8-10 years of education]|0.141***|0.143***|0.141***|0.143***|
||(0.033)|(0.033)|(0.033)|(0.033)|
|11+ years of education|0.323***|0.327***|0.323***|0.325***|
||(0.055)|(0.055)|(0.055)|(0.055)|
|Female-headed HH|0.049|0.046|0.045|0.046|
||(0.034)|(0.034)|(0.034)|(0.034)|
|_Mother's Education_|||||
|functional education and/or < 5 yrs of education|0.030|0.030|0.028|0.027|
||(0.034)|(0.034)|(0.034)|(0.034)|
|[5-7 years of education]|0.033|0.037|0.033|0.035|
||(0.030)|(0.030)|(0.030)|(0.030)|
|[8-10 years of education]|0.153***|0.158***|0.150***|0.155***|
||(0.035)|(0.035)|(0.035)|(0.035)|
|11+ years of education|0.305***|0.307***|0.294***|0.303***|

48
is very important in their lives, 71%, favor making religious law the law of the land, and 93% agree that religious judges should have the power to decide family law and property disputes (Pew Research Center’s Religion & Public Life Project 2013). Unlike much of the Western world, religious leaders in the Muslim world can issue religious rulings and opinions that can be binding in certain cases. Usually, most religious opinions ( _fatwa_ ) are non-binding, but a prominent religious leader’s ( _imam_ ) opinion can hold a lot of weight in the decisions made by an observant Muslim.

The importance of religion in everyday life manifests itself in financial decisions according to surveys exploring issues of financial inclusion. As discussed above, survey evidence points to religion having nontrivial impacts on financial decisions in Jordan. While there are several Islamic banks in Jordan there were no official Islamic microfinance institutions in Jordan at the time of the study.<sup>10</sup> A large proportion of the population is too poor to avail themselves of financial services from large banks. Taken together this leads to many of the poor being financially underserved, setting the stage for natural demand for religious products, including Islamic lending.

Even though the law allows the Central Bank of Jordan the right to regulate Islamic banking differently from conventional banks, they do not treat them any differently with respect to the normal levers that central banks regulate like lending limits, liquidity ratios and capital adequacy ratios (Abdel Al, 2004). This is slowly changing, with a new set of regulations put into place in 2017, but even those regulations are lax. This is partially due to the wide range of religious opinions about what is permissible. No country is known to regulate the Sharia jurisprudence used to determine whether a product is actually sharia compliant (CGAP, 2008).

This study choses Jordan as the site of our study for several reasons. First the context is one that is common across many Muslim-majority countries. Jordan has nearly the median GDP per capita for middle income countries as defined by the World Bank. While it contains a sizeable middle-class, there are still many individuals who do not have access to formal finance, as is the case with many Muslim majority countries. Second, religion continues to play an important role in people’s lives with evidence that it directly impacts their financing decisions. Finally, we were fortunate to find a partner who provided conventional loans and was in the process of expanding operations to begin providing sharia-compliant loans which allows us to test both at the same time, as discussed below. There are not

> 10 The first official Islamic MFI in Jordan was the entity that was begun by the founders of our partner bank after the

> completion of this study: http://www.islamicfinance.de/?q=node/8055

9
Table 3 shows the one-way average travel times and distances of commuters by mode of travel and by the four residence-job location arrangements.

**TABLE 2: Percentage distribution by mode of commuting and residence-workplace location (Beijing,** **_circa_ 2005)**

|_Residence_|**_Core_**|**_Core_**|**_Periphery_**|**_Periphery_**|||
|---|---|---|---|---|---|---|
|_Workplace_|**_Core_**|**_Periphery_**|**_Core_**|**_Periphery_**|||
|_Commute mode_ |||||||
|Walk, bicycle +|_188_|_315_|_183_|_1242_|**1928**|46.24%|
|Bus or Subway|_152_|_240_|_161_|_899_|**1452**|34.82%|
|Car +|_94_|_80_|_77_|_539_|**790**|18.94%|
||**434**|**635**|**421**|**2680**|**4170**||
||10.40%|15.23%|10.10%|64.27%||100.00%|

**TABLE 3: Average travel times and distances by mode of travel and commuting arrangement** **<u>(Beijing</u>** **_<u>circa</u>_** **<u>2005)</u>**

||**_Co_**<br>O|_Work_<br>**_re_**|_place_<br>**_Perip_**<br>O|**_hery_**|
|---|---|---|---|---|
||-||-||
|**Walk or bicycle**|<br>travel<br>time<br>(min)|One-way<br>distance<br>(km)|<br>travel<br>time<br>(min)|One-way<br>distance<br>(km)|
|_Rid_<br>**_Core_**|33.66|_2.26_|39.36|_10.75_|
|_esence_<br>**_Periphery_**|42.81|_11.51_|37.44|_11.45_|
|**Public bus or subway**|||||
|_Rid_<br>**_Core_**|32.77|_2.96_|37.65|_9.75_|
|_esence_<br>**_Periphery_**|41.34|_9.93_|35.93|_11.18_|
|**Car+**|||||
|_Rid_<br>**_Core_**|34.71|_2.53_|39.14|_9.40_|
|_esence_<br>**_Periphery_**|43.09|_11.42_|33.92|_10.26_|

# **2.1 The demand for commute mode, housing and location**

We assume that the consumer’s direct utility function is the log-of-Cobb-Douglas: where<sup>_b_</sup> _f_<sup>is the share of income after commuting that the consumer spends on renting housing floor</sup> space, _hijm_ | _f_ , and where _zijm_ | _f_ is consumption of goods other than housing. The subscript _f_ denotes the income quintile of the commuter. The choice bundle is denoted by ( , _i j_ , _m_ ) where _i_ is the location

8
is always significant and that the size of the coefficient increases from Equation (1) to Equation (3). This shows that those who opt to migrate are the maxmin (risk averse) individuals and those who stay are the maxmax (risk tolerant) individuals as predicted by the QM model with the dominance analysis. It also shows that the positive association between staying and risk-tolerance is stronger for people who sustained longer spells of violence.

As a fourth and last step, we test for a possible confounding factor. Recall that conflict could increase risk-tolerance as found elsewhere and that the lower risk-aversion that we find among stayers in Table 7 could be explained solely by the fact that stayers have experienced a longer period of violence as compared to leavers. To test for this confounding factor we carry out a Difference-in-Differences (DiD) test.

Results are reported in Table 8 and show that conflict and the interaction between these two variables (the DiD coefficient) are both non significant. Note also that the low R-squared and the insignificance of the independent variables are further evidence that the covariates of risk-aversion in Table 7 are independent. We can therefore reject the hypothesis that conflict affects risk preferences and explains the difference in risk preferences between movers and stayers.

# **6 Conclusion**

The paper departed from standard utility theory under uncertainty positing that individuals living in conflict areas are quantile maximizers as opposed to expected utility maximizers. In its most extreme form, quantile maximization implies that individuals tend to cluster into two groups, maxmin (risk-averse) and maxmax (risk-tolerant) individuals.

We then proposed a four-step approach to test the model with empirical data: 1) Estimation of the distributions of outcomes (lotteries) for people living under conflict who decide to stay or leave; 2) Predictions of migration choices for people with different risk preferences (focusing on different quantiles of the distributions) based on the QM model; 3) Test whether the QM predictions match observed risk preferences among stayers and leavers; 4) Test for a potential cunfounding factor.

Using household and conflict data from Nigeria, we found that the predictions of the QM model correctly identify stayers and leavers based on risk preferences. The CDFs of observed and predicted gains from staying and migrating crossed in one point. We also found that risk-averse individuals have a greater propensity to migrate whereas risk-tolerant individuals have a greater propensity to stay as predicted by the dominance analysis. This association is very significant and increases passing from general migration to migration from high conflict areas. We also tested for a possible confounding factor related to the possibility that conflict affects risk preferences and explains the observed difference between migrants and nonmigrants. We used a DiD estimator to test for this possibility and found no evidence that risk-aversion.

15
later. To study the link with future-oriented behavior, rather than focus on contemporaneous performance measures where causality is difficult to establish, we analyze behaviors that are indicative of how businesses will perform in the future, such as plans for business enhancement and expansion. We also measure attainment directly and study how baseline aspirations predict actual business outcomes at endline.

One commonality of the relevant theoretical literature is that aspirations affect the level of costly investment (see, e.g., Dalton, Ghosal, and Mani, 2016; Genicot and Ray, 2017; Lybbert and Wydick, 2017). The empirical literature by and large corroborates this prediction. For example, in a cross-section of rural households in Nepal, Janzen, Magnan, Sharma, and Thompson (2017) find aspirations to positively predict investment in education, loan usage for investment purposes, and saving behavior. Likewise, in an experiment in rural Ethiopia, Bernard, Dercon, Orkin, and Taffesse (2014) show that watching an inspirational movie increases loan usage and total savings. Kosec and Mo (2017) find that higher levels of aspirations are positively associated with the total volume of cash loans outstanding in a cross-section from rural Pakistan. Bernard, Dercon, and Taffesse (2012) complement this finding with cross-sectional data on households from rural Ethiopia. The authors show that narrow aspirations gaps correlate with both lower demand for credit and smaller loan sizes. Consistent with these established studies, we expect our aspirations measures to have predictive power on future-oriented behaviors such as plans for business innovation, and consequently, on business indicators such as sales, profits, employees, customers and shop size.

# **III Empirical Method and Data**

## **A Study Location and Sample**

This study was conducted in urban Jakarta, Indonesia. For logistical reasons, we limited the area of study to the urban core of Jakarta (”DKI Jakarta”), home to approximately 10.1 million inhabitants. Our sample comprises businesses in the traditional retail sector (called _Warung_ or _Toko Kelontong_ ), which employs about 35% of the working population of Jakarta (Statistics Indonesia, 2016).

We randomly selected 29 districts ( _Kelurahans_ ) out of the 112 districts in all of Jakarta. We then applied a series of inclusion criteria to cull down to a list of 2,042 eligible businesses. The inclusion criteria were as follows: (i) the business is a multi-product retail store, (ii) the size of the business is at least 4 square meters, (iii) the shop is an independent store and not part of any large retail chain, and (iv) there is a linear distance of at least 30 meters between each shop. This last criterion 9
# **Table 7. The Effect of PMT on Probability of Stunting Among Children 6-60 Months: Using Binary Indicator for Program Exposure**

||Poo<br>Linear Pr|led<br>obability|Linear Pr<br>w/ Comm|obability<br>unity FEs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**_A. 6-12 Months_**|||||
|Year Dummy (2000=1)|0.084|0.101|0.231|0.286|
||(0.106)|(0.107)|(0.200)|(0.206)|
|Program Since ’98 (Yes=1)*Year|-0.130|-0.151|-0.330*|-0.395*|
||(0.103)|(0.105)|(0.198)|(0.206)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-squared|0.09|0.11|0.53|0.53|
|**_B. 12-24 Months_**|||||
|Year Dummy (2000=1)|-0.070|-0.054|0.011|0.033|
||(0.087)|(0.089)|(0.148)|(0.153)|
|Program Since ’98 (Yes=1)*Year|0.019|-0.004|-0.088|-0.108|
||(0.085)|(0.087)|(0.153)|(0.159)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-Squared|0.08|0.09|0.37|0.38|
|**_C. 24-60 Months_**|||||
|Year Dummy (2000=1)|0.012|0.005|-0.021|-0.024|
||(0.053)|(0.054)|(0.071)|(0.070)|
|Program Since ’98 (Yes=1)*Year|0.001|0.009|0.039|0.044|
||(0.052)|(0.053)|(0.072)|(0.072)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-squared|0.11|0.11|0.21|0.22|

Note: There are 4788 observations. The dependent is an indicator for whether the child has a height for age z score less than -2, and the program effect is a dummy for whether the community had a supplemental feeding program interacted with a dummy for year=2000. Other covariates (with coefficients reported in Appendix Tables A7.1, A7.2 and A7.3) include: gender (boy=1), age (months), father and mother’s education and height, male head of household, farm household, household access to private toilet, sanitation and free health services, distance of village to bus station, community’s access to sewerage and piped water, number of village midwives, presence of a _posyandu_ in the community, and an indicator for whether the community is urban. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5% and 1%.

33
# **Table 6 Interactlon Effects of Curricular Content and Educational Expansion** on **Economic Growth, 1960-1985 (Full sample)**

(Unstandardized regression coefficients are reported; dependent variable is per capita RGDP 1985)

||Equation:|(A)|(B)|(C)|
|---|---|---|---|---|
||Average Yearly<br>Instructional Time (Total)||.00020*||
|Proportion|Mathematics|||-.00053|
|of School-age|Sciences|||.00125**|
|Cohort|Languages|||.00044*|
|Exposed to|ArtsMusic|||.00031|
|Instructional|All Social Studies|||.00022|
|Hours in|MoraVReligion|||.00055|
|Subject Area|Physical Ed.|||-.00024|
|(circa 1960):|Pre-Vocational Ed.|||-.00040*|
|Educational<br>|Primary<br>Education|.0043**|.0027**|.0028*|
|Enrollment<br>Rates:|Secondary<br>Educadon|.0034*|.0037**|.0034**|
|Control|Lagged<br>Dependent Var.|.68**|.67**|.70**|
|Variables:|Mining/Oil<br>Exporters|.08*|.08*|.08*|
||Export Commod.<br>Concentration|-.003**|-.003**|-.003**|
||Total Ferdlity<br>Rate (1965)|.015|.021|.020|
||Adjusted R<sup>2</sup>|.918|.920|.920|
||Constant|.91|.90|.82|
||Number of Cases|63|63|63|

> * Unstandardized regression coefficient is at least 1.5 times its standard error

> ** Unstandardized regression coefficient is at least 2.0 times its standard error