rafmacalaba/datause-encoder-data huggingface dataset:
Table A 1: The impact of 3G/4G access measured in months on welfare and labor outcomes

||Tot Cons|Food<br>Cons|Non-<br>food<br>Cons|Pov<br>$1.90|Pov<br>$3.20|Lab<br>Force|Farm<br>self-<br>employm<br>ent|Wage/sal<br>aried<br>employm<br>ent|Non-<br>farm self-<br>employm<br>ent|
|---|---|---|---|---|---|---|---|---|---|
|_Access to_:||||||||||
|Months covered 3 or 4G|0.003***|0.004***|0.002***|-0.003***|-0.001**|0.001***|0.000|0.000***|0.000|
||(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|# of observations|13,732|13,732|13,732|13,732|13,732|40,636|40,608|40,630|40,621|
|R-squared|0.092|0.079|0.088|0.049|0.044|0.009|0.003|0.002|0.003|
|Number of individuals|5,495|5,495|5,495|5,495|5,495|19,194|19,182|19,193|19,191|
|F statistics|77.67|68.36|64.96|40.15|33.16|10.50|2.985|2.943|3.538|

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. All these regressions include the additional set of controls (e.g., access to electricity, ownership of dwelling, household size, and a wealth index) although the coefficients for these variables are not reported to save space. *** p<0.01, ** p<0.05, * p<0.1.

Table A 2: The impact of 3G/4G access on welfare and labor outcomes (in first differences)

||Tot Cons|Food<br>Cons|Non-<br>food<br>Cons|Pov$1.90|Pov$3.20|Lab<br>Force|Farm<br>self-<br>employm<br>ent|Wage/sal<br>aried<br>employm<br>ent|Non-<br>farm self-<br>employm<br>ent|
|---|---|---|---|---|---|---|---|---|---|
|_Access to_:||||||||||
|Medium or strong 3G/4G|-0.027|-0.034|-0.007|-0.001|0.002|-0.000|-0.003|0.006|0.008|
||(0.025)|(0.031)|(0.025)|(0.019)|(0.015)|(0.012)|(0.014)|(0.005)|(0.009)|
|Medium or strong 3G/4G|0.020|0.019|0.025|-0.013|-0.006|0.005|-0.001|-0.002|-0.002|
|for one year|(0.026)|(0.031)|(0.024)|(0.018)|(0.019)|(0.011)|(0.014)|(0.006)|(0.008)|
|Medium or strong 3G/4G|0.031|0.023|0.024|-0.038**|-0.007|0.017|0.011|-0.002|0.001|
|for two years or more|(0.026)|(0.031)|(0.024)|(0.017)|(0.019)|(0.011)|(0.012)|(0.006)|(0.008)|
|Medium or strong 3G/4G|0.066**|0.054|0.058**|-0.055***|-0.032|0.023**|0.012|0.011*|0.004|
|for three years or more|(0.028)|(0.034)|(0.025)|(0.018)|(0.020)|(0.011)|(0.012)|(0.006)|(0.008)|
|# of observations|8,471|8,471|8,471|8,471|8,471|23,857|23,847|23,851|23,843|

_Notes_ : Standard errors reported in parentheses are clustered by LGAs. All these regressions include the additional set of controls (e.g., access to electricity, ownership of dwelling, household size, and a wealth index) although the coefficients for these variables are not reported to save space. *** p<0.01, ** p<0.05, * p<0.1.
_Table 4: Regions’ distribution by high- vs. not high-income, long-run growth rate and country._

|||||||Long-|Run Grow|th Rate||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Country|High-|Income,High-<br>|Growth<br>|High-|Income,Low-<br>|Growth<br>|Not Hig|h-Income,Hig<br>|h-Growth<br>|Not Hi|gh-Income,Lo<br>|w-Growth<br>|Total|
|||||||||||||||
||No|||No|||No|||No|||No|
||.|Category|Country|.|Category|Country|.|Category|Country|.|Category|Country|.|
|Austria|31|6.46|88.57|0|0.00|0.00|4|1.00|11.43|0|0.00|0.00|35|
|Belgium|30|6.25|68.18|1|0.37|2.27|11|2.75|25.00|2|1.17|4.55|44|
|<br>Bulgaria|0|0.00|0.00|0|0.00|0.00|28|7.00|100.00|0|0.00|0.00|28|
|<br>Cyprus|0|0.00|0.00|0|0.00|0.00|1|0.25|100.00|0|0.00|0.00|1|
|Czech Republic|1|0.21|7.14|0|0.00|0.00|13|3.25|92.86|0|0.00|0.00|14|
|Germany|273|56.88|68.08|24|8.89|5.99|103|25.75|25.69|1|0.58|0.25|401|
|Denmark|11|2.29|100.00|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|11|
|Estonia|0|0.00|0.00|0|0.00|0.00|5|1.25|100.00|0|0.00|0.00|5|
|Greece|1|0.21|1.92|4|1.48|7.69|0|0.00|0.00|47|27.49|90.38|52|
|Spain|13|2.71|22.81|4|1.48|7.02|26|6.50|45.61|14|8.19|24.56|57|
|Finland|18|3.75|94.74|1|0.37|5.26|0|0.00|0.00|0|0.00|0.00|19|
|France|18|3.75|18.75|39|14.44|40.63|1|0.25|1.04|38|22.22|39.58|96|
|Croatia|0|0.00|0.00|0|0.00|0.00|20|5.00|95.24|1|0.58|4.76|21|
|Hungary|0|0.00|0.00|0|0.00|0.00|20|5.00|100.00|0|0.00|0.00|20|
|Ireland|3|0.63|37.50|5|1.85|62.50|0|0.00|0.00|0|0.00|0.00|8|
|Italy|7|1.46|6.36|62|22.96|56.36|3|0.75|2.73|38|22.22|34.55|110|
|Lithuania|0|0.00|0.00|0|0.00|0.00|10|2.50|100.00|0|0.00|0.00|10|
|Luxembourg|1|0.21|100.00|0|0.00|0.00|0|0.00|0.00|0|0.00|0.00|1|
|Latvia|0|0.00|0.00|0|0.00|0.00|6|1.50|100.00|0|0.00|0.00|6|
|Malta|0|0.00|0.00|0|0.00|0.00|2|0.50|100.00|0|0.00|0.00|2|
|Netherlands|28|5.83|70.00|10|3.70|25.00|2|0.50|5.00|0|0.00|0.00|40|
|Poland|1|0.21|1.89|0|0.00|0.00|52|13.00|98.11|0|0.00|0.00|53|
|Portugal|0|0.00|0.00|1|0.37|4.00|23|5.75|92.00|1|0.58|4.00|25|
|<br>Romania|0|0.00|0.00|0|0.00|0.00|42|10.50|100.00|0|0.00|0.00|42|
|Sweden|20|4.17|95.24|1|0.37|4.76|0|0.00|0.00|0|0.00|0.00|21|
|Slovenia|1|0.21|8.33|0|0.00|0.00|11|2.75|91.67|0|0.00|0.00|12|
|Slovak Republic|1|0.21|12.50|0|0.00|0.00|7|1.75|87.50|0|0.00|0.00|8|
|United Kingdom|22|4.58|12.29|118|43.70|65.92|10|2.50|5.59|29|16.96|16.20|179|
|Total|480|100.00|36.34|270|100.00|20.44|400|100.00|30.28|171|100.00|12.94|1,321|

Notes: Authors’ elaboration on Eurostat data. By-category percentages are defined on column totals. By-country percentages are defined on row totals. High- (not high-)income regions are those with an average yearly GDP per capita over the period 2003-2017 equal to or greater than (lower than) the 90% of the sample average. High- (low-)growth regions are those recording a long-run growth rate of GDP per capita between the years 2003 and 2017 equal to or greater than (lower than) the 50% of the sample average.

9
Search costs are closely related to information asymmetries and the bargaining process. The lack of information drives potential buyers or sellers to search for each other. Having found one another, the outcome of their subsequent bargaining is affected by the search costs. A buyer who has incurred high search costs in attempting to find an appropriate seller is likely to pay a higher price simply to avoid recommencing the costly search as long as the premium paid to the first seller does not exceed the cost of the search process itself. This is the intuitive result that underlies the outcomes of some of the important contributions to search theory that establish a link between search costs, competition and price dispersion, namely, that for homogenous goods, even modest buyer search costs can lead to monopoly pricing by sellers (Salop & Stiglitz, 1977; Stiglitz, 1989 and Diamond, 1971). Once monopoly prices are established throughout the market, buyers will no longer expect any value in searching, leading to a situation referred to as the Diamond Paradox: monopoly pricing without search. Most price dispersion models conclude that search costs reduce both consumer and social welfare and that lowering search costs for one buyer will have the positive externality of reducing prices for all buyers by stimulating competition.

Much of the literature relating to search costs is theoretical but some applied empirical studies exist, such as Vukina & Zheng‟s (2007) analysis of search costs and price dispersion in the US hog market. What most of this literature (theoretical or applied) has in common is that it refers to buyers who are price takers. Therefore, the buyer absorbs the search costs allowing for the possibility for sellers to engage in monopoly pricing. Some literature, such as Vakis, Sadoulet and de Janvry‟s (2003) study of Peruvian producers, considers search costs incurred by the sellers. This study demonstrates that households that sell in distant markets to attain better prices incur “not trivial” costs in searching for buyers. Almost two-thirds of the households in their survey that sold at distance markets found the buyer in advance by incurring some search costs. They also importantly note that: “...transaction costs have a large unobservable component, and hence their measure can only be indirectly revealed from the behavior of potential agents in these markets. In addition, with the exception of transaction costs attributes like distance to markets and transportation costs, aspects like market information or search and bargaining procedures are rarely included in most surveys and are unlikely to be comprehensive when included.” _(Vakis et al, 2003, page 2)._ 8
in developing countries actually have a bank account. As shown in the following chart, on average less than 40% of adults in developing countries have a savings or deposit account at a bank or micro-finance institution. As discussed below in Sector 4, the lack of a bank account prevents remittances beneficiaries in developing countries from using the remittances funds they receive regularly to access other financial products that may help them to improve their living conditions, such as consumer loans, mortgages, life and non-life insurance products, pension plans, etc.

<!-- Start of picture text --> Chart 6. Percentage of Population (15 - 65 years) with Savings<br>Accounts in Banks or Micro-finance Institutions in Selected<br>Countries<br>60%<br>40%<br>20%<br>0%<br>Bangladesh Bolivia Costa Rica Croatia Ecuador Egypt Guatemala Guyana Honduras India Indonesia Pakistan Philippines Slovenia Tanzania Thailand Tunisia Turkey Uganda Uruguay<br><!-- End of picture text --> Source: With data from CGAP (2004) and World Bank’s World Development Indicators (2004).

# **Access to Central Banks’ Clearing and Settlement Systems**

Another proposal to lower the cost of remittances has been to grant non-bank financial institutions direct access to the clearing and settlement systems that commercial banks use for their international operations – which in most cases are administered by central banks. This survey found that in practically all countries money transfer companies and other non-bank financial institutions – such as credit unions -- do not have direct access to the central banks’ clearing and settlement systems. They must clear and settle their remittances transactions through commercial banks, which allegedly increases the cost they face in their remittances operations. By granting non-bank financial institutions direct access to clearing and settlement systems, it is believed that remittances fees would be reduced.

This survey found enormous skepticism among developing countries about the feasibility of this proposal. 35 out of 40 countries are not interested in pursuing this type of proposal, because they are concerned about the integrity of their clearing and settlement systems. Central banks think that most non-bank financial institutions in developing countries do not have the adequate technological infrastructure to directly participate in clearing and settlement systems. Furthermore, the volume of transactions that most of these institutions conduct is not large. Also, central banks believe that the reduction in remittances fees charged by non-bank financial institutions might not be significant just by granting them direct access to the central 20
Table <u>5. Mean and Median Exporter Size</u><sup>23</sup>

||log(**Mean**Ex|porter Size)|log(**Median**|Exporter Size)|
|---|---|---|---|---|
||(1a)|(2a)|(1b)|(2b)|
|Private Credit / GDP<br>Private Credit / GDP * EXTFIN|0.00185<br>(0.00229)<br>0.00656***<br>(0.00133)|0.00237<br>(0.00255)<br>0.00692***<br>(0.00133)|0.00150<br>(0.00243)<br>0.00428***<br>(0.00110)|0.00247<br>(0.00289)<br>0.00450***<br>(0.00111)|
|Private Credit / GDP * MENA|-0.0200***<br>(0.00583)||0.00823<br>(0.00864)||
|Private Credit / GDP * EXTFIN * MENA|0.00441***<br>(0.00150)||-3.78e-05<br>(0.00220)||
|Property Rights|-0.0725|0.0634|-0.132|0.0238|
||(0.141)|(0.140)|(0.143)|(0.151)|
|Property Rights *TAN|-0.102<br>(0.327)|-0.109<br>(0.316)|-0.254<br>(0.244)|-0.229<br>(0.241)|
|Property Rights *MENA|0.558***<br>(0.0896)||0.229*<br>(0.123)||
|Property Rights *TAN *MENA|-0.202**<br>(0.0957)||-0.191***<br>(0.0641)||
|Constant|7.571***|8.695***|10.71***|12.47***|
||(2.263)|(2.322)|(2.193)|(2.259)|
|Observations|9,816|9,816|9,816|9,816|
|R-squared|0.451|0.430|0.451|0.430|
|Number of MENA countries|3||3||

_Source: EDD (2005-2010)_ _Notes: Three MENA countries include Morocco, Egypt, and Jordan. Results without country controls to allow for the inclusion of more MENA countries yield similar results and are available upon request. Full industry, country, and year controls are included. Errors are clustered by country-year. Standard errors are in parentheses. Significance level markers: *0.10 ** 0.05 *** 0.01_ Next, the _Property Rights*TAN_ interaction term is discussed. In developing countries, banks prefer immovable assets (i.e., a high degree of asset tangibility) such as land for collateral, which highlights an agency problem. The influence of asset tangibility on trade specialization can be magnified or diminished by the development of legal framework and other governance-related factors such as property rights and credit protection.

The interaction of property rights and asset tangibility with exporter size ( _Property Rights *TAN_ ) is insignificant. Strong property rights are not correlated with larger mean or median exporter size in industries characterized by higher asset intangible. However, despite the insignificance, the effects are significantly different in MENA and non-MENA regions ( _Property Rights*MENA_ ). One explanation is that the GCI Property Rights Index is higher in MENA than other regions, and offers more effective protection than in non-MENA. Property Rights in MENA also appear to be

> _23 We estimate regressions with average or weighted tariffs. Trade regulations are generally viewed to be much more restrictive in MENA countries. Weighted tariff rates in MENA average 17.4 percent, and 8.7 percent in non-MENA countries. Tariff data from the WDI is unavailable for Lebanon or Kuwait. Egypt, Iran, and Yemen have only two years of tariff data. The availability of tariff data does not always coincide with availability of EDD or business climate data._

20
<!-- Start of picture text --> Focus Choice by Student Race and Poverty Status — Observed and Predicted Values, All Years<br>| | Observed Values(%) | Predicted Values(%)<br>Focus Focus<br>[LowInc. _|80.56{ 2.17] 4.53] 3.14] 9.60] 79.98] 2.33] 4.04] 3.49] 10.16 |<br>Note: For each row, sum across columns equals 100. For a given group of students, a cell denotes the percent of students of that group that attend a<br>school with a particular focus.<br><!-- End of picture text -->

<!-- Start of picture text --> All Wards Wards 7 and 8<br>Except Ward 3<br>[School Level | Public_| Charter _| Public | Charter |<br><!-- End of picture text -->

Note: school quality is the residual of the minimum-distance estimation regression of campus fixed effects on timeinvariant school characteristics. MHS= | if school level is middle, middle/high, high, or elementary/middle/high. Ward corresponds to the school’s location. Statistics are weighted by enrollment in the last year of the school in the sample.

<!-- Start of picture text --> (0.232)<br>(180.0)<br>(82.77)<br>(122.8)<br>eineeee’Mixed levels (=1 if school serves mixed levels) | an |88.90<br>(88.66)<br>Number of observations 64<br>R? 0.950<br>s.e. of regression 296.8<br>Mean of dependent variable 2526<br><!-- End of picture text -->

Note: Unit of observation: charter school campus. Dependent variable: total costs at the campus level, expressed in $1,000. OLS; standard errors in parentheses. Data: charter schools budgets for school year 2009-2010 from www.depesb.org. Before running the regression, the dependent variable was multiplied by (0.82)*(7/10) to incorporate the following: 1) charter reimbursements account for about 82 percent of charter revenues (which also include sources not modeled in this paper, such as federal grants for specific programs); 2) charter average reimbursements in 2003 and 2009 were approximately equal to $7,000 and $10,000 respectively
other ties as well as for any donor-specific biases regarding quality of systems, to the extent they are invariant over the three periods. Despite the stringent nature of this test, several of the variables significant in previous tests remain significant in equation 3.4. Most notably, PFM Quality is significant at the .05 level, with a coefficient only slightly smaller than in many of the other regressions. Year dummies are not statistically significant, although the coefficient on the 2010 year dummy can be interpreted as a 2.6 percentage point secular increase in use of country PFM systems relative to the 2005 base year, controlling for the other variables in the model.

In Table 4 we exploit our larger dataset to test the robustness of findings from IEG (2011) and Ellmers (2011). Equation 4.1 substitutes an index of PEFA indicators for the CPIA measure of PFM Quality. The coefficient is positive -- a one-standard-deviation increase in the index value is associated with a 1.6 percentage point increase in use of country PFM systems – but not significant. This finding is consistent with the IEG’s (2011) study of World Bank aid to lowincome countries, where PEFA is not significantly related to use of country PFM systems.

Even in our larger study, more than two-fifths of the observations are lost by using PEFA instead of CPIA. However, when the CPIA indicator of PFM Quality is tested on the PEFA sample in equation 4.2, its coefficient is positive, highly significant and of about the same magnitude as in the larger sample. The PEFA and CPIA indicators are correlated at .77, but they differ enough that they perform very differently in our tests, even in the same restricted sample. The findings from equations 4.1 and 4.2 confirm that the empirical link between quality and use of recipient PFM systems is sensitive to the measure of quality. There is no way of ascertaining which indicator, if either, is a more accurate or relevant measure of PFM quality, so it would be more reassuring if use of PFM systems were strongly related to both of them. However, it is meaningful that of the two, the one specified in the PD goals is the one that does significantly 22
Ln I Table 1: Number of Product Categoriesa Facing Quotas in the EC and4 the US. 1985-1989 (in parentheses, rmtier of bindingb quotas)

|||EUROPEA<br>C|N<br>OMUITY|MARKET|||UNITED|STATES MA|RKET||
|---|---|---|---|---|---|---|---|---|---|---|
||1985|1986|1987|1988|1989|1985|1986|1987|1988|1989|
|EAST EUROPEAN<br>FIVEC<br>|71<br>(44)|71<br>(47)|54<br>(41)|54<br>(35)|54<br>(37)|104<br>(4)|105<br>(15)|107<br>(16)|144<br>(7)|145<br>(6)|
|Bulgaria<br>|25<br>(7)|25<br>(8)|11<br>(4)|11<br>(2)|11<br>(1)|0<br>(0)|1<br>(0)|1<br>(0)|1<br>(0)|n.a.'<br>(n.a.)|
|Czechoslovakia<br>|54<br>(36)<br>|55<br>(37)<br>|41<br>(35)<br>|40<br>(30)|40<br>(26)|0<br>(0)|2<br>(1)|2<br>(0)|2<br>(1)|n.a.<br>(n.a.)|
|Hungary<br>|37<br>(22)|37<br>(22)|29<br>(19)|29<br>(18)|29<br>(17)|7<br>(2)|7<br>(2)|10<br>(4)|16<br>(1)|17<br>(3)|
|Peland<br>|37<br>(18)|37<br>(21)|31<br>(18)|31<br>(20)|31<br>(20)|104<br>(0)|105<br>(1)|104<br>(2)|123<br>(2)|139<br>(5)|
|Romania<br>|42<br>(24)|42<br>(25)|35<br>(21)|35<br>(17)|35<br>(20)|96<br>(2)|96<br>(13)|94<br>(15)|138<br>(3)|138<br>(0)|
|EAST ASIAN FOUR'<br>|71<br>(46)|71<br>(56)|67<br>(50)|68<br>(43)|61<br>(39)|82<br>(72)|90<br>(79)|92<br>(77)|109<br>(74)|113<br> <br>(89)|
|China<br>|50<br>(26)|50<br>(30)|50<br>(31)|51<br>(30)|42<br>(24)|56<br>(25)|68<br>(65)|76<br>(54)|87<br>(45)|88<br> <br>(57)|
|Hong Kong,China<br>|46<br>(19)|47<br>(27)|31<br>(23)|31<br>(21)|31<br>(21)|57<br>(37)|66<br>(49)|66<br>(42)|69<br>(41)|73<br> <br>(40)|
|Korea, Rep. of<br>|52<br>(26)|54<br>(37)|46<br>(35)|46<br>(27)|46<br>(21)|71<br>(50)|82<br>(57)|84<br>(48)|94<br>(36)|93<br> <br>(50)|
|Taiwan (China)|50<br>(26)|50<br>(32)|41<br>(31)|41<br>(26)|42<br>(21)|79<br>(52)|81<br>(50)|81<br>(36)|93<br>(32)|92<br> <br>(36)|
|ALL HFA EXPORTERSCd|<br>87<br>(62)|87<br>(68)|79<br>(60)|80<br>(53)|74<br>(52)|109<br>(76)|118<br>(84)|145<br>(96)|147<br>(91)|160<br>(99)|

Source: The Yorld Bank computer files on the MFA.

Notes:

- a The MFA product categories are market specific. The table excludes subcategories, group limits and aggregate limits. b A bound category is defined as one with a quota utilizatian rate of 90 percent or greater.

c d Where several exporters are aggregated, All exporters subject to MFA restrictions.a quota category is counted if it applied to (was b;nding on) at least one exporter in the group.

- n.a. = not available.
**<u>Table 8: Robustness:  Excluding Amhara Region</u>**

||**E**<br>|**xcludes Am**<br>|**hara Regio**<br>|**n**<br>|**Urban On**<br>|**ly & Exclu**<br>|**des Amhar**<br>|**a Region**<br>|
|---|---|---|---|---|---|---|---|---|
||**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|Reform* Non-<br>homework|0.200*<br>(1.74)||||0.257*<br>(1.91)||||
|Reform* Paid-<br>work||0.183*<br>(1.82)||||-0.189**<br>(-1.99)|||
|Reform* All-year-<br>work|||0.184<br>(0.76)||||0.159<br>(0.91)||
|Reform*Education||||0.008<br>(0.96)||||0.004<br>(0.76)|
|Constant|0.696***<br>(17.07)|0.701***<br>(17.77)|0.697***<br>(17.33)|0.677***<br>(14.6)|0.566***<br>(11.49)|0.579**<br>(11.33)|0.570**<br>(11.73)|0.256*<br>(1.87)|
|N|157|157|157|157|157|157|157|157|
|R sq.|0.71|0.71|0.71|0.71|0.71|0.71|0.71|0.71|

The result from estimating equation 1 for all occupations and all geographic areas but Amhara. The dependent variable is women share in occupation and t-stats are in parentheses. Occupation share in the geographic area, the geographic area-time and occupation dummies were controlled for but omitted from table. Standard errors are clustered at the level of the geographic areas. ***significant at 1%, **significant at 5%, *significant at 10%.

Another land reform related issue is that the Tigray region started the land registration on a limited scale in 1998 (Haile et al 2005; Deininger et al 2008), much earlier than other areas. So we examined the robustness of our results by excluding this region from our analysis. Table 9 shows that our results are indeed robust to excluding Tigray, as well as restricting the sample to only urban areas and further excluding Tigray. In all areas outside Tigray, the effect of the law remains significant in increasing women’s share. In non-Tigray urban areas, the effect of the law is significant with regards to non-home-based employment but not in other dimensions. As in the case with excluding Amhara, part of the reason why statistical significance fell in the second panel of Table 9 could be loss of statistical power.

32