rafmacalaba/datause-encoder-data huggingface dataset:
shallow wells resid. water 0 0 9S0 Average distance of these water sources (outside yard/household) but used by the consumers themselves) from place of consumption: handpump bucket other sources of shallow wells resid. water meter 0 0 0 Average price of water sold by vendors (carriers): Rp/cum 0 Do you consider this price an acceptable indicator of the cost of water (considerlng hauling cost and the cost at the source of the water)? If answer Is yes, change Indicator to 1" and go to paragraph 12, If no write 10" and continue here.

Indic. 0 Average quantity supplied by those water sources which the vendors use to purchase the water they distribute: cum/day/ source 0 Cost of that water at the source (different from price -- exclude remunerationlnet income of concessionalres/owners): Rp/cum 0 Average delivery rate of vendors: cumLday/ vendor 0 THIS IS THE END OF DATA INPUT FOR THE 'WITHOUT' PROJECT CASE. PLEASE PRESS CALC (F9) AND REVIEW THE RESULTS BELOW. THE ONLY PROPER WAY TO MODIFY RESULTS IS TO MODIFY THE INPUT DATA. IF FINISHED, PROCEED TO SECTION C (PARA13). 12. Economic cost of water to be replaced:

|Rp/cum|electric<br>tandp<br>shallow well|ump bucket<br>other<br>source||vendors|non-resid.|
|---|---|---|---|---|---|
|capital|0|0<br>0|0||0|
|O&M|0|0<br>0|0|0|<br>0|
|hauling|0|0<br>0|0||0|

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the distance between them. Hence, we will observe a zero θ even though diffusion across network links is taking place. The second exception is when the strategic complementarities and substitutes precisely offset each other. Although it is possible, this situation seems unlikely. If unobserved contextual effects can only generate positive correlations in technology and business practices, which is likely, then a nonsignificant θ indicates that the network diffusion is zero, and a positive θ suggests that the presence of strategic substitution affects adoption decisions. However, we cannot completely rule out the possibility that a negative correlation between practices could be the result of a negative correlation in the profitability of adoption α _i_ . For instance, if an innovation such as subcontracting is profitable for upstream firms but not for downstream firms, then firms that are linked as suppliers and clients will have negatively correlated practices because suppliers, by definition, are upstream relative to their clients.

Equation (3) is a dyadic regression. The dependent and independent variables are defined for every pair of firms _i_ , _j_ in the data, which implies that there are _n_ × ( _n_ − 1 ) observations that underlie the regression ( _n_ denotes the number of firms). Dyadic observations are not independent because the residual _uij_ is correlated with _uik_ . To compute standard errors that are robust with respect to the correlation in the error term across firms, we use the bootstrapping procedure that is described in section 1 in our online appendix.

# **<<A>>III. Data**

To implement our testing strategy, we use detailed firm-level data that were collected under the leadership of the World Bank in Ethiopia and Sudan. Virtually the same questionnaire and sampling strategies were used in the two countries. The data on the Ethiopian firms were collected as part of the Ethiopia Investment Climate Survey, which was implemented by the Ethiopian Development Research Institute in mid-2006.<sup>8</sup> The survey covered 14 major cities 14
Table 4). It is, however, di¢ cult to compare the incidence of livestock transactions with those of other physical assets because the data for the latter is over ...ve years preceding the ...rst round while the recall period for livestock is only four months before the ...rst round.

# 4 Estimating Equation for Net Sales of Livestock

As indicated earlier the focus of this study is to investigate the hypothesis that households smooth their consumption in the face of income shocks using livestock as a bu¤er stock. It should, however, be noted from the outset that livestock transactions alone do not fully represent net savings even if livestock accounts for a large proportion of the portfolio of households. There are other forms of savings, but we do not have a reliable time series data on other assets and grain stocks due to some shortcomings in the frame of the questionnaire administered on the survey sites. It is, therefore, impossible to estimate a model of optimal savings and portfolio selection.

As shown in the previous section more than 60 percent of net livestock sale observations are zero. This implies that the decision to participate in the livestock market is nonlinear, signalling the possible presence of transactions costs that, in turn, limit the role of livestock as a bu¤er stock in response to income shocks. This calls for the use of a friction model to account for the possible impact of transactions costs on livestock transactions.

The limited dependent panel data model that we estimate distinguishes between desired and actual net sales of livestock. Due to the presence of transactions costs, not all desired net sales are realized, resulting in three distinct observations: positive, negative and zero net sales of livestock. The model that takes these three observations into account can be written as with a two-threshold Tobit speci...cation characterized by the following censoring rules where yit<sup>�is desired or latent net sales of livestock per adult equivalent units8of household</sup> i in period t; yit is observed net sales of livestock per adult equivalent units, � 1 < 0 and � 2 > 0 are threshold values re‡ecting the unobserved transactions costs that relate the

> 8Adult equivalent units are calculated using World Health Organization (WHO, 1985) codes. The codes were constructed based on nutritional equivalence scales on data speci...c for East Africa.

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# Estimation Results

Our baseline results are summarized in Tables 4-6. Each table presents results for a different measure of institutional quality – Bureaucratic Quality, Control of Corruption and Protection of Property Rights. As mentioned above, we estimate separate equations for each indicator due to their high correlation. Since our tariff data is available for only 43% of the sample for which trade data are available, we estimate all specifications first without tariffs and then add them as a regressor. We address the potential endogeneity of institutional quality to trade by instrumenting for the sum of institutional quality with the sum of absolute latitude for each country-pair.

Our estimates imply that different legal origins do have a detrimental effect on trade, between 10% and 25%, depending on the specification of our regressions<sup>26</sup> . This is a large effect. Using a similar variable, Helpman Melitz and Rubinstein (2005) find a slightly larger impact in their regressions; this could be because they do not control for institutional quality. Our estimate for the detrimental effect of tariffs yields an elasticity of 1 to 2, thus every percentage point increase in tariffs decreases trade by 1%-2% on average.

However, we also find large positive effects of institutional quality on trade – in some cases very large. In order to assess this effect, the last line in Tables 4-6 report the effect of a change of one standard deviation in institutional quality on trade<sup>27</sup> . For instance, take Bureaucratic Quality in Table 4; our estimates imply that one standard deviation increases trade by 50%-120%. This is a much larger effect than that of differences in legal origins. A similar

> 26 The largest point estimate is in table 6, column 4. The effect is exp{-0.29}-1 = -0.25. 27

> The percent increase in trade due to 1 s.d. of institutional quality is calculated as follows: exp{coef*sd}-1.

19
by _nondiscriminatory_ but WTO-inconsistent trade policies under formal DSU litigation. That investigation found some evidence consistent with the hypothesis that legal capacity and political-economy relationships negatively affect the willingness of countries to engage in the dispute resolution process. That sample of data under investigation was much different, however, in that it analyzed WTO disputes initiated against a variety of countries (not solely the U.S.) that were applied on a quasi-MFN basis, thus negatively affecting many exporting countries.

The next two specifications (7) and (8) are crude attempts to address an econometric issue of the nonindependence of some of the observations in the estimation, given the pooling of the trade remedy data over time.<sup>29</sup> The concern that we address in specification (7) is that the disputes involving an EU member country as a complainant are somehow driving the results. This could be the case given that there are a number of instances in which multiple EU-member countries are involved in related trade remedy investigations over the same product, which may make it more likely for those related cases to be jointly challenged at the WTO. For example, common elements (WTO-inconsistencies) across cases against different EU members could make it cost effective to spread the litigation burden across the member states. Nevertheless, when we include an indicator for the affected foreign country being an EU-member, the qualitative pattern of results are virtually unchanged, and the EU indicator itself is negative, though it is not statistically significant.<sup>30</sup> Next, in specification (8) we attempt another approach, which is to include an indicator for the instances in which an imposed trade remedy is one of multiple remedies that the U.S. imposed against the same set of products from any set of different countries. In this specification, there is evidence that such a remedy is more likely to be challenged at the WTO. This could be indicative of the foreign country seeking to increase market access while its primary competitors would still be constrained by other U.S.-imposed remedies. If it were successful, the country would essentially receive _preferential_ access to the U.S. market. Nevertheless, we also note that inclusion of this variable does not substantively affect the qualitative pattern of results regarding the other variables of interest to the estimation.

> 29 In future research, as more data becomes available, it would also be useful to estimate a model with country fixed effects and which also investigates dynamic questions such as when (e.g., how long after imposition) trade remedies get challenged, etc.

> 30 On the other hand, to the extent that a U.S. trade remedy investigation leads to differential duties imposed on firms from different EU member states (implicitly providing firms from some EU states preferential access to the U.S. market relative to other EU competitors, this could make it more difficult for the EU to act jointly.

22
Table A5.5: Achieved FGD sample by gender (F=female, M=male), age and city

||||**Age**<br>|**bracket of pa**<br>|**rticipants (ye**<br>|**ars)**<br>|||
|---|---|---|---|---|---|---|---|---|
||**<=**|**35**|**>35 bu**|**t <=40**|**>40 b**|**ut <45**|**>=**|**45**|
|**City**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|
|Bengaluru|5|2|1|3|0|2|2|1|
|Ahmedabad|2|4|2|1|2|2|2|0|
|Mumbai|2|2|2|4|0|2|4|0|
|Pimpri-Chinchwad|4|6|4|3|0|0|0|0|
|Bhubaneswar|6|1|1|4|0|3|1|0|
|Delhi|1|2|5|4|0|2|2|0|
|TOTAL|20|17|15|19|2|11|11|1|

Table A5.6: Achieved FGD sample by gender (F=female, M=male), length of property ownership and city

|**Length of property ownership (years)**|
|---|

||**1**|**-3**|**4-**|**6**|**7**|**-9**|**1**|**0+**|
|---|---|---|---|---|---|---|---|---|
|**City**|**F**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|
|Bengaluru|2|3|2|4|1|0|3|1|
|Ahmedabad|1|3|5|2|0|1|2|1|
|Mumbai|2|3|1|1|1|1|4|3|
|Pimpri-Chinchwad|2|4|1|1|3|3|2|1|
|Bhubaneswar|1|3|5|3|0|1|2|1|
|Delhi|4|3|3|3|0|0|1|2|
|TOTAL|12|19|17|14|5|6|14|9|

Table A5.7: Main languages used in each FGD

|**City**|**Females**|**Males**|
|---|---|---|
|Bengaluru|Kannada and English|English|
|Ahmedabad|Hindi|Hindi|

45
conversely, refrained from FX purchases, or sold foreign currency, when the exchange rate was under pressure to depreciate.

<!-- Start of picture text --> Figure 3.2: Colombia -- FX Interventions and nominal effective<br>exchange rate (2000-2012)<br>0.5% 140<br>ρ= 0.43<br>0.4% 130<br>0.3% 120<br>0.2% 110<br>0.1% 100<br>0.0% 90<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>-0.1% 80<br>-0.2% 70<br>6-month Moving Average of FX Interventions in % of GDP NEER of Colombian peso (right axis)<br>Sources: IMF World Economic Outlook and World Bank Global Economic Monitor.<br><!-- End of picture text -->

# **3.3.  Turkey**

Turkey too operates a floating exchange rate regime with inflation targeting. The Central Bank of the Republic of Turkey (CBRT) reserves the right to intervene to mitigate exchange rate volatility and manage foreign exchange reserves. A strong foreign exchange reserve position has been consistently stated as a prime CBRT objective, especially in the aftermath of the 2001 economic crisis when Turkey switched the lira’s crawling peg to floating (February 2001). When accelerated reserve accumulation is deemed necessary, CBRT holds FX buying auctions under preannounced terms and conditions (Ozatay, 2005). The program was first put in place in 2002 to build up the FX reserve position with minimum interference in exchange rate movements. In addition, CBRT holds FX selling auctions when it deems necessary to provide liquidity to the market. Auction programs may be complemented by discretionary intervention. The CBRT publishes the results of its daily FX auctions.

In September 2011, the CBRT introduced a new instrument, the Reserve Option Mechanism (ROM), as a complementary tool to mitigate the impact of volatile capital flows on the exchange 12
migrating alone or with others, their participation in education and the labor market, the types of jobs they work in, and whether or not they ever return to their home countries.

All these factors are important for helping to assess the potential impact of migration on young migrants and on the development of their sending countries. This paper aims to be a first step in filling these knowledge gaps, by using microdata from national censuses and large scale household surveys in order to provide a detailed description of the lives and circumstances of young migrants from developing countries. Detailed data are available for migration to developed and developing countries, allowing comparison in South-North and South-South migration patterns.

Some of the main patterns observed are:

- Youth are a high share of the flow of migrants, especially in migration to developing countries. The share of 12 to 24 year olds in total migration is much smaller in migration to countries such as Canada, which rely on skill-intensive admission criteria.

- The share of females among youth migrants varies from 39% in South Africa and 42% in the U.S. to 65% in Argentina, averaging 50 percent across all countries looked at.

- Youth migrating to other developing countries are less likely to be accompanying a parent migrating. About 80 percent of 12 to 14 year olds accompany a parent, compared to 50 percent of 15 to 17 year olds and less than 20 percent of 18 to 24 year olds.

- 3 -
# **Account penetration among mobile money users in economies with the highest use of mobile money**

Mobile money users by whether with or without a formal account (as % of all adults) <!-- Start of picture text --> 100<br>80 Mobile<br>money<br>users<br>60<br>WITHOUT<br>ACCOUNT<br>40<br>20 WITH<br>ACCOUNT<br>0<br>ANGOLA UGANDA TAJIKISTAN ALBANIA SOMALIA CONGO, REP. ALGERIA GABON SUDAN KENYA<br>Note: Mobile money users are adults who report having used a mobile phone in the past year to pay bills or send or receive money.<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> or send or receive money. This means that 86 percent of all mobile phone users in the country are mobile money users. By comparison, the share in all of SubSaharan Africa is 23 percent.<sup>26</sup> Many of those who use alternative banking tools may also use formal financial services. But a growing share of people—especially in the developing world—rely solely on systems outside the formal banking sector. In the 10 economies with the highest reported use of mobile payments, many mobile money users are not otherwise included in the formal financial system (figure 1.21). In Kenya 43 percent of adults who report having used mobile money in the past 12 months do not have a formal account. In Sudan 92 percent do not. Overall in Sub-Saharan Africa, 12 percent of those without a formal account use a mobile phone to conduct financial transactions.

The degree to which mobile money is capturing the nonbanked market clearly differs across economies. This may reflect the varied and quickly evolving public policies surrounding mobile money. When M-PESA began in Kenya, it had no association with the formal banking sector and mobile banking customers there were exempt from the documentation requirements imposed by banks.<sup>27</sup> But governments increasingly are favoring bank-led models in which mobile money providers have partnerships with or are formed directly through banks. In India the government introduced regulations in 2008 requiring that mobile money schemes be operated by banks, making it difficult for an M-PESA–type market entrant to lead the nascent mobile money movement.<sup>28</sup> This has probably contributed to the slow growth of mobile money in India, where only 4 percent of adults in the Global Findex sample report having used a mobile phone in the past 12 months to pay bills or send or receive money.

**27**

MEASURING FINANCIAL INCLUSION
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