rafmacalaba/datause-encoder-data huggingface dataset:
and 1999q1. Table 2 shows the results of the _SEQT_ ( _l_ + 1 _|l_ ) test along with the estimated break dates with their 95% confidence intervals.

**Table 1. WDmax Test for up to 4 Breaks. VAR with no Linear Restrictions**

||VAR with X=GDP|VAR with X=C|
|---|---|---|
|WDmax statistic|54.13|50.75|

Critical value at 5%: 30.62 Next we apply the procedure setting X=C. The _WD_ max _LRT_ ( _M_ ) statistic allows us to reject the null hypothesis at the 5% level (Table 1). The value of the _WD_ max _LRT_ ( _M_ ) statistic is 50.75, whereas the critical value at the 5% level is 30.62. We conclude then that there is at least one structural break in the considered VAR model. The _SEQT_ ( _l_ + 1 _|l_ ) test allows us to reject the null hypothesis of one break against the alternative of two breaks as _Seq_ (2 _|_ 1) = 39 _._ 32 with the 5% critical value being 32.60. Moreover, _SEQT_ ( _l_ + 1 _|l_ ) also allows us to reject the null hypothesis of two breaks against the alternative of three breaks as _Seq_ (3 _|_ 2) = 37 _._ 88 with the 5% critical value being 33.96. Nevertheless, the null hypothesis of three breaks against four breaks is not rejected as _Seq_ (4 _|_ 3) = 0 _._ 00 where the 5% critical value is 34.89. Thus, the performed tests allow us to conclude that there are three structural breaks in the considered VAR specification which includes a private consumption variable: 1970q2, 1985q1 and 1999q1. Table 3 shows the 95% confidence intervals for each break.

**Table 2. The** _SEQT_ ( _l_ + 1 _|l_ ) **Test for a VAR with no Linear Restrictions with X=GDP**

||Statistic|Critical value<br>at 5% level|Signifcant break|
|---|---|---|---|
|Seq( 2_|_1|)<br>40.84|32.60|-|
||||1969q3 (1968q1-1971q1);|
|Seq( 3_|_2|)<br>37.88|33.96|1982q4 (1981q2-1984q2);<br>and 1999q1 (1997q1-2001q1)|
|Seq( 4_|_3|)<br>0.00|34.89|-|

Afterwards, we apply the tests using a VAR with a lag fixed at _p_ = 2. The number and location of the break-dates remain significantly unchanged.<sup>8</sup> In both cases, the VAR model with one lag can then be considered as a valid and parsimonious restriction of the VAR model with two lags.

_3.2 Application of the Qu and Perron’s Algorithm in a VAR with Linear_ . _Constraints_

> 8The test results are not reported because of space considerations but are available by request.
17

**Table 7. Alternative Specifications (probit)**

|**Estimates for graduate (1= graduate,**|**0= non-grad**|**uate)**||||
|---|---|---|---|---|---|
||**FY89–**<br>**FY18**|**FY89–**<br>**FY18**|**FY89–**<br>**FY18**|**FY89–**<br>**FY18**|**FY00–**<br>**FY18**|
|GNIper capita/operational cutoff|0.087***|0.088***|0.094***|0.090***|0.088***|
||(4.94)|(5.12)|(5.43)|(5.48)|(4.75)|
|Logofyears over cutoff - capped at 9|0.115***|0.106***|0.111***|0.107***|0.061***|
||(5.50)|(5.56)|(6.59)|(5.60)|(2.42)|
|Log of years above cutoff in excess of<br>9|−0.035**|−0.035**|−0.034**|−0.033**|−0.026**|
||(−2.32)|(−2.47)|(−2.37)|(−2.32)|(−2.19)|
|Creditworthiness|0.007***|0.006***|0.006***|0.006***|0.002**|
||(5.31)|(5.23)|(5.41)|(5.31)|(2.58)|
|Povertyheadcount|−0.418**|−0.379**|−0.325*||−0.501**|
||(−2.03)|(−1.97)|(−1.96)||(−2.29)|
|Poverty gap||||−1.101*||
|||||(−1.85)||
|Logofpopulation|0.049***|0.051***|0.051***|0.049***|0.096***|
||(3.88)|(5.11)|(5.27)|(4.79)|(6.35)|
|Logof life expectancy|0.749***|0.666***|0.665***|0.695***|1.458***|
||(2.90)|(2.92)|(3.00)|(3.04)|(2.76)|
|Urbanpopulation share|0.419**|0.409**|0.392**|0.431***|0.881***|
||(2.44)|(2.51)|(2.56)|(2.63)|(4.20)|
|Institutional development|0.598**|0.516**|0.511**|0.476**|0.569***|
||(2.43)|(2.32)|(2.40)|(2.11)|(3.58)|
|Resource rents/GDP|0.001|−0.031|0.013|−0.008|−0.238|
||(−0.00)|(−0.14)|(−0.06)|(−0.04)|(−1.60)|
|Logof OECD GNIper capita/cutoff|1394***|1.326***|1.397***|1.350***|1.088***|
||(7.49)|(7.36)|(8.53)|(7.61)|(6.20)|
|Angola dummy|0.188*|0.146|0.124|0.138|0.292**|
||(1.83)|(1.54)|(1.45)|(1.51)|(2.13)|
|Exports/GDP|−0.070|||||
||(−0.63)|||||
|Battle dummy||−0.018||||
|||(−1.34)||||
|Conflict ongoing|||0.017|||
||||(0.54)|||
|_Number of observations_|_674_|_705_|_705_|_705_|_581_|
|_Number of countries_|_64_|_65_|_65_|_65_|_65_|
|_pseudo-R_<sup>_2_</sup>|_0.78_|_0.78_|_0.79_|_0.78_|_0.85_|
|_Mean dependent variable_|_0.28_|_0.27_|_0.27_|_0.27_|_0.23_|
|_Correctly predicted(%)_|_94.4_|_94.5_|_94.5_|_94.6_|_96.0_|

_Note:_ t-statistics (in parentheses, beneath marginal effects) are adjusted for clustering by country; * indicates significance at the 10 percent level, ** at the 5 percent level, and *** at the 1 percent level for two-tailed tests.
were randomly selected to include an offer for a monetary payment of 75 Pesos (about US$5) to the first 200 clients who submitted their answer.

The total number of letters sent (40,000) was chosen based on information from our partner financial institution that typically only 2-3 percent of their clients reply to any sort of mail offer sent by them. This expected response rate would yield 800-1,200 clients who would form the sample for our randomized experiment. However, we received much fewer responses than anticipated – only 42 responses. That is, only 0.1 percent of clients expressed interest in a financial literacy training program.

We suspect that this low response rate is in part due to the climate of insecurity that has prevailed in Mexico City during the past few years and that has made people distrustful of unsolicited requests for data. Around the time of our study, fake phone and mail extortions had become a threat to the general population. In fact, both the federal government and commercial banks launched a campaign asking people to avoid giving personal information to strangers, especially through phone or mail communications offering money or prizes in return for personal data. Our letters were sent out by our partner financial institution, bearing their logo, but people may still have been suspicious of the request for data.<sup>10</sup>

# **4.2 Approach Two: Low Demand for Financial Literacy Demonstrated through an Online Campaign**

Our second strategy for obtaining a study sample was to conduct a screener survey through Facebook. We created a Facebook page for financial literacy and launched a Facebook ad that pointed to this page. The Facebook page included the same information as the letters sent in the mail, mentioning the importance of financial literacy. The page invited people to indicate their interest in participating in a (generic) financial literacy course by clicking on a link that redirected them to a page where they could answer our screener survey online. This survey contained the same questions as one mailed to our partner financial institution’s clients. We did not offer a financial incentive for completing the online survey.

The Facebook ad was targeted to individuals residing in Mexico City and ran for two months, from mid-February to mid-April, 2011.<sup>11</sup> It was displayed about 16 million times. We obtained a total of 1,240 fans of our Facebook page and 119 responses to the online survey. Since this

> 10 All letters included a toll free number where people could make enquiries. We did not receive any phone calls enquiring about training in response to this mailer, suggesting a low demand for such training. 11 At the time, Facebook had 7,743,220 registered users who reside in Mexico City (approximately 87 percent of the population).

12
# **1. Introduction**

Migrants send home colossal sums of money—sums that appear to be soaring. Workers’ remittances to developing countries were just US$47 billion in 1980 (constant 2011 dollars) in the best estimates we have. After barely rising by 1990 ($49 billion), they doubled by 2000 ($102 billion), and from there, _tripled_ by 2010 ($321 billion). Estimated remittances now far exceed the amounts of aid that flow to developing countries and are equal in scale to total private debt and portfolio equity flows (Figure 1).

But this explosive change has left economists surprisingly unsure of its broad development effects. One narrative to policymakers is optimistic: _“Remittances have a large positive effect on national income in many developing countries ... [I]n economies where the financial system is underdeveloped, remittances appear to alleviate credit constraints and may stimulate economic growth”_ (World Bank 2006, 86).

A competing narrative is strikingly pessimistic: _“Remittances do not seem to make a positive contribution to economic growth . . . Perhaps the most persuasive evidence in support of this finding is the lack of a single example of a remittances success story: a country in which remittances-led growth contributed significantly to its development . . . But no nation can credibly claim that remittances have funded or catalyzed significant economic development”_ (Barajas et al. 2009, 16–17).

How could such vast and soaring financial flows leave no trace in growth and development? In this paper, we review previous explanations and offer three new ones. The new explanations relate to measurement error, statistical power, and the effect of emigration on domestic labor stocks.

First, much of the increase in remittances across time may be illusory, arising from large changes in non-classical measurement error. To show this we start with macro data on remittances and compare the growth rate in remittances recorded to what one would expect based on the growth in migration and the growth in incomes in destination countries over the same period. Based on 2
Policy Research Working Paper 8725

# **Abstract**

Expectations of future socioeconomic mobility are an important determinant of cur- rent policy preferences. But how may these expectations be formed? Using Life in Transition survey data for a large set of transition economies and several Western European countries, this paper examines the link between beliefs about the importance of personal connections for getting access to opportunities, such as a good job or university education, and expectations of future socioeconomic mobility. The analysis of survey data finds evidence that: (i) lack of connections is associated with expectations of a lower position on the future social ladder; and (ii) when informal connections are unavailable, it matters for your aspirations whether you perceive connections to be vital or not. There is also some evidence that in the European Union, where formal institutions are stronger, individuals are less likely to resort to informal institutions such as personal connections, even when these are available. Perceptions of unequal access to opportunities are also linked with stronger redistributive preferences. Finally, there is some evidence that unequal access to opportunities is associated not only with lower intragenerational mobility, but also with lower intergenerational mobility.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at acojocaru@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 3: Mean values of variables used in the empirical analysis

||**NSS 61**|**NSS 68**|
|---|---|---|
|labor force participation|0.46|0.32|
|age|36.60|37.38|
|age_sq|1593.10|1654.60|
|schooling|3.46|4.54|
|schooling_sq|31.56|43.70|
|marital_dummy|0.72|0.72|
|log_land|-2.28|-2.58|
|log_land_sq|11.66|12.73|
|log_hhsize|1.64|1.57|
|share children_under_6|0.07|0.06|
|share children_above_6|0.09|0.09|
|share female_adult|0.20|0.22|
|share female_dependent|0.05|0.06|
|share male_dependent|0.04|0.05|
|female_headed_household|0.11|0.12|
|max_schooling_household|7.69|8.84|
|st_dummy|0.08|0.09|
|sc_dummy|0.19|0.19|
|obc_dummy|0.41|0.44|
|hindu_dummy|0.83|0.82|
|muslim_dummy|0.12|0.13|
|survey|1.00|2.00|
|_Irank_2|0.43|0.41|
|_Irank_3|0.17|0.15|
|_Irank_4|0.06|0.07|
|_Irank_5|0.11|0.11|
|_Irank_6|0.05|0.06|
|gap|0.18|0.20|
|all_farmers_share|0.26|0.19|
|all_non_farm_self_share|0.12|0.11|
|all_casual_share|0.19|0.17|
|all_non_farm_regular_share|0.07|0.08|

# 5. Main results

The four models presented above are estimated for all women aged 15 years and above, with participation defined based on the US definition of employment in the NSS (table 4). The estimated marginal effects are reported for each explanatory variable in all four models. Marginal effects indicate by how much the probability of labor force participation changes for a unit increase in the corresponding explanatory variable.

21
The next step involves obtaining the identities A1.2 throuah A1.4. These identities are derived as flows from the right hand side of equation Al.l. Given the set limitations on the equations we get 12 equations each having three variables but only 11 equations are linearly independent. In the Turkish case not enough data are available to solve the entire system without making further assumptions.

Given the 11 independent equations we can determine 11 variables provided they appear in different equations. The 11 equations contain 33 variables of which 11 are known from the flow version of equation Al.l. We have additional independent information from the balance of payments on reserve flows (K dRESc and K dRESd) and from fiscal data on the financing of the public sector (K dFGb) and K dFGo). This leaves us with 18 variables to be determined. However, given equation Al.l we also know the net asset position of the country ind we know that the sum of all net positions of the individual sectors must add up to that total. This provides us with an additional constraint so that we are left with 17 unknowns of which 11 can be determined. Hence, we need to Lind additional information for 6 variables.

For 1988, our base year, we choose to make the assumptions on the cross-currency effects as we have some additional information on the currency composition of external debts and reserves as well as the cross-exchange rates. We assume that the currency composition of foreign exchange deposits is similar to that of Central Bank reserves (approximately 19 percent DM, the remainder US dollars). Given the cross-exchange rates, we can determine the cross-currency effects on the foreign exchange deposits held by 8
we will include a full set of country and location dummy variables to capture local effects. We also rely on a rich set of sensitivity checks with different country groups, firm groups, and variations in bribery measurement.

# **3. Data**

Firm-level data for the LAC region are available from the World Bank’s Enterprise Surveys (ES) database. Enterprise Surveys is a primary data source for studying firm growth, obstacles, and performance.<sup>6</sup> There is great variability in the income and development levels of LAC countries yielding a heterogeneous group of firms. ES data for the LAC region is also unique because it is the first region to have completed two rounds of surveys following the same sampling methodology with a focus on panel data collection. A panel data set of firms interviewed in 2006 and 2010 will also be utilized to study the effects of corruption on firm growth.

Firms are asked if they were solicited for bribes when conducting a specific set of business transactions. Data on solicitations for six business transactions are available; requesting electricity or water connections; obtaining a construction, operating, or import license; or meetings with tax officials (Table 2). Note that we are only informed about bribery activity conditional on a firm participating in a business transaction within the last complete fiscal year, and there may be other instances of bribery not captured in the surveys. This is a clear disadvantage, however, since we are capturing instances of corruption occurring when a firm is trying to “get things done”, the data will capture how often bribery affects business operations when it matters. Framing in the last complete fiscal year is also consistent with reported annual sales.

As discussed in the previous section, we restrict the data to firms located in country-locationsector cells with at least thirty responses to bribery questions. A description of the variables is listed in Table 3, and a comparison of summary statistics for firms in the full and regression samples is presented in Table 4. Means and the standard deviation across countries are presented. There are 29 countries in the study. Brazil is excluded since it was surveyed independently in

> 6 See http://www.enterprisesurveys.org/ for the methodology and details of the data.

9
Concerning any possible simultaneity in the estimation of (7) we cannot rule out a priori an _X_<sup></sup> influence of TFP growth on investment in infrastructure, . Possible causes of simultaneity _X_ include endogenous responses of infrastructure policies to TFP growth, making it necessary to test the presence of reverse causation in the data. This will need to be tested.

Country-specific estimations, as opposed to panel estimations, call for longer time series in order to produce efficient estimators. Two sets of long time series can be considered. First, physical indicators of infrastructure stocks have been used in the literature. Canning (1999) uses indicators of telephones lines availability, electricity generating power and length of paved roads and railways to estimate an aggregate production function. This dataset and the one available from the World Bank Indicators (WDI) database include time series of usable length for key infrastructures (excluding water) for all countries included in our exercise. Second, it is in theory possible to build time series of infrastructure stocks based on investment data together with the perpetual inventory method – just as time series of _K_ are normally constructed. Unfortunately, in practice financial data on infrastructure (in monetary terms or as percentage of GDP) are scarce for the sample countries. Also, some authors (see Pritchett, 1996) have warned against the poor quality of financial indicators of public investment. For these reasons, we concentrate on physical indicators of infrastructure.

With respect to explanatory variables, we use data from WDI, covering a time period up to 2005:

- Number of mobile and fixed-lines telephone subscribers  [WDI variable name: **it_tel_totl** ];

- Electricity:

- Electricity generation in kwh [WDI variable name: **eg_elc_prod_kh** ];

- Electricity power transmission and distribution losses [WDI variable name: **eg_elc_loss_zs];**

- These two variables were combined to calculate the Electricity generation in kwh, net of transmission and distribution losses . In most of the estimations below, this quality-adjusted time series was used.

- Railway lines, total route in kms [WDI variable name: **is_rrs_totl_km** ] .

8
Despite the improvements undertaken in the present analysis, we have several suggestions for future research. Because AMR is an evolving future threat, improving the basis and particulars of the underlying assumptions will yield better estimates of the impact. These include the links between all potential pathogens and infectious diseases, and how these may affect mortality and labor productivity in varied countries; the implications on health care cost, public versus private, by the level of income among countries and households within countries; and better estimates of the effects on livestock production by type of animals. Regarding the methodology, a better framework is needed to model the snowballing effects of multiple factors and their uncertain propagation and interactions.

# **References**

- Ahmed, Amer S., Maurizio Bussolo, Marcio Cruz, Delfin S. Go, and Israel Osorio-Rodarte. 2017. “Global Inequality in a More Educated World.” Forthcoming. World Bank Policy Research Working Paper.

- Ahmed, Amer S., Marcio Cruz, Delfin S. Go, Maryla Maliszewska, and Israel Osorio-Rodarte. 2016a. “How Significant Is Africa’s Demographic Dividend for Its Future Growth and Poverty Reduction?” _Review of Development Economics._ DOI:10.1111/roe.12227 (online version).

- Ahmed, Amer S., Delfin S. Go, and Dirk Willenbockel. 2016b. “Global Migration Revisited: Short-Term Pains, Long-Term Gains, and the Potential of South-South Migration.”  World Bank Policy Research Working Paper 7628, April 2016.

- Arndt, Channing and Jeffrey D. Lewis, 2001. “The HIV/AIDS pandemic in South Africa: sectoral impacts and unemployment,” _Journal of International Development_ , 13(4): 427449.

- Baris, E. and M. Ezzati. 2004. “Should Interventions to Reduce Respirable Pollutants be Linked to Tuberculous Control Program?” _British Medical Journal._ 329:1090-3.

Barry, J.M. (2005). _The Great Influenza._ Penguin Books.

- Benedictow, O.J. (2004). _Black Death 1246-1353: The Complete History._ UK: The Boydell Press.

- Borowitz, B., E. Wiley, F. Saadah, and E. Baris. 2003. “Responding to HIV/AIDS in East Asia and Pacific Region: A Strategy Note for the World Bank. Discussion Paper, the World Bank.

- Bourguignon, François, and Maurizio Bussolo. 2013. “Income Distribution in Computable General Equilibrium Modeling.” In Dixon, P.B., and D.W. Jorgenson, eds., _Handbook of Computable General Equilibrium Modeling._ North Holland, Elsevier B.V., pp. 1383– 1437.

18
# **Table 7. Cross section 1984-1995, OLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|OLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-5.691|-6.385|-6.289|-8.614|-4.570|-4.183|
||(1.813)***|(1.744)***|(1.919)***|(1.483)***|(2.326)*|(3.437)|
|GDPpc71|-0.000|0.000|-0.000|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)**|(0.000)|(0.000)|
|prim_enrol8495|0.007|0.014|0.013|0.020|-0.010|0.018|
||(0.013)|(0.013)|(0.013)|(0.011)*|(0.014)|(0.033)|
|Invt_GDP8495|0.261|0.272|0.297|0.281|0.238|0.290|
||(0.066)***|(0.063)***|(0.065)***|(0.054)***|(0.075)***|(0.086)***|
|Govstab8495|0.070|0.045|-0.002|0.533|0.344|-0.113|
||(0.305)|(0.317)|(0.320)|(0.293)*|(0.321)|(0.417)|
|Corrup8495|0.110|0.202|0.151|0.140|-0.478|0.107|
||(0.356)|(0.326)|(0.417)|(0.337)|(0.459)|(0.463)|
|main8495|0.005||||0.012||
||(0.004)||||(0.005)**||
|egc8495||-0.411||||-1.629|
|||(1.343)||||(1.777)|
|rail8495|||1.262||||
||||(1.720)||||
|roads8495||||-1.414|||
|||||(0.631)**|||
|tel_faults8495|||||0.006||
||||||(0.005)||
|elec_loss8495||||||-0.104|
|||||||(0.095)|
|Observations|55|54|47|49|48|42|
|R-squared|0.51|0.48|0.48|0.60|0.55|0.43|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

27
**Figure 2:  Changes Over Time in Governance Indicators 2002-2005**

<!-- Start of picture text --> Political Stability and Absence of Violence<br>3<br>2005<br>y = 0.9184x - 0.0266<br>2 R 2 = 0.8701<br>1<br>0<br>-3 -2 -1 0 T HA 1 2 3<br>GEOA GO 2002<br>B OL -1 TGO<br>BGD<br>-2<br>IR Q<br>-3<br>Control of Corruption<br>3<br>2005<br>y = 0.9554x - 0.011<br>2 R 2 = 0.9303<br>1<br>V UT<br>SLB TU R<br>0<br>-3 -2 GEO -1 0 1 2 3<br>2002<br>-1<br>-2<br>-3<br><!-- End of picture text --> 34