rafmacalaba/datause-encoder-data huggingface dataset:
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Büchel, F. and A. Mertens (2000): _Overeducation, Undereducation and the Theory of Career Mobility_ . IZA Discussion Paper No. 195.

Büchel, F. and A. Mertens (2004): Overeducation, Undereducation and the Theory of Career Mobility. _Applied Economics_ , 36:803-816.

Cain, G.G. (1986): The Economic Analysis of Labor Market Discrimination; A Survey. In Ashenfelter, O. and D. Card (eds.): _Handbook of Labor Economics_ , 1.

Chevalier, A. (2003): Measuring over-education. _Economica_ , 70:509-531.

Chevalier, A. and J. Lindley (2006): Over-Education and the Skills of UK Graduates. Unpublished, draft 1.5.

Chiswick, B.R. (1978) “The Effect of Americanization on the Earnings of Foreign-born Men” _Journal of Political Economicy_ , 86(5): 897-921.

Chiswick, B.R. (1997): _Interpreting the Coefficient of Schooling in the Human Capital Earnings Function_ . Downloaded from the United Nations Online Network in Public Administration and Finance.

http://unpan1.un.org/intradoc/groups/public/documents/UNPAN/UNPAN002467.pdf Chiswick, B.R. and P.W. Miller (2005) _Why is the Payoff to Schooling Smaller for Immigrants?_ IZA Discussion Paper no. 1731.

Dolton, P. and A. Vignoles (1997): The incidence and effects of overeducation in the UK graduate labor market. _Economics of Education Review_ , 19:179-98.

Duleep, H.O. and M.C. Regets (1997) “The Decline in Immigrant Entry Earnings: Less Transferable Skills or Lower Ability?” _The Quarterly Review of Economics and Finance_ , 37: 189-208.

Duncan, G.J. and S.D. Hoffman (1981): The Incidence and Wage Effects of Overeducation. _Economics of Education Review_ , 1(1):75-86.

Ehrenberg, R.G. and R.S. Smith (1994): _Modern Labor Economics. Theory and Public Policy_ . HarperCollins College Publishers.

Ferrer, A.; D.A. Green and W.C .Riddel (2006): The Effect of Literacy on Immigrant Earnings. _The Journal of Human Resources_ , 41(2):380-410.

Friedberg, R.M. (2000): You Can’t Take It with You? Immigrant Assimilation and the Portability of Human Capital. _Journal of Labor Economics_ , 18(2):221-251.

31
**Table** **<u>8: Disentangling the effects of closures on the labor market by place of work</u>**

||(1)<br>Emp|(2)<br>Emp|(3)<br>Wage|(4)<br>W_days|(5)<br>W_hours|
|---|---|---|---|---|---|
|_CP_<sup>_30min_</sup>|-0.0031***<br>(0.001)|-0.0030***<br>(0.001)|-0.0623***<br>(0.014)|-0.0267***<br>(0.009)|0.0476***<br>(0.011)|
|_REG_<sup>_30min_</sup>|-0.0011***|-0.0012***|-0.0008|-0.0037*|0.0004|
||(0.000)|(0.000)|(0.004)|(0.002)|(0.003)|
|Workplace out of district x<br>|-0.0025*||-0.0033|0.0037|-0.0146*|
|_CP_<sup>_30min_</sup>|(0.001)||(0.011)|(0.007)|(0.008)|
|Workplace out of district x<br>|0.0039***||0.0047|0.0031|0.0009|
|_REG_<sup>_30min_</sup>|(0.001)||(0.004)|(0.002)|(0.003)|
|Workplace out of dist. in WB<br>x_CP_<sup>_30min_</sup>||-0.0004<br>(0.001)||||
|Workplace out of dist. in WB<br>x_REG_<sup>_30min_</sup>||-0.0003<br>(0.000)||||
|Workplace out of dist. Israel<br>x_CP_<sup>_30min_</sup>||-0.0035*<br>(0.002)||||
|Workplace out of dist. Israel<br>x_REG_<sup>_30min_</sup>||0.0069***<br>(0.001)||||
|Observations|92,335|92,335|82,005|89,952|85,118|
|R-sq. within|0.113|0.117|0.034|0.022|0.011|
|Nr. of workers|44,172|44,172|41,278|43,152|42,414|

_Robust standard errors (Huber-White method) clustered at individual level in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. All regressions include individual fixed effects, time effects, sector-round dummies and a set of controls including years of schooling, age, age squared, marital status, tenure, urban area / refugee camp residence dummies, a set of work place dummies, Hebron district-time effects, locality’s number of fatalities in the preceding quarter, locality’s length of the West Bank wall, the locality’s share of workers employed in Israel the year before and sector-year effects._ 42
2011a: 50) echoes the earlier ones in concluding that “the relationship between the quality of a country’s PFM systems and donors’ use of them is at best weak.” This conclusion is based on simple bivariate analyses of data on use of PFM systems from the PDMS, and the indicator of PFM quality specified in the Paris Declaration (from the World Bank’s Country Policy and Institutional Assessments, or CPIA).

Based on similar methods but using somewhat different data, IEG (2011) and Ellmers (2011) arrive at a similar conclusion. According to IEG (2011), there is no association between the World Bank’s use of PFM systems and their quality in low-income countries, as measured by countries’ scores from PEFA (Public Expenditure and Financial Accountability) assessments.<sup>1</sup> The analysis was limited to only 17 countries, however, because it used PEFA to measure quality of PFM systems instead of CPIA, which is available for many more countries. Ellmers (2011) concludes there is no correlation between donors’ use of country procurement systems (as measured by the 2007 PDMS) and quality of PFM systems as measured by the CPIA. However, that study is limited to only 9 countries in which Eurodad has also sponsored case studies of procurement systems. Ellmers (2011), IEG (2011) and OECD (2011a) all present simple bivariate correlations, each based on a single year of data.

Using data from all three rounds of the PDMS, Knack (2013) showed that a given donor’s use of country PFM systems was positively related to (1) its share of the total aid provided to a recipient, (2) public opinion in support of aid provision in the donor country, and (3) the trustworthiness of recipient country systems. In measuring the latter, however, Knack (2013) did not employ the CPIA indicator of PFM quality specified in the Paris Declaration. Rather, trustworthiness of country systems was measured primarily using the Control of Corruption indicator from the Worldwide Governance Indicators (WGI) project.

> 1 See www.pefa.org for more information on PEFA assessments and indicators.

5
*36-

pension level. This is in agreement with the philosophy of defined contribution plans.

As the. average covered wage can be estimated at US$18,441 in the United States in 1992 19, the annual cost while active of the annuity portion of this private pension is $320.9 for individual contracts and $130.9 for a group contract. This allows us to obtain the following results:

# Table 11

_Lifetime charges in the US Private Pension system_ (US dollars per covered worker per year)

|Costs/Charges|DC Plans|**DB**Plans|
|---|---|---|
|Active Life<br>|46.7|included|
|Passive Life|||
|a) Individual<br>|320.9<br>|included|
|b) Group<br>|130.9||
|Total|||
|a) Individual|367.6||
|b) Gwup|177.6|187.3|

This estimate suggests that defined contribution and defined benefit plans have versr similar costs for the covered worker on a lifetime basis, provided that the annuity is purchased in groups. The lifetime cost is close to double that in Chile.

4.4 **The** Social Security Administration in the U.S.

The mandatory pension system in the US pays old-age, disability and survivors pensions using a defined benefit formula. This system also generates income redistribution through two mechanisms. The first is that the actuarial factors used to calculate benefits are not individually fair, so they redistribute, for example, in favor of those that have the expectation to live more than average. The second mechanism for redistribution is that the benefit formula has a floor and caps, so it pays a higher rate of return to those that have contributed less in the past.

Contributions are 12.4% of eamings, with a maximum taxable earning of 55,500 dollars per year in 1-992. In 1988, 94% cf contributors had earnings below this threshold, and covered earnings were 89% of all wages and salaries. Approximately 80% of workers between 21 and 64 are covered in 19 Source: EBRI (1992) page 161 dividing the tomal taxable earnings of covered workers for 1990. and then inflating bv 4% to consider nominal wage growth.
Policy Research Working Paper 10738

# **Abstract**

Survey data on household consumption are often unavailable or incomparable over time in many low- and middle-income countries. Based on a unique randomized survey experiment implemented in Tanzania, this study offers new and rigorous evidence demonstrating that survey-to-survey imputation can fill consumption data gaps and provide low-cost and reliable poverty estimates. Basic imputation models featuring utility expenditures, together with a modest set of predictors on demographics, employment, household assets, and housing, yield accurate predictions. Imputation accuracy is robust to varying the survey questionnaire length, the choice of base surveys for estimating the imputation model, different poverty lines, and alternative (quarterly or monthly) Consumer Price Index deflators. The proposed approach to imputation also performs better than multiple imputation and a range of machine learning techniques. In the case of a target survey with modified (shortened or aggregated) food or non-food consumption modules, imputation models including food or non-food consumption as predictors do well only if the distributions of the predictors are standardized vis-à-vis the base survey. For the best-performing models to reach acceptable levels of accuracy, the minimum required sample size should be 1,000 for both the base and target surveys. The discussion expands on the implications of the findings for the design of future surveys.

This paper is a product of the Development Data Group, Development Economics. 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/prwp. The authors may be contacted at hdang@worldbank.org and tkilic@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
moment, expectations about changes in sales; and the second moment, uncertainty.<sup>8</sup> Such measures are also used in Avalos et al. (2022) and Cirera et al. (2021).<sup>9</sup> On average, the expectations about change in sales are close to zero (-0.76) even when the recovery phase is included, while the average uncertainty remained relatively high (a standard deviation of 22 percentage points), driven mainly by the second round, where many lockdown restrictions were still in place in the region (Table B1).

_Investments_ : For investment in digital technologies, we rely on a binary variable that indicates if the firm invested in these technologies. The average across all observations in the sample is 20% (Table B1). Though the share of firms that invested in these technologies increased over time, it peaked in the third round of data collection, where almost 22% reported investing in digital technologies. Information on investment in other assets, a variable that we use for robustness checks, was collected only in the third round of the survey. On average, 33% of the firms invested in other assets.

: _Employment adjustments_ Our survey allows us to directly observe employment adjustments on both the extensive margin (firing) and the intensive margin (reduction of hours of operation, salaries, and other benefits). The former is observed in 21%, while the latter in 41% of observations. The share of firms adjusting on the intensive margin was particularly higher during the first wave, where almost 70% of the firms reported these adjustments.

# **3. Results**

# **3.1 Financial fragility and credit constraints are heightened in informal firms both prior to and during the crisis and especially those emerging from necessity.**

Credit constraints and financial fragility are widely cited as the most binding constraint for informal firms. Applying this to the context of a crisis, we observe a negative (but weak) relationship between the probability of falling into arrears and informality (PCA5, see Figure 1, panel a). The result is intuitive because informal firms are less likely to run into problems complying with their financial obligations because they have lower access to

> 8These moments are computed as follows: _E_ [ _yi_ ] =<sup>�3</sup> _i_ =1<sup>_piyi_</sup> <u>1</u> _σ_ = [<sup>�3</sup> _i_ =1<sup>_pi_(</sup><sup>_yi −E_[</sup><sup>_yi_])2]</sup> 2 , where _i_ are the three scenarios: pessimistic, regular, and optimistic, _pi_ , are, therefore, the probabilities of each scenario occurring, and _yi_ is the performance variable that respondents are asked to forecast; in this case, change in sales.

> 9Since these questions are time-intensive, they were not included in Pakistan and Sri Lanka in the first round of data collection, which reduces our sample size.

8
10 higher proportion of young dependents, it is likely that countries with a higher proportion of people under age 14 will receive more remittances. At the same time, if migrants are motivated by investment returns at home, then the variable measuring the real interest rate should have a positive and significant impact on the receipt of remittances. Several macro-economic studies have found that migrants tend to remit more money when interest rates at home are high and positive (El-Sakka and McNabb, 1999; Lianos, 1997).<sup>13</sup> Finally, with respect to financial and political variables, various studies have suggested that factors like the costs of remitting money home, exchange rate spread and the presence of civil unrest may act as determinants of remittances (Freund and Spatafora, 2005; Higgins et al, 2004). For example, it has been suggested that as the costs of remitting money through official channels rise, and the spread between the exchange rates offered by remitting agencies and the central exchange increase, then the amount of remittance monies will fall. Similarly, it has been argued that political or civil unrest in the country of origin will also cause the amount of remittances to decline. To test the impact of these factors, this study includes the following 3 variables: (1) the costs of remitting US $200 to a labor-sending country; (2) the implicit exchange rate spread (defined as the difference between the exchange rate offered by remittance agencies and the central exchange rate as quoted by Bloomberg); and (3) the presence of civil war in a labor-sending country (1 if yes, otherwise zero).<sup>14</sup>
Table 3: <u>Sri Lanka. Characteristics of the Employed Population</u>

||**2002**|**2006/07**|**2009/10**|**2012/13**|**% Change**<br>**2002-2012/13**|
|---|---|---|---|---|---|
|**Labor relation**||||||
|Salaried farm|12.4|10.6|9.6|9.3|-25.1|
|Salaried nonfarm|47.1|49.2|48.6|48.1|2.1|
|Self-employed farm|22.2|16.2|18.3|16.5|-25.4|
|Self-employed nonfarm|13.2|16.8|16.7|17.1|30.0|
|Unpaid|5.3|7.4|7.0|8.9|68.7|
|**Economic Sector**||||||
|Agriculture|39.8|32.2|33.2|32.6|-18.3|
|Industry|19.5|24.8|23.2|25.6|31.4|
|Services|40.7|42.9|43.7|41.8|2.8|
|**Type of Settlement**||||||
|Urban|11.6|12.9|12.1|15.9|37.3|
|Rural|81.5|80.9|81.5|78.4|-3.7|
|Estate|7.0|6.3|6.4|5.7|-18.4|
|**Ethnicity**||||||
|Sinhala|84.9|85.3|84.4|85.6|0.7|
|Sri Lanka Tamil|4.5|4.4|5.0|3.5|-21.3|
|Indian Tamil|5.3|5.3|4.6|5.2|-1.9|
|Sri Lanka Moors|4.9|4.8|5.7|5.3|9.3|
|Other ethnicities|0.4|0.3|0.3|0.3|-8.9|
|**Age structure**||||||
|15-29|25.7|23.5|21.0|19.6|-24.0|
|30-49|49.5|48.4|48.9|48.3|-2.3|
|50-64|19.6|22.8|24.6|26.0|32.6|
|65+|5.1|5.3|5.5|6.1|18.0|
|**Gender**||||||
|Female|34.5|32.4|32.6|34.3|-0.6|
|Male|65.5|67.6|67.4|65.7|0.3|
|**Districts**||||||
|Colombo|11.9|12.6|12.4|12.3|2.9|
|Gampaha|12.9|12.9|13.0|12.4|-4.0|
|Kalutara|6.4|6.6|6.5|7.0|9.8|
|Kandy|7.0|7.0|6.9|7.0|0.1|
|Matale|3.0|2.7|2.8|2.8|-6.7|
|Nuwara Eliya|4.7|4.6|4.4|4.2|-11.6|
|Galle|5.8|6.0|5.8|6.0|2.7|
|Matara|4.7|4.8|4.5|4.6|-2.3|
|Hambantota|3.0|3.1|3.3|3.5|17.9|
|Kurunegala|9.8|9.7|9.2|9.6|-1.8|
|Puttalam|3.9|4.3|4.3|4.3|8.7|
|Anuradhapura|5.4|5.2|5.3|5.1|-5.0|
|<br>Polonnaruwa|1.8|2.1|2.1|2.0|13.2|
|Badulla|5.9|4.9|5.1|4.8|-18.6|
|Monaragala|2.6|2.5|2.7|2.6|-0.3|
|Ratnapura|6.5|6.4|6.9|7.0|7.4|
|Kegalle|4.6|4.8|5.0|4.8|4.7|

Source: Own estimates using HIES 2002, 2006/07, 2009/10 and 2012/13.

25
market where a handful of investors trade to provide liquidity. Second, and more importantly, holding periods are based on trading decisions of investors who, ex-ante, consider the transaction costs of the underlying securities they trade. In a concurrent paper, Naes and Odegaard (2008) use transaction-level Norwegian data to show that turnover is indeed a poor proxy for actual holding periods of investors. Their focus is on asset pricing, and they show that turnover is priced in size-sorted portfolios while average holding period is not.

The remainder of the paper is organized as follows. The next section describes the empirical questions pursued in this paper. Section 3 describes the liquidity measures and the individual trade data used herein. Sections 4 to 6 present and discuss the main findings, and Section 7 concludes.

# **2. Hypotheses and Related Literature**

Although empirical studies document that the effects of transaction costs on asset prices are both statistically and economically significant, there is a debate in the theoretical literature as to the direction and the magnitude of this relationship.<sup>4</sup> The debate centers on how investors make optimal trading decisions in the presence of transaction costs. The basic premise that the rate of return on a security should incorporate transaction costs is straightforward and uncontroversial. An investor who buys a security and expects to pay transaction costs when selling it will take this into account in valuing that security. An investor‟s required return on a stock will equal her required return in the absence of transaction costs plus these costs amortized over the investor‟s expected holding period. The liquidity premium required by investors to hold illiquid securities thus depends strongly on investors‟ holding periods. The theoretical debate over the effect of transaction costs on asset prices arises primarily from differences in how investors‟ holding periods are modeled.

One of the earlier papers to incorporate investors‟ holding periods into asset pricing with market frictions is Amihud and Mendelson (1986). They develop a model where

> 4 For empirical studies, see, for instance, Pastor and Stambaugh (2003), Acharya and Pedersen (2005), Amihud (2002), Chordia et al. (2000, 2001), Hasbrouck and Seppi (2001), and Huberman and Halka (1999).

4
**Table 4:** Determinants of log trip speed

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|log trip length|0.24<sup>_a_</sup><br>(0.0036)|0.14<sup>_a_</sup><br> (0.012)|0.14<sup>_a_</sup><br> (0.012)|0.24<sup>_a_</sup><br> (0.0036)|0.14<sup>_a_</sup><br> (0.012)|0.14<sup>_a_</sup><br> (0.012)|0.13<sup>_a_</sup><br> (0.015)|
|log trip length<sup>2</sup>||0.014<sup>_a_</sup><br>(0.0034)|0.014<sup>_a_</sup><br>(0.0035)||0.014<sup>_a_</sup><br>(0.0034)|0.014<sup>_a_</sup><br>(0.0034)|0.016<sup>_a_</sup><br>(0.0045)|
|log distance to center||0.15<sup>_a_</sup>|0.15<sup>_a_</sup>||0.14<sup>_a_</sup>|0.14<sup>_a_</sup>|0.098|
|||(0.042)|(0.042)||(0.041)|(0.041)|(0.063)|
|log distance to center<sup>2</sup>||0.025|0.025||0.031|0.031|0.041|
|||(0.023)|(0.023)||(0.022)|(0.022)|(0.034)|
|Type: circumferential|-0.015<sup>_a_ </sup><br>(0.0020)|-0.0039<sup>_b_</sup><br>(0.0016)|-0.0040<sup>_b_ </sup><br>(0.0016)|-0.015<sup>_a_ </sup><br>(0.0020)|-0.0037<sup>_b_</sup><br>(0.0016)|-0.0038<sup>_b_ </sup><br>(0.0016)|-0.0017<br>(0.0019)|
|Type: gravity|0.077<sup>_a_</sup>|-0.0032|-0.0032|0.079<sup>_a_</sup>|-0.0027|-0.0027|0.00098|
||(0.0065)|(0.0032)|(0.0032)|(0.0066)|(0.0032)|(0.0033)|(0.0043)|
|Type: amenity|0.082<sup>_a_</sup>|0.0064<sup>_c_ </sup>|0.0063<sup>_c_</sup>|0.083<sup>_a_</sup>|0.0066<sup>_c_ </sup>|0.0065<sup>_c_</sup>|0.0087|
||(0.0058)|(0.0036)|(0.0036)|(0.0057)|(0.0036)|(0.0036)|(0.0054)|
|City effect|Y|Y|Y|Y|Y|Y|Y|
|Day effect|Y|Y|Y|weekd.|weekd.|weekd.|Y|
|Time effect|Y|Y|Y|Y|Y|Y|Y|
|Weather|N|N|Y|N|N|Y|only|
|Observations|22,744,156|-|-<br>|19,385,656|-|-|10,319,939|
|R-squared|0.48|0.53|0.53|0.48|0.53|0.53|0.51|
|Cities|154|154|154|154|154|154|107|

_Notes:_ OLS regressions with city, day, and time of day (for each 30 minute period) indicators. Log speed is the dependent variable in all columns. Robust standard errors in parentheses. _a_ , _b_ , _c_ : significant at 1%, 5%, 10%. All trip instances in columns 1-3. Only weekday trip instances in columns 4-6. Sample sizes for columns 1 and 4 apply to columns 1–3 and 4–6, respectively. Only weekday trip instances for which we have weather information in column 7. Weather in column 3 and 6 consists of indicators for rain (yes, no, missing), thunderstorms (yes, no, missing), wind speed (13 indicator variables), humidity (12 indicator variables), and temperature (8 indicator variables). These variables are introduced as continuous variables in column 7.

A first series of results is reported in table 4. Column 1 regresses log trip speed on city fixed effects controlling for log trip length, an indicator for each type of trip, each day of the week, and each thirty-minute period during the day. Column 2 introduces further controls: the of the distance to the center a location as the square log trip length, log (defining trip’s midpoint between its origin and destination), and its square. Column 3 further adds weather variables (and indicators for missing weather data). Columns 4 to 6 repeat the specifications of columns 1 to 3 on a sample of only weekday trips. Column 7 is restricted to observations with non-missing weather data.

16