rafmacalaba/datause-encoder-data huggingface dataset:
the population.<sup>4</sup> For costs of education and taxes, we rely on administrative data. More detailed information can be found in the Annex 2.

The rate of return is calculated for adults at different ages of re-entering the education system starting with adults at the age of 20 ending at the age of 55 with five-year intervals.

# **Benefits from additional education**

Additional income as a consequence of improved formal education depends primarily on value of prior professional experience, unemployment, gender, and marginal tax-rate: _(i) Value of prior professional experience_ <u>. The value of experience and education is inter-</u> dependent because the embedded level of knowledge accumulated through experience in a job-position is closely tied to the employee’s level of education. While a part of job experience, such as integration into a large company, is generic across education levels, other kinds of job-experiences are job-specific, and therefore not valuable in other jobpositions. The calculations assume that prior knowledge have some value, namely that an adult completing a degree will have a similar wage growth and similar nominal wage premium as a young graduate. Since the wage premium is added to the wage level the student had before reassuming studies, prior knowledge is given some value. This assumption follows Blondahl _et al_ (2002).<sup>5</sup> _(ii)_ _<u>Unemployment</u>_ <u>.</u> The risk of unemployment varies with age and education. Annex table 3 details the unemployment rate by education level. We also propose an alternative scenario that disregards the difference in unemployment across education levels. This alternative scenario shed light on the impact of unemployment on returns.

> 4 They are Barranquilla, Bogotá, Bucaramanga, Cali, Medellin, and Pasto.

> 5 Because of lack of longitudinal data, little is known about how labor markets respond to timing of education. Less ability to learn, less flexibility and a shorter time for companies to reap benefits from the additional education could argue for lower wages for workers that graduated mid-way in life, while experience, and maturity while studying are arguments for higher wages of those mid-life graduates.

9
# _2.4. Is a self-reported maximum actually a maximum?_

As we have discussed in Section 2.2, several of the methods of eliciting the subjective distribution ask respondents for a maximum and a minimum, and then assume that the distribution is truly bounded by the elicited maximum and minimum. However, just as simply asking what you expect is unlikely to give you a statistical mean, we believe that in practice individuals are unlikely to reply with a statistical maximum or minimum to such questions, but rather answer with some percentiles on their subjective earnings.

To demonstrate this in practice, we use McKenzie et al. (2007)’s data on expectations of incomes in New Zealand for Tongans in Tonga, and on expectations of income in Tonga for Tongan migrants in New Zealand. The survey first asked individuals what the lowest amount and the highest amount they think they could be earning per week if they were in the other country was. The midpoint of this high and low was then used to define four threshold values, and respondents were asked the percent chance that their income would be less than each of these four threshold values. It transpires that for 95 individuals, the top threshold value was exactly equal to their reported highest amount.<sup>8</sup> Table 2 then tabulates these respondents’ answers of the percent chance of being above the self-reported maximum.<sup>9</sup> Only 17 percent say there is 0 percent chance of being above the maximum, with a median of 5 percent and mean of 9 percent as the chance of being above. There are a few observations with even higher reports. These results are then consistent with individuals giving the 95<sup>th</sup> or 90<sup>th</sup> percentile of their subjective distribution as the answer when asked for a maximum, and suggest caution in assuming that one gets a true maximum through a non-probabilistic question.

> 8 Respondents were asked a maximum and a minimum for the income they could earn. The midpoint of this was then used to choose which of 26 pre-assigned sets of Y1, Y2, Y3, and Y4 thresholds to ask the respondent about. For example, if the respondent said a minimum income of $300 and maximum of $500, the midpoint would be $400. Anyone with a midpoint between $400 and $449 was asked the percent chance their income was less than thresholds Y1=350, Y2=400, Y3=450, and Y4=500. Thus in this example, the Y4 threshold and maximum would coincide. In contrast, if they said a maximum of $550, they would still be asked about the same thresholds, but the maximum and upper threshold would not coincide.

> 9 Calculated as 100 minus the percent chance of being below this threshold.

- 13 -
**Table 2:** Balance

|||Phase 1|||Phase 2||
|---|---|---|---|---|---|---|
|Variable|Control<br>Mean/SD<br>(1)|Private - Control<br>Diference/SE<br>(2)|P-value<br>of diference<br>(3)|Non-Private <br>Mean/SD<br>(4)|Private-Non Private<br>Diference/SE<br>(5)|P-value<br>of diference<br>(6)|
|**Baseline labor supply**|||||||
|Tenure at factory|651|-1|.961|576|0|.997|
||[241]|(27)||[244]|(26)||
|Earnings|1822|-106|.296|1726|-105|.200|
||[863]|(101)||[670]|(82)||
|Earnings above median|.52|-.042|.439|.478|.041|.467|
||[.501]|(.054)||[.501]|(.056)||
|Attendance|.666|-.051|.034|.771|-.066|.028|
||[.207]|(.024)||[.223]|(.03)||
|Attendance above median|.414<br>[.494]|-.049<br>(.051)|.340|.472<br>[.501]|-.094<br>(.055)|.092|
|**Workers’ characteristics**|||||||
|Has an ID|.618|-.031|.594|.765|.04|.451|
||[.487]|(.058)||[.426]|(.054)||
|Is a woman|.986|-.004|.738|.994|-.013|.305|
||[.116]|(.012)||[.079]|(.013)||
|Speaks Dioula|.554|-.05|.356|.41|-.025|.646|
||[.498]|(.054)||[.493]|(.055)||
|Speaks Baoule|.261<br>[.44]|.052<br>(.049)|.287|.255<br>[.437]|.04<br>(.05)|.424|
|**Heterogeneity variables**|||||||
|Savings not taxed|.24<br>[.43]|-.108<br>(.063)|.088|.266<br>[.444]|.004<br>(.054)|.934|
|Not taxed by acquaintances|.<br>[.]|.<br>(.)|.|.713<br>[.454]|.063<br>(.053)|.238|
|Has a partner|.467|.141|.081|.683|-.093|.105|
||[.502]|(.08)||[.467]|(.057)||
|Limited control over earnings|.231|-.066|.334|.17|-.026|.546|
||[.425]|(.068)||[.377]|(.044)||

_Notes:_ Summary statistics and tests for baseline balance by treatment group. Cols. (1)-(3) use Phase 1 data, and Cols. (4)-(6) use Phase 2 data. Cols. (1) and (4) present the sample mean, with standard deviations in brackets, for workers in Control and Non-Private, respectively. Cols. (2) and (5) report the coefficient from a regression of the baseline covariate on an indicator for being assigned to the Private arm, controlling for strata fixed effects, with the standard error in parentheses; Cols. (3) and (6) report the associated p-values. Standard errors clustered at the worker level. Definitions of heterogeneity variables provided in notes to Table 4. Note that the “Not taxed by acquaintances” was not collected in the Phase 1 baseline. Worker earnings reflect the fact that attendance is on average 68% at baseline in our sample. Earnings at the factory are set so as to exceed Côte d’Ivoire’s minimum wage for full-time attendance.

treatment status, but before workers submit paperwork and blocked accounts take effect);<sup>23</sup> these time indicators are mutually exclusive. The _γi_ and _δt_ are worker and paycycle fixed

> 23This announcement period covers a 3-4 week gap between when treatment is announced and accounts take effect. It is comprised of 1-2 weeks of a pure announcement period after workers are informed of their treatment status, and an additional 2 weeks to get paperwork in place.

23
# **Table 13 Implications of Tunisian unilateral and bilateral services trade reform initiatives for sectoral output in Tunisia (percentage deviation from baseline)**

||_Tunisian fi_|_rms owned by_||
|---|---|---|---|
|_Sector_|_Tunisia_|_EU_|_Rest of world_|
|Live animals, products|-0.4|0.0|0.0|
|Dairy products|0.2|0.0|0.0|
|Coffee, etc, sugar, cut flowers|-0.6|-0.5|-0.5|
|Fruit and veg|-0.5|-0.4|-0.4|
|Cereals|-1.3|-1.2|-1.2|
|Oil seeds and fats|-0.9|0.0|0.0|
|Beverages and tobacco|-0.3|-0.2|-0.2|
|Other agricultural products|-1.3|-1.2|-1.2|
|Fish and products|0.0|0.0|0.0|
|Mineral products|-0.1|-0.1|-0.1|
|Metals, products|-1.3|-1.2|-1.2|
|Chemical etc|-0.9|-0.9|-0.9|
|Leather products|-0.8|-0.7|-0.7|
|Wood products, pulp, paper|-0.5|-0.4|-0.4|
|Textiles and apparel|-1.0|-0.9|-0.9|
|Transport equipment|-0.7|-0.6|-0.6|
|Other machinery and equipment|1.3|1.4|1.4|
|Electrical machinery|-0.8|-0.8|-0.8|
|Other manufacturing|-0.7|-0.6|-0.6|
|Electricity|-0.3|0.0|0.0|
|Gas and water|-0.5|0.0|0.0|
|Construction|0.1|0.2|0.2|
|Trade|0.3|0.4|0.5|
|Other transport|-1.1|-1.0|0.0|
|Air transport|16.1|16.6|16.6|
|Communications|1.2|10.4|10.4|
|Other financial services|0.5|7.0|7.0|
|Insurance|0.4|0.5|0.5|
|Other business services|-0.7|14.9|7.8|
|Other services|-1.4|-1.3|-1.3|

_Source_ : FTAP model projections.

43
dummy variables for each industry; represents dummies for each country. _EXTDEP_ is the industry-level dependence on external finance based on U.S. data, which is equal to external finance (capital expenditures minus cash flow from operations) divided by capital expenditures, as defined in Rajan and Zingales (1998). We then interact that variable with _FIN_ , which represents our indicators of financial development (private credit/GDP and stock market capitalization/GDP) in each country. A positive, significant coefficient for that interaction term is supportive of the notion that the labor growth of firms in externally dependent industries is faster relative to other industries in countries with relatively well developed financial sectors. Since our aggregate variable now varies at the country-industry level, we cluster our standard errors at the country-industry level in our analysis.

Because it focuses on within-industry, between-country differences in labor growth, this approach is complementary to our instrumental variables regressions (which focus on crosscountry differences). Moreover, the inclusion of country and industry dummy variables makes this approach less susceptible to criticism about omitted variable bias and model misspecification than more traditional cross-country regressions. RZ relied on industry-level data because it provided the most disaggregated comprehensive data on growth at that time, whereas our surveys enable us to examine firm-level growth in a consistent manner across countries. Finer disaggregation should improve the precision of our estimates relative to industry-level approaches.

Our main finding is that the interaction between industry-level dependence on external finance and private credit/GDP is significantly positively linked to labor growth in the sample of low-income countries (Table 7). By contrast, there is no significant relationship between labor growth and the interaction between stock market development and dependence on external finance in the sample of low-income countries. This is the same pattern as in our instrumental variables regressions. Our finding indicates that, when evaluated at the median industry level of external finance (0.24), the country at the 90<sup>th</sup> percentile of banking development would have a labor growth rate higher by 8.4 percentage points relative to the country at the 10<sup>th</sup> percentile of 22
Stock, **=** LC Stock, + FC Stock and b) Convert foreign currency stocks to US dollars using end-of-period exchange rate (E,), then obtain flows: FC Stock-, = FC_Stock, / Ee, FC_Flow', = (FC_Stock- - FC Stock ,.,) c) Correct dollar flows by cross-currency effect (CCE): FCC Flow', = FC_Flow, - CCE, d) If E, is the period average exchange rate, then compute the flow as: Flow, = (LC_Stock, - LC_Stock,.,) + **E, *** FCC Flow', e) The discrepancy between the first difference of original local currency stocks, and the computed flows:

[(Stock, - Stock,)j] - Flow, = (El, - E)eFC Flow', + (El, - E'.,)*FC Stock, l + E,*CCE, amounts exactly to the timing, valuatio,., and cross-currency effects of the foreign currency component respectively.

This methodology should be applied to all transaction flows, except accumulated retained earnings. If we then subtract total liability flows from total asset flows, we should obtain the implicit net cash flow, equivalent to savings of the monetary sector in our current account matrix.

# Monetary Sector Current Accou **nt**

The Monetary Sector current account consists primarily of interest payments and receipts, as shown in Table A. 1. As we have discussed, the information needed for the current account is basically that found in the profit & loss statement of a particular financial institution, excluding items like provision, and depreciation, ie, accounting flows. All payments and receipts, including interest differential and fee income, are contained in the profit & loss statement, which also follows the flow concept. Unfortunately, this data is not directly available in the Banking Survey for most countries. Thus, the current account most be constructed from the balance sheet from the previous period by applying the corresponding interest rates to the respective assets and liabilities. The values for interest payments on foreign debt and interest received on reserves may be obtained from balance of payment sources. Also, data for interest rates for items such as CB rediscounts, treasury paper, and demand deposits is available for most countries. Then, it should be relatively easy to calculate interest payments and receipts of the Monetary Sector.

As we discuss shortly, the data for the changes in the net worth or net cash flow is determined 11
prices, and so on. The price of households can be understood as a cost of living index since it is computed as a weighted average of all the goods the households buy (in and outside the household), plus tax payments. In this paper, we consider the Rest of the World account to be the only exogenous account.

Since we are assuming that the price of oil (or food) is given by the international market, oil (or food) is modeled as a fixed price sector (the equivalent of a supply constrained sector in the value model). This means that the price of the sector can only be increased exogenously from its current level. Following the notation used by Lewis and Thorbecke (1992) after adapting it to the price model, it can be shown that the final effects on prices, given an exogenous price shock, are given by where _pnc_ is a vector of prices of unconstrained sectors;<sup>_b_</sup> _c_ is a vector of endogenous costs for fixed price sectors; _Cnc_ is a matrix of expenditure propensities among unconstrained sectors; _R_ is a matrix of expenditure propensities of unconstrained sectors on fixed price sectors; _Q_ is a matrix of expenditure propensities of fixed price sectors on unconstrained sectors; _Cc_ is a matrix of expenditure propensities among fixed price sectors; _bnc_ is a vector of exogenous costs for unconstrained sectors;<sup>_p_</sup> _c_<sup>is a vector of exogenous prices of fixed price sectors; and</sup><sup>_I_and0</sup> are the conformable identity and the null matrices, respectively; _M m_ is called the mixed multiplier matrix, and the prime symbol (‘) denotes the transpose of a matrix.

In the next section all the computations are performed using SimSIP SAM, a powerful and easy to use Microsoft ® Excel based application with MATLAB ® running in the background 8
at Harvard and correlated their data with the distance between the authors’ offices, they found that being less than one kilometer away raised the quality of research, as defined by how many other researchers cited the article. The effect was even larger if the authors were in the same building.

Innovative firms thus have an incentive to locate near other innovative firms. In the same way that having a good colleague next door affects my creativity, having good neighbors—even competitors—improves the creativity of companies and workers. This in turn helps explain why workers in cities with many well-educated residents earn higher salaries than identical workers in other areas. By clustering near each other, innovators foster each other’s creative spirit and become more successful. These effects have not faded over time. While many people think that e-mail, cell phones, and the Internet have made physical proximity less important to the creative process, in reality the opposite is true. Location is more important than ever, in part because knowledge spillovers are more important than ever.

A large number of highly educated workers in a city is also associated with more creativity and a better ability to invent new ways of working. One way to see this is to look at what Jane Jacobs called “new work,” novel occupations that did not exist before. The economist Jeffrey Lin (2011) has studied which cities are the most creative, in the sense that they generate the most new work as measured by jobs that did not exist 10 years earlier. Between 5 percent and 8 percent of workers are engaged in new work at any time in the United States, but this number is much higher in cities that have a high density of college graduates—the ones in table 1—and a diverse set of industries. Lin also found that creativity pays off: for the first few years after a new kind of job is created, workers in those positions earn significantly higher wages than identical workers in old jobs.

# **Implications for regional economic development policies**

The discussion here has important implications for the future of cities and for the policy makers interested in shaping it.

There is something phenomenal about the forces of agglomeration just uncovered. They are responsible for turning a collection of individual workers and firms into something much larger than the sum of its parts. This generates what economists call localized economies of scale. The term economies of scale usually refers to the ability of companies to become more efficient as they grow. For example, large car manufacturers are more efficient than small ones. But instead of applying to a single company, these economies of scale apply to all the companies in a geographical area. Larger cities, and cities with larger industrial clusters, tend to be more efficient because they have a thicker labor market, a more specialized supply of business services, and more opportunities for knowledge spillovers.

While individual companies in a cluster do not necessarily become more efficient as they grow, all companies taken together become more efficient as the cluster grows. A surprising 12
records household expenditures, which are not observed in the UDB, as well as information on the receipt of Jamkesmas and BLT before the establishment of the UDB. Using this data, we investigate how the distribution of benefits within the study population changes when moving from past programspecific targeting allocations to the UDB.

We find that targeting using the UDB is more progressive than previous program-specific approaches to beneficiary selection. In particular, the UDB leads to a substantial reduction in leakage of benefits to non-poor households. For example, the proportion of the richest 40% of households receiving Jamkesmas is expected to fall from nearly 40% to 25%. These reductions in leakage have important welfare consequences as well as implications for the political economy of redistribution (see Pritchett, 2005).

However, we find more limited improvements in undercoverage that can be explained by two key challenges that arise in the development of any targeting registry. The first challenge is how to identify households for inclusion in the registry, or in other words _who to survey_ within the entire population. Properly addressing this issue is essential to ensuring that poor households are included in the registry in the first place, thereby avoiding what we refer to as _misenumeration_ errors. The second challenge is _how to assess the eligibility of those surveyed_ , or how to estimate their socioeconomic status in order to rank or classify them. The main concern in this step is to minimize what we refer to as _misclassification_ errors that stem from surveyed poor households being deemed ineligible and from non-poor surveyed households being wrongly classified as poor. Misenumeration and misclassification are key determinants of targeting accuracy. To date, however, little is known about the relative importance of registration and ranking in determining the accuracy of targeting registries, as most existing studies focus on errors due to misclassification.<sup>7</sup> Our unique combination of administrative and independent survey data allows us to compare the relative importance of these distinct errors to overall targeting accuracy.

> 7 One notable exception is Alatas et al. (2016), who show that self-targeting has the potential to reduce misenumeration errors at the registration stage.

4
|**Table A2:**|**Summary**|**Statistics**||||
|---|---|---|---|---|---|
|Variable|Mean|Std.<br>deviation|Min.|Max.|No. of<br>Observations|
|Female Workers (% of total workers)|29.217|28.009|0|100|44539|
|Exports (%, cell average)|9.452|13.96|0|100|44539|
|No. of Workers (logs)|3.061|1.231|0|10.06|44539|
|Herfindahl-Hirschman Index (HHI)|0.102|0.147|0|1|44539|
|Age of Firm (logs)|2.53|0.833|0|5.737|43911|
|Foreign Ownership (proportion)|0.089|0.266|0|1|44217|
|Female Ownership Y:1 N:0|0.34|0.474|0|1|43506|
|Skills Obstacle (0-4)|1.35|1.301|0|4|43892|
|How Much Of An Obstacle: Transport?|1.259|1.282|0|4|44074|
|<br>Firm Purchased Fixed Assets Y:1 N:0|0.481|0.5|0|1|44182|
|Power Outages (days)|1.316|3.541|0|30|42105|
|Courts Obstacle (cell average)|1.016|0.715|0|3.955|43198|
|Crime Losses Y:1 N:0|0.178|0.383|0|1|43173|
|Firm Inspected Y:1 N:0|0.598|0.49|0|1|44070|
|How Much Of An Obstacle: Tax Rates|1.768|1.322|0|4|43885|
|How Much Of An Obstacle: Labor Regulations?|0.963|1.132|0|4|44003|
|GDP per capita (logs)|8.598|1.005|6.462|10.264|44539|
|Population (logs)|16.266|1.35|13.329|19.345|44539|
|Urbanization (%)|48.113|19.99|0|95.045|44539|
|<br>WBL Index|0.691|0.164|0.176|1|44539|
|Women can work the same night hours as men|0.825|0.38|0|1|44539|
|Women can work in the same industries as men|0.463|0.499|0|1|44539|
|Ratio of Female to Male Population|50.251|5.534|0|56.84|44539|
|Fertility Rate|3.296|1.693|1.23|7.57|44539|
|<br>Starting a Business (DTF)|67.956|20.73|0|95.81|44539|
|Inflation (%)|7.028|7.885|-8.975|81.138|44539|
|Labor Productivity (2009 USD, logs)|9.738|2.145|-5.089|22.663|38742|
|Quality Certificate Y:1 N:0|0.185|0.389|0|1|43171|
|<br>Employment Growth (%)|4.874|17.859|-97.044|100|40435|
|Firms' Top Manger is Female Y:1 N:0|0.166|0.372|0|1|37489|
|<br>Firm Spent on Security Y:1 N:0|54.704|49.779|0|100|44409|
|Firm Has Own Website Y:1 N:0|0.394|0.489|0|1|44381|
|Firm Provides Training to Workers Y:1 N:0|0.32|0.467|0|1|42208|
|Polity index|4.028|5.596|-9|10|44539|
|Real growth rate of GDP per capita|3.7|5.457|-22.331|38.69|44539|
|Primary Enrollment (Gross, %)|102.905|14.373|47.618|144.338|<br>42363|

53
Table 2: Changes in Reforms and Pre-Reform Trends in Industry Characteristics

||∆Final Goods Tariffs|∆Input Tariffs|∆FDI Reform|∆Delicensing|
|---|---|---|---|---|
|∆log(wage)|0.002<br>(0.19)|-0.042<br>(0.052)|0.088<br>(0.12)|-0.14<br>(0.26)|
|∆Production Share|-0.052<br>(0.92)|-0.16<br>(0.25)|0.17<br>(0.60)|0.82<br>(1.27)|
|∆log(K/L Ratio)|-0.12|0.007|0.041|0.043|
||(0.080)|(0.022)|(0.052)|(0.11)|
|∆log(Employment)|-0.062<br>(0.061)|-0.024<br>(0.016)|-0.0085<br>(0.04)|-0.034<br>(0.084)|
|∆log(Firm Size)|-0.096<br>(0.12)|-0.035<br>(0.032)|0.018<br>(0.077)|-0.026<br>(0.16)|
|∆log(Output)|-0.037<br>(0.040)|-0.0088<br>(0.011)|0.02<br>(0.026)|-0.0028<br>(0.055)|
|∆TFP (Total)|0.038<br>(0.072)|-0.0066<br>(0.020)|0.062<br>(0.047)|0.014<br>(0.099)|
|Observations|136|136|136|136|

Results are coefficients from regressions of the change in reforms (final goods tariffs, input tariffs, delicensing, FDI reform) from 1990 to 2004 on changes in industry characteristics from 1985 to 1989. Each value represents a result from a separate regression. Standard errors are in parentheses.

34