rafmacalaba/datause-encoder-data huggingface dataset:
goal in terms of search hours are more likely to have increased the time spent searching at follow-up. An additional hour in their stated goal is associated with a 0.4 hour increase, from baseline, in actual behavior (Columns 2 and 3). A positive correlation is also found between goals and behavior change in terms of submitted applications. A unit addition in the stated goal is associated to a 0.11 increase in applications (Columns 4 and 6). Notably, goals are significant predictors only of a change in the corresponding behavior, suggesting that the observed pattern is not driven by motivation or other unobservable character traits.<sup>15</sup>

**Table 6:** Action Plan Subgroup Analysis: Correlation with Search Intensity

|||Search Hours||Sub|mitted Applica|tions|
|---|---|---|---|---|---|---|
||(1)<br>Search Hours|(2)<br>Search Hours|(3)<br>Search Hours|(4)<br>Applications|(5)<br>Applications|(6)<br>Applications|
|Goal: Applications|_−_0_._131||_−_0_._267<sup>_∗∗_</sup>|0_._113<sup>_∗∗_</sup>||0_._106<sup>_∗_</sup>|
||(0_._119)||(0_._119)|(0_._055)||(0_._054)|
|Goal: Hours||0_._405<sup>_∗∗∗_</sup>|0_._460<sup>_∗∗∗_</sup>||0_._048|0_._035|
|||(0_._105)|(0_._107)||(0_._037)|(0_._036)|
|Observations|582|590|576|580|589|574|
|_R_<sup>2</sup>|0_._177|0_._207|0_._218|0_._352|0_._341|0_._358|
|Baseline Mean|12_._013|12_._013|12_._013|4_._647|4_._647|4_._647|

Notes: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. Regressions use panel data over two follow-up periods. Standard errors (reported in parentheses) are clustered at the individual level. Subsample consists of those assigned to Workshop Plus. All regressions control for location-fixed effects and baseline values of goal applications and goal search hours. Outcome variables are winsorized at the 5% level.

The evidence of a positive correlation between goals and behavior change for participants in the plan-making treatment group is consistent with studies in the psychology literature showing that goal-directed actions (i.e. implementation intentions) can support goal achievement. See Brandst ̈atter et al. (2001) for a review.<sup>16</sup>

> follow-up, which implies that we cannot check the correlation between goals and behavior for this variable. 15Column 1 in Table 6 shows that the intended search hours are not correlated with subsequent changes in submitting applications, while Column 5 shows that the intended applications are uncorrelated with changes in hours searched.

> 16van Hooft and Noordzij (2009) experimentally test the effectiveness of a workshop focused on goal setting. They find suggestive evidence of a positive effect on search intensity and employment. However, the study is limited by a small sample size and selective attrition.

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sector to the government. This dual need to transfer resources abroad and, internally, from the private to the public sector, has become known as the "twin transfer problem".

How important has been this fiocal crisis and how have countries reacted to the crisis? To answer properly this question requires comparable data on the consolidated public rector (inclusive of the Central Bank, local governments and public enterprises). As mentioned earlier, many fiscal operations li%.e foreign exchange subsidies were undertaken by the Central Bank. These operations often amounted to a eizable percentage of GDP. Unfortunately, they do not figure in the widely available data sets like the International Monetary Fund's Government Financial Statistics (GFS) which only report Central Government operations. Still, these statistics are useful to indicate broad trends for the universe of developing countries and are used in the tables that follow.

Tables 1 and 2 give averages for middle and low-income countries between 1975 and 1988. The breakdown into period averages is to smooth out year-toyear fluctuations, and to try to capture the effects of significant changes in the external environment. The breakdown into two country groupings reflects the widely held view that low-income countries have less resilience in their fiscal systems and a different tax revenue structure because of a larger share of agriculture in GDP (a sector difficult to tax) and because of less-developed administrative capabilities.<sup>1</sup> The two tables suggest several stylized developments. Start with fiscal deficit and expenditure trends in Table 1.2 First,the overall deficit showed a worsening trend during the period of easy money (1979-81) and during the aftermath of the debt crisis (1982-85) for both low-income and middle-income countries. This worsening trend was only arrested for middle-income countries in the last period (1986-88) when the overall deficit was, on average, reduced by two percentage points of GDP. During the first sub-period this trend reflected expansionary fiscal policies, as indicated by the evolution of total expenditures in column 3. Starting in 1982, with the onset of the debt _3_
it is still possible that such effects may operate at the local level. The avoidance of the effect at the local level requires generation of demand (through income earning opportunities) in conjunction with the food aid.

The second major debate of macroeconomic relevance linked to food aid is the use of counterpart funds from the sale of conmmodity aid (or foreign exchange). The debate is admirably reviewed by Bruton and Hill (1990a and 1990b) who reaffirmn the view that such funds need MM91 be inflationary. It would be useful if studies of the impact of aid on government fiscal behaviour explicitly separated out counterpart funds in their analysis since: (i) many donors do not intend these funds to increase expenditure, but rather to offset deficit financing; and (ii) data on counterpart funds are frequently available from government accounts. In support of Bruton and Hill's position the analysis of Tanzanian data by Doriye _et aL._ (1994) found that counterpart.finds (unlike other forms of aid) did not increase government expenditLre.

# **5.3 Technical assistance and the displacement of skilled labour**

Aid projects may be expected to generate demand for unskilled labour. With the exception of (these days rare) ill-conceived use of young volunteers, this labour will be provided from domestic sources. By contrast, the requirements for skilled labour are likely to be met by -,xpatriate personnel. The issue of inierest here is whether the use of expatriates substitutes for domestic labour.

Chenery and Strout's (1966) presentation of the two gap model in fact identified three constraints - savings, forex and the capacity to invest, the last arising from a skills gap. Foreign assistancL may be required to fill this gap to attain the desired level of investment.4 But, as with the other two gaps, sustainable development is only achieved by the closing of the gap, and so the important question is whether TA has contributed to the closure of the gap as well as filling it. At first sight the -figures - which show increasing quantities of aid personnel - suggest that the gap has not closed. Why is this?

Technical assistance in fact covers two distinct areas - overseas training and employment of expatriates, the latter nearly always including an on-thejob training component for counterpart staff. Ideally speaking, expatriates will be used where a genuine skills shortage exists and then be replaced by local personnel who become available through the two channels of taining provided by TA. But whilst the number of skilled people in recipient countries has grown so have the numbers of unemployed graduates and brain drain to developed countries. These phenomena exist despite the 87
**Table 1B. Summary Statistics on Public Spending, Governance and Outcomes** (Data from 98 countries over two years, 1990 and 1997)

|_Variables_|_Observations_|_Mean_|_Std.Dev._|**_Min_**|_Max_|
|---|---|---|---|---|---|
|Infant MortalityRate|169|42.3|37.6|3.7|150|
|Publichealthspending(share**in**GDP)|169|3.26|2.10|.18|8.25|
|Percapita GDP(inPPPadjusted1990$)|169|8,920|8,877|420|38,135|
|IndexofCorruption(leastcorrupt=6)|169|3.5|1.4|0|6|
|QualityofBureaucracy(highest**=6)**|169|3.6|_1.5_|1|6|

_Note:_ In addition to the countries listed in the above sample, the other countries are: Angola, Brunei, Dominican Republic, Gambia, Mali, Mongolia, United **States,** and Zambia.

# **_4.3.1 OLS regressions: Factors affecting health status_**

Table 2 contains the OLS estimates of our model given in equation (6). Equation (2.1) presents the results from estimating a simple version of equation (6)-one that does not include the governance variable. It indicates that a 1 percentage increase in per capita GDP at the margin is associated with a .44 percent reduction in child mortality, i.e., the estimated elasticity of mortality is -.44. At the same time, a one percent increase in the share of public health spending in GDP is linked with a .19 percent reduction in child mortality. These two and other non-health related variables explain 92 percent of the variation in cross-national child mortality rates.<sup>5</sup>

> 5 When estimated in a rather parsimonious form-witi only **two** regressors: per capita GDP and public health spendingthe model explains 88 percent of the variation in cross-national child mortality rates. Moreover, both variables are statistically significant and have the right signs.

**10**
is present in Germany, Poland and up to a certain degree also in Spain. In these three countries de‐ routinization seems to be a common trend.

<u>Figure 6: Change in employment shares, by country and occupation</u> <!-- Start of picture text --> Germany: 1994‐2013 Poland: 1992‐2013 Spain: 1990‐2013<br>15.6% 14.7%<br>12.0%<br>6.9%<br>1.6%<br>‐4.2%<br>‐8.5%<br>‐10.5%<br>‐27.6%<br>Non Routine Routine Non Routine Non Routine Routine Non Routine Non Routine Routine Non Routine<br>Man Cog Man Cog Man Cog<br>percent of wage employees<br><!-- End of picture text --> Source: Authors calculations using LIS harmonized household surveys. Note: This figure shows the change, in percentage points, of the share of total employment (wage employees, excluding self‐employed) over a period of about twenty years of three occupations categories: non‐routine manual task intensive occupations, routine ones, and non‐routine cognitive tasks intensive ones. The initial and final years depends on data availability. For more details on the construction of the occupation categories please see the appendix.

The two approaches presented above do not produce the exact same ranking of occupations, however the two rankings are not far apart. This is because, as illustrated in Figure 5, there is a correlation between the intensity of task contents of occupations and wages paid in these occupations. Using the normalized task index of occupations, Figure 7 shows that, for Germany and Spain, the intensity of non‐routine cognitive tasks is higher for jobs at the top of the wage distribution while routine task intensity is higher in the middle of the distribution. Low pay jobs are more intensive in non‐routine manual occupations.

In sum, no matter what the criteria for ordering of occupations is used – either the average wage paid in each occupation, or the intensity of their task content – there is evidence of a hollowing of the middle of the distribution of occupations, or of its polarization. For the three countries under study, jobs entailing a high degree of routine tasks and paid around the middle of the wage scale are declining.

19
16 Female workers who have completed primary and middle schooling earn relatively higher returns than their male counterparts. By contrast, male workers who have completed secondary schooling and tertiary education have higher returns to education than women **.** Returns are almost double for females relative to males completing primary and middle school, at an annual rate of 15.1 percent and 15.6 percent, respectively. However, this reverses with secondary schooling and higher levels of education. Males receive higher returns on having completed secondary schooling and tertiary education.

An alternative approach to measuring schooling returns is to observe the total differential private rate of return from completing an education level which is provided in Table 8. Looking at the final row, males enjoy a 31.6 percent return on completing a basic 3 year foreign university education over and above the return from secondary schooling. The total private returns to females having completed university education holding all else constant are less than half of their male counterpart at 12.8 percent. Most obvious is the disparity in private returns to university education between public and private sector workers. Public workers earn over 10 times the private returns of private sector employees with tertiary education<sup>9</sup> . For public sector employees who earn a 5 year foreign Master’s degree, their private returns would be as high as 45 percent.

**Table 8  Djibouti: Selectivity-Corrected Total Private Rate of Returns to Schooling, 1996**

|**Education Level**|**All**|**Males**|**Females**|**Public**|**Private**|
|---|---|---|---|---|---|
|Primary|59.7|49.2|90.8|14.1|40.2|
|Middle|40.6|29.6|62.4|23.3|35.4|
|Secondary|32.5|39.6|26.2|29.6|19.8|
|Vocational|34.4|37.8|32.7|28.4|5.9|
|University|26.8|31.6|12.8|27.1|2.3|

_Note. Estimates for primary category is the earnings premium over no-formal education. Similarly, middle school category shows earning differential over primary school. Secondary category shows differential over middle school, vocational shows differential over middle school and university is the differential over secondary school. Source: Authors' calculations based on wage regressions from Tables 3 and 6_

> 9There is also a large gap in returns between individuals who completed university education and those with vocational training.  The total post middle-school private returns for university degree holders in the public sector is 56.7 percent compared to 28.4 percent for those with vocational schooling. In the formal private sector, the post middle-school returns to education for university degree holders is 22 percent compared to 6 percent for individuals who completed vocational training.
Figure 11. Children in wealthier households have been more likely to be engaged in more interactive educational activities than poorer ones _Interactive distance learning by country and quintile_ <!-- Start of picture text --> Q1 Q2 Q3 Q4 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Pooled Indonesia Lao PDR Mongolia Myanmar Philippines Vietnam<br>before the pandemic<br>children attending school<br>Percent of households with<br><!-- End of picture text --> Note: Modes of interactive distance learning include mobile apps, online or in-person meetings/sessions with a teacher or tutor, or other online learning platforms. 95% confidence intervals for difference from Q1 are shown (rather than from zero). The pooled regression includes country and period fixed effects. Education data are only available for Round 4 in Myanmar and Round 2 and 4 in Indonesia. Source: HFPS

# 5. Prospects for inclusive recovery

This paper presents evidence from the High Frequency Phone Surveys (HFPS) indicative of the risk of rising inequality both in the short-and long-terms across a selected set of EAP countries.

Unlike in developed countries, work stoppages and labor income have been relatively widespread at times of economic closure and downturn, although there is some evidence that the top 20 have been able shield themselves more than workers at the bottom of the distribution when economic activity resumed. The data on potentially harmful coping mechanisms, food insecurity, and distance learning suggest that the impacts could be long lasting, suggesting that the recovery may be uneven.

These results from high-frequency survey data, should be taken as indicative of trends, in the absence of official household and labor force surveys. The HFPS, while timely and informative for looking at how households have weathered during the pandemic on a number of dimensions, is not without limitations. As mentioned in the paper, limited numbers of survey rounds and a respondent sample can only provide a partial (and imperfect) picture of the impacts. In addition, the rapid nature of the phone surveys means that the questionnaires may lack the level of detail necessary to look at precise mechanisms of the pandemic’s economic impact on workers and households. As nationally representative household or labor surveys become available, a more comprehensive assessment of the extent to which the pandemic and its recovery have increased inequality in the region will be important.

Not discussed in this note is the important role that the expansion of safety nets played in many countries in the region in mitigating to some extent these negative impacts. Several countries in EAP have made unprecedented efforts to scale up their social protection systems in a relatively short amount of time. In Mongolia, according to the December 2020 survey, recipients of the Child Money Program have largely used the benefit to purchase food, while a third to save for future usage, potentially increasing their buffer. Among recipients, 8 percent declared that the transfers have completely mitigated the negative impact of 26
**Plc, Planning, and Research**

**Word Development Report|**

Venture capital is a temporary-equity or quasiequity investment in a growth-oriented, usually small or medium-size business managed by a ;highly motivated entrepreneur. Management assistance often comes with the investment. For the investment, the investor expects either a minority share in the company or the irrevocable right to acquire it.

Unlike traditional investors, venture capitalists prefer good entrepreneurs to good projects - and minority rather than controlling interest, which tends to tum an entrepreneur into an efficient employee. Collateral ofteiu takes the form of such intangible assets as research results, innovative marketing ideas, or technical skills. Venture capitalists usually expect more risk and a longer initial period of negative cash flow than traditional investors.

Venture capital cannot be expected to grow in the developing countries at the same pace as it did in its early years of development in the United States and Canada. But there is no reason to believe than enabling conditions eannot be improved so that it can contribute to industrial and entrepreneurial development in **tLie** Third World. The same doubts being expressed about venture capital in the developing countries were expressed a decade ago about Europe and the Far East, two regions where venture capital is now growing fast. In many countres, the right conditions already exist for venture capital to succeed.

Governments can create an enabling climate for venture capital by improving the macroeconomic environment, trying to change attitudes about risk and entrepreneurship, improving information and infrastructure, and providing and promoting the availability of venture capital funds.

Hard facts about venture capital are scarce, as participants have not been eager to spread the word about the results of their investments. Most of the information available is through self-promoting success stories. The Bank might consider:

* A systematic review of the experience with venture capital in selected countries.

* Interviews with investee companies to assess the impact of different types of investment.

* Assessing the efficiency of different types and procedures of investment.

* Comparing the results of equity and venture financing with conventional or developmental term credit financing.

* Analyzing the essential characteristics of a successful entrepreneur.

This is a background paper for the 1989 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact the World Development Report office, room S13-060, extension 31393 (38 pages).

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
infrastructure capital investment) and physical indicators of service production or coverage.

Note first that in a world where in the last decades an increasing part of the infrastructure investment has been made by the private sector, public capital is unlikely to overlap completely with infrastructure investment. To take only a few examples, in the period 1996-2001, the share of total spending in infrastructure corresponding to the public and the private sector respectively amounted to 1.37 and 1.02% of GDP in Brazil, 0.27 and 0.98% of GDP in Mexico or to 2.93 and 4.35% of GDP in Bolivia (Calderón and Servén, 2004). To the extent that we are interested in the effect of infrastructure capital, however it is financed, on growth, rather than in the effect of fiscal policy, this is obviously problematic. If variations of the private share of infrastructure investment across sectors or countries are not random, an assumption that seems likely to be verified, relying on public capital as a proxy for total infrastructure investment therefore introduces a systematic measurement error.

The second problem has to do with the argument found in Pritchett (1996), who argues that whatever the measure of capital stock used, the numbers available overlook the fact that cumulated investment flows are not reasonable proxies for the true effective capital stocks, because the costs of these investments are likely to differ from their values. Justifications for this may include simple government inefficiency, potential corruption, or departure from efficiency for redistributive motives among others.

The weakness of public capital as a proxy for infrastructure services is reflected in the fact that estimations using aggregate public capital fail to find any effect about half of the time, as shown above in Table 1. Partly to circumvent the measurement problems discussed above, raw physical indicators, like kilometers of road, number of phone lines, or electricity generating capacity, have become a standard alternative.<sup>23</sup> However, their

> 23 These indicators, for which Canning (1998) put together a comprehensive cross-country database, have been used extensively. Examples include Canning (1999), Canning and Bennathan (2002), Canning and Pedroni (2004), Calderón and Servén (2003, 2004), Sanchez-Robles (1998), Estache, Speciale and Veredas (2005) inter alia.

23