rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3. Average marginal effects of subjective well-being over the lifespan: LiTS data (2006–2010)**

<!-- Start of picture text --> ECA ECA with cohort dummies<br>(1) squared (2) squared<br>4 4<br>3.5 3.5<br>3 3<br>2.5 2.5<br>2 2<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>(3) cubic (4) cubic<br>4 4<br>3.5 3.5<br>3 3<br>2.5 2.5<br>2 2<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>(5) non-parametric (6) non-parametric<br>4 4<br>3.5 3.5<br>3 3<br>2.5 2.5<br>2 2<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>Age Age<br>Linear Prediction<br>Linear Prediction<br>Linear Prediction<br><!-- End of picture text -->

# _3.2 Well-being in a synthetic panel using LiTS_

To account for the time-constant effects shared by certain population groups, we construct a synthetic panel from the ECA countries in the LiTS data, whose results support the findings from the cross-sectional analysis (Table 3). Above all, once fixed-group effects are accounted for, the estimates find an overall negative linear relation between age and well-being. In addition, the squared specification is highly significant, with a minimum around 60, while the cubic term supports the U-shape until age 80, after which well-being decreases. The estimates become less precise, however, at older ages, when the standard errors are quite large (see Appendix 3 for the corresponding marginal plots). We are therefore unable to reject either the U-shape or a second turning point.

14
<!-- Start of picture text --> Urban Boy 0-4<br>Rural Boy 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Urban Girl 0-4<br>Rural Girl 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Urban Girl 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girl 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Urban Boy 0-4<br>Rural Boy 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Urban Girl 0-4<br>Rural Girl 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Urban Girl 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girl 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Rural Boy 0-4<br><!-- End of picture text --> <!-- Start of picture text --> Rural Girl 0-4<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Estimates for Taraba urban girls, Ebonyi urban boy and girls, Enugu urban boys, Imo urban boys and girls, Cross River urban boys and girls and Ekiti rural boys and girls are excluded because of too limited sample size. The threshold for exclusion is set at less than 30 observations per demographic group.

# Health Deprivation

Health deprivation estimates, the share of children 0-1 not having received any vaccination based on the GHS waves 1 and 2 and the MICS 2016/17 are inconsistent. The aggregate rate of 17 percent from GHS wave 2 is however very close to the finding of 19 percent of children not having received any vaccination based on the latest Demographic and Health Survey (DHS) report with data from 2018 (see NPC and 33
As noted above, following a birth all three events can occur, or two of them can occur, or one, or none (since a death can preclude weaning). Due to censoring by the i.nterview, one or more of the events that will eventually occur may not be observad. The events can occur in any order, with the exception of weaning following death. We view the child as progressing through a series of states following its birth. For example, a few months after giving birth a woman may stop breastfeeding (i.e., the child enters the state of weaned); a few months later, she may become pregnant (i.e., the child enters the state of next pregnancy); and perhaps a few months later, the child may die (ie, the child enters the state of death). In this example, weaning is followed by next pregnancy which is followed by death; other sequences can easily be imagined.

In this analysis, we examine how the occurrence of one of the three events modifies the likelihood of the occurrence of each of the other two events. The occurrence of one event is hypothesized to shift (upward or downward) the hazard for each of the other two events. The method of estimation makes use of the precise dating (on a monthly basis) of each event, but the results are driven mainly by the ordering of the events. The estimation is robust to some imprecision in the dating of events so long as the ordering is accurate. In particular, accurate ordering of events is essential to the estimation of reciprocal effects, such as weaning on fertility and fertility on weaning. Disentanglement of the reciprocal effects is accomplished by assuming that chronological order implies causal order.

The approach mighe be criticized for not taking into account the timing of the events (i.e., months since the birth): it is plausible, for example, that early weaning (prior to age twelve months, for example) has different 7
- Scarpetta, Stefano (1996). “Assessing the Role of Labour Market Policies and Institutional Settings on Unemployment: A Cross-Country Study” _OECD Economic Studies_ 26, 43-98.

- Shea, John (1997). “Instrument Relevance in Multivariate Linear Models: A Simple Measure” _Review of Economist and Statistics_ 79(2), 348-52.

- Slaughter, Matthew J. (2001). “Does Trade Liberalization Converge Factor Prices? Evidence from the Antebellum Transportation Revolution” _International Trade and Economic Development_ 10(3), 339-62.

- Stock, James H. and Yogo, Motohiro (2005). “Testing for Weak Instruments in Linear IV Regression” in Stock, James H. and Andrews, Donald W. K. (Eds.) <u>Identification and Inference for Econometric Models: Essays in Honor of Thomas J. Rothenberg</u> New York: Cambridge University Press.

- Trefler, Daniel (2004). “The Long and Short of the Canada-U.S. Free Trade Agreement” _American Economics Review_ 94, 870-95.

- Wacziarg, Romain and Welch, Karen Horn (2008). “Trade Liberalization and Growth: New Evidence” _The World Bank Economic Review_ 22(2), 187-231.

World Bank (2012). World Development Indicators, Washington, D.C.

# **Figure 1**

Histogram of Year of Reform <!-- Start of picture text --> 1960 1970 1980 1990 2000<br>Year<br>15<br>10<br>Frequency<br>5<br>0<br><!-- End of picture text --> 24
The econometrics of finance and growth Thorsten Beck<sup>1</sup>

**Keywords** :  Finance and growth; instrumental variables; time-series econometrics; differences-in-differences estimation

**JEL Classification Codes** : C2; C3; G2; O4

1 World Bank, <u>tbeck@worldbank.org. I am grateful to George Clarke, Aart Kraay,</u> Luc Laeven, Ross Levine, Kerry Patterson, and Peter Rousseau for comments and useful discussions. This paper’s findings, interpretations, and conclusions are entirely those of the author and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. This paper was prepared for the Palgrave Handbook of Econometrics, Vol. 2.
government expenditures that they financed, minus the present value of the future tax increases or expenditures used to pay off the government debt. This report argues that this lack of transparency has unintended distributional and allocative effects and may also have reduced national savings.

Annex Table 4 shows real annual investment returns for various countries between 1977 and 1990 for selected countries. Figures 3.7 and 4.6 include returns for all mandatory, publicly-managed plans for which data were available for more than five years during the 1980s. Since the publication of the report, new data have been obtained for several countries and some revisions have been made to the data presented in Figure 3.7. Specifically, the returns for India have been recomputed using the IMF consumer price index instead of the urban price index used previously, to make it more comparable to other data used in the report. The result is that the 1980-1990 average shown in Figure 3.7 should actually read, 1.1 percent between 1980-1990 rather than .3 percent. This is a substantial difference but would not change the placement of the bar or the overall story conveyed by the graph.

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sizable trade imbalances between regions, trade costs are likely to be asymmetric. This is a simple demand-meets-supply outcome: total trade in one direction constitutes demand for shipping. If a truck is operating on a back-and-forth basis between two places, it has the same capacity (i.e., supply) in both directions. Given uniform supply, the cost should be higher in the direction featuring greater quantities being shipped on total.<sup>2</sup> In the extreme case, suppose that a truck company transports a certain freight from _o_ to _d_ but expects the vehicle to be empty on the return trip. The pricing of the _o_ -to- _d_ fronthaul will incorporate the additional cost of the return trip. This will be a systematic pattern if there is more freight on total from _o_ to _d_ than in return. Then, any potential shipper from _d_ to _o_ will enjoy lower shipping costs thanks to the competition between truckers that have to go back and will be doing so without cargo otherwise.

In principle, it is possible to circumvent the backhaul problem through trihaul routing ( _o → d_ 1 _→ d_ 2 _→ o_ ) or more complex itineraries. This is, however, complicated by the cost of obtaining ex-ante information for shipping demand in multiple locations and the uncertainty of finding shippers in spot markets (Brancaccio, Kalouptsidi and Papageorgiou, 2020). Applications of on-demand ride technologies to freight may alleviate some of these costs. In the relevant near future, incorporating potential asymmetries and their causes to transport cost estimates is important for accurately predicting how transport costs affect different regions.

With the conceptual and practical issues in mind, the next three sections introduce the three most common methods to measure and quantify transport costs. These are i) survey-based methods, ii) imputation-based methods, iii) estimation-based methods.

I start with direct measurement from surveys.

# **3 Survey-based Methods**

The best method to measure a phenomenon is to collect direct quantitative data about it. This goes for transportation costs as well. Since a survey can target its sample across different types of firms and entities, it is also the only avenue to potentially obtain information on the price of road transport paid by end users separate from its

> 2Wilson (1987) models and estimates the price structure using trucking data between a limited number of regions within the U.S., documenting the prevalence of backhaul problem. This same effect has been documented for international maritime shipping by Wong (2022).

6
20 Bover **(1995),** and Blundell and Bond **(1997).** The first is a Sargan test of over-identifying restrictions, which tests the overall validity of the instruments **by** analyzing the sample analog of the moment conditions used in the estimation process. The second test examines the hypothesis that the error term et, is not serially correlated. In the both the difference regression and the system difference-level regression we test whether the differenced error term is second-order serially correlated **(by** construction, it is likely that this differenced error term be first-order serially correlated even if the original error term is not). The third test is the difference Sargan statistic, which tests the additional set of restrictions of the system estimator. This "difference" Sargan statistic is asymptotically distributed as Chi-square under the null hypothesis of validity of the additional instruments." Failure to reject the null hypotheses of these tests gives support to the model.

# B. Results

The dynamic panel estimates suggest that financial intermediary development exerts a large, positive causal impact on economic growth. Table 4 presents the results using the _difference and system_ estimators described above. We also present the results when the panel estimation is performed purely in _levels_ for comparative purposes. In Table 4, only the results on the financial indicators are given. Table **_5_** gives the full results from _system_ dynamic-panel estimation. The analysis was conducted with two conditioning information sets. The first uses the simple conditioning information set, which includes initial income and educational attainment. The second uses the policy conditioning information set, and includes initial income, educational attainment, government size, openness to trade, inflation,

> **1s** The degrees of freedom of the "difference" Sargan test is given **by** the number of additional restrictions in the system estimator (which is given **by** the difference between the number of degrees of freedom of the system estimator and that of the difference estimator).
<!-- Start of picture text --> Inflows Outflows<br>10 6 10 6<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>1980 1990 2000 2010 1980 1990 2000 2010<br>Inflows Outflows<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>1980 1990 2000 2010 1980 1990 2000 2010<br><!-- End of picture text --> Figure 1: Gross capital flows, by country group: total flows (USD million, upper panels) and percentage shares (lower panels). Advanced (solid), emerging (dashed) and developing countries (dotted).

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_Table 17: Effect of CCT on Incidence of NDV (continuous measure of exposure to violence)_

|(1)<br>Incd. of physical<br>NDV: Sum of 6<br>answers|(2)<br>Incd. of sexual<br>NDV: Sum of 2<br>answers|(3)<br>Incd. of<br>emotional NDV:<br>Sum of 3<br>answers|(4)<br>Incd. of<br>economic NDV:<br>Sum of 4<br>answers|
|---|---|---|---|
|RD Effect<br>-0.013|-0.006|0.017|-0.013|
|(0.47)|(0.32)|(0.68)|(0.37)|
|FDR q-val<br>0.63|0.63|0.68|0.63|
|Observations<br>564|564|564|564|
|Control mean<br>0.014|0.000|0.036|0.007|
|P-values in parentheses: * p<0.1 ** p<0.05 **|* p<0.01|||

Note: All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all NDV regressions in the table.

_Table 18: Effect of CCT on Frequency of Exposure to IPV (continuous measure of exposure to violence)_

|(1)<br>Incd. of physical<br>IPV (freq): Sum<br>of 7 answers|(2)<br>Incd. of sexual<br>IPV (freq): Sum<br>of 3 answers|(3)<br>Incd. of<br>emotional IPV<br>(freq): Sum of 3<br>answers|(4)<br>Incd. of<br>economic IPV<br>(freq): Sum of 4<br>answers|
|---|---|---|---|
|RD Effect<br>-0.011|0.012|-0.024|-0.112|
|(0.98)|(0.81)|(0.88)|(0.54)|
|FDR q-val<br>0.98|0.98|0.98|0.98|
|Observations<br>562|562|562|562|
|Control mean<br>0.642|0.090|0.441|0.527|
|P-values in parentheses: * p<0.1 ** p<0.05 **|* p<0.01|||

Note: All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all regressions in the table.

# VII. Mechanisms of Impact

As described in Section 3, there could be multiple pathways underlying the effect of CCT on GBV. In this section, we examine the effect of the 4P program on an array of intermediate outcomes (as described in Section 4.2) to furnish evidence on plausible mechanisms of impact. Table 19a and Table 19b provide estimates on women’s and men’s outcomes, respectively.

We first test whether the 4P program had any effect on men’s and women’s well-being. Estimates from table 19a (column 4) as well as table 19b (column 1) indicate improvements in well-being (significantly so for men). Improved well-being within the household could have contributed towards lower likelihood of domestic violence perpetrated by intimate partner or non-intimate partner.

Notably, we find significant improvement in women’s bargaining power and empowerment (column 1 and 2 in Table 19a). At the same time, we find no evidence of deterioration (or backlash) in men’s 25
The fiscal policy response of subnational governments will thus be procyclical during a downturn as long as government revenues are procyclical, unless (a) there are additional compensating transfers from the central government, (b) withdrawals from contingency (rainy day) funds, or (c) adequate flexibility in the borrowing rules for subnational governments to respond to cycles within sustainable limits.

a. The condition is that R*-R, the revenue shortfall, is less than FD*-(E*-R*), the additional borrowing capacity, which translates into R-R*>E*-FD*-R*. Dropping R* and reversing the order yields E*<R+FD*.

Source: Authors.

budgets a certain level of expenditure based on estimated revenue and a borrowing target, it will not be able to meet its spending target if revenue falls short of the budget estimate by more than the room it has for borrowing in excess of its budget target (Box 3).

Aggregate _subnational debt_ in 2010 was about 16 percent of GDP in Brazil and 27 percent in both China and India. The scope of subnational governments contributing to countercyclical fiscal policy is determined by the level of debt and interest rates facing individual subnational governments. Some governments may be able to contribute more than others to aggregate fiscal expansion. Short-term stabilization as well as the pursuit of medium- and long-term goals require that governments at least avoid unplanned cuts in developmental expenditure— something that could be considered a minimum target for subnational fiscal policy during an economic downturn.

# **The Global Crisis and Subnational Finances**

After a decline of about 4 percent in 2009, real GDP grew in the advanced economies, by 3.0 percent in 2010 and 1.6 percent in 2011 (table 1). Emerging economies, which contracted by more than 5 percent in 2009, rebounded almost to pre-crisis rates of growth in the following two years.

The strong recovery in emerging economies resulted in multiple growth poles, which helped support the global recovery. This rebound in large part reflected these countries’ relatively healthy initial economic conditions, which allowed them to implement countercyclical 10
# **1. Introduction**

Global trade has grown slowly after the Great Recession, and 2019 is projected to continue the trend as one of the years with the weakest trade performance since the 2008-2009 global financial crisis. A growing body of work analyzes the determinants of the current slowdown in global trade (Constantinescu et al. 2018; Hoekman 2015; Haugh et al. 2016; IMF 2016). Recent analyses attribute the trade slowdown, in varying degrees, to such factors as changes in the composition of economic activity away from import-intensive investment, a slowing pace of global value chain growth and trade liberalization, and an increase in trade protectionism.

This paper draws upon recent pioneering work on the measurement and impact of economic policy uncertainty (Baker et al. 2016) to examine whether policy uncertainty can help explain the slowdown in world trade growth. To motivate the analysis, Figure 1 juxtaposes the growth in volume of world merchandise imports against an index of global economic policy uncertainty. The figure does suggest that there is a negative association between the two, and the particularly weak trade performance in 2019 has coincided with unusually high levels of economic policy uncertainty in that year.

**Figure 1.** **_World import growth and policy uncertainty, January 2012 to June 2019_**

<!-- Start of picture text --> 350 6<br>300 Monthly global economic policy uncertainty 5<br>index<br>4<br>250<br>3<br>200<br>2<br>150<br>1<br>100<br>0<br>World merchandise trade volume (year‐<br>50<br>over‐year percent change of 3 month ‐1<br>moving average, right axis)<br>0 ‐2<br>2012 2013 2014 2015 2016 2017 2018 2019<br>index<br>percent<br>Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun<br><!-- End of picture text --> Source: CPB Netherlands Bureau of Economic Policy Analysis, <u>www.PolicyUncertainty.com, Baker et al. (2016), and</u> authors’ calculations.

Economic policy uncertainty may lower trade growth in two ways. First, a rise in policy uncertainty reduces trade indirectly by reducing GDP growth. In a less-certain environment, firms may choose to postpone investment decisions, consumers may cut back spending, and banks may increase the cost of finance. Second, policy uncertainty, may affect trade directly, by affecting firms’ decisions to invest to serve foreign markets or to source inputs internationally.

2
<!-- Start of picture text --> Minerals nec, 2001 Minerals nec, 2011<br>COL<br>BRA<br>CHN TUR PERZAF<br>RUS CHN<br>ZAF BRA<br>IND TURRUS<br>PER CHL IND COL<br>THA CHL<br>THA<br>4 6 8 10 12 4 6 8 10 12<br>Log GDP per capita (current USD) Log GDP per capita (USD)<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>Total LVAX share (%) Total LVAX share (%)<br>10 10<br>0 0<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX and World Development Indicators.

The job intensity of South Africa’s manufacturing exports continues to be low vis-a-vis comparator countries, while the job intensity of mining and energy is higher than all other countries. The following two figures show a comparison of the job intensity of exports across sectors and comparator countries (in terms of the total number of jobs per $1 million of gross exports in Figure 16 and the direct number of jobs per $1 million of gross exports in Figure 17). Unlike the labor value added intensity of exports (which was lower than the BRIC countries but higher than other comparators), the job intensity of exports tends to be lower than all comparators, with the exception of mining and energy. Again, with the exception of mining and energy, the direct job intensity of exports tends to be lower than comparators across sectors.

**Figure 16: Total job content per $1 million of gross exports across macro sectors 2011**

<!-- Start of picture text --> Agriculture<br>Construction<br>Electricity, gas and water<br>Financial services<br>Manufacturing<br>Mining and Energy<br>Other commercial services<br>Public services<br>Real estate, renting<br>Trade, sales and accomodation<br>Transport and communication<br>0 100 200 300 400<br>Jobs per $1 mil. exports<br>ZAF BRA RUS CHL COL THA TUR<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

26