rafmacalaba/datause-encoder-data huggingface dataset:
household income as an indicator of a household’s economic status. The last column in Table 3 reports the estimated e¤ect of log of total income on the probability of paying bribes for education services. The estimated coe¢ cient is negative and large in magnitude (-0.50). It is also statistically signi...cant at the 5 percent level (P-value 0.02).

The last column in Table (3) reports the estimate from 2SLS where the interactions of the heavy rainfall dummy with age and religion of the household head are the identifying instruments, and the speci...cation is same as in column (1) of Table 3. As noted before, we control for the religion dummy and cubic polynomial terms of head’s age to control for any possible direct e¤ects on the propensity to pay bribes.<sup>34</sup> Using interactions of both religion and age as instruments increases the set of households whose income can be a¤ected by the identi...cation scheme, and the results thus have more external validity. To improve the power of the instrument and e¢ ciency of the 2SLS estimate, we follow the approach developed by Rajan and Subrahmanian (2008) and predict household income from a “zero stage" regression where the interactions of rainfall with household head’s age and religion are included as regressors in addition to the other controls used in the main speci...cation (column (1) Table 3). The predicted income from the “zero stage”then is used as the identifying instrument in 2SLS regression. The estimated e¤ect of household income from this exercise is a bit larger in magnitude compared to that reported in column (1) of Table (3).<sup>35</sup> The results in Table (3) thus provide robust evidence that the e¤ect of higher income on the probability of paying bribes for education of children is negative, thus con...rming the conjecture that in villages the rich wield signi...cant power and they are not likely to be subject to the bribe demands from school teachers.

# (7.2) Propensity to Pay Bribes: Disaggregated IV Results

> 34 In a similar interaction based identi...cation scheme, Carneiro et al. (forthcoming) use cubic polynomial terms to control for direct e¤ects. We also note here that the results do not depend on the exact form of polynomial function of age, or on the inclusion of interaction of religion dummy with other exogeneous household characteristics as additional controls.

> 35 Note that if we use only one interaction instrument at a time, the power of the instrument su¤ers, but the estimates of the income e¤ect are very similar to the ones reported in columns (1) and (5) in Table (3). Also, even though we have two interaction instruments, we do not report Hansen’s J statistics in Table (3), as the e¤ects of income may vary for di¤erent subgroups a¤ected by di¤erent instrument, and thus heterogeneity may lead to a "rejection" of the null hypothesis. To satisfy curiosity of a reader, we note that Hansen’s J test cannot reject the null of "valid" exclusion restriction.

30
nature of the data, (ii) the scale, multidimensionality and granularity of the information collected; and (iii) its ability to capture actual human behavior recorded in real-world transactions.

The emergence of big data has also enabled researchers to examine aspects of the labor market that have historically been difficult to analyze (Horton and Tambe, 2015). For instance, using data from the predominately US-focused online job portal “Career Builder,” Marinescu and Rathelot (2015) assessed the effects of geography on worker application patterns. Their research concluded that while job seekers typically do not want to apply for jobs far from where they live, the effects of geography are too small to explain much of the frictional unemployment in the labor market. This type of analysis would have been impossible without big data analytics. Other studies using data from job portals have been conducted in the US, Europe and middle-income countries like China and the Slovak Republic. Moreover, social networks such as LinkedIn, Facebook and Twitter, employment-information websites such as Glassdoor, and trendinformation providers like Google Trends, are also potential all new data sources for labor market analysis (Lenaerts, Bevlavy and Fabo, 2016).

The establishment and rapid expansion of online job platforms have dramatically reduced the cost of information access for both job seekers and employers. Kroft and Pope (2014) and Mang (2012) argue that there has been a large-scale shift from posting job listings in newspapers and other print media to websites and that this process has lowered the cost of acquiring employment information. Moreover, several studies from developed countries have found that the use of employment websites has reduced unemployment rates (Beard et al., 2012; Kuhn and Mansour, 2014), though other studies do not find strong evidence of an impact on wages (Kuhn and Mansour, 2014; Shahiri and Osman, 2014). However, as Shahiri and Osman (2014) point out, many people in developing countries—including both employers and job seekers—cannot take advantage of employment websites due to limited internet infrastructure, high user fees, and/or a lack of knowledge regarding information technology. Studies have also found that greater cellphone use improves employment outcomes in developing countries (Klonner and Nolen, 2010 for South Africa; Aker, 2011 for Niger; Burga and Barreto, 2014 for Peru), lending further support to the conclusion that information constraints are a significant, though often unrecognized, source of inefficiency in the labor market.

There are important challenges involved in using big data for labor market analysis. These include accessing and analyzing data in ways that respect the privacy and confidentiality of individuals, designing creative and scalable approaches to summarizing, describing and analyzing unstructured data sets, and dealing with the partial representativeness of the data. Methodological challenges and the ownership of data sets are also important considerations. One of the principal critiques of big data analytics is that the underlying empirical 5
12

# III. Initifal Financial Development. Growth. and the Sources of Growth

Cross-country studies of long-run growth typically evaluate the strength of partial correlations between growth and economic indicators that are almost certainly determined jointly with growth. With respect to financial services, the finding that financial development is strongly associated with contemporaneous economic growth may be interpreted in a number of ways. Joan Robinson, for example, argued that "By and large, it seems to be the case that where enterprise leads finance follows" 11952, p. 86]. Other observers may believe that the strong link between financial development and economic growth merely reflects a positive correlation arising from contemporaneous effects of various shocks on financial and economic development. Here, we investigate whether the predetermined component of financial sector development is strongly linked with subsequent growth and the sources of growth. Although we will note some qualifications, the evidence suggests that the predetermined component of financial development is a good predictor of long-run growth and that financial development predicts both the rate of physical capital accumulation and the rate of improvement in the efficiency with which economies allocate physical capital. These results have a number of implications. The link between growth and financial development is not just a contemporaneous association. Finance does not only follow growth; finance seems to importantly lead economic growth. Furthermore, a positive association between contemporaneous shocks to financial development and economic growth does not fully account for the finance-growth link. When countries have relatively high levels of financial development, economic growth tends to be relatively fast over the next 10 to 30 years.

# A. Initial values

We examine the relationship between the initial values of the financial development indicators at the beginning of the period andi subsequent economic growth using ordinary least squares regressions. Due to data availability, we
staff also overestimated the gain from training, with a mean expected treatment gain of 24.3 percentage points. The impact of training is thus much less than both the staff at ISKUR and the training applicants anticipated.

# **6. Why does training have such limited effects, and do certain types of training work better?**

The impact of training is less than expected by either training participants or the labor ministry staff, although the impacts on employment are not that different from those found in other evaluations. In order for training to be effective, our pre-analysis plan set out four intermediate steps in a causal chain through which we might expect to see selection into a course influencing employment outcomes. We examine each in turn, which also helps understand whether certain types of training have more impact.<sup>18</sup>

# **6.1 Individuals selected for courses must show up and complete training**

The first step in our causal chain is that individuals selected for training classes must actually show up and attend these classes. As noted above, 77 percent of those selected for treatment attended the course beyond the second day, and 72 percent completed it. The LATE estimates show impacts which adjust for attendance. However, we might also think that lower attendance and completion rates are an indicator of courses that participants find to be of lower quality or expect to be less useful. We therefore estimate equation (3) by interacting treatment with the percent of individuals assigned to a course who attended the course or who completed it. Since this characteristic varies only at the course level, we cluster standard errors by course (and also do this for all subsequent tests of interactions of treatment with course characteristics).

The first two rows of Table 9 show these treatment interactions. The interactions are positive, as hypothesized, suggesting that treatment effects are larger in courses with higher attendance rates. However, the results are almost all not statistically significant, and the magnitude of the impacts is not large: a one standard deviation increase in course attendance rates is associated with a 1 percentage point increase in employment. The administrative data shows a significant longer-term impact at the 10 percent level of the course having higher attendance, but the effect

> 18 We specified that we would look at these effects for two survey outcomes: being employed for 20+ hours in a week, and the aggregate employment index. Given that we find impacts vary over time, we also examine this heterogeneity for two measures of formal employment from our administrative data: ever having a formal job between course end and January 2012 (around the follow-up survey), and being formally employed as of August 2013.

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# **Some empirical values of** _wc_ **and** _wh_

To consider the values of _wc_ and _wh_ in a realistic setup, we take several prediction models and poverty lines used in small-area estimation or survey-to-survey imputation in several countries. For Cambodia, we adopt the consumption model and poverty line used in a small-area estimation project detailed in Ministry of Planning and United Nations World Food Programme (2002) and Fujii (2006). This project uses the Cambodia Socioeconomic Survey 1997. In each of the three strata (Phnom Penh, Other Urban and Rural), a separate consumption model and a separate poverty line are used. Our point estimates of _β_ (unreported) are slightly different from those used in this project because the heteroskedasticity of _ech_ is allowed for in the former and the estimation method is slightly different as a result. However, the difference is not important for our purpose because we are only interested in the plausible range of _wc_ and _wh_ .

For Malawi, Niger, and Tanzania, we used a prediction model and national poverty line from an independent survey-to-survey imputation project.<sup>17</sup> The datasets used are Malawi Third Integrated Household Survey 2010-2011 for Malawi, l’Enquˆete Nationale sur les Conditions de Vie des M ́enages et l’Agriculture 2011-12 for Niger, and Tanzania National Panel Survey 201011 for Tanzania. All these surveys are a part of the Living Standard Measurement Surveys by the World Bank.

Table 4 provides a summary of the estimates of _wc_ , _wh_ , _c_ ̃, _ψ_<sup> ̃</sup> , and _α_ for various surveys. The first two columns report the basic information about the survey such as the number of observations ( _n_ ) and number of clusters ( _K_ ). The next two columns show that the values of _wc_ and _wh_ vary substantially across countries. Based on this, we use _wc ∈{_ 0 _._ 6 _,_ 1 _._ 2 _,_ 1 _._ 8 _}_ and _wh ∈{_ 0 _._ 4 _,_ 1 _._ 2 _,_ 3 _._ 6 _}_ . The next two columns report the implied travel cost _tildec_ under the assumption of optimal sampling for the sample direct estimator and the ratio _ψ_<sup> ̃</sup> of data collection costs between the sample direct and single-sample prediction estimators with the implied travel cost. As shown in the last column, the ratio _α_ of variances between the cluster- and householdspecific error terms satisfies eq. (9), which is the condition for the approximation we use.

> 17Specific details, including the model specifications used, are available upon request.

42
2. Investments in shipping capacity occur across long cycles. Carriers must carefully anticipate demand to avoid costly overcapacity yet still meet upswings. Therefore, capacity-to-demand adjustments are imperfect. The 2021-22 combination of booming demand and congestion-induced capacity losses was unforeseeable and sparked a surge in orders for new (large) container vessels to be delivered in 202324 amid slowing demand.

<!-- Start of picture text --> Figure 10. Coevolution of Global Stress (MTEU) and Figure 11: Shanghai Freight Index and Stress<br>Freight Rates (USD/TEU), 2019-2024<br>2 6000 6,000<br>y = 2237.1x + 349.35<br>5000<br>1.5 5,000 R2 = 0.743<br>4000<br>4,000<br>1 3000<br>2000 3,000<br>0.5<br>1000<br>2,000<br>0 0<br>1,000<br>0<br>Global Supply Chain Stress Index (LHS) 0 0.5 1 1.5 2 2.5<br>Stress MTEU<br>Shanghai Containerized Freight Index (RHS)<br>Source: The World Bank, Shanghai Shipping Exchange Source: The World Bank, Shanghai Shipping Exchange<br>(https://en.sse.net.cn/). The Shanghai Containerized index (https://en.sse.net.cn/).<br>is a weighted average of Chinese Shipping rates (per TEU)<br>across global destinations.<br>Millions<br>TEU<br>USD/TEU<br>Rate USD<br>2019-01 2019-07 2020-01 2020-07 2021-01 2021-07 2022-01 2022-07 2023-01 2023-07 2024-01<br><!-- End of picture text --> Figure 12. Baltic Dry Index, 1985-2024 <!-- Start of picture text --> 14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>1/1/1985 2/1/1986 3/1/1987 4/1/1988 5/1/1989 6/1/1990 7/1/1991 8/1/1992 9/1/1993 10/1/1994 11/1/1995 12/1/1996 1/1/1998 2/1/1999 3/1/2000 4/1/2001 5/1/2002 6/1/2003 7/1/2004 8/1/2005 9/1/2006 10/1/2007 11/1/2008 12/1/2009 1/1/2011 2/1/2012 3/1/2013 4/1/2014 5/1/2015 6/1/2016 7/1/2017 8/1/2018 9/1/2019 10/1/2020 11/1/2021 12/1/2022 1/1/2024<br><!-- End of picture text --> Source: Baltic Exchange, the Baltic Dry Index is a global average of observed rates over a series of major routes.

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28

# Table 6: Probit estimates for NCMS enrollment

|_Variable_|_Coef._|_z_|
|---|---|---|
|Household size|0.1542|15.76|
|Ethnic majority household|-0.3384|-5.16|
|Share of household members over 65|0.0586|0.97|
|Share of household members under 10|0.1257|1.40|
|Share of household members chronically sick|0.2295|3.17|
|Share of household members with health fair or bad|0.2448|4.18|
|Head is rural wage laborer (omitted = professional worker)|0.0424|0.99|
|Head is farmer (omitted = professional worker)|-0.0767|-2.12|
|Head is retired (omitted = professional worker)|-0.3195|-4.61|
|Head is employee (omitted = professional worker)|-0.2802|-4.98|
|Head has primary education (omitted = illiterate or semi-literate)|-0.1711|-2.40|
|Head attained junior high school (omitted = illiterate or semi-literate)|-0.0217|-0.30|
|Head attained high or vocational school (omitted = illiterate or semi-literate)|0.0253|0.33|
|Head has college education (omitted = illiterate or semi-literate)|0.0180|0.22|
|Head's gender|-0.0131|-0.38|
|Household per capita income|0.0000|0.24|
|Household per capita income<sup>2</sup><br>|0.0000|-1.74|
|Household per capita income<sup>3</sup><br>|0.0000|2.44|
|Household per capita income<sup>4</sup>|0.0000|-2.48|
|Household per capita income<sup>5</sup>|0.0000|2.23|
|Distance to closest facility category 2|0.2946|9.94|
|Distance to closest facility category 3|0.2711|6.45|
|Distance to closest facility category 4|0.4135|5.81|
|Distance to closest facility category 5|-0.1064|-1.14|
|Distance to closest facility category 6|0.3521|5.09|
|_Dibao_household or classified as poor (_pinkun_)|-0.7140|-9.88|
|Received safety net payments last year|0.8258|13.75|
|Someone in household enrolled with GIS or LIS|-0.3330|-8.33|
|GDP per capita in county|0.0000|5.87|
|Central China (omitted category is East)|0.2007|5.02|
|Western China (omitted category is East)|0.1476|3.97|
|Constant|0.1806|1.36|
|Pseudo R-squared|0.072||
|N|17263||
In exploring the prospects for services-led development compared to the traditional export-led manufacturing model, the following is worth emphasizing. First, that services may “need” a manufacturing core to develop does not take away from the fact that many services, in turn, are vital inputs into the production of manufactured goods. This symbiotic relationship between the two sectors therefore requires a reexamination of the linear structural change process from agriculture to manufacturing and then services.

Second, productivity gains from resource reallocation will also happen within sectors, for which there is evidence of large heterogeneity across firms (McMillan and Rodrik, 2011; Bloom et al. 2010, Syverson 2011). Hsieh and Klenow (2009) find that between a third and half of the differences in manufacturing total factor productivity between China and India and the United States can be explained by the large number of inefficient firms. This dispersion in the productivity distribution of firms applies equally, if not more, to services. Using firm-level data from Portugal, for example, Dias et al. (2016) show that reducing misallocation in the service sector to manufacturing levels would boost aggregate value added by around 31 percent. The importance of productivity gains within sectors is underscored by China’s experience where reallocation accounted for only one-fourth of the productivity growth in the three decades between 1980 and 2010 (Herrendorf, Rogerson and Valentinyi 2013).

Third, firms are increasingly structured around the close interaction of ‘manufacturing’ and ‘services’, which makes it difficult to assign them exclusively to one sector. Manufacturing companies increasingly no longer sell only physical goods, but instead sell bundles including design, development, marketing, warranties and after-sales care, etc. Xerox, for example, has restructured itself into a ‘document solution’ company, offering technology advanced printer systems but also services like document managing and consulting; in fact, services represent around 40% of Xerox’s turnover and are soon expected to represent more than 50% (Benedettini et al. 2010). Similarly, many services firms are becoming more like manufacturing firms, as outputs are mass produced, and have even introduced new goods such as Google in the market for tablets and Amazon with its Kindle (Lopez-Bassols and Millot 2013).

16
Table 9 reports the LATE estimates for the pooled model. The results indicate that ENTRE reduces dropout by 3.1 percentage points for students in complier schools (column 3 of Table 9), roughly doubling the ITT estimates. As expected, the LATE estimations are significantly more imprecise, with the standard errors three times larger than those corresponding to the ITT. Table 10 shows a similar pattern of heterogeneous effects by dropout probability, school size, and gender. The results show that participating in the program reduces dropout by about 6 percentage points for the students with the highest risk, 4.5 percentage points for students in large schools, and approximately 4 percentage points for boys.

Table 9. Impact of the program on the probability of student dropout: LATE estimates.

|||LATE||
|---|---|---|---|
||(1)|(2)|(3)|
|_ENTRE j_|-0.025|-0.030**|-0.031**|
||(0.040)|(0.013)|(0.013)|
|Observations|130,762|124,626|124,626|
|R-squared|0.000|0.294|0.325|
|Controls at student level|NO|YES|YES|
|School fixed effects|NO|NO|YES|

Notes: (1) Local Average Treatment effects were estimated by instrumenting treatment (proxied as the effective reception of the ENTRE guide according to self-reported data in follow-up 3) with treatment assignment. (2) Standard errors clustered at schoolyear level in parenthesis. (3) *** p<0.01, ** p<0.05, * p<0.1. (4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year _t_ and year _t+1_ . (5) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where in this case _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that was treated) and _Postt_ : (1 = year of the intervention; 0 = year before the intervention). (6) All specifications include the corresponding _Postt_ and _Entrejt_ dummies. (7) Student characteristics used as control: age dummies, gender and risk (probability of dropout estimated in the Early Warning System).

34
# International Liquidity Rents

Maya Eden<sup>_∗_</sup> World Bank, DECMG May 23, 2013

**Abstract**

This paper presents a model of global liquidity shortages. Liquid claims are enforceable promises that play a transaction role. Since developed economies have a comparative advantage in creating liquidity, they export liquid claims to emerging economies, resulting in a permanent current account deficit. This model suggests that unrestricted liquidity flows are (a) welfare reducing for emerging economies and (b) Pareto inefficient. The inefficiency results both from excessive investment for the purpose of creating collateral-backed liquid claims, and from excessive global fragility with respect to collateral shocks.

**JEL Classification:** E44, F30, G15

**Keywords:** Liquidity shortage, the welfare effects of financial integration, international liquidity flows, liquidity creation **Sector board:** Economic Policy (EPOL)

> _∗_ I thank Alessandro Barattieri, Ricardo Caballero, Arnaud Costinot, John Duca, Ben Eden, Anton Korinek, Ha Nguyen, Vincenzo Quadrini, Luis Serven and Christoph Steffen for helpful comments. I have also benefited from comments made by seminar participants at MIT’s international breakfast and the World Bank and conference participants at IGIDR, INFINITI and the NBER summer institute, IFM workshop. This paper reflects my own views and not necessarily those of the World Bank, its Executive Directors or the countries they represent. Please send comments to meden@worldbank.org.

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