rafmacalaba/datause-encoder-data huggingface dataset:
Table 6. Estimates of Long-term Foreign Investment<sup>in the United States, 1853-1874 ($US miflions)</sup>

||||Governent|||||Pri|vae Sector||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Datw|Totdal|Podae|State|County/<br>city|Bank|Canal|Riroads|Isurance|Minig|Manufauing|Utilities|Other|
|1853<br>1854|22.2|27.0|111.0|21.5|6.7|2.5|52.1|0.4|0.7|0.1|0.1||
|1856<br>1859|241.0|15.0|111.0<br>40.0|21.5|6.7|2.5|82.9|0.4||||1.0|
|1861, Jan.<br>1863|444.0<br>200.0||||||100.0||||||
|1864, Mar.||150.0|||||||||||
|_1865,_Mar.<br>16S, Jue||320.0|||||||||||
|<br>1866|600.0|350.0|*-150.0o||||100.0||||||
|1867||486.0|||||IS0.0|||||21.0<br>|
|1868|938.0|700.0|60.0|||7|243.0||10.0|||25.0|
|1369|1,390.5|1,000.O|100.0|7.5||5|||||||
|1871||845.0|||||||||||
|1872|1,.0||||||||||||
|1873|1,S00.0||||||||||||
|1974|1,500.||92.9||||390.0||||||

Souc: Wikins 1989, p. 91.

35
playgroups until age 4. From age 5 children also have the option to go to kindergartens. Some stay in kindergartens till age 6, though, increasingly many enroll in primary school at age 6 despite the fact that the official school-starting age is 7.

In Figure 2 we highlight this using the two cohorts followed in this study. At the time of the baseline survey, 4 years old were immediately eligible for project-provided playgroups. In contrast, the cohort of children that was 1 years old was not age-eligible for project-provided playgroups, they became eligible 2 years later.

By midline the children in the older cohort were about 5 years old, when their families essentially had a choice between continuing to enroll them in playgroups or enrolling them in nonproject kindergartens. All of the children in the older cohort were enrolled in primary school by the time of the endline. The younger children however, were only about 2 years old at the time of the midline and still ineligible for project-provided services. By endline these children were between 4 and 5 years old and age-eligible for playgroups or kindergartens.

Figure 2: Children’s ages, the timing of the intervention and their eligibility for various services

||**Baseline**|**Midline**|||**Endline**|
|---|---|---|---|---|---|
|**Early beneficiaries**<br>**/ 4-year-old cohort**|**2009**|**2010**|**2011**|**2012**|**2013**|
|Age|4|5|6|7|8|
|Type of service<br>child is eligible to<br>enroll in|Children are<br>eligible to<br>enroll in<br>playgroups|Children ar<br>enroll in ki|e eligible to<br>ndergartens|Children ar<br>in pri|e eligible to enroll<br>mary school|
|**Later beneficiaries**<br>**/ 1-year-old cohort**||||||
|Age|1|2|3|4|5|
|Type of service<br>child is eligible to<br>enroll in|Children are t<br>enroll in pl|oo young to<br>aygroups|Children ar<br>enroll in p|e eligible to<br>laygroups|Children are<br>eligible to enroll<br>in kindergartens|

Note: Figure depicts ages of the two cohorts studied and what types of services they are eligible for at <u>each age.</u>

# **5.2. The centers they attend**

We corroborate these assertions in Figure 3, which compares the enrollment trends over time for the two cohorts side by side.<sup>6</sup> Each figure plots year-by-year trends in months of enrollment. The first panel displays the net enrollment patterns over time for children of different ages over all types of services and then presents the patterns separately for each type of service.

> 6 At the endline, parents were asked to report their children’s enrollment status in each year since the project started. We combine these event histories with their ages and report the year-by-year impacts on duration of enrollment.

10
thought to be a way to cope with the uncertainty involved in the baseline projections, their implementation is problematic for, at least, two reasons. First, the framework ignores the co-movements between the macroeconomic variables that determine the evolution of external debt. For example, a negative shock to GDP growth is assumed to have no impact on other macroeconomic variables, like the real exchange rate, the current account, or the interest rate. These variables are kept constant at their baseline projections. Moreover, shocks in the current framework do not have a meaningful economic interpretation. Where do shocks come from? What is their structural interpretation? Second, some argue that the stress tests are too stringent because they consider highly unlikely and pessimistic scenarios. For example, one of the stress tests in the current framework involves a simultaneous worsening in four determinants of debt for two years.<sup>3</sup> There is no rationale for the duration or the size of the proposed shock.

This paper proposes to alleviate these problems by pooling data from many countries and estimating the shocks and co-movements faced by a generic low income country. The methodology is an extension to that proposed by Garcia and Rigobon (2005). I rst derive a debt accumulation equation and identify key variables that determine the evolution of foreign debt.<sup>4</sup> Next, I estimate a panel vector autoregression with common slope coecients and covariance matrix to model the evolution of the determinants of debt. Finally, I perform simulations to generate a large number of future debt trajectories for a given country and compute several statistics on external debtmedian forecasts, condence regions, the probability of debt crossing some threshold, and so forth. These statistics can be used to assess whether external debt is, or is likely to be, too large relative to a predetermined criterion.

To perform the simulations, Garcia and Rigobon (2005) use a Monte Carlo method and assume that shocks are drawn from a normal distribution with mean zero and covariance matrix equal to that of the tted residuals. One contribution of this paper is to replace the

> 3This is a combined negative shock to real GDP growth, exports growth, the GDP deator measured in U.S. dollars, and private transfers and foreign direct investment.

> 4The terms external debt and foreign debt are used interchangeably throughout the paper.

2
15

|**Ta**<br>|**ble 2. Testing 5 hy**<br>|**potheses**<br>||
|---|---|---|---|
|Model|A|B|C|
|_Estimation Method_|_bet_|_ween panel estimat_|_or _|
|_Dependent variable_|_degr _|_ee of decentralizat_|_ion_|
|real GRP per capita<br>log,PPPadjusted|-0.111<br>(0.028)***||-0.272<br>(0.064)***|
|real GRP per capita<br>Log||-0.096<br>(0.027)***||
|Population<br>Log|0.044<br>(0.012)***|0.023<br>(0.013)*|0.070<br>(0.028)**|
|Area|0.007|0.021|-0.002|
|Log|(0.010)|(0.011)*|(0.015)|
|Ethnolinguistic<br>fractionalization|-0.174<br>(0.067)**|-0.163<br>(0.066)**|-0.259<br>(0.107)**|
|Election votes spread in<br>1st round|-0.00040<br>(0.0005)|-0.00043<br>(0.0005)||
|Corruption|||-0.016<br>(0.076)|
|Urbanization|0.0019<br>(0.0010)*|0.0024<br>(0.0011)**|0.0014<br>(0.0018)|
|Constant|0.340<br>(0.099)***|1.315<br>(0.255)|0.367<br>(0.226)|
|numberofobservations|546|619|259|
|numberofgroups (regions)|85|85|37|
|R<sup>2</sup>between|0.44|0.43|0.62|
|1. Standard errors in parenthes<br>5%with **,at 1%with ***.|Notes<br>es. Values significan|t at 10% level are m|arked with *, at|

# **III. Resolving the Russian Paradox**

**3.1. Income and decentralization.** The results of the previous section are in sharp contrast with the general consensus about the determinants of decentralization. In particular, with the work of Panizza (1999) who finds that fiscal decentralization is robustly associated with higher per capita incomes, higher ethnolinguistic fractionalization and higher levels of democracy. How can this “Russian paradox” be resolved or explained? Could it be driven by misspecification of the equation (1)? Or is the conventional theory not accurate indeed?

One must first explain the negative association between the measure of fiscal decentralization and per capita income. There can be several explanations for this unexpected relationship. Firstly, as already mentioned, a lower degree of fiscal decentralization in a region
the significant proportion of missing values for our 12 policy measures and 3 trade infrastructure variables, we choose _M_ = 80 in the first step so that we can obtain stable results (Kenward and Carpenter, 2007; Horton and Lipsitz, 2001). Summary statistics for the selected imputations shown in Table 2 look reasonable in that the summary statistics of the imputed data are quite stable across the first and last imputations, and are not overly different from those that are observed for the initial data set (in Table 1).

Table 2: Summary Statistics and Number of Observations for the 1st and 80th imputations

|||**the 1st**|**imputation**||||**the 80t**|**h imputatio**|**n**||
|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**# of Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|**# of Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|**OECD trade facil**|**itation indi**|**cators**|||||||||
|Information|182|1.50|0.46|0|2|182|1.51|0.43|0|2|
|Involvement|182|1.01|0.75|0|2|182|1.14|0.70|0|2|
|Advance Rulings|182|1.03|0.76|0|2|182|0.99|0.76|0|2|
|Appeal|182|1.31|0.53|0|2|182|1.32|0.46|0|2|
|Fees & charges|182|1.30|0.55|0|2|182|1.31|0.48|0|2|
|Documents|182|1.31|0.36|0.01|2|182|1.34|0.34|0.01|2|
|Automation|182|1.11|0.64|0|2|182|1.12|0.61|0|2|
|Procedures|182|1.14|0.40|0.30|2|182|1.12|0.38|0.3|2|
|Coop. - internal|182|1.22|0.72|0|2|182|1.21|0.75|0|2|
|Coop. - external|182|1.05|0.83|0|2|182|1.06|0.77|0|2|
|Consularization|182|1.31|0.95|0|2|182|1.31|0.95|0|2|
|Governance|182|1.51|0.46|0.33|2|182|1.44|0.49|0.33|2|
|**Trade infrastruct**|**ure variable**|**s**|||||||||
|Phys. Infra.|182|0.44|0.23|0.10|1|182|0.43|0.23|0.10|1|
|ICT|182|0.47|0.23|0.12|1|182|0.48|0.23|0.12|1|
|Bus. Environment|182|0.38|0.24|0.01|1|182|0.37|0.24|0.01|1|

Notes: The data report summary statistics for the first and last of 80 imputations of the policy and trade infrastructure data.

# **5 Results**

In this section we report empirical results from the estimation models, as well as some counterfactual calculations. The primary focus of attention is the set of results from the grouped duration models with import and export days in customs as the outcome variables. We also report results from the linear models of continuous outcomes (the logged cost of clearing import and export customs, from TAB, and an index of customs performance taken from the LPI). There are two reasons for the focus on the model of clearance times: a) the estimation model is less familiar than the linear model used in the other applications, b) our discussions with logistics professionals suggest that time savings are a useful summary indicator of the private sector costs associated with clearing goods at the border.

18
# **Reference:**

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Dooley Michael and K. M. Kletzer (1994). “Capital Flight, External Debt and Domestic Policies”, Federal Reserve Bank of San Francisco Economic Review, No. 3, pp. 29-37.

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Elbadawi Ibrahim, Benno Ndulu, Njuguna Ndung’u (1997). “Debt Overhang and Economic Growth in Sub-Saharan Africa”, in Iqbal and Kanbur eds. External Finance for Low-Income Countries, International Monetary Fund, Washington DC.

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22
Policy ReseaRch WoRking PaPeR 4625

# **Abstract**

Controversy exists over whether the estimated effects of schooling on health care use reflect the influence of unobserved factors. Existing estimates may overstate the schooling effect because of the failure to control for unobserved variables or may be downwardly biased due to measurement error. This paper contributes to the resolution of this debate by adopting an instrumental variable approach to estimate the impact of female schooling on maternal health care use. A school construction program in Indonesia in the 1970s is used to construct an instrumental variable for education. The choice between use and non-use of maternal health services is estimated as a function of schooling and other variables. Data from the Indonesia Family Life Survey are used for this paper.

indicate that each additional year of schooling does indeed have a significant, positive effect on maternal health care use. Instrumental variable estimates of the schooling effect are larger. The results suggest that schooling has a positive impact on maternal health care use even after eliminating the effect of unobserved variables and measurement error.

This paper moves beyond previous work on the impact of education on health care use by adopting an IV approach to address the problem of endogeneity and measurement error. IV methods have been used widely in the labour economics literature to examine the impact of schooling on wages and other labour market outcomes but rarely to estimate the effect of schooling on health outcomes.

Standard regression models estimated in the paper This paper—a product of the Health, Nutrition and Population Unit, Human Development Sector Department of East Asia and Pacific Region—is part of a larger effort in the department to disseminate working papers by staff members on issues related to human development. This paper was written as part of the author's doctoral thesis submitted to Harvard University in June 2006. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at asomanathan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Digital wage payments are also cheaper and safer for recipients. In Liberia, teachers who were paid digitally saw the cost of receiving their payments fall 92 percent (including bus fare) from $25 per paycheck to $2. This cost reduction was accompanied by an increase in time spent teaching—because they were paid digitally, teachers no longer had to travel to town to receive their wages and instead spent more time in the classroom (Dusza, 2016). Eliminating travel also improves recipient safety, as noted in the section on digital payments and crime (Wright et al., 2017).

Additional benefits of digital wage payments come from their speed and efficiency compared with manual cash payments. Outcome data collected from firms that digitized wage payments (i.e., not in comparison to a control group) suggests that these benefits apply also to the firms making them. In Jordan, three garment factories that digitized their wage payments saw the time needed to make payments decline by between 66 percent and 70 percent, depending on the type of account the wage was paid into (BTCA, 2021a). Similarly, in India, after transitioning to digital wage payments, the clothing company GAP saw savings equal to the wages for 16 fulltime workers per month (BTCA, 2021b). In Senegal, 82 percent of businesses reported that they felt safer with digital payments because they did not need to travel with all employee paychecks (BTCA, 2021a).

# **4.3 The benefits of digitalizing agriculture payments**

In developing economies, 160 million banked, and 145 unbanked adults received agricultural payments in cash (Global Findex 2021). In contrast to wage and government payments, agricultural payments have a very low rate of digitalization: of the 11 percent of adults in developing economies who received them, three quarters received them in cash. The one exception to this trend is Sub-Saharan Africa, where the share of adults receiving agricultural payments is higher at 26 percent—more than twice the developing economy average—and a higher share received them into an account. This is likely a byproduct of payment digitalization programs initiated by large supply chain wholesalers and enabled by large, rural, mobile money agent networks—a large share of the region’s digitalized agriculture payments are paid into mobile money accounts.

Digital agricultural payments allow farmers to more efficiently access new markets and better agricultural inputs, improve their resilience to shocks, and increase their productivity. When 11
quality of life. In Andrews, F.M. (Ed.), _Research on the Quality of Life_ . Ann Arbor: Institute for Social Research University of Michigan.

Ito, T.A. and Cacioppo, J.T. (1999). The psychophysiology of utility appraisals. In Kahneman, D., Diener, E. and Schwarz, N. (Eds), _Foundations of Hedonic Psychology: Scientific Perspectives on Enjoyment and Suffering_ , Chapter 24. New York: Russell Sage Foundation.

Kahneman, D. and Krueger, A.B. (2006). Developments in the measurement of subjective wellbeing. _Journal of Economic Perspectives_ , vol. 22, pp. 3-24.

Knight, J. and Song, L. (2006). Subjective well-being and its determinants in rural China. _University of Nottingham_ , mimeo.

Lelkes, O. (2006). Tasting freedom: Happiness, religion and economic transition. _Journal of Economic Behavior and Organization,_ vol. 59, pp. 173-194.

Lucas, R. (2005). Time does not heal all wounds - A longitudinal study of reaction and adaptation to divorce. _Psychological Science,_ vol. 16, pp. 945-950.

Lucas, R., Clark, A.E., Georgellis, Y. and Diener, E. (2004). Unemployment alters the set-point for life satisfaction. _Psychological Science,_ vol. 15, pp. 8-13.

Luttmer, E. (2005). Neighbors as negatives: Relative earnings and well-being. _Quarterly Journal of Economics,_ vol. 120, pp. 963-1002 Melenberg, B. (1992). _Micro-Economic Models of Consumer Behaviour and Welfare_ . PhD Thesis, Tilburg University.

Rayo, L. and Becker, G.S. (2007). Evolutionary efficiency and happiness. _Journal of Political Economy_ , vol. 115, pp. 302-337.

Veblen, T. (1899, 1949). _The Theory of the Leisure Class_ . London [Originally published by Macmillan New York], George Allen and Unwin.

Veenhoven, R. (1997). Quality-of-life in individualistic society: A comparison of 43 nations in the early 1990's. _Social Indicators Research_ , vol. 48, pp. 157-186.

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25
Driemeier and Rijkers (2013)<sup>15</sup> ). Even so, we restrict our discussion below to the sign and significance of the observed effect rather than discuss the magnitude of the effect, accordingly.

In addition to productivity, we also control for several other firm-level regressors in our specification. Several of these firm-level variables are taken from the ES data and correspond to values in the initial-round of survey (t). We control for the age of a firm, measured by the year of the first round survey minus the year in which the firm started operations; the size of the firm, measured by (log of ) the total employees during the first round of survey; the legal structure of the firm, measured by a dummy variable taking a value of 1 if the firm has limited liability and zero otherwise; ownership structure, capturing whether the firm is foreign-owned and whether there are female among its owners; exporting status, measured by whether the firm exports at least 10% of its total sales; access to external finance, measured by the usage of bank financing for working capital and/or fixed investment; and (log of) manager’s experience, measured by manager’s years of experience in similar industry.

As a second set of covariates, we include measures of local-market indicators to approximate the effect of meso-level market variables. First, we capture the level of competition in the local market by the variable _comp_ , given by the collapsed, weighted count of firms by location and industry (2-digit ISIC code) within a country. We further use _comp_ as a proxy for firm market share and thus capacity for pricing as a mechanism to absorb shocks and stay in business. Secondly, as we anticipate a firm’s relative productivity to its within-country competitors to matter, we include the country-sector<sup>16</sup> mean level of (log) sales per worker as a proxy for the (labor) productivity of a firm’s competition.

Finally, we also include sector- as well as country-level variables in the regression. Sector is broadly defined to indicate whether the firm is in manufacturing, retail or other services. We control for five country-level variables<sup>17</sup> - initial GDP per capita, GDP growth (over the period between survey rounds), openness – as measured by imports and exports as % of GDP, and measures of ease of entry and exit based on the Doing Business Indicators. Thus, we control for the, in effect, lagged state of

> 15 Hallward-Driemeier and Rijkers (2013) use value-added per worker, a widely used alternate definition of labor productivity, as well as several comparisons for robustness using alternate TFP measures. The ES data have limited cost variables for service firms and so we utilize a simpler sales per worker measure.

> 16 Due to issues of sample coverage and survey design, we estimate these averages over broadly defined sectors: manufacturing, retail, and other services.

> 17 Some of the country-level variables account for the period average in the years between rounds and the aggregate business environment.

18