rafmacalaba/data-use-mentions-extended huggingface dataset:
variables related to management information systems for public finance, tax administration, customs, procurement, and human resources, as well as digital identification schemes.

Our explanatory variables of interest are indicators showing when e-government systems were adopted by the central government of each country and the type of the functionality of the adopted systems. The e-filing implementation year refers to the year in which the country first introduced an e-filing system at the given level of functionality. Typically, this occurs after core tax administration systems are in place. Given that e-filing is a relatively new e-government technology, countries have only one iteration of an e-filing system at a given level of functionality. System upgrades that do not constitute an upgrade in system functionality are not recorded in the data set. E-filing functionality is assessed at three levels: 1) informational systems, which provide policy guidance and forms for download; 2) transactional systems, which allow taxes to be filed electronically; and 3) transactional systems with e-payment, which allow for electronic filing and payment of taxes. In the empirical analysis, we distinguish between transactional and transactional with e-payment functionalities. We do not have countries that implemented basic informational systems during our sample period.

The e-procurement implementation year refers to the year in which the most recent iteration of a country’s e-procurement system was introduced. E-procurement functionality is also assessed at three levels: 1) informational systems, which provide information about tenders and the results of bid evaluations; 2) transactional systems, which also allow tenders and supporting documents to be submitted and evaluated electronically; and 3) connected systems, in which the transactional system is integrated with other financial management information systems so that budgets, contractual commitments, and payments to vendors are automated. In our analysis we distinguish between informational and transactional systems. Four countries had adopted connected systems by 2014, but data on procurement competitiveness is not available for them, and they are excluded from our analysis.

Figure 1 shows the total number of e-filing and e-procurement systems adopted each year, by functionality. Overall
voter theorem is absent. However, it is not clear that this would fully neutralize the relevance of income distribution; even dictators cannot entirely ignore their citizens’ wishes.<sup>9</sup> A more compelling reason to question this hypothesis is that the evidence suggests that poorer countries tend to have more unequal distributions.<sup>10</sup> The (small) literature on the role of distribution in influencing public spending has almost solely used the Gini index (as in, for example, Shelton 2007), rather than skewness, but the two are likely to be highly correlated. Empirically, we find that skewness is actually a more common feature of income distributions in low-income countries than in high-income ones; we find that the correlation coefficient between (log) GDP per capita and the ratio of the mean to the median incomes is -0.405.<sup>11</sup> Thus, if anything, the political economy of redistribution (or at least the median-voter version) implies <u>more redistributive effort in poorer countries. So, we appear to know enough already to reject</u> this hypothesis as an explanation of the rising SPEC, and we will not test it further.

**_Distribution 2: The unequal growth hypothesis._** _Economic growth in market economies tends to be inequality increasing. This leads to political demands for redistributive SP policies._ The idea that higher inequality creates demands for redistribution is familiar from the political economy literature (including, for example, Alesina and Rodrik 1994). In the present context, the concern is that the positive income effect on the SPEC is actually picking up the effect of economic growth on inequality, leading to higher SP spending.

However, in light of accumulated evidence from household survey data, the basic premise of this hypothesis can be questioned. Empirically, we see a far more mixed picture, with inequality decreasing roughly as often as it increases in growing economies.<sup>12
|Yes|Yes|
|p||||Others|Occasional||||
|Main Items<br>Excluded|Weddings,<br>celebrations,<br>donations,<br>talisman and<br>other<br>miscellaneous<br>expenditures|Occasional<br>expenditures,<br>income tax,<br>interest<br>charges and<br>insurance.|Agricultu<br>ral input,<br>tax and<br>insurance<br>and other.|<br>None|expenditures<br>, durables,<br>rent,<br>mortgage<br>and loan<br>brokerage<br>services|Occasional<br>expenses<sup>(11)</sup>|Occasional<br>expenditures,<br>durables, tax,<br>annual items<sup>(12)</sup>|Occasional<br>expenditures,<br>tax/insurance/<br>contribution to|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD).

(1) Total consumption, including self-production, is reported in one question per item. In other countries, market and self-production are reported separately for each item.

(2) Self production is accounted for also among non-food items (Block 10 of the questionnaire).

(3) Questionnaire: Section 9A2 item 14.

(4) Questionnaire: variables at the end of Block 8 (5) Questionnaire: Block 12, served processed food and packaged processed food.

(6) Coicop: 1111.

(7) Section 5: coicop 131.

(8) No consumption flow estimated, expenditure items included at purchase price.

(9) Questionnaire reports market equivalent values.

(10) Questionnaire reports market equivalent values; outliers and missing imputed using prediction from hedonic regressions.

(11) Some of the infrequent expenses have been kept, most notably
_Notes:_ Means calculated using sample-weighted individual-level data from the 2014-17 Gallup World Poll. Standard errors are in parentheses. Asterisks indicate whether the difference in means is statistically significantly different from low-income countries at the 0.01 level. Low-income countries are defined as those with a gross national income (GNI) per capita of $995 or less, lower-middle-income countries are those with a GNI per capita of more than $996 but less than $3,895, and upper-middle-income countries are those with a GNI per capita of more than $3,895 but less than $12,055, and high-income countries are those with a GNI per capita of more than $12,055.

The remaining results are from the regression model discussed in section 2. Table 2 examines how food insecurity affects trust in national institutions (vertical trust, column 1) and trust in friends and family (horizontal trust, column 2). The estimates presented are the average marginal effects estimated by the linear model with country and year fixed effects.

Table 2: The effect of food insecurity on vertical and horizontal trust.

||Tru|st|
|---|---|---|
|Variables|Trust in national institutions|Trust in friends and family|
|Food insecurity|-0.027***|-0.117***|
||(0.004)|(0.005)|
|Female|-0.010***|0.013***|
||(0.003)|(0.002)|
|Age|-0.004***|-0.008***|
||(0.000)|(0.000)|
|Age squared|0.000***|0.000***|
||(0.000)|(0.000)|
|Number of adults|0.001|0.
higher education. Moreover, the urban focussed short-cycle courses in vocational universities, supported by municipal governments became more popular than provincial universities' 4-year academic programs that typically lead to prefecture and county level jobs.

**(b) Structural Equity:** In Table 1, the data show the shape of the enrollment pyramid which reflects the access to various levels of education and determines the distribution of educational attainment of a generation. While public expenditure by level of education is another structural dimension of equity as it influences the distribution of resources in the sector. But, structural equity alone does not necessarily imply that people from all socio-economic backgrounds and from all geographic regions have equal access to schooling. Therefore, accessibility to education and educational achievements by different income groups and across all provinces are examined on the basis of both time-series and survey data later.

For empirical evidence in a number of countries see, Jere Behrman and Nancy Birdsall,"The Quality of Schooling: Quantity Alone is Misleading" _American Economic Review,_ 1983. Jere behrman, Donald Ross and Richard Sabot, "Inproving the Quality Versus Increasing Quantity of Schooling" 1992, _Colby College (Mimeo),_ Waterville, MA

> 2 Higher education was perceived very inegalitarian, creating an exclusive elite group. See Julia Kwong,"Chinese Education in Transition: Prelude to the Cultural Revolution" Montreal: McGill Queens University Press, 1979.
Urban<br>52.6|51.1|58.4|8.8|
|2015 (NFHS-4)<br>51.9|50.5|58.7|8.6|
|2015 Rural<br>49.8|48.7|57.0|8.5|
|2015 Urban<br>56.1|54.1|61.7|8.9|
|Panel C: AL from Time Use Su<br>Male 15+|rvey (TUS) 199<br>Female 15+|8-1999<br>Male 6-14|Female|
|1998-99 mean<br>1.80|1.82|1.43|1.45|
|1998-1999 SD<br>0.45|0.34|0.24|0.24|
|1998-99 Rural mean<br>1.87|1.89|1.45|1.48|
|1998-99 Urban mean<br>1.62|1.66|1.39|1.39|

_Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS) rounds 2, 3 and 4. _Note_ : Panel A uses the (Indian Council of Medical Research, 2009) REE equations, which are based on the (FAO/WHO/UNU Expert Consultation, 2001) equations with a 5% downward adjustment for children. Panel B reports weight in kilograms. Panel C uses the activity factors found in Ainsworth et al. (2000). All statistics use sample weights.
62 percent of the items were performed. There is a positive association between wealth and each individual procedure of antenatal care. All the coefficients are significant at least at the 95 percent level. A one standard deviation increase in wealth is associated with a seven percentage‐point higher likelihood of having blood and urine samples taken for testing, a five‐percentage‐point increase in the likelihood of being offered an HIV test, and a five‐percentage‐point higher likelihood of receiving nutritional counseling.

Table 2 presents the results of regressions of the quality‐of‐care indexes on the wealth index. Columns 1 to 3 present the results from analysis of the household survey data, and columns 4 to 9 present the results from analysis of the direct observation data. Given that women in the household surveys report on antenatal care coverage over the entire pregnancy, positive wealth gradients could appear due to the difference in individual visits or because wealthier women attended more consultations. For the household survey data, we find that each standard deviation increase in wealth is associated with a 4‐ percentage‐point increase in the completeness of antenatal care. When we focus on the first antenatal care consultation in directly observed visits (column 4), the gradient drops to 1.6 percentage points. In both cases, the coefficient representing the wealth‐quality relationship remains statistically significant at the 99 percent level when controlling for women’s education level, age, and marital status (columns 3 and 6). It is important to keep in mind that the items in the quality indexes are different. That is, although the estimated coefficients both represent percentage changes in protocol compliance, the clinical implications of observed changes might differ.

We use the rich data collected through direct observation of antenatal consultations to create an index of non‐clinical quality of care, which primarily focuses on the interpersonal behavior of the providers. The index gives equal weight to the following six indicators: filling an antenatal care card, taking sufficient time for the consultation (at
and land.

c. In addition, tourists in direct transit (that is, tourists that spend less than four hours in Panama and do not leave the airport) have spent a further US$313 million in 2006. This constitutes an additional 3 percent of the GDP of Panama Province. This brings total tourism expenditures for the whole country to 7 to 9 percent of the GDP.

_Source:_ Authors’ estimates from tourism survey data for the Tourism Satellite Accounts, 2008.

Tourism revenues have benefits beyond those accruing directly to hotel operators and employees, tour operators, restaurants and shops who sell goods and services to tourists. Incomes earned from these expenditures by tourists are typically spent at least in part on local goods and services, thereby further raising output and incomes. To the extent that local goods and services are elastic in supply and can thus expand in the face of increased demand, the multiplier effects of tourism (or increases in other sector outputs) can be substantial.

province (adjusting for the higher spending in Panama and Colon). The second method uses the estimated daily per capita expenditures by foreign tourists (all tourists and tourists that only went to Panama and Colon) and multiplies these expenditures by the cumulative number of days spent at each destination. This method can be described as a ―bottom-up‖ approach while the previous method is ―top-down.‖ Estimates using the second method result in the upper bound estimates of total tourist expenditures by province in Table 4. Expenditures by tourists in direct transit (not part of the TSA survey) are assumed to occur in Panama Province and contribute a further 3 percent to its GDP. These results are shown in square brackets in Table 4.

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# _2.3 Other explanatory variables_

We control for several firm-level and country-level variables. The degree of crime a firm faces may depend on its size, the sector it belongs to, and its locale (Amin, 2009; Glaeser and Sacerdote, 1999). Big cities tend to provide higher pecuniary benefits for criminals and also criminal activity may involve lower costs due to a high probability of anonymity and low likelihood of arrest. We control for firm size using dummies for small and medium firms. A small firm is defined as a firm with less than 20 employees, while a medium firm has workers between 20 and 99. We also capture the economic sector of the firm by using a dummy variable for manufacturing firms. A priori it is not clear whether a manufacturing firm should have higher crime losses with respect to other sectors. We also include a city size dummy variable which takes the value of 1 if the city has a population of 250,000 of greater, or is a capital city, and 0 otherwise. There is also evidence that female-owned households as well as female-owned firms have a positive association with higher crime rates (Glaeser and Sacerdote, 1999; Islam, 2013; Kelly 2000). We thus include a dummy variable for whether a firm has at least one female owner. All these variables are from the Enterprise Surveys data set.

At the country level, we control for real GDP per capita growth, real GNI per capita, and the Gini index, given their prominence in the literature (Rogers 1989; Fajnzylber et al., 2002; Soares, 2004; Demombynes and Ozler, 2005; Dahlberg and Gustavsson, 2008). Increasing inequality typically increases the rewards of engaging in criminal activity due to a presence of well off potential victims and also increases the proportion of poor who may engage in criminal activity. For cases where the GINI index or GNI per capita information is missing for the year corresponding to the enterprise surveys data, we use the estimate of the most recent year available.
is suggestive evidence that shifting the age when tracking begins is associated with a change in student performance. Van Elk, van der Steeg, and Webbink (2009) study the effect of differentiated starting age geographically in the Netherlands at which the students are placed into different types of schools. Their results suggest that when students are tracked at an earlier age, their average probability of participating in postsecondary education falls as a result. Studies on specific European countries include Bauer and Riphahn (2006) on the correlation between parents’ and children’s education across Swiss cantons, which set the different grades at which tracking begins. The paper finds that tracking that starts at a later grade is associated with increased probability that children enroll in the secondary track that is considered “university bound” regardless of the parental education level.

- Effort in boosting general education at the cost of vocational education yielded mixed results. Malamud and Pop-Eleches (2010) look into the relative benefits of general education and vocational training during Romania’s transition to a market economy. They examine a 1973 educational reform that shifted a large proportion of students from vocational training to general education. Based on census and household survey data, it is found that men affected by the policy are significantly less likely to work in manual or craft-related occupations but have similar levels of labor market participation and earnings compared to their counterparts unaffected by the policy. The same authors also find that although students from poor households, rural areas, and with less educated parents were significantly more likely to finish an academic track and become eligible to apply for

19
based on consumption per person, while about half of them are not found in the poorest 40%. For children, we find that about three-quarters of stunted children are not found in the poorest 20%, and similarly for wasted children. More than half of wasted and stunted children are not found in the poorest 40%.

On combining Tables 2 and 8, we can use equation (2) to infer the joint probabilities of being both undernourished and wealth-poor, Pr( _n_  ,1 _w_  )1 . The empirical values for the DHS data are given in Table 10. For underweight women and the poorest 20%, the joint probability is under 0.04 for 22 countries. The mean joint probability of a woman being underweight and living in the poorest 20% of households is only 0.03, rising to 0.06 for the poorest 40%. For child wasting the probabilities are even lower than for underweight women, at under 0.02 for two-thirds of all countries. The joint probabilities are higher for stunting, with a mean of 0.08 and 0.16 for the poorest 20% and 40%, respectively. While for child stunting the probabilities span a wider range, it remains that all but two are under 0.1 for the poorest 20%.

As expected, the joint probabilities tend to be positively correlated with the marginals; the bottom row of Table 10 gives the correlation coefficients. The table also gives the OLS elasticities across countries (regression coefficients of the log joint probability on the log marginal probabilities). The elasticities are all less than unity. So a higher rate of undernutrition should reduce the conditional probability. On balance, we find that countries with a higher overall incidence of women’s undernutrition or a higher incidence of child undernutrition (whether stunting or wasting) tend to have a higher share of these disadvantageous outcomes among the “non-poor” based on wealth. Table 11 shows the correlations between the conditional probabilities. For women’s
to balance the government's budget constraint:1<sup>4</sup> _-_ The term (c **_cT)_** represents the adjustment in real balances that takes place at time T in response to the discontinuous decline in inflation to its new level, **_Ai._** It is evident from (3.14) that there is a continuum of _(MT,_ **_j)_** combinations that satisfy the government's budget constraint. If we fix _MT_ we can then solve for the values of c and y that simultaneously solve (2.4) and (3.2). Proceeding analogously we can fix p and solve for c and _MT._ Since it is not possible to carry out these steps analytically we now turn to calibrated versions of the model.

# **4. Numerical Experiments**

Here we study the determinants of speculative attacks in versions of our the model that have been calibrated using Korean and Thai data.

## **4.1. Model Calibration**

Table 1 summarizes the parameter values of our benchmark models. Throughout we use 1996 as the baseline period for time series that are sampled at annual frequencies. In addition we normalize both output and the time 0 exchange rate, S, to be one.

> "<sup>4</sup> The seignorage collected between time t* and time _T, ft._ e-rt(rit + 7rtmt)dt was omitted from this equation because it is equal to zero. This follows from the fact that _Mt =_ O during this interval of time and from the cash in advance constraint, M* = _Stct._

16
scenario for a large increase in the CPI/PPI ratio that uses private sector CPI inflation estimates, consistent with adjustments made to the REER. This implies a cumulative 70% increase in the CPI/PPI ratio over 2008-2015, such that average annual CPI inflation is underestimated by 5% relative to PPI during the period (see Figure 3).

Figures for domestic output suffer from similar measurement discrepancies (see Figure 4).<sup>25</sup> Inflated official growth estimates would be reflected in monthly industrial production and annual GDP per capita differentials. We compute two alternative measures of output that adjust for this: (i) industrial production corrected for growth rates estimated by Orlando J. Ferreres & Asociados since 2007, and (ii) GDP per capita deflated by PriceStats CPI inflation starting in 2009, when the GDP deflator and PriceStats inflation began to diverge substantially. The latter measure implies GDP in 2014 was overstated by 26 percent.

Finally, an improved measure of productivity differentials is constructed using annual data following Ricci et al. (2008), measuring the ratio of real value added per worker in the agricultural sector vis-à-vis the nontradable sector in Argentina, relative to its main trade-weighted partners, excluding China.<sup>26</sup> We obtain data on value added and employment for 10-sectors from the Groningen Growth and Development Centre (GGDC) and classify sectors into tradables versus nontradables using three alternative definitions.<sup>27</sup> Figure 2: Prices - Official vs. Private Sector Estimates <!-- Start of picture text --> Annual CPI Inflation (%) Real GDP per capita: different deflators<br>Source: INDEC, PriceStats, and unionportodos.org Source: INDEC, PriceStats, and WEO (Constant 2005 prices)<br>80 PriceStats Congress CPI Official CPI 45000<br>CPI - INDEC CPI - PriceStats GDP deflator<br>70 40000<br>60 35000<br>50 30000<br>40 25000<br
Workers’ Age and the Impact of Trade Shocks Erhan Artu ̧c<sup>1</sup> April, 2012 _Abstract_ Do trade shocks affect workers differently because of their age? This paper examines the issue by estimating the lifetime mobility of workers based on the sectors in which they work. Using U.S. data, the paper shows that mobility costs rise with a worker’s age and years of experience, but stay the same regardless of his or her education level. In addition, using a general-equilibrium simulation of counterfactual trade-liberalization policies in the metal manufacturing sector, the paper shows that trade shocks affect workers with higher mobility costs more, for both winners and losers of the policy shocks. But the effects taper off over a worker’s lifetime, especially when they are close to retirement.

JEL Classification: F1, D58, J2, J6 Keywords: Trade Liberalization, Sectoral Mobility, Labor Market Equilibrium.

> 1The views in this paper are the author’s and not those of the World Bank Group or any other institution. Artuc: The World Bank, Development Economics Research Group (Economic Policy), 1818 H St NW Washington DC, 20433, USA. Email: eartuc@worldbank.org. I would like to thank Leora Friedberg and John McLaren for their valuable comments, Turkish Academy of Sciences (TUBA) and Scientific and Technological Research Council of Turkey (TUBITAK) for their financial support between 2007-2010, and Jane Zhang for her editorial comments. All errors are mine.

1