rafmacalaba/datause-encoder-data huggingface dataset:
_Source:_ Authors’ calculation based on _Statistical Yearbook of National Tax_ (NTS, 1988–2015).

_Note:_ The wage and salary income taxpayer ratio precipitated in 2014 due to changes in the PIT income deduction system in 2014. The income deduction for education expenses, medical expenses, private insurance premiums, and charity contributions were shifted to tax credits, which made many marginal taxpayers tax exempt.

# Effective PIT Rate of Owner‐Operated Businesses

The effective PIT rates are estimated through micro‐simulation analysis developed by Sung (2008) using Household Income and Expenditure Survey (HIES) data, a household survey data set, collected monthly and compiled, and released by Statistics Korea every quarter year. HIES data provide monthly and quarterly income and expenditures, along with each household’s demographic characteristics, that can be used to estimate household PIT. Detailed PIT estimation methods using the HIES and summary statistics of HIES are attached in Appendixes 1 and 2.

Figure 6 illustrates the longitudinal changes of effective PIT rates both for self‐employed business income and wage and salary income for the last three decades. The two effective PIT rates have different trends. The effective PIT rate of wage and salary income increased from 1.6 percent to 4.0 percent from 1982 to 1988 and then oscillated within the range of 2.2 percent to 3.5 percent from 1989 until recently. For business income, the effective PIT rate decreased from 5.6 percent to 3.4 percent from 1982 to 1998 but turned to a continuous upward trend from 3.4 percent in 1998 to 6.3 percent in 2013 after the introduction of TIETP, except for the two dips caused by Korea’s economic crises in 2002–2003 and 2008–2009.

Figure 6 Effective PIT Rate for Business Income and Wage and Salary Income (%) <!-- Start of picture text --> 7<br>PIT for Business Income 6.3 6.2 6.3 6.2<br>6 PIT for Wage and Salary Income 5.9 6.0 6.0<br>5.6<br>5.6 5.4<br>5 4.7 5.0 4.9 5.1<br>4.7 4.7<br>4.5 4.6<br>4.3<br>4.4 4.2 4.1<br>4.2 4.2 4.0 4.0<br>4 4.0 3.8 3.8 3.8 3.6 3.5 3.8 3.4<br>3.5 3.2 3.2<br>3.0 3.0 3.1 3.1<br>3 3.3 3.1 3.3 2.9 2.9 2.7<br>2.6 3.0 2.6 2.6<br>2.9 2.7 2.5<br>2.3 2.6 2.7 2.4 2.5 2.5<br>2 1.8 2.2 2.3 2.4<br>1.6<br>1.6<br>1<br>1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> _Source:_ Author’s estimation using Household Income and Expenditure Survey (Statistics Korea, 1982–2014).

34
find that inflation is not robust. Although these findings suggest to be cautious when interpreting results from cross-country regressions-specially when they are estimations of loosely specified reduced forms-it does not necessarily imply that a certain (non-robust) variable does not affect growth. This may also be the consequence of high degree of correlation among independent variables, what makes difficult to disentangle the individual effect of each variable. This section indeed shows that the negative correlation between inflation and growth appears to be robust to several changes in specification, which is confirmed by most, but of course not all, of the work reviewed here.

# **4.1 Inflation and Growth**

The early empirical work on inflation and growth focused on estimating Phillips curve-type relationships, and hence, it used high frequency data to capture the shortrun tradeoff between inflation and growth. Fischer (1983) shows, in a panel for 53 countries and yearly data for the periods 1961-73 and 1973-81, that there is a negative correlation between inflation and growth even at yearly frequency."<sup>6</sup> In the growth literature, in turn, most of the early work was on growth accounting, which aimed to decompose the sources of growth into growth of inputs and factor productivity growth. It was not until Kormendi and Meguire (1985) that macroeconomic factors were incorporated as determinants of long-run growth in cross-section analysis for relatively long periods of time (1950-7,7). The basic regression they run, and that has been basically the same in later work. is: where 'y is the rate of growth, X is a set of independent variables, _Y0_ is the initial level of per-capita GDP, and e is the error term. The rationale to include _Y0_ is to control for convergence of income across countries. It has been widely documented (see Barro and Sala-i-Martin, 1992) that after controlling for variables that explain differences in steady state output across countries (X), economies with lower GDP per capita grow faster than richer ones.

An important aspect of estimating (7) is whether the rate of investment should

**1**<sup>**6**</sup> **For another review of the evidence with additional references on time series studies see Briault (1995).**

19
instantaneously, suggesting that much of R&D expenditures are dedicated to development as opposed to basic or applied research. Blundell, Griffith and Windmeijer (2002), however find the long run elasticity substantially larger than the short run. Since spillovers may occur only with some lag from the initial discovery, we may expect larger lags at the aggregate level. In using both annual and quinquennial data, we find that in fact, the lags are quite long.

We then divide the sample into OECD and the developed countries and identify significant and important differences in scale economies and very large differences in implicit returns to R&D between the two samples. To examine the determinants of the differences in observed elasticities, we introduce interactively several variables likely to capture, especially, the degree of spillovers. In this sense we broadly parallel the literature on National Innovation Systems (NIS)<sup>7</sup> that focuses on how the deployment of human capital and financial resources in the various national institutions – universities, public think tanks, firms- and the interactions among these institutions affect the capacity of a country to generate knowledge. Furman, Porter and Stern’s work on the OECD established that such factors often enter additively in determining the number of patents. Given the greater range of endowments, institutions, and institutional quality across our sample, we expect, and find, significant and important effects of these factors on the patenting elasticity.

The remainder of the paper is organized as follows. Section 2 describes the data, section 3 outlines the econometric approach, and section 4 presents the main results regarding the relationship between patents and R&D expenditures. Section 5 examines elements of the NIS that may influence the corresponding elasticity. Section 6 concludes.

> 7 See Nelson 1993; OECD 1998, 2001; Lundvall et al. 2002, Furman et al 2002

4
In 1980, Tanzania’s production was close to trend (hence the column label: Tanzania 0), while Uganda – a major source of maize imports into Tanzania – experienced a large negative production shock. Globally, many major maize producers and exporters experienced below-trend production in that same year. In contrast, Tanzania had below average production in 1982 and 1983. In 1982, major maize exporters had above-average production, putting downward pressure on prices. In contrast, in 1983, some of those major exporters had below-average production pushing up global maize prices to the advantage of smaller maize exporters that had good harvests in that year.

International maize trade in each of these scenarios could have had beneficial or detrimental impacts on Tanzania. However, an export ban on grains would have made it impossible for Tanzania to take advantage of higher world prices, especially in years like 1995 and 1971, when it had aboveaverage production while other African countries and major global traders had low production. Analysis of FAOSTAT maize trade shows that there were 14 years when Tanzania did not officially export any maize<sup>6</sup> . In six of these 14 years – 1976, 1977, 1981, 1986, 1996, and 1996 – Tanzania had a positive maize production deviation from trend. In some of these cases, the world price of maize was also above trend, suggesting that the country was missing an opportunity to take advantage of the higher commodity prices. While import restrictions in partner countries surely played a role in limiting Tanzania’s exports, Tanzania has had a range of different initiatives in place that have also contributed to low levels of exports<sup>7</sup> . For example, in 1995, when Tanzania had higher-than-usual maize production, the world price of maize was 38% above trend<sup>8</sup> . However, there was an export ban in place which limited the country’s ability to capitalize on this market development. This was finally lifted at the end of 1996, allowing Tanzania to export at greater-than-trend world maize prices till 1998, after which maize prices dropped below trend again. All of this suggests the need to systematically investigate the role of Tanzanian trade policy in the context of volatile grains production nationally, regionally and globally.

> 6 1972-1974, 1976, 1977, 1981-1986, 1994-1996

> 7 Please see Anderson and Valenzuela (2008), Chapoto and Jayne (2010), Morrissey and Leyaro (2007), and World Bank (2009) for detailed reviews.

> 8 World maize prices are obtained from the World Bank’s Development Prospect Group’s GEM database. The price series for maize is in constant 2000 USD/MT. The time series is de-trended with maize prices explained by a quadratic function of time. The world price percent deviations from trend are then estimated from the ratio of the estimated residuals to the predicted prices. The world maize price time series and the percent deviations from trend are available in Appendix A.

9
the results attribute a greater role to the banking sector and its efficiency in fostering economic growth. Therefore, our results suggest that the quality of financial intermediation rather than the quantity of finance matters for growth. Our results thus stand in contrast to the recent papers suggesting that too much finance harms growth. We show that once we distinguish between quality and quantity of finance, we find that quality matters and quantity is largely irrelevant for long– term real growth. In terms of policy implications, our results indicate that the current wave of regulatory changes intended to safeguard financial stability should carefully analyze the consequences for the efficiency of financial intermediaries.

# REFERENCES

- Acemoglu, D., S. Johnson, and J. Robinson. 2001. “Colonial Origin of Comparative Development: An Empirical Investigation.” _American Economic Review 91_ , 1369–401.

- Ang, J. B. 2008. “A Survey of Recent Developments in the Literature of Finance and Growth.” _Journal of Economic Surveys 22_ , 536–76.

- Ang, J. B. 2013. “Are Modern Financial Systems Shaped by State Antiquity?” _Journal of Banking & Finance 37_ (11), 4038–58.

- Ang, J. B., and S. Kumar. 2014. “Financial Development and Barriers to the CrossBorder Diffusion of Financial Innovation.” _Journal of Banking & Finance 39_ (C), 43–56.

- Arcand, J., E. Berkes, and U. Panizza. 2012. “Too Much Finance?” Working Paper 12/161, Internation Monetary Fund.

- Atje, R., and B. Jovanovic. 1993. “Stock Markets and Development.” _European Economic Review 37_ , 632–40.

- Barro, R. J. 1996. “Determinants of Economic Growth: A Cross-Country Empirical Study.” Working paper 5698, National Bureau of Economic Research.

- Barro, R. J., and X. Sala-i-Martin. 1992. “Convergence.” _Journal of Political Economy 100_ (2), 223–51.

- Beck, T., A. Demirguc-Kunt, and V. Maksimovic. 2005. “Financial and Legal constraints to growth: Does firm size matter? _The Journal of Finance 60_ , 137–77.

46
# **But... What Is The Poverty Rate Today? Testing Poverty Nowcasting Methods in Latin America and the Caribbean** 

German Caruso<sup></sup> Eduardo Malasquez<sup>†</sup> Leonardo Lucchetti<sup>§</sup> Thiago Scot<sup>‡</sup> R. Andrés Castañeda<sup>††</sup> JEL classification: D31, I3, I31, D6 _Key words:_ Poverty, Nowcasting, Latin America and the Caribbean, Validation We are grateful to Oscar Calvo-Gonzalez, Samuel Jaime Pienknagura, German Reyes, Liliana Sousa, Daniel Valderrama, Carlos Vegh, and Nobuo Yoshida whose suggestions greatly improved earlier drafts of the paper. All remaining errors are ours.

>  World Bank. E-mail: gcaruso@worldbank.org

§ World Bank. E-mail: llucchetti@worldbank.org † World Bank. E-mail: emalasquez@worldbank.org ‡ University of California at Berkeley. E-mail: thiago_scot@berkeley.edu †† World Bank. E-mail: acastanedaa@worldbank.org
# **Table 2:  Prediction regression results by time scale**

Dependent Variable: Actual ΔLC (=Log[Caseratet]- Log[Caseratet-k])

|Country<br>Forecast Period Days|US<br>14|US<br>28|Philippines<br>14|Philippines<br>28|Western Cape<sup>a</sup> <br>14|Western Cape<sup>a</sup><br>28|
|---|---|---|---|---|---|---|
|Predicted ΔLC|0.456<br>(23.52)**|0.402<br>(30.43)**|0.249<br>(9.69)**|0.075<br>(9.11)**|0.724<br>(8.04)**|0.620<br>(3.88)**|
|Constant|0.157|0.271|0.005|0.059|0.294|0.850|
||(3.88)**|(5.27)**|(0.36)|(2.67)**|(3.00)**|(2.62)*|
|Observations|3,084|3,092|1,531|1,531|31|32|
|R<sup>2</sup>|0.15|0.23|0.06|0.05|0.69|0.33|

a Robust regression Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 13
and objectivity.<sup>2</sup> Having an enormous number of indicators, which could not be scrutinized, is nothing but a distinct disadvantage for a measure that aims for wider acceptance and confidence.

Implementation of the above framework requires a worldwide survey with uniform questionnaire honing on the four dimensions of governance identified above across countries. Given that such a survey is not available and costly to commission, in the following section, we take a pragmatic approach based upon available survey data to develop rough indexes of governance quality.

# 3 Citizen-centric governance: Empirical framework

Following Table 1, public interest is characterized by four dimensions of governance outcomes - responsive governance, fair governance, responsible governance, and accountable governance. Each of these categories is split further on sub-categories in order to characterize a concrete governance outcome (such as improvements in quality of life, safety, peace, etc.). Public opinion survey, with the questions assigned to each subcategory, should be used for the assessment of governance.

The procedure of the assessment consists of the two main steps. First, data source - the raw data from inter-country public opinion survey - is chosen. The responses on questions in the survey, which characterize governance outcomes, are recorded. Second, the responses are aggregated in order to achieve governance index for each country from the sample.

In what follows, we consider both steps in detail.

## 3.1 Data

Reliable, comprehensive and consistent through time and space source of data is essential for qualitative estimation of citizen-centric governance indicators (CGIs). With an additional requirement of being publicly accessible and, preferably, free of charge, such data source hardly exists at present. There is a database of governancerelated questions included into different surveys across the world (Governance Surveys Database published by the World Bank). In principle, each of these questions could be included into our estimation (questions taken separately from different polls) if the data is available. However, as the experiments in the construction of surveys suggest (see Bertrand and Mullainathan, 2001, for examples), even the small difference in the formulation of a question (assigned to the same sub-criterion) or the sequence of questions in a survey may bring significant discrepancies in the responses for the same country and same sub-criterion. Therefore, we decided to use only one data source, which covers sufficient amount of countries. Effectively it means, that almost the same questionnaire is used in all participating countries.

> 2see Andrews and Shah (2005) for details and relevant indicators of an approach that emphasizes citizen-centric governance and Shah and Shah (2006) for citizen-centered local governance and relevant indicators

7
In this paper, we examine the relationship between e-commerce development and household consumption growth in China using three data sources: Alibaba e-commerce data, household survey data, and official statistics. First, we construct a series of indicators to measure the development of e-commerce at the county level using online sales and purchase information provided by the Alibaba Group, gross domestic product (GDP) from the China Statistical Yearbook, and population drawn from the Population Census 2010. Second, we merge the county-level e-commerce development measures with household consumption data obtained from the CFPS survey administered by Peking University.

# _<u>Indicator construction</u>_

We measure county-level e-commerce development on three dimensions: penetration, intensity, and market size (as a share of the national online market). For each of the three dimensions, we construct two indicators measuring, respectively, the online sales and purchase using the amount of online sales and online purchases (Gross Merchandise Value, or GMV), numbers of online sellers and online purchasers, and numbers of packages sent and received associated with online transactions. The three indicators are comparable over time. Therefore, compared with Alibaba’s three official e-commerce indicators, Alibaba e-commerce development indicators (aEDI), OBI, and OSI, which use different weights across years, our three indicators are comparable over years.

We constructed the indicators in three steps. First, we extracted the original indicators for each index. Second, we normalized the indicators and transformed them into z-scores for easy comparison.<sup>13</sup> The number of buyers, annual online purchase GMV, national online purchase GMV, and total number of national online buyers are from Alibaba. GDP is obtained from China Statistical Yearbooks. Residential population is from the Population Census 2010.<sup>14</sup> The three sets of indicators are defined as follows:

# _E-commerce penetration indexes:_

> 13 See Appendix 1 for a detailed description of the construction of the indicators.

> 14 Measures using different sources of population yield consistent results. We use census population information in our main analysis, rather than the population reported in the statistics year books. In addition, we apply the 2013 residential population at the county level collected from local statistical bureaus as a robust check.

·· 9
Table 4: The growth elasticity of poverty based on household consumption as measured from surveys

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|||||
|gr_change|-1.847***|-1.966***|-4.030***|-1.664***|
||(0.160)|(0.159)|(0.607)|(0.289)|
|initial poverty rate|0.171***|0.1000**|0.181***|0.183***|
||(0.0373)|(0.0418)|(0.0381)|(0.0374)|
|initial inequality|-0.148|-0.181*|-0.285***|-0.170*|
||(0.0909)|(0.101)|(0.0937)|(0.0936)|
|intial survey mean|0.00791**|0.00634*|0.009***|0.00858***|
||(0.00327)|(0.00331)|(0.003)|(0.00324)|
|interval|0.00902***|0.00612**|0.009***|0.00842***|
||(0.00325)|(0.00305)|(0.003)|(0.00310)|
|Baseline: other<br>regions|||||
|SSA||0.0182|||
|||(0.0234)|||
|gr_change x|||||
|SSA||1.072*|||
|||(0.571)|||
|initial headcount gini|||5.111***||
||||(1.239)||
|Baseline: <2000|||||
|2000-2010||||-0.0402**<br>(0.0186)|
|2011 and above||||-0.0270*<br>(0.0159)|
|gr_change x|||||
|2000-2010||||-0.176|
|||||(0.369)|
|2011 and above||||-0.298|
|||||(0.386)|
|Baseline:|||||
|Consumption|||||
|Income|0.0170|0.0265|0.0163|0.00977|
||(0.0292)|(0.0301)|(0.0279)|(0.0287)|
|Constant|-0.0914***|-0.0515|-0.0383|-0.0585*|
||(0.0350)|(0.0387)|(0.0345)|(0.0349)|
|country fixed effect|No|No|No|No|
|Observations|574|574|574|574|
|R-squared|0.466|0.481|0.489|0.473|

Notes: Table shows results of a regression of the annualized log change in poverty headcount on the annualized log change in survey means. Sample consists of all consecutive spells between 1981 and 2021, dropping spells with baseline and endline poverty rates lower than 2 percent. Robust standard errors in brackets. ***: Statistically significant at 1%; **: Statistically significant at 5%; *: Statistically significant at 10%.

21
effects of season-induced food deprivation in Bangladesh, locally known as _Monga_ , which has historically been a cause of famines, particularly for rural households in the Rangpur region. While agriculture is the classic causal source of seasonality, even countries with relatively low levels of agricultural production experience seasonality (as indicated by Barsky and Miron for the United States). In the case of Jordan, the period of Ramadan is a time of increased consumption, with consumption levels that are 11 percent greater than the other periods of the year.

Seasonality in itself is not necessarily a problem for measuring poverty based on interviewing households about their consumption during a single visit. A common approach is to ask the household how much food they consumed during the last seven days and then multiply this value by 52 to get an annual estimate. Households interviewed during the lean season will appear to be poorer than they would seem if they had been interviewed after harvest, but if households are randomly interviewed during the year, these differences will average out for the national poverty rate. Grosh and Glewwe (2000) note that one reason a “prototypical LSMS [Living Standards Measurement Study] spreads fieldwork evenly over a 12-month period [is] ... to study or even out seasonality effects” (p. 44). The underlying assumption to this approach is that single-visit interviews with short recall periods scaled up uniformly to annual estimates are expected to create type 1 errors (identifying someone as poor when they are not) and type 2 errors (failing to identify someone as poor when they are) in equal proportions, if fieldwork is randomly allocated over the year.

While the single-enumeration approach is by far the most commonplace, some countries do extract their official poverty estimates from data based on multiple visits. As one example, Niger 3
(Percentage of women in each labor status)

||**2001**|**Rural**<br>**2010**|**2018**|**2001**|**Urban**<br>**2010**|**2018**|
|---|---|---|---|---|---|---|
|Employed|32.9|36.2|29|15.1|14.7|13.7|
|Unemployed|.54|.743|.73|4.96|3.64|4.39|
|Inactive|66.5|63.1|70.3|79.9|81.6|81.9|
|Paid workers|17.3|25.2|29.4|93.2|94.7|96|
|Formal workers|.549|1.75|3.05|32.9|44.3|48.1|
|Public Salaried|.|7.09|10.7|.|25.3|22|
|Private Salaried|.|88.1|82.8|.|65.6|68.2|

Source: HCP Enquête National sur l’emploi. Paid workers category includes wage workers, independent, employers and others; it excludes apprentices and cooperatives workers. Formal workers is the share of employed that have health insurance (CNSS). Public and Private categories -unavailable in 2001- are calculated as shares of employed (they do not sum up to 100 because of a third “other” marginal category).

In urban areas, the decline in female participation rates is less accentuated, smoother, and spread across age groups (figure 10 panel b). A phenomenon specific to urban areas is the increase in female unemployment compared to the steady decline in unemployment for men between 2001 and 2018. At the same time, female employment rates remained stable at around 14 to 15 percent throughout the period (table 1). It is worth noting, however, that the quality of jobs women accessed suffered a slight deterioration—with the number of remunerated, formal, and private sector salaried jobs marginally lower for women relative to men. The dynamics of job creation also reveals that the likelihood for a woman to be employed in a new job fell over the period. Around 21 percent of jobs created over 20012007 in urban areas employed a woman, while only 13 percent did so over 2007- 2018.

# **VI. Methodology**

In the previous sections, we profile different groups of women regarding their labor status and demographics, and we show the evolution of these variables over time. In the following equation, we summarize how the multiple variables interact to determine the inactivity of 23