rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Tax. Revenue (percent of GDP)

||1988|1989|1990|1991|1992|1993|994|
|---|---|---|---|---|---|---|---|
|Payment Tax|Revenue at B|aseline Em|ployment|||||
|Actual payroll tax revenue of which:|11.9|11.7|11.2|14.6|19.6|21.4|21.6|
|Wage tax or PIT4|3.5|3.3|3.0|2.6|6.3|7.7|8.2|
|ZUSb|8.4|8.4|8.0|11.4|12.9|12.9|12.6|
|FPc|0.0|0.0|0.2|0.6|0.6|0.7|0.8|
|**Correction Coneficients**||||||||
|Constant employment coefficientd|-|1.0|1.07|1.17|1.27|1.33|1.34|
|for wage tax.'|-|1.0|1.09|1.22|n.a.|n.a.|n.a.|
|Constant PIT coverage coefficient|-|n.a.|n.a.|n.a.|0.38|0.38||
|Constant tax rate coefficientg|-|1.0|0.87|0.87|0.76|0.74|-|
|For ZUS only|-|1.0|0.88|0.88|0.70|0.70|0.7|
|**Counterfactual Tax Revenue**||||||||
|Counterfactual 1989 revenue of which:|-|11.7|10.8|14.9|14.8|15.9|-|
|Wage tax or PIT|-|3.3|3.3|3.2|3.3|3.9|-|
|ZUS rate|-|8.4|7.0|10.0|9.0|9.0|8.8|
|ZUS|-|8.4|7.5|11.7|**11.5**|12.0|11.8|
||Profit Ta|x<br>|_<br>_<br>_<br>_|_<br>_<br>_|_<br>_|_<br>_||
|**Actual profit tax**|12.9|9.7|14.0|6.5|4.4|4.1|3.2|
|Correction for profit tax with amortizationh|1.0|1.63|1.63|1.67|1.7|1.7|-|
|Counterfactual profit tax|12.9|15.8|22.8|10.9|7.3|6.8|-|
|A|ctualTempor|ary Taxes_|___-|||____||
|Dywidenda|n.a.|1.7|2.1|1.4|0.6|0.5|0.2|
|Popiwek|0.7|1.7|1.4|3.3|1.5|0.6|0.2|

Note: The data are not consolidated, that is, they include social security contributions with respect to government employees, and since 1992 include personal income taxes paid by government employees and pensioners on their grossed up earnings.

- n.a.: Not applicable; -: Not available.

- a. PIT = Personal Income Tax introduced in 1992, replaces the wage tax.

- b. ZUS = Zaklad Ubezpiecien Socialnych, own revenue from contributions.

c. FP = Fundusz Pracy, own revenue from contributions.

- d. Rocznik 1994, p. 120, tab. 12. and 1995, p. 122, tab. 9 [see also number of contributors to ZUS, rocznik 1995, p. LVI, line 31, table 8 and 9.

- e. For the wage tax, government employees are subtracted (they were exempt).

_f._ 1993: /non-gov. empl. 10/[iabor force 17.7 + pensioners 8.71 =1/2.64

- g. Table 2.

- h. Table 2 and Schaffer's 4 percent effect of amortization on profits; 1988 for comparability without inflation bias.

means that the higher tax rate and average wage base do not compensate for the employment loss.

The opposite is true if the counterfactual is lower than the actual revenue. Thus in 1990 there was a gain, and in 1991 there was a loss.

11
problems between surveys arising from differences in questionnaires, interviewing procedures or processing methods. These errors can greatly weaken the power of the tests found in the literature using cross-country and inter-temporal comparisons for detecting the true relationship.

There are a couple of things we can do to test robustness to time varying measurement errors. One is to use data over longer periods. Figures 1 and 2 use whatever time periods are available between successive surveys. If instead one uses changes over three surveys (taking the log difference between the survey for date t and t-2) the correlation over the whole period becomes significantly negative (r=0.24, n=206), and that remains true for the data points after 1992. Alternatively, one can use the longest spell for each country; again there is no significant correlation (r=0.10, n=80).

A second test is to use growth rates in consumption from national accounts (NAS) as the instrumental variable for the growth rates based on the survey means. This assumes that the measurement errors in the two data sets are uncorrelated. While in practice there are sometimes overlaps in the underlying data sources used (such as when specific consumption items in the national accounts are benchmarked from household survey data), by-and-large the assumption is probably defensible for the purpose of testing robustness. Using this test, one finds no significant correlation (in either direction) between changes in inequality and (instrumented) growth in survey means for either the 1990s, or the period as a whole since the early 1980s. When estimated over all available observations, the IV estimate of the regression coefficient of the change in log Gini index on change in log survey mean using the change in log private consumption per capita from the NAS as the instrument is 0.04 with a standard error of 0.26. Confining the estimation to the post 1992 period, the IV regression coefficient rises substantially to 0.15, but this is only significantly different from zero at the 15% level (White standard error of 5
country-specific time trend in reading achievement, using data from all PIRLS rounds available for this country. Column 5 provides similar results, but additionally adjusting for student age and grade. However, these adjustments are made using country-specific data only and not using information on age or grade effects from other countries’ data.

Table A2 provides model-based estimates of the learning loss for all ECA+ countries. The first column compares 2021 results to those from the previous rounds. The second column shows the estimate of linear trend departure, with the linear trend fixed at the same value for all ECA+ countries. These estimates are based on strong assumptions that there is no trend in the data or that the trend is the same for all countries. Column 3 reports similar estimates to those reported in Table A1 in column 4 but testing whether the change is statistically different from zero. Finally, column 5 provides results controlling for the country-specific time trend but also for age and grade effects estimated for all ECA+ countries.

Table A3 provides calculations for EU countries that did not participate in PIRLS 2021. Learning loss is estimated for these countries considering the UNESCO data about the number of weeks with full closure and taking the average EU estimates from Table 1 for the overall decline and the decline associated with one week of closures.

_Within-countries differences in the learning decline_ Some research has shown that boys suffered larger learning losses during the pandemic. However, interactions of the COVID-19-related effects for boys show no difference in the decline of achievement by gender. In general, we found no evidence of gender differences in the impact of school closures on reading achievement of 4<sup>th</sup> grade students in EU or ECA+ countries.

However, as expected, quantile regression results show that lower-achieving students experienced much larger losses. Considering principal-provided data, presented for the EU countries on Figure 1A, students at the 1<sup>st</sup> achievement decile (lowest achieving students) in schools affected for more than eight weeks suffer a loss of nearly 25 points compared to less than 15 points for the top students (9<sup>th</sup> decile). Similarly, for students in schools affected between 2 and 8 weeks, the lowest achieving students lost more than 20 points, while those at the top lost less than 10 points. Figure 1A also shows that in schools not affected or affected by less than two weeks, the lowest achieving students lost only 10 points, while those at the top lost around 5 points. Overall, for EU countries, these results suggest that longer school closures affected the low-achieving students more, extending inequalities.

9
# **Introduction**

One of the biggest challenges in development is urbanization.<sup>1</sup> Within developing countries, nearly two billion people are expected to move from rural regions into cities in the next two decades (World Bank, 2011a). The pace at which this will happen will be much faster than what the world has experienced before. For instance, China’s and India’s economic transformation and urbanization is happening at 100 times the scale of the first country in the world to urbanize—the United Kingdom—and in just one-tenth of the time. Most countries have only one chance to get it right, and there is no “one size fits all” recipe.

McKinsey Global Institute (2010) contends that cities in India have the potential to generate 70% of the country’s new jobs and GDP over the next 20 years, a process that could drive a four-fold increase in per capita incomes. While promising and greatly desired, such urbanization imposes unprecedented managerial and policy challenges. However, in spite of the potential for urbanization in the country, research on spatial location and concentration of economic activity in cities is still at an early stage.

Given the great challenges ahead, we look backward to describe how the urbanization process has proceeded (or not) for the Indian manufacturing sector over the 1989-2005 period. We have two goals. The first goal is to trace the trends and depth of India’s manufacturing urbanization across states and industries. This description can yield important insights for researchers and policy makers going forward. Our second goal is to examine whether localized education and infrastructure are linked to urbanization and more efficient spatial allocation of India’s industries and plants since 1989. As we discuss below, there are many explanations for urbanization drivers in India, too many in fact for one study to accurately assess. We seek to quantify whether and how these local conditions, along with other factors like wage costs, promoted or discouraged the urbanization process.

Our study combines data from the Annual Survey of Industries for the organized (formal) sector and from the National Sample Survey for the unorganized (informal) sector. On the whole, India’s manufacturing sector became more urbanized, with the share of workers in urban areas rising from 33% of employees in 1989 to 41% in 2005 (Figure 1a). Urbanization growth was most dramatic from 1989 to 1994, but slowed down from 1994 to 2000. The urbanized employment share was basically flat from 2000 to 2005. This pattern of increasing urbanization was also present when looking just at manufacturing plant counts, but the opposite trend is 1 Key references in this area include Duranton and Puga (2001), Glaeser et al. (1992, 1995), Henderson (2005, 2010), Henderson and Venables (2009), Henderson and Wang (2005, 2007), World Bank (2009, 2011a), Moretti (2004), Rossi-Hansberg and Wright (2007), and McKinsey (2010).

2
- 124 -

Where significant, the coefficients of all schooling variables (except secondary) and experience are greater for men. The diploma variables on the other hand have systematically larger coefficients for women. It is, however, beyond the scope of this paper to investigate further sex differences in the returns to human capital.

# Cognitive Skills

The CILSS recorded self-reported literacy and numeracy for respondents above the age of 4. It is of interest to know whether these skills have an effect on earnings independent of the years of schooling. To that effect, we split the people in the sample with no diploma into UBKiWt W.1d read, E--c, r-- do - **_n,_A_ nthn1aQ** The results indicated no significant effect from these skills that could be separated from the years of primary schooling during which the skills are acquired. (There is no need to introduce these skills at a higher level, since everyone in the sample with at least a CEPE reported being literate and numerate.)

# Hours Worked

In the main text of the paper, we argued for the use of monthly earnings as dependent variable in the earnings functions (rather than hourly wage) because a month is the most relevant time period for supply, demand, and remuneration of wage labor in C8te d' Ivoire. Nevertheless,
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economic activity is directly located in areas with significant flood risks (note that this refers to exposed economic activity, not annually lost activity).

Figure 12 demonstrates that the use of income levels or GDP for assessing flood risks mostly highlights risks in high-income countries. Of the $5.3 trillion of economic activity in flood risk areas, 77 percent is in upper middle- and high-income countries (UMC, HIC). Low-income countries (LIC) account for 48 percent of the world’s flood exposed population, but only 22.6 percent of exposed economic activity. In contrast, high-income countries account for 11 percent of people exposed, but for 53 percent of at-risk economic output. Among countries with the largest economic value at risk, the United States leads the ranking with $1.9 billion in economic output exposed, followed by Japan with $1.3 billion; no low-income country is among the top 10 countries in terms of economic value at risk (Figure 13). Figure 14 confirms this picture at the global scale, as the highest economic exposure is highlighted in North America, Europe and East Asia, while Sub-Saharan Africa is classified as “low exposure” in monetary terms.

In interpreting these results, it is important to note that the flood risk exposure does not account for existing flood protection measures. Such protection measures tend to be better developed in high-income countries, meaning that the fraction of exposed economic activity that actually gets lost during a flood is likely to be higher in low-income countries.

<!-- Start of picture text --> LIC: 101.8bn<br>LMIC: 1,123.3bn<br>UMIC: 1,260.8bn HIC: 2,827.8bn<br>(21.1%)<br>0 2,000 4,000 6,000<br>USD billions<br><!-- End of picture text -->

# **LIC: 101.8bn**

_Figure 12. Flood exposed economic activity, disaggregated by countries’ income groups. Computed as exposure headcount per country multiplied by GDP per capita. (LIC: low-income countries; LMIC/UMIC: lower/upper middle-income countries; HIC: high income countries, based on World Bank income classifications)_ <!-- Start of picture text --> United States (HIC) 1,926.4<br>Japan (HIC) 1,342.5<br>India (LMIC) 898.6<br>Germany (HIC) 829.3<br>Netherlands (HIC) 548.9<br>Vietnam (LMIC) 503.0<br>Indonesia (UMIC) 498.7<br>Thailand (UMIC) 442.0<br>Pakistan (LMIC) 413.9<br>South Korea (HIC) 396.8<br>0 500 1000 1500 2000 2500<br>USD millions<br><!-- End of picture text --> _Figure 13. Top 10 countries: Economic value at risk, computed as exposure headcount multiplied by subnational income per capita. Note: no subnational income data are available for several countries, including China._ 17
Teacher Sensitivity, in which case there is no spread between their scores. But if one rater scores 2 and the other 5, there is a spread of 3. A score of 3 or more indicates a large discrepancy in judgment.

Figure 1 shows that a few dimensions have a significant proportion of coding with a spread of 3 or more. For Teacher Sensitivity, Instructional Dialogue, Analysis and Inquiry, and Regard for Student Perspectives, at least 10 percent of discrepancies had a spread of 3 or more.

**Figure 1: Extent of Coding Differences for Each Dimension**

<!-- Start of picture text --> Teacher Sensitivity<br>Instructional Dialogue<br>Analysis and Inquiry<br>Regard for Student Perspectives<br>Student Engagement<br>Behavior Management<br>Content Understanding<br>Quality of Feedback<br>Instructional Learning Formats<br>Positive Climate<br>Productivity<br>Negative Climate<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>5 4 3 2 1 0<br><!-- End of picture text --> Source: Guangdong Pilot Study; authors’ calculations.

A session was held with the raters in a June 2017 workshop to understand why these discrepancies arose. The raters indicated that the main factors were technical and cultural. For Analysis and Inquiry and Instructional Dialogue, the challenges appear to be mainly technical understanding of the dimensions and could be addressed through more intensive training. While the technical element also played a role for Teacher Sensitivity and Regard for Student Perspectives, there were important cultural elements to consider. These dimensions bring in new concepts that are not typically considered in the lesson observations taking place in Guangdong. The raters generally saw value in these dimensions, but proposed that the indicators be examined to consider how they might be modified to better reflect the Guangdong context. For further measures of observation reliability, including intraclass correlation coefficient and Cronbach’s alpha analysis, see Appendix B.

# **3 RESULTS**

## **3.1 Overall**

The data suggest that teachers have strong Classroom Organization skills. The scores on Emotional Support and Instructional Support are lower. This pattern is in line with evidence collected in other settings around the world. Teachers were notably weaker in Emotional Support and Instructional Support than they were in Classroom Organization. Low Emotional Support and Low Instructional Support are the two most highly correlated domains. While not necessarily a causal relationship, a focus on either domain will likely lead to improvements in both.

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# _<u>Figure 6 – Meta‐estimates for Productive Assets</u>_

<!-- Start of picture text --> (a) Productive Assets Estimates (%)<br>200%<br>150%<br>100%<br>50%<br>0%<br>‐50%<br>Effect Mean Effect CI Livestock Durables Land Fertilizer/seed use<br>(b) Productive Assets Impacts (%) and Transfer Sizes<br>1 300<br>0.8<br>250<br>0.6<br>0.4<br>200<br>0.2<br>0 150<br>‐0.2<br>100<br>‐0.4<br>‐0.6<br>50<br>‐0.8<br>‐1 0<br>Baseline Mean Estimate Monthly HH Transfer (2011 PPP USD)<br>Percentage of baseline<br>Monthly HH Trasnfer<br>Baseline Mean and Estimate<br><!-- End of picture text --> 36
it is "identical" or "equivalent" to an ISO standards. This variable is used as a proxy for de facto international harmonization. All information is cross-checked against CEN’s own on-line standards catalogue, before being manually mapped to the Harmonized System product classification (http://www.cen.eu/catweb/cwsen.htm).

For each 2- and 4-digit HS code, EUSDB provides a count of the number of CEN standards in force in a given year over the sample period (1995-2003).! It also counts the number of those standards that are treated as being internationally harmonized using the above definition. Table 1 presents basic descriptive statistics, which disclose a number of notable features (see Czubala et al., 2007, for a complete discussion). All three sectors have undergone rapid growth in terms of the total number of standards in force. However, the bulk of standards remain concentrated in the textiles sector (84%). Although the proportion of internationally harmonized standards has generally risen, the pattern across sectors is by no means uniform. In the context of instrumental variables estimates below, some of the possible reasons for this non-uniformity will become apparent.

# 3.2 Measuring Export Variety

To examine the impact of standards and international harmonization on the extensive margin of trade, I construct new measures of export variety covering up to 200 countries. I follow the recent empirical literature on product variety in trade (e.g., Hummels and Klenow, 2005; and Broda and Weinstein, 2006), in building on the theory-consistent measure of variety developed by Feenstra (1994). I use the version of his measure set out by Feenstra and Kee (2006): P ~~ra~~ Negt = ~~oi~~ " ~~YS~~ <u>pra</u> levee (14) The denominator is the total value of world exports in a particular sector, summing across all <u>~ Counts include standards that entered into force prior to</u> 1995 provided they were still in force at some point during the sample period. A standard is considered to be in force for a given year if it came into force before or during that year. If it is withdrawn at some point during the year, it is still assumed to be in force for the entire year. Amendments to existing standards are counted as additional standards.

15
**Table 5: Effects of Household Income on the Amount of Bribe Paid: IV estimates (2SLS)**

|||**Amount of B**|<br>**ribe Paid**||
|---|---|---|---|---|
||**Main**|**Extended**|**Alt. Income**|**Interaction**|
||**Specification**|**Controls**|<br>**Variable**|**Instrument**|
||**(1)**|**(2)**|**(3)**|**(4)**|
||**Con**|**ditional on P**|**aying Bribes**||
|**Per Capita Income**|**-0.006**|**-0.063**||**-0.033**|
||**(-0.07)**|**(-0.64)**||**(-0.35)**|
|**Log(Household Income)**|||**-0.017**<br>**(-0.07)**||
|**Sign and significance of the Instr**|**ument in First stage**||||
|Heavy Rainfall Dummy|-0.570|-0.450|-0.213||
||(-3.26)***|(-2.99)***|(-3.50)***||
|Interactions Based Instrument||||1.995<br>(3.48)***|
|Angrist-Pischke F Statistic|10.62|8.95|12.23|12.11|
||**E**|**stimates Wit**|**h Selection Co**|**rrection**|
|**Per Capita Income**|**-0.142**|**-0.124**||**-0.168**|
||**(-0.97)**|**(0.88)**||**(1.21)**|
|**Log(Household Income)**|||**-0.363**||
||||**(-0.99)**||
|Selection Term|-3.311|-5.03|-3.141|-6.23|
||(-2.02)**|(1.27)|(-2.16)**|(-1.60)|
|**Sign and significance of the Instr**|**ument in First stage**||||
|Heavy Rainfall|-0.287|-0.295|-0.113||
||(-2.65)***|(-3.32)|(-3.05)***||
|Interaction Instrument||||1.14|
|||||(3.80)***|
|Angrist-Pischke F Statistic|7.04|11.04|9.30|14.46|

Notes: (1) All regressions include regional dummmies. Standard errors are clustered at primary sampling unit level (2) Robust z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
innovations explain only about half of the modest variation of out of school children and age-forgrade distortion over the time-period 1981-1993, that the impact of financial autonomy and election of principals is significant but small in explaining test scores and the one of school councils not even significant. A possible explanation for this heterogeneous and often weak impact lies in the uncoordinated adoption of these innovations in the Brazil states. While a majority of the states were, for instance, transferring funds to the schools and had school councils in the early 1990’s, only four of them implemented these innovations at the same time, while in the others these measures were unsynchronised, subject to reversals, etc. The results also suggest, however, that even the combination of these measures might not necessarily be successful in the same way in all states, suggesting the need for more specific analysis of the institutional characteristics of the environment in which school autonomy develops.

A recent study of Di Gropello and Marshall (2004), using regression functions to measure the first and second-order effects of PROHECO (autonomous) schools and controlling for sample selection bias, finds that school autonomy in the administrative area, in combination with higher community monitoring, translates into better capacity utilization in PROHECO schools, under the form of less school closings, more individualized learning, higher frequency of homework, etc, and that, in turn, differences in school capacity utilization explain a substantial proportion of the observed differences in academic achievement between PROHECO and control schools.

Finally, Di Gropello (2002) shows that there is a significant positive relationship between test score and the level of pedagogical decentralization of the school and that there is an improvement of municipal intra-area equity, i.e. that the relation between the socio-economic level of the municipal school and school educational achievement is weaker, when schools have a more active role in local decision-making (which would suggest that giving more responsibilities to schools ensures better resource targeting, i.e. favorable to the poor, and promotes convergence).

31