rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6 –Descriptive statistics for high‐bias observations**

_Bias = GPS ‐ CR (acres)_

|||GPS over<br>‐<br>|GPS unde~~r~~<br>~~‐~~<br><br>Eth|iopia||Difference<br>in Means<br>||GPS over<br>‐<br>|GPS und~~er~~<br>~~‐~~<br><br>Tan|zania||Difference<br>in Means<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Relative<br>Bias > 10%|reported<br>by > 10%|reported<br>by > 10%|Relative<br>Bias < 10%|All Plots|~~(~~ >10% Bias<br>~~v~~s. < 10% )|Relative<br>Bias > 10%|reported<br>by > 10%|reported<br>by > 10~~%~~|Relative<br>Bias < 10%|All Plots|~~(~~ >10% Bias<br>vs. < 10% )|
|N:|542|305|237|1223|1765||388|251|137|1520|1908||
|% of Total Plot Sample|31%|17%|13%|69%|100%||20%|13%|7%|80%|100%||
|_Average:_|||||||||||||
|CR Area (acres)|0.22|0.23|0.20|0.45|0.38|***|0.21|0.23|0.17|0.28|0.27|***|
|GPS Area (acres)|0.23|0.28|0.16|0.45|0.38|***|0.22|0.27|0.14|0.29|0.28|***|
|Bias (GPS ‐ CR)|0.01|0.05|‐0.03|0.00|0.01|***|0.02|0.04|‐0.03|0.01|0.01|***|
||% Bias||22.74|23.18|22.18|3.41|9.34|***|16.95|16.61|17.57|4.13|6.74|***|
|Closing Error (%)|2.24|2.31|2.14|2.22|2.23|‐|2.09|2.22|1.86|1.97|2.00|*|
|Number of Corners|5.94|5.98|5.89|6.48|6.32|***|8.09|8.25|7.80|8.35|8.30|‐|
|Per : Area Ratio (GPS)|0.41|0.27|0.59|0.20|0.26|***|0.19|0.16|0.24|0.15|0.16|***|
|Number of Satellites|17.0|17.1|16.8|17.4|17.3|***|16.5|16.6|16.4|16.8|16.7|**|
|Walking Speed (m/min)|37.0|37.8|36.1|43.5|41.5|***|42.1|42.4|41.6|44.3|43.9|***|
|_Treecover:_|||||||||||||
|Partial (n)|139|77|62|292|431||207|132|75|739|946||
|(%)|26%|25%|26%|24%|24%|‐|53%|53%|55%|49%|50%|*|
|Heavy (n)|39|20|19|50|89||26|16|10|78|104||
|(%)|7%|7%|8%|4%|5%|***|7%|6%|7%|5%|5%|‐|
||||Nig|eria|||||Po|oled|||
|||||||Difference||||||Difference|
|||GPS over<br>‐|GPS unde~~r~~<br>~~‐~~|||in Means||GPS over<br>‐|GPS und~~er~~<br>~~‐~~|||in Means|
||Relative|reported|reported|Relative||~~(~~ >10% Bias|Relative|reported|reported|Relative||~~(~~ >10% Bias|
||Bias > 10%|by > 10%|by > 10%|Bias < 10%|All Plots|~~v~~s. < 10% )|Bias > 10%|by > 10%|by > 10~~%~~|Bias < 10%|All Plots|vs. < 10% )|
|N:|83|30|53|402|485||1013|586|427|3145|4158||
|% of Total Plot Sample|17%|6%|11%|83%|100%||24%|14%|10%|76%|100%||
|_Average:_|||||||||||||
|CR Area (acres)|0.94|1.31|0.72|1.38|1.31|**|0.27|0.29|0.25|0.49|0.44|***|
|GPS Area (acres)|0.96|1.56|0.61|1.37|1.30|*|0.29|0.34|0.21|0.49|0.44|***|
|Bias (GPS ‐ CR)|0.02|0.25|‐0.11|‐0.02|‐0.01|**|0.01|0.05|‐0.04|0.00|0.00|***|
||% Bias||18.96|23.60|16.33|3.93|6.50|***|20.21|20.39|19.97|3.82|7.82|***|
|Closing Error (%)|2.05|2.68|1.69|1.54|1.62|***|2.16|2.29|1.99|2.01|2.05|***|
|Number of Corners|9.17|9.50|8.98|10.24|10.06|‐|7.03|7.13|6.89|7.87|7.66|***|
|Per : Area Ratio (GPS)|0.13|0.08|0.15|0.09|0.09|***|0.30|0.21|0.42|0.16|0.20|***|
|Number of Satellites|‐|‐|‐|‐|‐|‐|‐|‐|‐|‐|‐|‐|
|Walking Speed (m/min)|58.6|67.5|53.3|61.8|61.3|*|40.7|41.3|39.9|46.0|44.7|***|
|_Treecover:_|||||||||||||
|Partial (n)|50|18|32|228|278||396|227|169|1259|1655||
|(%)|60%|60%|60%|57%|57%|‐|39%|39%|40%|40%|40%|‐|
|Heavy (n)|9|3|6|48|57||74|39|35|176|250||
|(%)<br>******|11%|10%|11%|12%|12%|‐|7%|7%|8%|6%|6%|**|

*p<.1; ** p<.05; *** p<.01

36
# **Appendix C: Additional Formulas and Proofs**

We briefly review below some relevant technical details and the intuition of our proposed imputation method from Dang _et al._ (2017), Dang _et al._ (2019), and Dang and Lanjouw (2023). Further details and formal proofs are provided in these studies.

Given our consumption model for the base survey (survey 1) and the target survey (survey 2), we can write out Equation (1) in full as follows 1 1 1 1 (C.1) 2 2 2 2 (C.2) cc1 yy = ββ ′xx + υυ + εε cc2 By Assumption 1, since both yy = ββ _x1_ and ′xx + υυ _x2_ are representative of the population at the two survey + εε periods (or at the same time), we can replace _x1_ with _x2_ in Equation (C.1) to obtain the imputed household consumption in survey 2 as 1 2 1 2 1 (C.3) cc1 Further given Assumption 2 that changes in the distributions of the explanatory variables yy = ββ ′xx + υυ + εε between the two periods can capture the change in the poverty rate in the next period, we can jj obtain poverty estimates in the absence of actual consumption data in the target survey xx 1 2 1 2 2) (C.4) where (.) is the given poverty function and is the poverty line in survey _j_ , _j_ = 1 and 2. PP(yy ≤zz ) = PP(yy ≤zz jj Since the poverty function PP (.) is defined as the averaged poverty rate for the population, it is zz an expectation function. Using the iterated expectation rule, combining Equations (C.3) and (C.4) and plugging in the estimated parameters for PP 1 and the estimated distributions of the error terms for and 1, we have 1 2 1 ββ 1 2 2) (C.5) cc1 υυ εε cc1 EE(PP(ββ̂ ′xx + υυ� + εε̂ ≤zz )) = PP(yy ≤zz Since we do not exactly know the predicted error terms and 1 in survey 1 that are associated with the characteristics _x2_ for each household in survey 2, we can simulate these error terms from cc1 their estimated distributions and approximate the first term on the left hand side in equality (C.5) as υυ� εε̂ 1 ∑ 1 1) 1 2 1 1)) (C.6) pp SS ss=1 2,ss cc1 SS PP(yy� ≤zz →EE(PP(ββ̂ ′xx + υυ� + εε̂ ≤zz 1 _th_ where 1 2 , and and represent the _s_ random draw from their estimated distributions for and 1, for s= 1,..., S (see, e.g., Gourieroux and Monfort, 1997). The 2,ss cc1,ss 1,ss cc1,ss 1,ss number of simulations S should thus be large enough for the term on the left-hand side to converge yy� = ββ̂ ′xx + υυ�� + εε̂̃ υυ�� εε̂̃ cc1 to the term on the right-hand side of Equation (C.6), which results in Equation (4). We recommend υυ εε using 1,000 simulations. The terms on the left-hand side of Equations (C.4) and (C.5) also make it clear that we should use the poverty line 1 in the base survey (rather than the poverty line 2 in the target survey) together with the imputed consumption for the base survey 21 to obtain poverty estimates. Put differently, since all the estimates for zz 1, , and 1 in Equation (C.5) come from the base survey, the poverty zz yy� cc1 ββ υυ εε 74
The challenge with comparing results from various studies covering different countries or regions calls for the use of standardized datasets that are comparable across countries. Our second contribution is to offer new, global estimates for the impacts of hotter temperature on inequality and poverty dynamics (including chronic and transient poverty) at the subnational level. We analyze the Subnational Poverty and Inequality Database (SPID), a recently constructed global database derived from official national household income and consumption surveys, incorporating over 1,594 subnational units from 134 countries, and spanning the period from 2003 to 2019 (Nguyen _et al._ , 2023).

Using this database, we obtain new global headcount poverty rates at the subnational unit level, using different poverty lines (from the extreme poverty line at $2.15/day to the higher poverty line at $6.85/day).<sup>2</sup> We further analyze richer poverty outcomes by classifying each subnational unit as being in chronic poverty (i.e., poverty extending over multiple consecutive periods), or transient poverty (i.e., temporary poverty lasting over non-consecutive periods). To our knowledge, these estimates on chronic and transient poverty at a global scale were not available before. The policies to address transient poverty are distinctly different from those for chronic poverty. Specifically, strong social protection programs would most effectively address transient poverty (e.g., as they help prevent the non-poor but vulnerable households from falling into poverty), but longer-term investments in human capital and infrastructure would better tackle chronic poverty.<sup>3</sup> Our analysis suggests that change in temperature has a large impact on poverty at the subnational unit level, with larger impacts over the short-term than over the long-term,

> 2 Unless otherwise noted, all our estimates in this paper are at the subnational unit level. Our poverty measures are adjusted to real values using 2017 PPP dollars.

> 3 See, e.g., Barret (2005) and Ravallion (2016) for further discussion on different policy interventions regarding chronic poverty versus transitory poverty. Furthermore, distinguishing between chronic and transient poverty has much policy relevance, given the debate over whether and how much shocks that trigger transient poverty can generate chronic poverty, especially in places with low income and limited access to financial tools, through poverty traps (Barrett and Carter, 2013).

3
**-** **<u>Table 4: Estimated agglomeration wage premium using 2 Stage Least Squares Regressions</u>**

||**[1]**|**[2]**|**[3]**|**[4]**|
|---|---|---|---|---|
|**District sample:**|**All**|**All**|**Metro**|**Metro**|
|**Worker sample:**|**All**|**Urban**|**All**|**Urban**|
|**Metro definition:**||**Weighted**|**population**||
|District|0.141***<br>[0.026]|0.153***<br>[0.025]|||
|Duranton: 10 %|0.151***|0.164***|0.168***|0.179***|
||[0.023]|[0.025]|[0.038]|[0.044]|
|Duranton: 7 %|0.172***|0.192***|0.204***|0.217***|
||[0.041]|[0.048]|[0.052]|[0.045]|
|NTL: 80<sup>th</sup>percentile|0.165***|0.183***|0.682**|0.796**|
||[0.046]|[0.054]|[0.278]|[0.285]|
|NTL: 25<sup>th</sup>percentile|0.195***|0.220***|0.547***|0.584***|
||[0.072]|[0.083]|[0.065]|[0.109]|
|AI|0.199**|0.224**|0.389**|0.123**|
||[0.080]|[0.093]|[0.127]|[0.051]|
|Cluster algorithm|0.161***|0.178***|0.452***|0.490***|
|(High density)|[0.042]|[0.049]|[0.091]|[0.099]|
|Cluster algorithm|0.207***|0.234***|0.409***|0.466***|
|(Urban cluster)|[0.073]|[0.085]|[0.127]|[0.132]|
|n|47,513|33,243|13,610|11,018|

**_Notes:_** The dependent variable in all columns is the natural log of hourly wage. Standard errors clustered at the metro level are reported in brackets. ***, **, *, denotes significance at the 1 percent, 5 percent and 10 percent levels respectively. All results include the full set of job and worker characteristics discussed in Section 4.1.1. Number of observations varies by metro area definition used. To give an idea of the relative sample size, the reported number of observations denotes the number of observations using the “Duranton: 10 %” approach to define metro areas. In all regressions, the population measure (in natural log) is instrumented using the set of instruments set out in the text above Table 4. Instruments are generally relevant, and pass the usual overidentification tests. First stage results are available upon request.

# _4.4. Extension – Controlling for human capital, sectoral specialization and market access_

As a final extension, or robustness check (see also Tables A2 – A10 in the Appendix), to our findings, we also include three other, frequently used agglomeration variables in our regressions. Specifically, we add a metro/district’s sectoral specialization, a measure of its overall level of human capital, and its market access to other Indonesian districts’ markets to equation (1). This allows us to verify the extent to which our findings are sensitive to taking explicit account of these alternative explanations for agglomeration economies.

25
|Mumbai|Hindi|Hindi and English|
|---|---|---|
|Pimpri-Chinchwad|Hindi and English|Hindi|
|Bhubaneswar|Hindi and Oriya|Hindi|
|Delhi|Hindi|Hindi and English|

# Interviews with government officials

Two interviews in each of eight cities were planned: one with a revenue officer to gain insights on aspects property tax issues, and the other with an officer from the stamp duty and registration office to gain similar insights on those topics; both interviews were expected to provide general insights about gender and property ownership locally. Additionally, gender-disaggregated data on property ownership, including data from different time points over recent years to observe any gender trends, were to be sought from all interviewees for the relevant geographies.

The eight cities selected reflect a range of discounts on stamp duty and property taxes. Table A5.8 shows the two additional cities, beyond those listed for FGDs in Table 6 (see section 4.2), where interviews with government officials were sought. Legislation for these cities includes a stamp duty discount for females. The eight cities include those where both gender-based stamp duty and property tax discounts exist, where neither exists and where the legal framework suggests gender-based discounts exist but do not seem to be implemented in practice.

Table A5.8: Cities where interviews with government officials were requested

|**State**|**City**|**Stamp Duty discount for females**|**Property tax discount for females**|
|---|---|---|---|
|Punjab|Ludhiana|Yes|No|
|Uttarakhand|Dehradun|Yes|No|

## Profile of Participants

### _Revenue officers_

Two types of revenue officers were considered for this interview, listed here in order of preference:

1. Officers with 8 to 10 years of experience as head/superintendent of the revenue/property tax department.

2. The additional/joint commissioner (the usual designation in municipal corporations), or in town panchayats, since these positions as unlikely in very small urban local bodies (ULBs), the executive officer directly.

These officers’ typical responsibilities include:

- Managing a team of tax collectors.

- Assigning targets to the tax collectors.

- Managing the cash collected at the municipal office and from the field.

46
# **Figure 6a. Estimated Shifts in Sugarcane Supply and Sugar Demand**

<!-- Start of picture text --> Sugarcane<br>S'SC SSC<br>$/tonne<br>P'SC<br>PSC<br>a<br>Q'SC QSC tonnes of sugarcane<br>Sugar<br>$/tonne<br>P'S<br>b<br>PS<br>DS D'S<br>C'S CS tonnes of sugar<br><!-- End of picture text --> 15
This paper employs a cross-country study to estimate _tax capacity_ from a sample of 110 developing and developed countries during 1994-2009 and the two sub-periods of 1994-2001 and 2002-09. In this study, we extend the empirical methodology applied by Tanzi and Davoodi (1997), and Bird, Vazquez, and Torgler (2004). The estimation results are used as benchmarks to compare taxable capacity and tax effort in different countries. _Taxable capacity_ refers to the predicted tax-to-GDP ratio that can be estimated with regression analyses, taking into account a country’s specific macroeconomic, demographic, and institutional features. _Tax effort_ is defined as an index of the ratio between the share of the actual tax collection in GDP and the taxable capacity. The concepts of taxable capacity and tax effort are also extended to measure total fiscal revenue capacity and revenue effort.

Calculating tax effort and actual tax collection benchmarks allows us to rank countries into four different groups: (i) low tax collection, low tax effort; (ii) high tax collection, high tax effort; (iii) low tax collection, high tax effort; and (iv) high tax collection, low tax effort. This classification is based on the global average of tax collection and a tax effort index of 1, corresponding to the case when tax collection is exactly the same as estimated taxable capacity.

The analysis provides guidance for countries with various levels of tax collection and tax effort. The authors argue that taxation is always a critical dimension of fiscal policy for all countries, while countries at various stages of development and with different initial levels of tax collection and effort should rely on different strategies for tax reforms. Our analysis focuses on tax performance and provides broad directions for reforms in developing countries.

Section II provides an overview of the worldwide trend in tax revenue collection across incomegroups and geographic regions, using the tax-to-GDP ratio as a measure of tax collection. Section III highlights alternative measures of the tax performance of countries and extends the existing literature to the empirical estimation of a country’s taxable capacity and tax effort. This section also investigates the trends in taxable capacity and tax effort across regions. Based on the level of tax collection and the tax effort index, countries are classified into different groups. This section also compares the new results with the ones reported in Le, Moreno-Dodson, and Rojchaichaninthorn (2008), which was covering a shorter time period. Some policy implications for fiscal revenue reforms follow. Section IV concludes.

# **II.  TRENDS IN TAXATION**

# **_Data_**

The simplest definition of tax effort, which is commonly used in the literature, is the share of tax revenue in percentage of GDP. It does not give detailed information on tax collection relative to taxable capacity, but still it provides us with a simple measure to see the trends across countries, income groups, as well as regions.

3
# **3. Co-integration analysis and innovation accounting**

In this section we first use cointegration analysis to test for potential long-run (equilibrium) relationships between the price series, and then apply impulse response analysis and variance decomposition to shed light on the relative importance of Lao, Thai and Vietnamese price shocks for the evolution of rice prices in Lao PDR.

The Johansen procedure<sup>9</sup> is used to test for cointegrating vectors, since it allows for multiple cointegrating vectors and short run dynamics. The trace test statistics, reported in Table 2, indicate that there is one cointegrating vector: the test for no stationary vector is clearly rejected, p-value=0.008, while the test for at most one vector is insignificant. The sizes of the eigenvalues also indicate that there is one cointegrating vector: the largest one is 0.18, i.e., well over zero, while the other two are close to zero. Fig. 4 (upper panel) shows recursive estimates of the largest eigenvalue and 95% confidence bands for the period 2004:1-2012:12. The eigenvalue is close to 0.20 over the whole sample.

Table 2: Trace test statistics of the number of cointegrating vectors

||None|At most 1|At most 2|
|---|---|---|---|
|Statistic|36.089|8.154|0.266|
|Critical value, 0.05|29.797|15.490|3.841|
|_p_-value|0.008|0.449|0.606|
|Eigenvalue|0.183|0.056|0.002|

Note: The data is for January 2001-December 2012. The VAR has six lags and seasonal dummies. The test is based on unrestricted constant.

> 9 See Juselius (2006) for a detailed description of the Johansen procedure for cointegration analysis.

12
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- Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. _Journal of the American Statistical Association 103_ (484), 1481–1495.

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- Becker, A., T. Deckers, T. Dohmen, A. Falk, and F. Kosse (2012). The Relationship Between Economic Preferences and Psychological Personality Measures. _Annual Review of Economics 4_ , 453–78.

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- Bernard, T., S. Dercon, K. Orkin, and A. S. Taffesse (2014). The Future in Mind: Aspirations and Forward-Looking Behaviour in Rural Ethiopia. _Working paper_ .

- Bianchi, M. and M. Bobba (2013). Liquidity, Risk, and Occupational Choices. _Review of Economic Studies 80_ (2), 491–511.

- Blattman, C. and J. Annan (2015). Can Employment Reduce Lawlessness and Rebellion? A Field Experiment with High-Risk Men in a Fragile State. _forthcoming in American Political Science Review_ .

- Blattman, C., J. Annan, E. P. Green, C. Lehmann, and J. Jamison (2015). The returns to microenterprise development among the ultra-poor: A field experiment in post-war Uganda. _forthcoming in American Economic Journal: Applied Economics_ .

- Blattman, C., N. Fiala, and S. Martinez (2014). Generating skilled employment in developing countries: Experimental evidence from Uganda. _Quarterly Journal of Economics 129_ (2), 697–752.

- Blattman, C., J. Jamison, T. Koroknay-Palicz, K. Rodrigues, and M. Sheridan (2015). Measuring the measurement error: A method to qualitatively validate survey data. _forthcoming in Journal of Development Economics_ .

- Borghans, L., A. L. Duckworth, J. J. Heckman, and B. t. Weel (2008, September). The Economics and Psychology of Personality Traits. _Journal of Human Resources 43_ (4), 972–1059.

42
<!-- Start of picture text --> Market potential, inhabitants [number of cells]<br>> 5 milion[32] mm 2 - 5 million[258] =m 1 - 2 million [379] |= 500 000 - 1 million (460) | 200 000 - 500 000 [541] < 200 000 (269)<br>Sources : Africapolis: www.oecd.org/swac/topics/africapolis and Prieto Curiel, R., P. Heinrigs and |. Heo (2017),<br>“Cities and Spatial Interactions in West Africa: A Clustering Analysis of the Local Interactions of Urban Agglomerations”,<br>West African Papers, No. 05, OECD Publishing, Paris. © 2017. Sahel and West Africa Club Secretariat (SWAC/OECD)<br><!-- End of picture text -->
# **I.3 Quantitative results for Section 4.3.2**

Table I.3 shows the values of internally calibrated parameters with alternative local and central debt interest elasticities.

**Table I.3:** Internally Calibrated Parameters with alternative local and central debt interest elasticities

|Parameter|Target|C|alibrated Va|lues|
|---|---|---|---|---|
|||1988|1996|2006|
| ̄_B_|Local government debt|0.0345|0.129|0.126|
| ̄_D_|Central government debt|0.220|0.538|0.280|
|_θ_|Central-local spending ratio|0.791|0.653|0.515|
|_ε_|Central-to-local transfer|0.014|0.014*|0.014*|

Note: Using _ψb_ = 0.05 and _ψd_ = 0.01 and re-calibrating each year. Calibration strategy is identical to the baseline. Debt and transfer levels are normalized by national GDP. *We assume _ε_ for 1996 and 2006 is the same as 1988.

42
<!-- Start of picture text --> Informal tradables<br>1989 1994 2000<br>°<br>o<br>bs<br>N<br>a a<br>£ Oo<br>= a<br>Ss ) eS 10<br>2 2005 2010<br>2 ow<br>oO<br>;. no! L<br>a as<br>) 5 10 O 5 10<br>Firm-size<br>Graphs by Year of majority coverage<br><!-- End of picture text --> <!-- Start of picture text --> Informal non-tradables<br>1989 1994 2000<br>t+<br>oO<br>N<br>=<br>g<br>= Oo<br>= Sa aT<br>= () 5 10<br>2 2005 2010<br>Ss<br>oO<br>.<br>a rs<br>0 5 10 O 5 10<br>Firm-size<br>Graphs by Year of majority coverage<br><!-- End of picture text --> <!-- Start of picture text --> Informal apparel sector<br>1989 1994 2000<br>oO<br>Earg ..——_$___ a L<br>= ——<br>5 0 5 10<br>2 2005 2010<br>2<br>=<br>oO ——<br>0 5 10 O 5 10<br>Firm-size<br>Graphs by Year of majority coverage<br><!-- End of picture text -->