rafmacalaba/datause-encoder-data huggingface dataset:
GDP, employment, exports, and attraction of foreign investment and new technologies, as well as adoption of modern management practices, among others. In 2006, the five initial SEZs accounted for 5 percent of China’s total real GDP, 22 percent of total merchandise exports, and 9 percent of total FDI inflows. At the same time, the 54 national ETDZs accounted for 5 percent of total GDP, 15 percent of exports, and 22 percent of total FDI inflows (see table 1.3).

**<mark>Table 1.3    Performance of Initial Five Special Economic Zones and National</mark> E** **<mark>conomic and</mark> T** **<mark>echnological Development Zones, 2006</mark>**

|_National_<br>_Indicator_|_SEZs_|_ETDZs_|_China_|
|---|---|---|---|
|Totalemployment||||
|(millions)|15|4|758|
|as%ofChinatotal|2.0|0.5|100|
|RealGDP||||
|(RMB100 millions)|9,101|8,195|183,085|
|as%ofChinatotal|5.0|4.5|100|
|UtilizedFDI||||
|(US$100millions)|55|130|603|
|as%ofChinatotal|9.1|21.6|100|
|Merchandise exports||||
|(US$100millions)|1,686|1,138|7,620|
|as%ofChinatotal|22.1|14.9|100|
|Totalpopulation||||
|(millions)|25|—|1,308|
|as%ofChinatotal|1.9|—|100|

_Source:_ National Statistics Bureau 2006. _<mark>Note:</mark>_ <mark>— = not available</mark> .

Because of the large number of SEZs of various types and the difficulty of obtaining recent data (especially from those at the subnational level), it is hard to paint an overall picture of the contributions of the SEZs, but some estimated aggregations could be obtained based on available data for 2006 and 2007. In 2006, the 54 state-level ETDZs, 53 state-level HIDZs,<sup>4</sup> and 15 FTZs accounted for a combined 11.1 percent of China’s total GDP and 29.8 percent of exports (China Knowledge Online 2009). The same year, the total GDP for Shanghai Pudong and Tianjin Binhai was RMB 236.53 billion and RMB 196.05 billion, respectively; and their exports were US$44.5 billion and US$18.5 billion (Shanghai Statistics Bureau 2008; Tianjin Statistics Bureau 2008). If the figures cited in table 1.3 are added, then the total GDP of the majority of the state-level SEZs (including the seven comprehensive SEZs, ETDZs, HIDZs, and FTZs) would account for about 18.5 percent of China’s total GDP and about 60 percent of total exports. In 2007, the five initial SEZs produced a total GDP of RMB 1,110.7 billion, and Shanghai Pudong and Tianjin Binhai 13
<!-- Start of picture text --> Figure 3: Shock decomposition during key episodes of sharp U.S. interest rates movements<br>A. Taper tantrum B. COVID-19, war in Ukraine, and rising inflation<br>Percentage points Yield, change Percentage points Yield, change<br>Reaction shocks Real shocks Reaction shocks Real shocks<br>4 Inflation shocks 2-year bond (RHS) 1.5 10 Inflation shocks 2-year bond (RHS) 2.0<br>3 10-year bond (RHS) 8 10-year bond (RHS) 1.5<br>2 1.0 6 1.0<br>1 0.5 4 0.5<br>0 0.0 2 0.0<br>-1 0<br>-2 -0.5 -2 -0.5<br>-3 -1.0 -4 -1.0<br>Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 -6 -1.5<br>Jan-20 May-20 Sep-20 Jan-21 May-21 Sep-21 Jan-22 May-22 Sep-22<br><!-- End of picture text --> _Sources:_ Federal Reserve Bank of St Louis; World Bank _Notes:_ Based on a sign-restricted Bayesian VAR model with stochastic volatility (see section 2.1 for more details). “Shocks” reflect 3-month moving sum of identified monthly shocks. Yields are 3-month-on-3-month changes. B. Last observation is September 2022.

24
Table B.2 shows that our results are robust to conducting a “donut RD” in which we remove observations around the reform time to account for potential selective sorting (i.e. retiming of purchases in our case). Another potential challenge with our estimation procedure is that shorter bandwidths, which allow us to achieve a better fit of the data around the reform, require us to estimate the month fixed effects on fewer observations. Table B.3 shows that this is not a concern, as our results are almost identical when using an alternative two-step estimation procedure. This procedure is similar to the “augmented local linear” methodology suggested in Hausman and Rapson (2018). We first estimate equation 1 on the full 2010-2016 data to estimate the month-of-year fixed effects with the highest possible degree of precision. We then ˆ recover the de-seasonalized outcomes _log_ ( _Z_<sup> ̃</sup> _t_ ) = _log_ ( _Zt,m_ ) _− gm_ and estimate the regression discontinuity with a shorter data set (bandwidth) around the reform. In this second step, we estimate equation 1 without the month-of-year fixed effects _gm_ and use the de-seasonalized outcomes as dependent variable. The point estimates from this procedure are hardly distinguishable from our main estimates.

Tables B.4 and B.5 show the results when controlling for potential autorcorrelation in the outcome by including the first and second lag of the dependent variable in the estimation. With this correction, the effects are only slightly smaller than in our main estimations, suggesting that the number of card transactions increased by 40 percent and the volume of transactions increased by 20-30 percent. All point estimates continue to be highly statistically significant.<sup>19</sup> Finally, it is possible that our specification is affected by serially correlated unobservables and hence autocorrelation in the error term. We thus rerun the RD estimation using the Prais and Winsten (1954) correction for autocorrelated errors (see also Judge et al. (1985) and Davidson and MacKinnon (1993)). The results shown in Table B.6 are very similar to our main results, suggesting that autocorrelated errors are not an important concern.

# **5 Tax Compliance**

Having established that the VAT rebates lead to a large increase in the number and volume of card transactions, and to a smaller but still non-negligible increase in the number of POS in use, we now turn to analyze the impact on tax compliance. Applying an RD estimation, as used in

> 19As Hausman and Rapson (2018) discuss, the point estimate on the treatment indicator in an estimation that includes the lagged outcome variable captures only the short-run effect of the policy change, while our main estimates capture the medium-term effect, i.e. the short-term effect plus any additional impact that arises from a combination of the short-term effect and the autoregressive nature of the outcome. This latter effect is arguably the policy-relevant one in our context, which is why we use it for our main analysis, but it is reassuring that the short and medium-term are similar.

15
- **Goldberg, Linda S, and Signe Krogstrup.** 2018. “International Capital Flow Pressures.” Working Paper 24286, National Bureau of Economic Research.

- **Ilzetzki, Ethan, Carmen M. Reinhart, and Kenneth S. Rogoff.** 2019. “Exchange Arrangements Entering the 21st Century: Which Anchor Will Hold?” _Quarterly Journal of Economics_ , 134.

- **Kose, M. Ayhan, Christopher Otrok, and Charles H. Whiteman.** 2003. “International Business Cycles: World, Region, and Country-Specific Factors.” _American Economic Review_ , 93(4): 1216–1239.

- **Longstaff, Francis A., Jun Pan, Lasse H. Pedersen, and Kenneth J. Singleton.** 2011. “How Sovereign Is Sovereign Credit Risk?” _American Economic Journal: Macroeconomics_ , 3(2): 75–103.

- **Mandalinci, Zeyyad, and Haroon Mumtaz.** 2019. “Global Economic Divergence and Portfolio Capital Flows to Emerging Markets.” _Journal of Money, Credit and Banking_ , 0(0): .

- **McCauley, Robert N., Agustín Bénétrix, Patrick M. McGuire, and Goetz von Peter.** 2017. “Financial Deglobalisation in Banking?” BIS Working Paper No. 650.

- **Miranda-Agrippino, Silvia, and Hélène Rey.** 2015. “US Monetary Policy and the Global Financial Cycle.” Working Paper 21722, National Bureau of Economic Research.

- **Obstfeld, Maurice, Jonathan D. Ostry, and Mahvash S. Qureshi.** 2018. “Global Financial Cycles and the Exchange Rate Regime: A Perspective from Emerging Markets.” _AEA Papers and Proceedings_ , 108 499–504.

- **Onatski, Alexei.** 2012. “Asymptotics of the principal components estimator of large factor models with weakly influential factors.” _Journal of Econometrics_ , 168(2): 244 – 258.

- **Passari, Evgenia, and Hélène Rey.** 2015. “Financial Flows and the International Monetary System.” _The Economic Journal_ , 125(584): 675–698.

- **Patton, Andrew, Dimitris N. Politis, and Halbert White.** 2009. “Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White.” _Econometric Reviews_ , 28(4): 372–375.

74
<!-- Start of picture text --> 5.3.<br>Section<br>in<br>1 1 2 discussed<br>1 and<br>Specifications 0 Wholesale 0 Wholesale Wholesale 1<br>0<br>Liability<br>-1 -1 Table<br>Years to Reform Retail Data Years to Reform Retail Data -1 Years to Reform Retail in<br>VAT Data<br>-2 -2<br>2016 2016 -2<br>Alternative Net 2016<br>to C. -3 OLS DiD (Log): -0.022 (0.074). N Retailers:2822, N Wholesalers=3014 -3 OLS DiD (Log): 0.020 (0.049). N Retailers:17356, N Wholesalers=13077 -3 OLS DiD (Log): 0.026 (0.049). N Retailers:17416, N Wholesalers=13176 presented<br>Appendix<br>1 Excludes 1 Includes 2 tests<br>Excludes<br>99,<br>p95, p 1<br>0 0<br>p99,<br>Estimations Wholesale at Wholesale at Wholesale robustness<br>VAT at 0<br>-1 -1 the<br>Years to Reform Retail Years to Reform Retail -1 Years to Reform Retail of<br>Output -2 -2<br>-2<br>B. winsorinzing winsorizing<br>Winsorizing -3 OLS DiD (Log): 0.047 (0.041). N Retailers:2822, N Wholesalers=3014 -3 OLS DiD (Log): -0.027 (0.036). N Retailers:17356, N Wholesalers=13077 -3 OLS DiD (Log): 0.004 (0.037). N Retailers:17416, N Wholesalers=13176<br>representation<br>1 1 Sample, 2<br>Sample,<br>Sample,<br>Difference-in-Difference<br>1<br>of Sales 0 0<br>graphical<br>Wholesale Wholesale Wholesale<br>Difference-in-Difference 0<br>the<br>-1 -1<br>Balanced Unbalanced<br>C Taxable I. -2 Years to Reform Retail UnbalancedII. -2 Years to Reform Retail III. -1 Years to Reform Retail<br>Robustness -2 provides<br>It<br>Total<br>C.1: -3 OLS DiD (Log): -0.059 (0.046). N Retailers:2822, N Wholesalers=3014 -3 OLS DiD (Log): 0.043 (0.040). N Retailers:17356, N Wholesalers=13077 -3 OLS DiD (Log): 0.060 (0.041). N Retailers:17416, N Wholesalers=13176 3.<br>A.<br>Figure<br>Figure<br>to<br>similar<br>is<br>figure<br>This<br>Notes:<br>1.1 1.05 1 .95 1.1 1.05 1 .95 1.1 1.05 1 .95<br>Normalized Log Outcome Normalized Log Outcome Normalized Log Outcome<br>1.1 1.05 1 .95 1.1 1.05 1 .95 1.1 1.05 1 .95<br>Normalized Log Outcome Normalized Log Outcome Normalized Log Outcome<br>1.1 1.05 1 .95 1.1 1.05 1 .95 1.1 1.05 1 .95<br>Normalized Log Outcome Normalized Log Outcome Normalized Log Outcome<br><!-- End of picture text --> 65
THS 'FINAL' MODELS FOR ZJC MATHEMATICS AND ENGLISH ACHIEVEMENTs COMPARISON WITH THE RESULTS OF THE 1985 STUDY Models 11 for both subjects are the 'final' models, not in any definitive sense, such as that no other variable combinacLons are possible or, lndeed, that all possLble explanatory variables have been tested. Rather, they are adequate final models glven the purpose of this study, to identlfy some of the most effective and the least effective secondary schools in Zimbabwe, and also that **the models** include signifLcant explanatory variables and indeed go a long way towards explaining school differences.

Comparison with the final models used ln the 1985 study shows that a similar percentage of the total varlatlon ln secondary school achievement **has been** explained by the final equation for Mathematlce achlevement, but in **fact** consLderably more for English achlivement. Whereas 48% of the overall variation ln Mathematics achlevment (at '0' level) was explaLned by the fLnal model ln the 1985 study, 44% of the overall varlatlon ln MathematLcs achLevement (at ZJC) was explained by the final model in the current study. Despito **the** time constraLnts and the limited varLables able to be consLdered for possible inclusLon in the current study, 63% of the total variation in Englsh achievement at ZJC was explaLned, whereas the much mo-e comprehenslve data collectlon carried out in 1985 culminated ln a model for Englih '0' level achlevement which only explained 38% of the total varLation.

36
input to confectionary and chocolate products while coffee and tea are consumed as beverages in quite distinct markets (developing countries for the former and high income countries for the latter).

The elasticities of raw materials were quite diverse. Cotton and rubber estimates (0.14 and 0.17) are in the same range as those of food commodities. Note that both cotton and natural rubber compete with synthetic products (manmade fibers and synthetic rubber, respectively) whose main input is crude oil. The remaining elasticities, however, were either negative (-0.05 for logs and -0.13 for sawnwood) or close to zero (0.04 for tobacco).

Natural gas exhibited the highest elasticity (0.64 with the highest _t_ -ratio and the second highest _R_<sup>_2_</sup> ), not surprisingly given its use as a key source of energy. The precious metals also exhibited high elasticities (0.34 for gold and 0.58 for silver) implying that high crude oil prices are indeed associated with inflationary pressures by households and investors.

The picture for metals was as mixed as that of raw materials, with elasticities ranging from a negative and insignificant estimate for copper to almost zero for aluminum and zinc and a highly significant estimate for tin. Of the seven metal commodities examined, only two were highly significant—iron ore (0.18) and tin (0.56)—and one was significant at the 5 percent level—lead (0.21).<sup>**8**</sup> The different performance between the food and metals equations was also evident in the unit root statistics. With the exception of beef and soybean oil, both ADF and PP statistics were significant either at the 1 or 5 percent level, which indicates that the respective equations form strong cointegrating relationships. However, this was not the case with the metals equations, of which for three (copper, iron ore, and lead) the PP statistic was not significant while for zinc it was significant only at the 10 percent level.

Aluminum’s near-zero elasticity (0.04) is quite surprising, considering that its energy costs are larger than for any other metal examined in this study. Aluminum

**_— 8 —_**
Tanzania’s national CPI reflects the prices surveyed in urban areas in 21 regions across the country. Meanwhile, Madagascar’s national CPI reflects the prices of goods and services in the capital, Antananarivo, only. Similarly, before 2011, the National Statistical Institute of the Democratic Republic of Congo did not collect prices in any administrative province except Kinshasa, the capital. When price data in rural areas are not available, CPIs are based on urban price data only, with the (arguably strong) assumption that urban and rural prices move in parallel. What is more, even when HBS data are nationally representative, some countries compute budget shares for urban households only. In Tanzania, for instance, CPI weights until 2009 were based on consumption patterns among urban households in the 2001 HBS. In 2010, as part of the process of rebasing the index to the HBS 2007, the reference population for the weights was broadened to include rural households (Tanzania NBS 2010).

Finally, for the purpose of poverty analysis, concerns arise over the fact that the weights are not representative of poor households’ consumption patterns. CPI weights are computed as the consumption shares of an aggregate reference population, which attaches a weight to each household in proportion to its total expenditures. Because of this plutocratic bias, CPI weights reflect consumption patterns of households at the upper end of the distribution (Nicholson 1975, Deaton 1998). In times of changing relative prices, such as during food price shocks, inflation measured by the CPI can then differ from the inflation experienced by poorer population groups (Günther and Grimm 2007).

# _The Engel curve method_

The Engel curve method attempts to address some of the above biases, mostly the commodity and outlet substitution biases.<sup>4</sup> The method takes as a starting point Engel’s Law and the idea that movements in food budget shares that cannot be explained by changes in relative prices and household characteristics reveal changes in real incomes. As a result, any systematic difference over time in the food budget shares of demographically similar households at the same level of real income (CPI deflated) and facing the same relative prices is assumed to reflect mismeasurement in the CPI. This Engel curve method was first introduced by Costa (2001) and

> 4 According to Hausman (2003), the method only accounts for commodity and outlet substitution bias. Beatty and Larsen (2005), however, argue that it also captures some quality change bias arising from increased durability.

6
garments versus non-woven garments), and by location (Chittagong, Chittagong-EPZ, Dhaka, and Dhaka-EPZ). After cleaning up the data to exclude outliers and ...rms with incomplete information, there are a total of 297 ...rms in the ...ve year unbalanced panel data set of 1213 observations, from 1999 to 2003.<sup>17</sup> In this unbalanced panel data set, the composition is 68 percent in woven industry and 32 percent in non-woven industry, roughly re‡ecting the population of ...rms in the garment sector. Among the sampled ...rms, 14 percent have positive foreign equity, while the remaining 86 percent are purely domestic owned.

Table 1 presents the sample means of the key variables by woven and non-woven industries and by equity ownership. It is clear that in both industries FDI ...rms are, in general, larger in sales and exports, purchase more material inputs, including imported materials, hire more employees, and have more capital. All these presumably suggest that foreign ...rms are more productive.

Note that, to promote the improvement and expansion of locally available inputs, it is necessary for FDI ...rms to increase the industry demand for these inputs through greater local input intensity, a point emphasized greatly in Rodriguez-Clare (1996), but not necessarily more productive. Given that FDI ...rms are much larger on average in the current data set, this is likely to be the case. In fact, given its size, a typical FDI ...rm in the current sample source 83 percent more local inputs than domestic ...rms, even though only 20 percent of their inputs are locally supplied, while the comparable ...gure for a domestic ...rm is 32 percent. More evidence on FDI ...rms increase the industry demand of local intermediate inputs in Bangladesh is presented in Section 6.4, where we examine the local intermediate input intensity of FDI ...rms. This is essential to support SSS, whereby, the presence of foreign ...rms signi...cantly increases the industry demand for local inputs, which may then lead to quality upgrading and variety expansion in the intermediate input industry.

Table 2 presents the sample means of industry foreign presence and FDI sibling and foreign sibling presence of domestic ...rms in the sample, by industries. On average, there is more foreign presence in the woven industry than in the non-woven industry, judging by their industry foreign presence, although the di¤erence is only about 8 percentage points. The contrast is larger between two industries when focussing on the siblings. On average, 52 percent of domestic ...rms in the woven industry have FDI siblings, while only 16 percent of domestic ...rms in the non-woven industry

> 17 In the survey, the accounting managers of the ...rms were asked to provide ...nancial information from 1999 to 2003, which is what I used to conduct the analysis. Some of these ...rms stopped production and exited the industry during the sample period but have resumed production in 2004. These ...rms are the exiters in the sample.

12
_9_ Figure 2

# PERCENTAGE CHANGES **IN** CIVIL SERVICE<sup>EMPLOYMENT</sup> FOR SELECTED OECD COUNTRIES,<sup>1984-89</sup>

<!-- Start of picture text --> United States<br>France<br>udx<br>Germany<br>Sweden -2s5<br>Japan ±z7%<br>Canada<br>Australia<br>-9.~~~~~~ .t1%1<br>UnRed Kingdom -a .-<br>7 Total change Average annual change<br><!-- End of picture text --> Note: Changes in Gennany are<sup>based on 1985-89timeperiod.</sup> Source: CECPS
Policy ReseaRch WoRking PaPeR 4334

# **Abstract**

This paper measures the economic impact of climate on crops in Kenya. The analysis is based on cross-sectional climate, hydrological, soil, and household level data for a sample of 816 households, and uses a seasonal Ricardian model.

Estimated marginal impacts of climate variables suggest that global warming is harmful for agricultural productivity and that changes in temperature are much more important than changes in precipitation. This result is confirmed by the predicted impact of various climate change scenarios on agriculture. The results further confirm that the temperature component of global warming is much more important than precipitation. The authors analyze farmers’ perceptions of climate variations and their adaptation to these, and also constraints on adaptation mechanisms. The results suggest that farmers in Kenya are aware of short-term climate change, that most of them have noticed an increase in temperatures, and that some have taken adaptive measures.

This paper—a product of the Sustainable Rural and Urban Development Division, Development Economics Research Group Department—is part of a larger effort in the department to mainstream economic research on climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jmariara@mail.uonbi.ac.ke.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team