rafmacalaba/datause-encoder-data huggingface dataset:
**Housing Allowance** <u>.</u> In addition to compensation for the reacquired land use right, government provides a House Purchase Allowance (HPA).  In statutory terms, the HPA is defined as the difference between the market value of the current house and a notional replacement flat based on a 7-year old unit of similar size and other characteristics.  In practice, the norm has been to compensate house owners with payments equal to three times the value of their current house. Commercial properties are compensated at four times existing building value.

**Squatter Clearance.** Squatters—those without land leases--are not eligible for statutory compensation if their settlements are cleared.  Hong Kong SAR, China has recognized the right of displaced households to social housing.  Government policy holds that social housing of at least comparable quality (in physical terms) should be provided to displaced squatters.  Government retains the statutory right to evict squatters without compensation.  The physically comparable social housing provided in resettlement often is not comparable in terms of location or community cohesion.

**Social and Economic Protections** <u>.  Hong Kong SAR, China follows a specific procedure in</u> terms of social and economic protections.  The Urban Renewal Authority (URA) Ordinance requires URA to take a census and inventory of population in the redevelopment area as of the date that a redevelopment project is announced.  Social service teams are required to conduct two Social Impact Assessments.  The first Assessment is called the Freezing Survey.  It is designed to inventory ex-ante conditions.  Typically, the Freezing Survey is conducted immediately before (same day as) announcement of redevelopment.  It is intended to exclude from compensation benefits those who would move into the area upon public announcement in order to capture compensation payments.

The second Social Impact Assessment gathers information on households’ resettlement needs, employment, work-place locations, and community networks. The intent is to match Housing Payment Allowances and resettlement to social housing as closely as feasible with household needs. The findings and recommendations of the Social Impact Assessments are submitted to the Town Planning Board and Secretary for Development for approval.

**Special Rules to Facilitate Urban Redevelopment.** Majority (80%) owners of buildings more than 50 years old and in poor condition have the right to force minority owners to sell to enable redevelopment.  There is no requirement to establish a public purpose.  In practice, this means that developers can acquire 80% interest in old properties through voluntary negotiation, then compulsorily acquire the remaining interest.  Land assembly has been greatly facilitated by this ordinance.

**______________________________________________________________________**

Source: Drawn primarily from LaGrange, 2011.

57
**Figure 2. Electricity-Generating Capacity and Income per Capita**

<!-- Start of picture text --> 2008 - 2012 Benchmark (average)<br>ZAF MUS<br>GAB<br>ZWE ZMB<br>MOZ CIVGHA SDN AGO<br>GIN GMB SENLSOKENCMR NGA<br>MDGETHMWITGOBFAMLIUGABENTZA<br>NER<br>RWA<br>-3 -2 -1 0 1 2<br>GDP per capita<br>90% CI Linear Fit<br>EAP ECA IND LAC MENA SA SSA<br>R-sq: 0.8993; Observations 94; Using PWT 9.0<br>Adjusted by demographic and geographic features.<br>4<br>2<br>0<br>EGC/labor<br>-2<br>-4<br><!-- End of picture text --> _Sources: International Energy Agency, World Energy Outlook. GDP per capita is from Feenstra, Inklaar, and Timmer 2015. Note: CI = confidence interval; EAP = East Asia and the Pacific; ECA = Europe and Central Asia; EGC = electricity-generating capacity; GDP = gross domestic product; IND = Industrial Countries; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa._

**Figure 3. Road Density and Income per Capita**

<!-- Start of picture text --> 2008 - 2012 Benchmark (average)<br>GHA<br>GIN<br>MDG MWI BENTZA KENCMR NGA AGO MUS<br>MOZ BFAMLI SEN<br>NER<br>-3 -2 -1 0 1 2<br>GDP per capita<br>90% CI Linear Fit<br>EAP ECA IND LAC MENA SA SSA<br>R-sq: 0.6025; Observations 77; Using PWT 9.0<br>Adjusted by demographic and geographic features.<br>2<br>1<br>0<br>-1<br>-2<br>Total Road Network / area<br>-3<br><!-- End of picture text --> _Sources: International Road Federation, World Road Statistics; World Bank, World Development Indicators. GDP per capita is from Feenstra, Inklaar, and Timmer 2015. Note: CI = confidence interval; EAP = East Asia and the Pacific; ECA = Europe and Central Asia; GDP = gross domestic product; IND = Industrial Countries; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; SA = South Asia; SSA = Sub-Saharan Africa._ Another important mode of transportation infrastructure is the railroad sector. The data on the length of the railroad network are not as complete as those of the road network. There is information available only _Infrastructure in Sub-Saharan Africa - 14_
three protein-rich foods but not for other foods (and here some of these difference estimates reach standard levels of precision).

We now turn from provincial level price data, where inferential power may be limited by the small number of provinces, to price variation at the village level. Specifically, we document the relationship between village-level unit values from FIES and village-level saturation of _Pantawid_ . Figure 4 confirms the visual pattern that we saw in the household survey data now with the national expenditure data: the price (or unit value) of eggs increases in program saturation, with a steeper slope past approximately 65 percent saturation. Table 3 summarizes regression results not just for fresh eggs but for the prices of 6 categories of food goods from FIES. The food goods include several largely non-tradable goods—fresh eggs, fresh fish, fresh meat, fresh vegetables, and milk products—as well as one tradable ones—raw regular milled rice (the full set of results for 15 food goods are reported in Annex 2). All prices are converted into natural logs. Consistent with our experimental sample, we relate the log unit value, _Pivy_ , for each good _i_ in village _v_ and year _y_ (for the years 2009, 2012, and 2015) to program saturation according to the following specification: where _S_ is the village-year-specific saturation measure for _v_ and _y_ . The model allows for the relationship between prices and saturation to vary above and below a cutoff value (we estimate this via an indicator variable Dvyc equal to 1 if saturation exceeds _c_ which is included in the model directly as well as interacted with saturation). Alternative models change the cutoff _c_ at which we allow the relationship with saturation to vary—setting it at 50, 65, and 75 percent saturation respectively. In addition, we also estimate a simple linear model where log prices are just a function of saturation. All specifications also include village and year fixed effects, _Fv_ and _Fy_ respectively. In each interactive model the mean effect of interest is γ1+γ2 which is reported below the model coefficient estimates—this captures the total effect of saturation in high-saturation areas on each relative price.

The results show that the prices of all the largely non-tradable goods increase with program saturation above the 65 percent threshold (recall that 65 percent is the median saturation in the experimental sample). Moving from a saturation level of 65 to 100 percent between 2009 and 2015 increases the relative price of eggs by almost 3 percent. The magnitudes of the price changes for 19
_The special case of customs unions_ Arndt et al. (2007) suggest that a benefit of opting for a customs union model over a simple free trade agreement could be to register more progress on “deep integration” issues such as trade facilitation or liberalization of services. We find that this is most likely to be indeed the case for trade facilitation.

The preservation of tax revenues is very much at the forefront of customs and other trade facilitation related concerns in regional trade agreements. This is in this respect a notable difference between cooperation agreements (which are not necessarily signed in the context of tariff reductions) and RTAs. For developing countries and even more least developed ones trade taxes are a big share of all government revenue (Keen and Simone, 2004). This is why emphasis has most often only been on the few aspects of customs that would enable to secure trade tax revenues while allowing preferential trade.

The incentives related to optimizing revenue collection are modified in a customs union, which we first define broadly as a regional trade agreement with a common external tariff and no tariffs among its members.<sup>44</sup> In a “true” customs union, revenues will be collected at the initial port of entry in the customs union. That is revenue is collected by each member on behalf of the union and subsequently either redistributed or spent by common institutions. This substantive difference between customs union and other regional trade agreements bears important consequences on the scope for trade facilitation reform at the regional level.

First, customs unions do not require the implementation of rules of origin among members.<sup>45</sup> They also either eliminate or diminish the need for transit bond regimes for goods destined to markets within the union. Both are significant facilitations of trade. Further, forming a customs union implies further incentives for trade facilitation reform in member countries to harmonize their regime, starting with adopting common customs legislations, classification, and tariff rates.

Like in any other preferential trade arrangements there are incentives for trade deflection in customs union, to take advantage of borders in the region where protection is the lowest. Because tariffs are uniform, the cause of trade deflection is solely due to non-tariff barriers, therefore putting a specific emphasis on them. Among them, lack of trade facilitation in one

> 44 The World Customs Organization (1995) defines a customs union as the union of two or more customs territories sharing a common tariff, where customs duties and restrictive regulations of commerce have been abolished within the customs union.

> 45 At least for “true” customs unions that collect revenue at entry.

23
What I object to is the use of the word “because.” That demonstrates a mistake in reasoning, which is my concern in this section.

Let me now, in keeping with the focus of this paper, turn to the more specific matter concerning anti-poverty policies and growth. This is best posed in terms of the recent paper by Dollar, Kleineberg and Kraay (2013), who empirically analyze the relation between growth and poverty. This is a remarkable study because it draws on high-quality survey-based data from 118 countries and brings in state-of-the-art statistics to analyze this challenge.<sup>8</sup> And it reaches an important conclusion, namely, that the bulk of poverty eradication that took place over the past decades was driven by the overall income growth of economies. More specifically, by using the method of standard variance decomposition, the authors find that 77% of the cross-country variation in growth in the incomes of the poorest 40% of the population is explained by the growth of average incomes.

Unfortunately, the policy takeaway that many have read into this important empirical finding is that to eradicate poverty, we have no choice but to rely on economic growth and that direct policy interventions in the market to eradicate poverty are not worth it. So if we want to have pro-poor policies or promote shared prosperity, we have to rely on the benefits of growth to trickle down to the poor.

What I argue here is that the empirical finding referred to above is valid; the policy conclusion stated in the previous paragraph is wrong. It is an illustration of an interesting and, at the same time, common lapse in deductive reasoning. Given that many fall into this trap, this is a point worth explaining.<sup>9</sup> I shall do so using a parable.

> 8 This new paper revisits an earlier debate on the subject of growth and poverty (see, for instance, Datt and Ravallion 2002; Dollar and Kraay 2002).

> 9 A part of this section was published as an article entitled “Reason and the End of Poverty” in Project Syndicate (7 October, 2013):  http://www.project-syndicate.org/commentary/the-logical-flaw-that-keeps-people-poor-by- <u>kaushik-basu.</u>

25
communist elite would be replaced by a new generation of political leaders. While we view the effect of literacy on the possibilities for independent political judgment as unambiguous, we cannot rule out the possibility that whatever aspects of a society led to broader literacy in the precommunist period also led to better governance in the post-communist period.

A second instrument that we use for political alternation is urbanization in 1989-1990.

This is a plausible instrument because greater urbanization lowers the cost of voter mobilization, increasing the likelihood that several genuinely different parties will emerge. However, urbanization can have this effect only if certain other conditions are present—such as the emergence of leaders who feel free to oppose the governing party—and so we view this as a weaker predictor of political alternation.

Table A-2 (Appendix) shows the first-stage IV regressions. As expected, the level of precommunist literacy is a strong predictor of cumulative political alternation, but the rate of urbanization in 1989-90 is not significant. The _F_ statistic regression is 11.5, which is significant, and Anderson’s identification test for the irrelevance of instruments is strongly rejected. Based on Hansen’s _J_ statistics, reported in Table 6, column 3, the null hypothesis that instruments are correctly excluded from the second stage regression is easily accepted.

Column 3 of Table 6 shows the second-stage regression. Instrumented political alternation is a significant predictor of rule of law score ( _p_ =0.00) and the effect is very large. In this sample, one additional alternation is associated with an improvement of 0.71 points in the rule of law score. Recall that for the post-communist countries, the standard deviation in rule of law scores is 0.7, so the coefficient means that one additional alternation predicts an improvement in governance of one full standard deviation in rule of law scores in our sample.

Earlier literature has used the number of years of communist rule as an explanatory variable (see e.g., Treisman, 2002) and shown that it is associated with a large change in the quality of governance in the post-communist period (with countries with longer years of rule having worse governance outcomes). However, our results in both the OLS and IV regressions show that the 30
22 IV.C) Limitations of the Empirical Results As with all empirical work, there are limitations to t.,e present study. We will discuss two classes of limitations: data related and conceptual.

First, official trade statistics for Africa are extremely defective. They ignore a great deal of trans-border trade which routinely takes place amorJ, neighboring countriesZ'. It is estimated that for some countries the unrecorded trans-border trade (UT!) may be as high if not higher than recorded trade'. Because of the magnitude of the UTT in Africa, it is believed that the use of official statistics may seriously under-estimate the true amount of intra-SSA trade. Moreover, for reasons that range from the degree of competence of customs officials to under- or over-invoicing and the use of artificial prices in evaluating the flow of t;ade, whatever trade is detected, is recorded with a great deal of error. For all these reasons, there often exists a large discrepancy between reporter and partner recorded trade flows in SSA. For example, Yeats (1990a) shows that the average discrepancy between matched export and import values for thirty-five SSA countries in 1982-83 was more than sixty percent. However, the existence of UTT likely strengthens our conclusions.

The existence of UTI flows should lead to a serious under-estimation of the true magnitude of intra-SSA trade relative to non-SSA trade. This creates a bias to find trade too low, not too high or about right. First, in recorded border trade, although U=T is large, it is known that most of this trade takes place in goods of foreign origin and/or goods of domestic origin but ultimately destined I' On the importance, institutions and consequences of unrecorded trans-border trade in SSA see Azam (1990), Barad (1990), and Deardorff and Stolper (1990).

2' See Barad, op. cit.
# **Table VI**

## **Host Country Patenting Activity and IPR Regime Changes**

The dependent variable in the first four specifications is the log of the number of patents filied in a particular country by residents of that country, and the dependent variable in the last four specifications in the log of the number of patents filed in a particular country by non-residents. The Post Reform Dummy is a dummy equal to one in the year of reform and the years following the reforms identified in Table II. The Difference of Host Country Tax Rate and US Tax Rate is the difference between the corporate income tax rate in the host country and the US corporate income tax rate. The Host Country Withholding Tax Rate is the annual median tax rates paid by affiliates in a host country. Host Country Inward FDI Restrictions is a dummy equal to one when inward FDI restrictions exist, and it is drawn from Brune (2001). Host Country Trade Openness is the index of constant price openness taken from Heston, Summers, and Aten (2002). The Log of Host Country GDP per capita is derived from data provided in the World Bank World Development Indicators (2003). All specifications include country fixed effects as well as country specific time trends. Heteroskedasticity-consistent standard errors appear in parentheses.

|Dependent Variable:|L|ogof Resident P|atent Filings||Logo|f Non-Resident|Patent Filiings||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Constant|7.0197|20.7387|6.6135|19.7757|7.8368|-8.1660|6.8564|-4.1342|
||(0.6487)|(10.2948)|(0.7204)|(9.7460)|(0.4270)|(6.8139)|(0.5344)|(6.4278)|
|Post Reform Dummy|0.0183|0.0800|0.0792|0.1605|0.3841|0.4257|0.5435|0.5881|
||(0.2114)|(0.2203)|(0.2063)|(0.2163)|(0.1352)|(0.1329)|(0.1295)|(0.1255)|
|Post Reform Dummy * Time Trend|||0.1065|0.1269|||0.2706|0.2488|
||||(0.0962)|(0.1012)|||(0.0690)|(0.0679)|
|Host Country Withholding Tax Rate|-0.2781|0.1048|0.5517|1.1393|1.0122|0.6577|3.1513|2.7043|
||(1.4100)|(1.3565)|(1.4292)|(1.4784)|(0.8211)|(0.7593)|(0.9240)|(0.9864)|
|Host Country Inward FDI Restrictions|0.7600|0.8600|0.7804|0.9036|0.0231|-0.2307|0.0742|-0.1413|
||(0.5375)|(0.5554)|(0.5331)|(0.5474)|(0.2130)|(0.1541)|(0.2233)|(0.1651)|
|Host Country Trade Openness|0.0036|0.0083|0.0080|0.0144|0.0214|0.0243|0.0307|0.0361|
||(0.0153)|(0.0161)|(0.0160)|(0.0170)|(0.0120)|(0.0076)|(0.0126)|(0.0082)|
|Log of Host Country GDP per Capita||-1.3475||-1.6205||1.7457||1.1949|
|||(1.1136)||(1.1458)||(0.7324)||(0.6941)|
|Log of Host Country FDI||-0.0588||-0.0566||-0.0222||-0.0176|
|||(0.0565)||(0.0549)||(0.0404)||(0.0368)|
|Country and Year Fixed Effects?|Y|Y|Y|Y|Y|Y|Y|Y|
|Country Specific Time Trends?|Y|Y|Y|Y|Y|Y|Y|Y|
|No. of Obs.|160|157|160|157|163|158|163|158|
|R-Squared|0.9545|0.9511|0.9548|0.9516|0.9618|0.9715|0.9672|0.9759|
# **APPENDIX TABLE 2**

# **Additional Covariates (Corresponding to Table 2, Column 5)**

|(Cluster-co|rrected Standa|rd Errors in Par|entheses)|||
|---|---|---|---|---|---|
|**Endpoint:**|**1990-91**|**1995-96**|**1997-98**|**1999-00**|**2001-02**|
|Initial Inequality (MlnD, Jacknifed)|**_-0.354*_**|**_-0.240*_**|**_-0.197*_**|**_-0.173*_**|-0.111|
||(0.173)|(0.084)|(0.085)|(0.071)|(0.068)|
|**Household Variables:**||||||
|Ln Initial Income (Y0)|-0.321|-0.133|-0.249|-0.024|-0.252|
||(0.938)|(0.234)|(0.278)|(0.210)|(0.185)|
|(Ln Y0)-Squared|0.014|0.009|0.028|-0.009|0.027|
||(0.149)|(0.039)|(0.046)|(0.034)|(0.030)|
|(Ln Y0)-Cubed|0.000|-0.001|-0.002|0.001|-0.001|
||(0.008)|(0.002)|(0.003)|(0.002)|(0.002)|
|Household Education 8788|**_0.004*_**|**_0.002*_**|0.000|0.001|0.000|
||(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log Family Size|0.013|**_0.011*_**|**_0.008*_**|0.005|0.000|
||(0.009)|(0.003)|(0.003)|(0.002)|(0.002)|
|Dependency Ratio|**_-0.041*_**|0.013|**_0.024*_**|**_0.029*_**|**_0.030*_**|
||(0.014)|(0.007)|(0.006)|(0.005)|(0.005)|
|Log Cultivated Land|0.000|0.001|0.000|**_-0.002*_**|0.001|
||(0.004)|(0.001)|(0.001)|(0.001)|(0.001)|
|Age <= 30|-0.002|**_-0.011*_**|**_-0.008*_**|-0.006|-0.005|
||(0.009)|(0.004)|(0.003)|(0.003)|(0.003)|
|31 <= Age <= 40|**_-0.024*_**|**_-0.011*_**|**_-0.007*_**|**_-0.004*_**|**_-0.004*_**|
||(0.005)|(0.003)|(0.002)|(0.002)|(0.002)|
|51 <= Age <= 60|0.002|-0.006|**_-0.009*_**|**_-0.007*_**|**_-0.010*_**|
||(0.007)|(0.003)|(0.002)|(0.002)|(0.002)|
|Age > 60|**_-0.035*_**|**_-0.027*_**|**_-0.018*_**|**_-0.016*_**|**_-0.019*_**|
||(0.009)|(0.005)|(0.004)|(0.003)|(0.003)|
|**Village Variables:**||||||
|Log Mean Initial Income|0.023|0.026|**_0.034*_**|**_0.028*_**|0.017|
||(0.028)|(0.014)|(0.014)|(0.011)|(0.009)|
|Avg Education|-0.001|0.001|0.002|0.003|0.003|
||(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|Log Per Capita Land|**_0.033*_**|0.006|0.006|0.003|0.003|
||(0.015)|(0.008)|(0.008)|(0.007)|(0.006)|
|Dependency Ratio|**_-0.336*_**|-0.109|-0.080|**_-0.120*_**|**_-0.116*_**|
||(0.144)|(0.066)|(0.057)|(0.044)|(0.037)|
|Share of HH Income from Agriculture|-0.063|0.035|0.015|0.058|0.045|
||(0.143)|(0.052)|(0.053)|(0.049)|(0.042)|
|Share of HH Income from Wages|0.047|0.068|0.019|0.057|0.024|
||(0.160)|(0.054)|(0.053)|(0.050)|(0.046)|
|Share of HH Income from Fam Business|-0.004|0.059|0.020|0.093|0.045|
||(0.150)|(0.067)|(0.067)|(0.057)|(0.051)|
|Tax Revenue Per Capita|-0.028|-0.001|-0.011|-0.005|-0.006|
||(0.056)|(0.022)|(0.020)|(0.015)|(0.015)|
|Gov Expenditure Per Capita|0.050|0.003|0.011|0.010|0.015|
||(0.061)|(0.021)|(0.020)|(0.016)|(0.017)|
|_N_|3424|3424|3424|3424|3424|

_Notes:_ 1) All specifications employ attrition weights; 2) Village mean log income and education are Jack-Knifed.

3) For further details, see the notes to Table 2