rafmacalaba/datause-encoder-data huggingface dataset:
32 investing across borders: do fdi-specific regulations matter?

than countries like Afghanistan and Haiti which receive insignificant amounts of FDI. Obviously BRIC offers conspicuous advantages like market size to investors that small, conflict-ridden economies do not. That, on paper, the poorer countries allow 100 percent ownership of equity should foreigners invest does not seem to matter when most other determinants of FDI are accounted for. This is illustrated in Figure 7 with outward _sectoral_ FDI data from the United States to 87 IAB countries.<sup>28</sup> There is no systematic relationship between countries’ openness score and actual FDI received by sector.

In the bias-corrected models of column 3 and 4 in Table 1, the elasticity of FDI with respect to distance drops dramatically, by two-third, from that in the (biased) benchmark model in column 2. The positive effect of a shared border increases, but the coefficient of common colony falls modestly in the bias-corrected models. Coefficients on remoteness and GDP are also subdued in the bias-corrected models. The coefficient of GDP per capita is not statistically significant in any of the regressions. Coefficients on the average education level of the adult population and the mean level of weighted tariff on manufactured import in host countries are also not statistically different from zero. I detail the estimation procedure and results in the rest of this section.

In Table 2, FDI-receiving OECD countries in the sample are dropped so that the FDI relationship is now between the 24 OECD source countries and 65 nonOECD host countries. Coefficients on FDI Arbitration and FDI Procedures are both statistically significant although the magnitudes are lower than in the fuller sample (Table 1). The elasticity of distance in the biased regression is much higher than in the main regression, which drops by about one-third when biases are corrected. This indicates that the inclusion of 15 OECD countries in the sample overestimated the effect of FDI-specific provisions and underestimated the hurdle posed by bilateral distance. When the proportion of developing countries in the sample grows,

> 28 The 5 sectors are mining, manufacturing, banking, insurance and telecommunications. Concordance between the sectors defined by the US Bureau of Economic Analysis and IAB is not exact. FDI stock figures from the US are averaged between 2007 and 2008.
**Table 7:Trade Openness and Industry Labor Demand for Blue Collar Workers in Chile (1982-95) (standard errors in parenthesis)**

||Ln L t-1|Ln WBt|Ln WBt-l|Ln WWt|Ln WWt-l|Constant|Wald<br>Test: p-<br>value(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|---|---|---|---|---|---|---|---|---|---|---|
|Trade Policy<br>Specification|0.488(*)<br>(0.039)|0.005<br>(0.106)|-0.042<br>(0.089)|0.118(**)<br>_(0.051)_|-0.097(**)<br>(0.047)|-0.110(***)<br>(0.057)|<br>--|0.188|0.571|21014<br>(1501)|
|Variable* Tariff Rate<br>Variable* Real|0.001<br>(0.001)<br>0.0002|0.001<br>(0.002)<br>-0.002(**)|-0.004(***)<br>(0.002)<br>0.001(**)|-0.001<br>(0.001)<br>-0.0004|0.003(*)<br>(0.001)<br>0.0003||0.007<br>0.227||||
|Exchange Rate|(0.0002)|(0.001)|(0.001)|(0.0004)|(0.0004)||||||
|Wald Test: p-value(b)|0.065|0.074|0.085|0.203|0.004||||||
|Price-Cost-Margin|0.496(*)|-0.255(*)|0.149(*)|0.067(*)|-0.022|-0.183(*)|--|0.261|0.520|21014|
|Specification(c)|(0.054)|(0.028)|(0.033)|(0.021)|(0.026)|(0.010)||||(1501)|
|Variable* Price Cost<br>Margin|0.001(*)<br>(0.0005)|0.0002<br>(0.0007)|-0.001<br>(0.0009)|-0.001(***)<br>(0.0006)|0.0001<br>(0.0008)|0.004<br>(0.003)|0.000||||

Notes: GMM estimates with first-differenced data. Time dummies were included but are here omitted. All variables are assumed exogenous except for lagged employment. Instruments are based on second and further lags of employment and output. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Wald Test of Joint Significance of the variables interacted with a given openness variable. (b) Wald Test of Joint Significance of openness variables interacted with a given variable. (c) Additional Instruments: second and further lags of price-cost-margins.
**Figure 4** : Cumulative middle-class bounds for LAC countries in 2017 PPP ordered by country GDP

<!-- Start of picture text --> 15 85<br>80<br>14<br>75<br>13<br>70<br>12 High income (H) 65<br>11 60<br>Upper middle income<br>(UM) 55<br>10<br>Lower middle 50<br>income (LM)<br>9<br>45<br>8 40<br>hnd bol slv ecu per pry col bra dom mex cri ury arg chl pan<br>Country<br>Vulnerability line Middle-class line (right axis)<br>Vulnerability line Middle-class line<br><!-- End of picture text --> Source: Own estimations based on SEDLAC (2022) Note: World Bank Analytical Classifications using data for 2017. Results were obtained using 92 synthetic panels built with available surveys from 2010 to 2019 and 25 bootstrap repetitions.

# Robustness check: Implementing other methodologies

For completeness, the analysis estimates the middle-class lines using previous methodologies. If the Lopez-Calva and Ortiz-Juarez (2014) methodology is applied only to Chile, Mexico, and Peru -as in the original study- or to Argentina, Colombia, and Costa Rica -as in Ferreira et al. (2013)-, the lower bound would be $12 and $16.1, respectively, and the upper bound $85.8 and $109.3, correspondingly. If this method is expanded to all countries over the same period, the lower bound would end up being $13.6 and the upper bound $89.1. Moreover, replicating the simple method applied, the lower and upper bounds would be $14.7 and $79.3, respectively.

# Identifying the drivers of change in the thresholds

While the update of PPPs from 2011 to 2017 contributed to the observed changes in the middle-class thresholds ($ 14 and $ 81) compared to the previous lines ($ 13 and $ 70, respectively), it is not the only factor. One way to initially assess the impact of updating purchasing power parity (PPP) rates is to calculate the ratio ( ) of the relative change in the consumer price index (CPI) between 2011 and 2017 to the relative change in PPP between those same years: δδ C �<sup>C</sup> C<sup>CC2017CC2017</sup> δδ=<sup>C</sup> CC2011 CC2011 15
# **Tables and Figures**

Table 1: Background information about energy subsidies in these 12 countries

||Cost<br>% GDP|Size of<br>Subsidy (%)|Subsidy per<br>capita (US$)|Internet access<br>(% pop)|Internet pop<br>18yrs+ (mil)|Main energy<br>subsidized|Other subsidized<br>energy sources|
|---|---|---|---|---|---|---|---|
|Angola|2.6|70|96|36.0|6.6|Fuel|Electricity|
|Argentina|2.7|41|338|85.5|29.6|Fuel|Electricity|
|Bangladesh|5.6|34|45|24.8|30.3|Electricity|Gas|
|Bolivia|4.6|28|106|59.9|4.9|Fuel|Electricity|
|Ecuador|1.7|15|85|70.7|9.1|Fuel|Electricity|
|Egypt|9.7|56|259|71.9|51.6|Electricity|Fuel|
|Ghana|1.0|10|17|58.0|11.7|Electricity|N/A|
|Indonesia|2.7|32|89|53.7|108.5|Fuel|Electricity|
|Kazakhstan|5.7|52|627|85.9|11.4|Fuel|Electricity, Gas|
|Nigeria|1.1|28|25|35.5|41.8|Fuel|Electricity|
|Pakistan|3.3|21|37|25.0|35.6|Electricity|Gas|
|Vietnam|2.3|11|46|70.3|52.5|Electricity|N/A|

Note: This table provides background information about energy subsidies in the 12 study countries. The size of subsidy is the weighted average discount provided across all types of energy due to subsidies. Sources: IEA 2023 (for cost of subsidy, subsidization rate, and subsidy per capita; World Bank 2023A (for internet access and internet population 18+); IMF 2023 (types of energy subsidized).
**<u>Table 3: Employment by Sector and Occupation</u>**

|Occupational Types|Agriculture|Industry|Services|Total|
|---|---|---|---|---|
|1. Formal Low –Skilled Workers|6.0|2.9|5.7|14.6|
|2. Formal Semi –Skilled Workers|6.2|8.7|16.5|31.3|
|3. Formal High- Skilled Workers|0.7|1.3|9.6|11.6|
|4. Informal Sector Workers*|2.7|2.5|13.9|19.2|
|5. Self-employed*|9.1|2.8|11.5|23.4|
|Total|24.6|18.2|57.2|100.0|

Notes: *Self-employed and informal labor are further distributed by skill (not shown). Source: Authors’ calculations from LFS 2000.

Self-employed and informal sector workers make up about 43 percent of the total employed labor force. A large proportion of informal labor (including domestic workers) and self-employed are working in the services sector, which is the biggest employer of the work force and also employs the largest share of the high-skilled workers.

**Table 4:   Formal Wage Employment by Economic Sector, 2000 (%)**

|Economic Sector|Working,VeryPoor*|Working,Poor**|Non-<br>Poor***|
|---|---|---|---|
|Agriculture|36.5|16.0|1.1|
|Mining|2.1|8.0|7.3|
|Manufacturing|11.0|18.0|18.8|
|Electricity, gas and water supply|0.5|1.0|1.6|
|Construction|5.6|6.0|2.8|
|Wholesale and retail trade|23.3|23.0|12.1|
|Transport|2.3|5.0|6.8|
|Financial Services|5.2|9.0|13.9|
|Community, Social and Personal||||
|Services|9.8|14.0|35.6|
|Private households|3.6|2.0|0.2|
|Overall(allsectors)|100|100|100|
|Notes:||||

*Working, very poor: annual wage for working very poor is calculated using R1000 per month (2004).Using the CPI for 2004 and 2000, the annual wage of working very poor comes to about R9,695. [1000/(123.8/100)]*12.

**Working, Poor: annual wage for working poor is calculated using R2500 per month (2004) benchmark   In 2000 prices, the annual wage for this group is about R24,233.[2500/(123.8/100)]*12.

***Non poor: Formal workers <u>are those with an annual income higher than R24,233.</u>

<u>Source: Authors’ calculations from LFS 2000.</u>

- 10 -
the impact of external shocks. Motivated by the policy debate about external shocks and debt sustainability (see World Bank, 2003), we first look at the effect of external shocks on government expenditure and the current account. We find that, in response to shocks, government expenditure tends to move in tandem with total GDP. A negative shock thus reduces both real activity and government expenditure. Interestingly, we find that in the case of natural disasters the decline of government expenditure is proportionally smaller than the decline in output. This suggests that governments try at least partially to compensate for the negative impact of these events. With respect to the current account, the evidence suggests that it moves procyclically in response to external shocks. Low-income countries tend to borrow when facing favorable external conditions and increase their foreign asset position during bad times. This behavior is similar to the procyclicality of the current account documented for emerging markets (see Aguiar and Gopinath, 2004), and goes against the conventional wisdom that poor countries get more indebted when facing adverse external shocks.

The second issue we explore is the response of aid flows to the different external shocks, which allow us to document, at least partially, the determinants of aid, and the relative importance of supply and demand factors. We find that aid flows increase significantly as a result of an increase in the GDP of high-income countries, but decline as a result of increases on the commodity prices faced by low-income countries and their output level. Interestingly, we do not find a significant effect of natural disasters on aid flows. Finally, we look at the impact of external shocks on different groups of low-income countries, separated by their degree of openness to trade, indebtedness, and institutional quality. We document a larger impact of commodity price and interest rate shocks on the output of low-income countries that are open to trade, which is consistent with the idea that these countries are more exposed to international variables. However, we also show that the impact of shocks, especially of commodity price shocks, is much shorter lived in open countries than in closed ones. It seems therefore that the higher exposure is compensated with a quicker adjustment to these types of shocks. We also find some support for the idea that highly indebted poor countries (HIPC) are more vulnerable to external shocks. The response of these countries to the world business cycle, commodity prices, and interest rate shocks is larger and more persistent than for the rest of low-income countries. Regarding the role of institutions, we document that better institutions seem to attenuate the impact of shocks to the world business cycle and commodity 4
|Table 2–Difference-in-Diffe|rences Esti<br>|mates for the<br>|Impact of th<br>|e Law of 1998<br>|on Extensiv<br>|e Margin of<br>|Labor Supply<br>|<br>||
|---|---|---|---|---|---|---|---|---|---|
||Labor Fo|rce Participatio|n Rates|Participation R|ates – Formal|Labor Force|Participation|Rates – Informa|l Labor Force|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|||||_Different_<br>|_cohorts and sa_<br>|_me ages_<br>||||
|Eligible*D99(_DD_)|-0.019**|-0.040***|0.0035|-0.0048|-0.0074|-0.0016|-0.014*|-0.032**|0.0051|
||(-2.40)|(-2.89)|(0.45)|(-1.42)|(-1.27)|(-0.47)|(-1.94)|(-2.55)|(0.73)|
|Eligible|-0.017***|-0.024***|-0.011**|-0.0091***|-0.016***|-0.0032|-0.0076*|-0.0089|-0.0076*|
||(-3.46)|(-2.87)|(-2.26)|(-4.41)|(-4.26)|(-1.58)|(-1.73)|(-1.14)|(-1.75)|
|D99(1998=0, 1999=1)|0.040***|0.068***|0.012**|0.0078***|0.011**|0.0046|0.032***|0.057***|0.0077|
||(6.53)|(6.25)|(2.05)|(2.59)|(2.10)|(1.58)|(5.88)|(5.81)|(1.45)|
|_Mean of the dependent variable_||||||||||
|_Comparison Group in 1998 _|_0.076 _|_0.112_|_0.040_|_0.021_|_0.032_|_0.010_|_0.055_|_0.08_|_0.030_|
|Observations|14641|7223|7418|14641|7223|7418|14641|7223|7418|
|AdjustedR2|0.035|0.026|0.008|0.010|0.013|0.001|0.030|0.024|0.005|
|||||_Different_|_ages and sam_|_e cohorts_||||
|Eligible*D99(_DD_)|-0.025***|-0.042***|-0.0067|-0.0039*|-0.0056|-0.0021|-0.020**|-0.035**|-0.0037|
||(-2.77)|(-2.66)|(-0.78)|(-1.86)|(-1.53)|(-0.97)|(-2.29)|(-2.33)|(-0.45)|
|Eligible|-0.0032|-0.0098|0.0021|-0.0010|-0.0022|0.0001|-0.0033|-0.0088|0.0011|
||(-0.63)|(-1.04)|(0.46)|(-1.37)|(-1.41)|(0.0001)|(-0.66)|(-0.95)|(0.25)|
|D99(1998=0, 1999=1)|0.036***|0.052***|0.020***|0.0050**|0.0067*|0.0032*|0.032***|0.047***|0.017***|
||(5.28)|(4.22)|(3.21)|(2.53)|(1.92)|(1.73)|(4.83)|(3.94)|(2.82)|
|_Mean of the dependent variable_||||||||||
|_Comparison Group in 1998 _|_0.031_|_0.053_|_0.010_|_0.002_|_0.004_|_0.001_|_0.028_|_0.048_|_0.01_|
|Observations|7354|3597|3757|7354|3597|3757|7354|3597|3757|
|AdjustedR2|0.027|0.024|0.005|0.003|0.005|0.000|0.026|0.024|0.004|

Note: Robust T-statistics in parentheses. *, **, *** Statistically significant at 10 percent 5 percent, and 1 percent respectively. The controls include age (in weeks), dummy variables for gender (male), ethnicity (white), head’s years of schooling, age, and gender (=1 if male), dummy for states and metropolitan area.

7
**Impact of Property Rights Reform to Support China’s Rural-Urban Integration: Household-Level Evidence from the Chengdu National Experiment**

# **1. Introduction**

Although the rapid economic growth observed in China over the last decade is the result of many factors, a land tenure system that strictly separates rural from urban land and that allows expropriation of rural land and its conversion to urban land in a way that provides large margins to local governments has significantly contributed to the associated expansion of industrial activity. At the same time, this dualism between rural and urban land and the ability of local governments to generate enormous amounts of revenue by expropriating land cheaply and selling it to developers at prices that are orders of magnitude higher than what is being paid in compensation has also contributed to income inequality. Moreover, it led to inefficient land use, large ‘land banks’ by local governments, rural unrest, and – given China’s limited agricultural land endowment – concerns about negative impacts on food security. The latter may arise either directly – by irreversibly converting land from agricultural to non-agricultural uses – or indirectly by reducing investment and efficiency-enhancing land transfers by farmers who fear expropriation with limited compensation. Most experts agree that this situation is not sustainable and that, to change it, simultaneous action in a number of areas is needed (World Bank and DRC, 2014).

Cognizant of this challenge, a number of national experiments were conducted to explore the scope for alternative and potentially more sustainable arrangements. In this context, the city of Chengdu in Sichuan Province was selected by the central government as a “rural-urban integration reform experiment zone” in 2008. Efforts undertaken in the context of the experiment are of interest as they involved systematic verification of all types of land assets, relaxation of the restrictions imposed by the _hukou_ system of urban residency permits, and measures to improve land market functioning. They were expected to enhance tenure security and reduce transaction costs in land and labor markets so as to encourage land-related investment, enhance allocative efficiency, create jobs, and improve overall economic performance. But there was also concern that far-reaching reforms in this area would be costly to implement, and give rise to disputes or socially undesirable land transfers and migration.

2
# _Appendix Table A.5.Evolution of Industrial Employment by the VSIC Sub-sectors (1000s)_

||2000|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|
|Apparel, textiles (17, 18)|355|394|509|602|666|700|789|901|
|Footwear, leather products (19)|297|332|397|472|518|551|582|615|
|Furniture, wood, manufact. n.e.s. (20, 36, 37)|129|157|203|257|329|376|435|483|
|Food products (15, 16)|280|311|358|392|425|442|454|478|
|Electronics, machinery, transport equipment (29-35)|153|183|221|253|287|319|370|440|
|Metal, non-metallic mineral products (26, 28)|179|206|248|287|332|350|378|418|
|Basic manufacturing (21, 22, 23, 27, 40, 41)|161|175|195|211|245|250|297|316|
|Chemical, rubber, plastic products (24, 25)|117|127|153|170|195|204|227|253|
|Mining (10, 11, 13, 14)|153|129|155|163|166|178|180|186|
|Total|1823|2014|2441|2807|3162|3370|3711|4091|

_Source_ : the _Enterprise Survey_ data 2000-2007 (GSO).

# _Appendix Table A.6. Evolution of Industrial Employment by Regions (1,000s)_

||2000|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|
|HCMC|485|547|672|742|836|869|879|918|
|Southeast<sup>a</sup>|300|359|478|586|702|782|916|1041|
|Hanoi|163|178|210|235|248|267|275|291|
|Red River Delta<sup>b</sup>|248|255|305|381|427|466|545|637|
|Northeast|171|173|200|214|238|244|265|298|
|Northwest|7|8|10|14|14|15|17|19|
|North Central Coast|58|66|75|85|91|93|103|114|
|South Central Coast|122|137|160|183|207|215|234|253|
|Central Highlands|26|31|35|40|43|46|47|51|
|Mekong River Delta|127|139|170|199|224|249|290|332|
|Others|115|122|126|128|131|125|140|138|
|Total|1823|2014|2441|2807|3162|3370|3711|4091|

_Source_ : the _Enterprise Survey_ data 2000-2007 (GSO). _Notes_ :<sup>a</sup> The Southeast region excludes HCMC. bThe Red River Delta region excludes Hanoi.

50
turn, comparisons of implicit exchange rates of currencies based on PPP lead to estimates of relative changes in price levels of given countries.

An important merit of the PPP technique is that it enables one to estimate the differences in price levels between countries. This is particularly important for countries in which the exchange rate does not provide meaningful information about the true scarcity of foreign exchange, a problem that was particularly serious in Czechoslovakia prior to the introduction of economic reform in 1990/91. On the other hand, the technique has a number of well-known shortcomings and limitations. The most serious are: (1) product coverage is incomplete and the analysis is derived from samples; (2) the information is subject to typical problems of sampling; (3) goods and services are not homogenous among these countries, and the estimators must take into account differences in quality; (4) consumer preferences may differ among countries and over time, which leads to the usual index-number problems; _(5)_ the choice of countries to be compared may be biased.

Given these shortcomings, the interpretations of PPP estimated are not straightforward. Nevertheless, the comparison of estimates is often interpreted in the literature on CPEs as an indicator of "hidden" price increases. We can report here a study of Havlik [1985], who compared purchasing power parity in Austria and Czechoslovakia. His conclusion was that purchasing power parity in 1980 amounted to 1 AS = 0.52 Kcs. A similar study performed by the Economic Institute of the Academy of Sciences in Prague in 1967 found PPP 0.70 in 1964. Comparing both PPP indices and using CPI in Austria as the base, Havlik obtained an increase of price level in Czechoslovakia during 1964-1980 amounting to 1.69.3 This means that the average annual inflation in the given period was 3.3 percent, whereas the Czechoslovak authorities admitted only 1.4 percent. Thus, Havlik states, hidden inflation was nearly 2 percent per annum.

A similar technique was used by Nachtigal [1990], who compared nominal and average r=tail prices.<sup>6</sup> The aim of his study was to separate the structural effect of forced substitution, which was assumed to push up the average prices. He estimated that this effect brought about 0.2-0.5 percentage point of "true" inflation. This figure is obviously much less than Havlik suggested.

Reservations to both Havlik's and Nachtigal's studies are quite obvious. Among the problems noted above, the issue of quality adjustments seems particularly serious. Both 2'CPICs = CPIA PPP8J/PPP6 = 2.27 * 0.52/0.72 = 1.69 2 The nominal price is the actual price in the market; the average price is obtained by dividing nominal retail sales for a given commodity by the physical amount of sold goods. The information about physical amounts was available only for some commodities so that this indicator cannot cover the whole consumer market.

17