rafmacalaba/datause-encoder-data huggingface dataset:
|Table G|4. Regulatory Bur|den||
|---|---|---|---|
|Code|Table<br>|Row|Concepts measured|
||(Appendix 1)|||
|Represe|ntative sources|||
|HFWSJ|A 1.9|9|Regulation (regulations that impose a burden on business)<br>|
|||3|Govemment interventon in economy|
|||7|WagePrice control|
|||5|Trade policy (tariff and no-tariff barriers to trade)|
||||Capital flows and foreign investment (financial regulations<br>for foreigners)|
|||6|Banking (free from govemment intervention, financial|
||||regulations domestic)|
|DRI|A 1.3|13|Export regulations|
|||14|Import regulations|
|||15|Other regulations (regulatory burden)|
|||10|Legal restrictions on ownership of business by non-residents|
|||11|Legal restrictions on ownership of equity by non-residents|
|WDR|A 1.13|9|Regulations of starting new business as an obstacle to<br>business development|
|||10|Price controls as an obstacle to business development|
|||11|Regulations on foreign trade as obstacle for business development|
|||12|Foreign currency regulations as obstacle for business development|
|||13|General uncertainty about the of costs of regulations as an<br>obstacle to business development|
|Non-rep|resentative sourc|es||
|EBRD8|<br>A 1.4|4|Price liberalization|
|||5|Trade regulations<br>|
||||Competition policy|
|||8|Banking: Extensiveness of legal rules|
|||9|Banking: Effectiveness of legal regulations|
|||8|Securities: Extensiveness of legal rules|
|||9|Securibes: Effectiveness of legal regulations|
|GCS98|A 1.8|10|Protection of domestic banks from foreign competition|
|||11|Barriers to entry in banking sector|
|||12|Interest rates are heavily regulated|
|||1|Participation of private sector in infrastructure projects|
|||15|<br>Extent of market competition|
|||16|Effectiveness of anti-trust policies|
|||I|Negative impact of tariffs on costs and availability of equipment|
||||and materials|
|||2|Negative impact of hidden barriers to trade|
|||7|The tax system hinders business competitiveness|

56
For each alternative scenario, the model generates results concerning the impact on real GDP, output, trade, value added, real wages, as well as the rental rate of capital and land. Our scenarios should be interpreted as controlled experiments rather than as forecasts of performance that might occur with each option. The actual growth pattern will be the result of many more factors than just trade policy, especially macroeconomic policies. Both the comparative static and dynamic-externality experiments are meant to describe the impact of different patterns of trade liberalization in the medium to long run. Use of the term "dynamic" here does not imply the actual path of the transition, but rather the net cumulative effect over time of positive productivity externalities that could potentially result from regional integration.

# **The Uruguay Round and Beyond**

The Uruguay Round conmnitments require action on a number of different policy fronts: (1) conversion of many existing NTBs into tariffs and a commitment to real tariff reductions (for some countries) or a promise to "bind" tariffs below certain levels; (2) reduction in export and production subsidies (primarily in the OECD economies); and (3) dismantling the Multifibre Agreement over the coming decade, thereby eliminating the massive distortions that have characterized these industries.

The importance of the tariff and NTB reductions varies substantially by country. Using data obtained from the GATT, we can calculate the change in tariff rates to which each country is committed.<sup>2</sup> ' Table 6 shows the average sectoral tariffs for each region before and after the Uruguay Round, along with figures on the percent reduction in tariffs that occurs from the UR commitmnents. Tariff reductions for developing countries range from a low of 4 percent in Indonesia (where modest sectoral tariff cuts are concentrated in sectors with few imports), to 8 percent for the Philippines, and 22-23 percent for Thailand and Singapore & Malaysia. The cuts for the OECD countries range from 26 percent in the EU to 40 percent in Japan.

As part of this experiment, we also assume that the non-agricultural NTBs in the OECD economies decline by half as part of the Uruguay Round, resulting in a drop in the average NTB to 7.9 percent in Japan, 2.3 percent in the US, and 0.9 percent in the EU. Agricultural production subsidies are reduced by 20 percent in OECD economies and 13 percent in developing economies, while agricultural export subsidies are reduced by 36 percent in the US and EU (see Table 5). And finally, the dismantling of the MFA is reflected in the elimination of export taxes on textiles and apparel in the major developing country exporters--Indonesia. Thailand, Philippines, Singapore & Malaysia, China, and Korea & Taiwan.

> **21** Because no information on the Uruguay Round commitments by China was available, we have assumed that sectoral tariffs for China will be reduced by the average sectoral reduction undertaken by Indonesia and the Phillipines.

19
**Table 4.3a** **<u>(continued): Non-response bias in HFPS</u>**

||||**Nigeria**|||||**Uganda**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**Sample**|**Co**|**ntacted**|**Int**|**erviewed**|**Sample**|**Co**|**ntacted**|**In**|**terviewed**|
||_Mean_|_Mean_|_Bias due to_<br>_non-contact†_<br>_(12) - (11)_|_Mean_|_Bias due to non-_<br>_interview†_<br>_(14) - (12)_|_Mean_|_Mean_|_Bias due to_<br>_non-contact†_<br>_(17) - (16)_|_Mean_|_Bias due to non-_<br>_interview†_<br>_(19) - (17)_|
|**Characteristic**|(11)|(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|
|**Sample size(number of households)**|_3,000_|_2,057_||_1,950_||_2,421_|_2,274_||_2,259_||
|Rural|0.687|0.629|-0.058|0.626|-0.003|0.659|0.661|0.002|0.662|0.002|
|Average household size|5.530|5.731|0.201|5.749|0.018|4.613|4.627|0.014|4.647|0.020|
|Dependencyratio|1.028|1.023|-0.005|1.023|0.000|1.062|1.047|-0.015|1.046|-0.001|
|Female head(%)|0.186|0.180|-0.006|0.178|-0.002|0.332|0.325|-0.006|0.324|-0.002|
|Age of head|48.646|48.830|0.184|48.706|-0.124|45.133|45.314|0.182|45.334|0.019|
|**_Religion of head_**|||||||||||
|Orthodox|||||||||||
|Muslim|0.425|0.407|-0.018|0.414|0.007||||||
|Christian|0.556|0.576|0.020|0.570|-0.007||||||
|**_Marital status of head_**|||||||||||
|Married|0.749|0.765|0.016|0.766|0.001|0.738|0.739|0.002|0.743|0.004|
|Formerlymarried|0.192|0.177|-0.015|0.174|-0.003|0.227|0.223|-0.004|0.220|-0.003|
|Never married|0.058|0.058|0.000|0.060|0.002|0.036|0.038|0.002|0.037|-0.001|
|**_Education level of head_**|||||||||||
|Head Literate|0.748|0.801|0.052***|0.803|0.002|0.809|0.819|0.01***|0.821|0.002|
|None|0.364|0.295|-0.069**|0.291|-0.003|0.076|0.076|0.000|0.077|0.001|
|Primary|0.253|0.260|0.006|0.260|0.001|0.555|0.541|-0.013*|0.540|-0.001|
|Secondary|0.231|0.256|0.025|0.256|0.000|0.307|0.313|0.006|0.315|0.002|
|Tertiary|0.152|0.190|0.038*|0.192|0.003|0.132|0.139|0.007|0.138|-0.001|
|**_Working status of head_**|||||||||||
|Any|0.830|0.838|0.009|0.840|0.001|0.865|0.866|0.002|0.869|0.003|
|Wage|0.186|0.213|0.027|0.217|0.004|0.326|0.330|0.004|0.330|0.000|
|Agriculture|0.430|0.376|-0.053*|0.375|-0.002|0.474|0.466|-0.008|0.470|0.005|
|Nonfarm enterprise|0.375|0.413|0.039|0.412|-0.001|0.288|0.295|0.007|0.296|0.001|
|Ganyu|||||||||||

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

23
the middle of 1980s, and reached its lowest level in 1990-1995; during this period migrations into cities increased significantly thanks to urban market reforms initiated in the middle 1980s and flexibility of urban _hukou_ system (Yang and Zhou 1999). During the period of 1995-1996, the migration into towns started to pick up, reaching 19 percent. This recent increase in the flow of migrants into towns probably has been due to the more favorable migration policy toward small cities and designated towns residents by granting them urban _hukou_ , and the increase in FDI in many small towns, especially in the coastal areas.

Table 1 Percentage of flow of in-migration to cities, towns and rural townships.

|place of destination|1982-87|1985-90|1990-95|1995-2000|
|---|---|---|---|---|
|____________________|_____________|____________<br>(%)|____________|_______________|
|city|36.6|61.7|61.4|59.4|
|town|39.8|20.1|10.0|19.16|
|rural|23.6|18.2|28.6|21.43|

Source: Data for the period of 1982-87, 1985-90, and 1990-95 are from Yan (1998), data for the period of 1995-2000 are derived from the tabulation of 2000 population census data (the long form of 9.5% of census sample data).

# **3.  Economic opportunities in towns in three provinces**

Before we elaborate the town in-migration in these three provinces, we present the population composition and employment opportunities in towns in these three provinces. Towns are well developed in Zhejiang, and less so in Henan and Sichuan. According to census data of 2000 (Table 2), there were 20.15 percent of population living in towns in Zhejiang province; in contrast, there was only 8.31 percent of population living in towns in Henan, and 12.26 percent in Sichuan. Zhejiang is also more urbanized than other two provinces. There was 28.5% of population living in cities whereas there was only 15% of population living in cities in Henan and Sichuan respectively, even lower than national average; majority of population, three quarters, lived in rural areas in Henan and Sichuan.

Table 2 City, town, and county <u>population in three provinces in China (in million): 2000</u>

|Province|total|city|town|county|
|---|---|---|---|---|
||(pe<br>|rcentage in pa<br>|renthesis)<br>||
|Zhejiang|45.93|13.10|9.25|23.57|
|||(28.53)|(20.15)|(51.33)|
|Henan|91.24|13.80|7.58|69.851|
|||(15.13)|(8.31)|(76.56)|
|Sichuan|82.35|12.21|10.10|60.04|
|||(14.83)|(12.26)|(72.91)|
|National|1,242.61|292.63|166.14|783.84|
|||(23.55)|(13.37)|(63.08)|

________________________________________________________________________ Source: NBS “Tabulation on the 2000 population census of the People’s Republic of China. Table 1-1, 1- 1a, 1-1b, 1-1c.

4
9 sequentially, up to 10 percent of the sample size'<sup>5</sup> . The human capital coefficient remained negative for all of these trial sub-samples.

The results are also robust to variations in data or estirnation technique. Estimates using World Bank constant price GDP growth rates instead of the PWT5 GDP data are roughly the same. Using growth of GDP per person or per work-aged labor force produces an even larger negative estirnate for education. Using either of the two physical capital stock series in conjunction with either human capital series gives similar results. Relaxing the assunption of constant returns to scale does not alter the negative estimate on human capital'<sup>6</sup> . Using weighted least squares using either (log of) population, GDP per capita, or total GDP as the weights also gives nearly identical results.

As surprising as these results are, they are actually quite close to what other researchers have found when they have examined the education/growth relationship using data on changes in the stock of education. Benhabib and Spiegel (1994) and Spiegel (1994) use a standard growth accounting framework (extended to include initial per capita income) and find the growth of years of schooling enters negatively (although it is statistically insignificant)". Spiegel (1994) shows the finding of a negative effect of educational growth is robust to the choice of sample and to the inclusion of a wide variety of ancillary variables (i.e. dummies for SSA and Latin America, size of the middle class, political instability, share of 15 Observations are identified asinfluential based on the difference in the estimates with and withou **t** he observation included, that is; _Influence,_ = L **_IPk_** - Pk I where (1) indicates the estinates with the ij observation dropped.

16 Others have tried relaxing the Cobb-Dougl **as** sumptions about substitution between the various factors but without much success (Judson, 1993).

17 The Benhabib and Spiegel (1994) and Spiegel (1994) papers use a coMpletely different set of estimates of humnan capital created by Kyriacou (1991), adding credence that the present results are not an artifact of the particular estimates of schooling.
Table 10: Garment Industry and Domestic Violence

|Dependent variable = 1(Ever|Been Bea|ten)|
|---|---|---|
||(1)|(2)|
|Work|0.094*|0.200|
||[0.055]|[0.265]|
|Work X Garment Sector|-0.095|-0.025|
||[0.061]|[0.056]|
|Age||-0.003|
|||[0.006]|
|Work X Age||0.000|
|||[0.002]|
|Age First Marriage||-0.014**<br>[0.006]|
|Work X Age First Marriage||-0.022|
|||[0.013]|
|Education||0.009**|
|||[0.004]|
|Work X Education||-0.009|
|||[0.009]|
|Observations|1,324|1,323|

_Standard errors in brackets, clustered at the level of the village. *** p<0.01, ** p<0.05, * p<0.1._
may take the finding from Milanovic, Lindert and Williamson (2011). The authors find particularly high IERs to have obtained in colonies: inequality there was pushed almost to its maximum, with IERs approaching 100%.<sup>2</sup> Figure 2 shows the actual Ginis in pre-industrial societies calculated from the social tables drawn against the Inequality Possibility Frontier. The data include new results for the United States in 1774 and 1860 and Russia in 1904 that had become available after the publication of the MLW paper.<sup>3</sup> It is easy to notice that the dots representing most of the colonies in the sample (Moghul India 1750 and British India 1938, Nueva España 1790, Maghreb 1880, and Kenya 1912 and 1927) lie around or slightly above the frontier.<sup>4</sup> <!-- Start of picture text --> Figure 2. Estimated Gini coefficients and the Inequality Possibility Frontier (pre-industrial economies)<br>80<br><!-- End of picture text --> <!-- Start of picture text --> IPF<br>70<br>Nueva España 1790 Chile 1861<br>Holland 1732<br>60<br>Maghreb 1880 Holl and 1561<br>India 1938<br>Old Castille 1752 France 1788 Netherlands. 1808 USA1860<br>50 India 1750<br>Siam 1929 USA 1774 England 1801<br>Kenya 1927 Brazil 1872<br>Peru 1876 Florence 1427 Engl1759<br>Engl 1688<br>40 Byzant 1000<br>Java 1880<br>Rome 14 Levant 1596 Russia 1904<br>Engl 1290<br>30 Kenya 1914 Bihar 1807 Java 1924Japan 1886<br>Naples 1811<br>China 1880<br>20<br>Serbia 1455<br>10<br>0<br>0 300 600 900 1200 1500 1800 2100 2400<br>GDI per capita (in 1990 $PPP)<br>Gini index<br><!-- End of picture text --> Source: Updated from MLW (2011).

2 The social minimum was assumed to be 300 international 1990 dollars (these are the same dollars that underlie Maddison’s tables). This amount is somewhat lower than the World Bank absolute poverty line (see discussion in MLW, 2011, p. 262, fn. 16). In the empirical work, we find that there are people with incomes even lower than the World Bank absolute poverty line, but their distance from the poverty line is typically low. Moreover, if one thought, based on this evidence, that 300 international dollars is greater than what is needed for subsistence, all relations given here can be recalculated with an even lower _s_ . The key point is that the subsistence minimum must be greater than 0.

> 3 US estimates were published by Lindert and Williamson (2011), Russia estimates by Nafziger and Lindert (2012).

> 4 The observations for two countries with at least three data points (Holland/Netherland and England) are linked by the dashed lines.

5
potential correlation of residuals across local markets with similar industry composition by calculating shift-share errors (Adao et al. (2019)).

For the plant level analysis, the main challenge is that we do not observe robots’ adoption at the plant level. Instead, we follow Autor et al. (2003) and assume that industrial robots are best suited to perform manual routine tasks. Since we observe the educational attainments of employment at the plant level, we calculate the typical level of education of routine task-intensive occupations based on the Indonesian labor force survey. This turns out to be either Junior or Senior High School Diploma (“secondary education” hereafter).<sup>4</sup> We measure plant level exposure to robots by interacting yearly imports of robots by 2-digit industry to plants’ share of secondary education workers in the base year. Under the assumption that within robot-adopting industries, plants with larger shares of secondary education have greater opportunities for automation, our exposure measure can be used to estimate the impact of robots by comparing the outcomes of firms with large and small shares of employment with secondary education.

We perform a battery of tests to assess the validity of our exposure measure and we show that it is strongly correlated with plant level investments in machinery and equipment. Our empirical methodology addresses concerns related to unobserved firm heterogeneity and time-varying industry characteristics that may be related with robot imports and firm outcomes. We also provide robustness tests instrumenting Indonesia’s robot imports with OECD imports, an approach similar to Acemoglu and Restrepo (2017), Dauth et al. (2017), Faber (2018) and Giuntella and Wang (2019).

Our paper is related to the growing literature on the impact of industrial automation on advanced economies at the industry and local labor market-level (Graetz and Michaels (2018); Acemoglu and Restrepo (2017); Dauth et al. (2017); Giuntella and Wang (2019); Artuc et al. (2019); Kugler et al. (2020)), and at the firm-level (Acemoglu et al. (2020); Koch et al. (2019); Stapleton and Webb (2020)). These contributions suggest that differently to Indonesia, robots have an overall negative impact on labor demand in high-income et al. (2017), among others.

> 4Our definitions of routine task-intensive occupations follow Graetz and Michaels (2018) and Frey and Osborne (2017). The details are presented in Section 2.1.

4
68 _Table 5.4: Zimbabwe: Change in Demandfor Electricity, 1 982-84 and Implied Arc Elasticities_

||Change in demand (mil. kWh)|Price clasLicitics*|
|---|---|---|
|Agriculturc|-81 (12.03%)|0.13|
|Mining|-58.8 (4.42%)|0.05|
|Manufacturing|-94.4 (2.65%)|0.03|
|Domcstic consumers|-62 (6.28%)|0.07|

Arc clnasticitics are **calculaled an** the **basis** ofthe price changes dunng 1983-4.

The low arc elasticities implied by these price increases indicate the inflexible nature of electricity demand in Zimbabwe. For thc industrial sector, the fall in demand was mainly due to the output effect of the price increase as interfuel substitution in the industiy is limited. The inelastic nature of electricity demand also indicates the revenue-raising potential of the electricity sector. However, to what extent this potential is actually realized depends on the govemment's ability to extract the increase in revenues from the state-owned electricity sector.

_Electricity Price Increases 1983-84: The Household Sector_ The structure of power supply at the time of the revisions in electricity prices during 1983-84 catered mostly to urban households and rural electrification was low. Given the disparities in income distribution in Zimbabwe, an important policy issue was the impact of electricity price changes on the welfare of the poor. Table 5.5 gives an estinate of the welfare impact of electricity price changes for a lowincome urban household.

_Table 5.5: Zimbabwe: Urban Low-Income *Household Electricity Consumnpion, Expenditure Shares, Elasticities and Estimated Welfare Effects_

||1982-83|1983-84|
|---|---|---|
|Expenditure share|0.04|0.05|
|Price elasticity|0.08|0.08|
|<br>Consumption(kWh)|177.78|177.73|
|Welfare effect*|-1.42%|-2.2%|

* Houscholds with incomc equal to ZS 100, i.c., thc median incomc of quintlc I and 2. **L- Ls** in consumer **surplus cpressed as** a **prctagc** of **total income.**
6 considering the asset replacement cost, such as is considered, for example, in current costs accounting (CCA).

The measure of the long-term avoidable cost obtained from the cost information submitted by the universal service provider will give the costs incurred by that operator. However, it is not necessarily the case that this measure represents an efficient degree of avoidable costs. In the event that a finance mechanism is determined, the other operators should not have to pay for the universal service provider's inefficiency. Thus, an efficiency adjustment must be applied to the long-term avoidable costs incurred so as to obtain an estimate of the efficient level of costs.<sup>5</sup> The financial approach uses the proper definition of costs, and introduces some interesting concepts into the analysis of the universal service burden (like the indirect financial benefits of being a universal service provider). It seems, though, that financial models share some of the undesirable properties of historical embedded cost (HEC) models (accounting auditing approach). Both types of models rely on firm-reported cost data; the major difference is that the financial model uses a current cost accounting methodology rather than the HEC standard.<sup>6</sup> Engineering-economic models have been developed in recent years as an alternative to the traditional econometric and accounting approaches to cost assessment. Engineering models offer a more detailed view of cost structures than is possible using econometric data. The engineering models (also known as _cost proxy models)_ could enable the regulator to estimate the forward-looking economic cost of the service without having to rely on detailed cost studies that otherwise would be necessary. Proxy models can be useful for many regulatory purposes, such as determining levels of universal service support in high cost areas and the pricing of unbundled network elements (e.g. interconnection). An economic cost proxy model begins with an engineering model of the physical local exchange network, and then makes a detailed set of assumptions about

> ' For the case of BT (British Telecom), the scope of the efficiency adjustment was taken from the analysis and the assumptions used by Oftel in the tariff revision for BT.

> **6** Financial models and engineering-economic models should not be seen as rival approaches, but rather as complementary methodologies. For example, the avoidable costs mentioned above could be estimated through a cost proxy model.
|**Tabl**|**To**<br>**e 2. Auditor ch**|**tal**<br>**aracteristics**|**Peru**|**Mexico**|**Ghana**|
|---|---|---|---|---|---|
||Mean|St. dev||Mean||
|Male (1 = Yes)|0.48|0.50|0.48|0.41|0.53|
|Age|31.91|8.54|31.57|32.50|31.76|
|Single / Divorced (1 = Yes)|0.56|0.50|0.74|0.36|0.56|
|Number of adults in household|2.99|1.54|2.96|3.00|3.00|
|Completed a university degree (1 = Yes)|0.44|0.50|0.48|0.32|0.50|
|Has an active loan (1 = Yes)|0.42|0.50|0.57|0.41|0.32|
|Has a savings account (1 = Yes)|0.73|0.44|0.61|0.50|0.97|
|Owns business / self-employed (1 = Yes)|0.39|0.49|0.30|0.32|0.50|
|Household monthly income (USD)|702.94|425.37|984.33|610.09|572.67|
|Number of auditors<br>This table reports summary statistics for audito<br>and credit products. Four savings auditors in M<br>survey firm that trained credit auditors.|r characteristics<br>exico were prof<br>7|. One auditor i<br>essionals from<br>9|23<br>n Peru comple<br>low-income h|22<br>ted visits for <br>ouseholds em|34<br> both savings<br>ployed by the|