rafmacalaba/datause-encoder-data huggingface dataset:
|FDI|||
|---|---|---|
|Employmentgeneration|||
|Government revenue|||
|Exportgrowth|||
|Skills upgrading|||
|Testingfield for wider economic reform|||
|Technologytransfer|||
|Demonstration effect|||
|Export diversification|||
|Enhancingtrade efficiencyof domestic firms|||

Source: World Bank staff.

The term _SEZ_ covers a broad range of zones, such as free trade zones, exportprocessing zones, industrial parks, free ports, enterprise zones, and others. As used in China, however, the term _SEZ_ refers to a complex of related economic activities and services rather than to a uni-functional entity (Wong 1987). As a result, Chinese SEZs are more functionally diverse and cover much larger land areas than other types of economic zones. In China, _SEZ_ normally refers to seven specific zones: Shenzhen, Zhuhai, Shantou, Xiamen, Hainan, Shanghai Pudong New Area, and Tianjin Binhai New Area, which will be discussed later. In this book, however, the term is used in a broad sense; that is, it refers not only to the seven special economic zones (hereafter referred to as comprehensive SEZs) but also to China’s economic and technological development zones (ETDZs), free trade zones (FTZs), exportprocessing zones (EPZs), high-tech industrial development zones (HIDZs), and the like.

# Industrial Clusters

An industrial cluster is generally defined as a geographic concentration of interconnected firms in a particular field with links to related institutions. Often included in this category are financial providers, educational institutions, and various levels of government. These entities are linked by externalities and complementarities of different types and are usually located near each other (World Bank 2009). Increasingly, both developed and developing countries use cluster initiatives to promote economic development, a concept supported by the development community at large. Popularized through such works as _The Competitive Advantage of Nations_ (Porter 1990, 1998) and others (Schmitz 1992, for example), clusters have been viewed as a mechanism for enabling firms to join their efforts and resources and work with government toward greater regional, national, and international competitiveness (World Bank 2010). Do clusters foster innovation? Nadvi’s collective efficiency model (1999) highlights four key variables that determine competitiveness in enterprise clusters: market access, labor-market pooling 5
# **References**

- Aker, J. C. 2011. “Dial ‘A’ for Agriculture: A Review of Information and Communication Technologies for Agricultural Extension in Developing Countries.” _Agricultural Economics_ 42 (6): 631–47.

- Aker, J. C. and I. M. Mbiti. 2010. “Mobile Phones and Economic Development in Africa.” _Journal of Economic Perspectives,_ 24 (3): 207–32.

- Alibhai, A.S., N. Buehren, R. D. Coleman, M. P. Goldstein and F. Strobbe. 2018. _Disruptive Finance: Using Psychometrics to Overcome Collateral Constraints in Ethiopia_ . Washington, DC: World Bank Group.

- Amatulli, G., S. Domisch, M. Tuanmu, B. Parmentier, A. Ranipeta, J. Malczyk and W. Jetz, (2018). “A suite of global, cross-scale topographic variables for environmental and biodiversity modeling”. _Scientific Data_ .

- Angrist, J. D. and J. Pischke. 2009. _Mostly Harmless Econometrics: An Empiricist’s Companion_ , Princeton: Princeton University Press.

- Aron, J. 2018. ‘Mobile Money and the Economy: A Review of the Evidence’, _The World Bank Research Observer_ , 33 (2): pp. 135–188

- Bertschek, I., W. Briglauer, K. Hüschelrath  et al. 2016. The Economic Impacts of Broadband Internet: A Survey. _Review of Network Economics_ , 14(4), pp. 201-227.

- Beuermann, D.W., C. McKelvey and R. Vakis. 2012. Mobile Phones and Economic Development in Rural Peru, _The Journal of Development Studies_ , 48:11, 1617-1628.

- Blumenstock, J., Keleher, N., Rezaee, A. and E. Troland. 2020. The Impact of Mobile Phones: Experimental Evidence from the Random Assignment of New Cell Towers. Unpublished manuscript.

- Chun, N. and H. Tang. 2018. Do Information and Communication Technologies Empower Female Workers? Firm-Level Evidence from Viet Nam (May). ADBI Working Paper 545.

- Debo, L. and G. Van Ryzin. 2013. “Leveraging Quality Information in Stock-Outs.” Research Paper No. 13–58, University of Chicago Booth School of Business, Chicago, IL.

- De los Rios, C. 2010. “Welfare Impact of Internet Use on Peruvian Households”. Lima: Instituto de Estudios Peruanos.

- Fernandes, A. M., A. Mattoo, H. Nguyen and M. Schiffbauer. 2019. “The internet and Chinese exports in the pre-ali baba era.” _Journal of Development Economics_ 138: 57-76.

- Goyal, A. 2020. “Information, direct access to farmers, and rural market performance in central India.” American Economic Journal: Applied Economics, 2(3): 22-45.

15
increases annual agric'i. tiral output, holding the agricJltural inputs constant, by approximately ten percent'. While the magnitudes of Lhese effects are not exceptional!y large, they can be economically significant because of the durability of educational capital which can be as high as fifty years.

There have also been numerous studies in which the economic benefit of education is measured in terms of its effect on the lifetime earnings of individual workers. The implied rate of return to the education is then estimated. This is the so-called 'Human Capital' approach, pioneered by Schultz (1961). PsacharopoLulos (1985) has summarized the many rate of return to education studies around the world. He found that the estimated rates of return to education arr .ypically above, and sometimes considerably above, ten percent per annum in real terms, which is generally considered a very respectable rate of return. The rates of return are higher for lower levels of education, that is, they are the highest for primary education, followed, in descending order, by respectively secondary and higher education. They are also higher in countries where educated manpower is more scarce.

What are some of the channels through which education affects economic growth, productivity and development in general? First, education enhances the ability of an individual to perform standard taskc and to learn to perform new tasks. Second, education enhances the ability of an individual to receive and process new information. Third, education enhances the ability of individuals to communicate and therefore to coordinate activities with one another. Fourth, education enhances the ability of an individual to evaluate and adjust to changed circumstances. Fifth, education helps to reduce subjective uncertainty and unnecessary anxiety as well as fatalistic acceptance of the status quo and thereby enhances the probability of adoption of new technologies or practices by an individual. Finally, at higher levels, education also helps to bring about innovations in the production technology.

Moreover, there is evidence from the experience of many countries that education, by enabling the acquisition of the necessary skills by the workers, is in fact a complementary input to physical capital 'See Lau and Yotopoulos (1989).

2
- 72 -

# Table Alt Rsgl Codiciers f Eing Empboyew **Mpliebs**

||AGW|AR|AP|AY|R2|Employment<br>Multiplier|
|---|---|---|---|---|---|---|
|District Data|||||||
|Rural Areas|.024<br>(3.3)|.0023<br>(3.7)|-1.32.4<br>(1.11)|-.0018<br>(2.5)|.02|.022|
|Rural Towns|.015<br>(2.9)|-.0002<br>(.6)|6.65,r<br>(.6)|-.0006<br>(.5)|-.02|.012|
|Rural Areas Plus<br>Rural Towns|-.035<br>(3.7)|.0025<br>(3.3)|-2.04.4<br>(1.2)|-.0017<br>(1.7)|-.02|.037|
||AGY|AR|AP|AY|R2|Employment<br>Multipliere'|
|State Data b/|||||||
|ruralareas|6.2.5<br>(4.3)|6.8.'7<br>(3.7)|-2.4." '<br>(.6)|-1.2.4<br>(.4)|.81|.021|
|Rural towns|3.8.5<br>(4.6)|9.9.4<br>(.9)|2.5.-1<br>(1.0)|-3.1.6"<br>(1.9)|.70|.016|
|Rural Areas Plus<br>Rural Towns|1.0.-4|7.8.'|-4.9e1|-1.4.4|.82|.037|
||(5.1)|(3.1)|(.8)|(3.7)|||

a/ _AGW_ = total agricultural wage eamings in each ditrict. AR = AGWYroad density. AP - AGW*popuation density. AY * AGW*daly **agricultural** wage rate (used as an altenative to agricultr wcome per agricuttural population). Left side - Rural nonfarm wage amirngs etimated as rural ronfarm labor*daily agricultural wage rate. All equations wer estimated uing ordnary least squares, with the ira-Houthaker adjustment for heteroskedasticity. There were 83 observations.

_bl_ AGY **=** totl **agricl** wage sarnins in ch state. AR **-** AGY*road denity. AP **-** AGY*population density. AY **=** AGY*daily agricural wage rat, (used as an alternatiw to agricultural income per agricural population). Left **side** Rural nonfarm iicomG Al equations wr estimated using ordiway lst squares. Heteroekedasticity was not a problem in th state data, so no adjustment wa _neczewy._ **_The_ wer** 17 **obsvations.** The t-aios are listed hi parenthe underneath rgression parametrs.
92 4. **Representative farmer:** defined as the farmer whose utility level is equal to _Vn_<sup>_F_,thedis-</sup> trict’s average farmer utility. Her EV can be obtained by substituting _v_ 1 = _Vn_<sup>_F_and</sup><sup>_y_0=</sup> _V_<sup>_−_1</sup> ( _Vn,_<sup>_F_</sup> 0<sup>;</sup><sup>_P A_</sup> _n,_ 0<sup>_, P_</sup> _n,_<sup>_M_</sup> 0<sup>) in equation (48):</sup> When reporting welfare effects in Section VII, we use the relative version (in %) of the EV to make relative welfare losses more comparable across regions and agent types. This relative EV (denoted _EV_ ̃ ) can be computed by dividing the EV by the corresponding initial income level: ̃ _EV_ =<sup>_<u>EV</u>_</sup> _y_ 0<sup>.</sup> Finally, for each district _n_ , we define the average (relative) EV across agent types as follows: ̃ _avg_ The variable _EV n_ is a weighted average of (relative) EV across agent types, with weights given by each type’s share of the population’s district. It is the main metric we use in our counterfactual exercises to measure changes in the average welfare of each district. By weighting typespecific relative EVs by types’ population shares, this metric comes as close to capturing the concept of real income loss faced by a district’s “typical” inhabitant as is possible in the context of a model with non-homothetic preferences and multiple types of (possibly heterogeneous) agents.

Note that equation (49) does not include any terms related to middlemen because, as we mentioned in Section V.A, their population is assumed to have measure zero. This assumption is empirically supported by the very low number of fertilizer suppliers in Sri Lanka.<sup>123</sup> Furthermore, in our model the middleman is both the richest agent type and the one who obtains an outsize financial gain from the ban (by construction, as middlemen own the domestic fertilizer endowment). Therefore, excluding middlemen from our welfare change metric makes it more representative of the overall population (as opposed to the super rich) and avoids the potential distortions that could be caused by including outliers.

> 123In 2022, the NFS listed 17 “fertilizer distributors” in Sri Lanka. It also lists six “local agents of international fertilizer suppliers”. Our Panjiva import data (see Sections III and A.B) shows that 310 firms imported fertilizer (defined as any product listed in Chapter 31 of the HS system) into the country at some point between January 2016 and October 2022. Finally, the NFS lists of fertilizer importers include 629 unique firm names. Using the highest of these numbers (i.e. 629), we obtain an average of 25.16 fertilizer suppliers per district, which is within the range that we examine in the robustness exercise described in footnote 73.
# **A2. Macroeconomic Variables**

The source of each macroeconomic variable is World Development Indicators published by World Bank.

**GDP per capita (constant LCU):** GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.

**GDP growth (annual %)** : Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 1995 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.

**Inflation, consumer prices (annual %)** : Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a fixed basket of goods and services.

**Real interest rate (%):** Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator.

**Domestic credit provided by banking sector (% of GDP):** Domestic credit provided by the banking sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The banking sector includes monetary authorities and deposit money banks, as well as other banking institutions where data are available (including institutions that do not accept transferable deposits but do incur such liabilities as time and savings deposits).

# **A3. Definition of Liberalization**

The liberalization process is defined as follows by Kaminsky and Schmukler (2003).

**Liberalization of domestic financial sector:** They evaluate the regulations on deposit interest rates, lending interest rates, allocation of credit, and foreign-currency deposits (Kaminsky and Schmukler, 2003 p.6).

**Liberalization of capital account:** They evaluate the regulations on offshore borrowing by domestic financial institutions, offshore borrowing by non-financial corporations, multiple exchange rate markets, and controls on capital outflows (Kaminsky and Schmukler, 2003 p.6).

**Liberalization of stock market:** They analyze the evolution of regulations on the acquisition of shares in the domestic stock market by foreigners, repatriation of capital, and repatriation of interest and dividends (Kaminsky and Schmukler, 2003 p.7).

24
Table 4. City Size Equations<sup>a,b,c,d</sup> (robust standard errors in parentheses)

||(1)|(2)|(3)|
|---|---|---|---|
||GMM-IV|OLS|The effect of<br>increase<br>in covariate<br>based on(1)|
|Ln(ruralpop. supply)|1.661***|1.216***|1.558|
||(0.643)|(0.425)||
|Ln(rural income opportunities)|-3.664***|-1.999***|-3.701|
||(0.894)|(0.600)||
|Ln(marketpotential)|2.693***|1.426**|2.720|
||(0.916)|(0.586)||
|Average Schooling|0.220**|0.231**|0.277|
||(0.091)|(0.106)||
|Ln(inter-citytransport costs)|-1.395***|0.081|-0.480|
||(0.337)|(0.110)||
|State capital dummy|-0.260|1.091***||
||(0.395)|(0.170)||
|time dummies|Yes|Yes||
|Observations|369|369||
|R<sup>2</sup>||0.801||
|Hansen J statistic<br>(overidentificationtest)|1.770|||
|(p-value)|(.880)|||
|Average of Partial R<sup>2</sup>|.477|||
|Average of Partial F's|129.47|||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, port dummy, illiteracy rate (1970), ln(industry capital per worker, 1970), ln(distance to state capital)*ln(market pot. agric. land availability, 1970), ln(humidity), ln(avg. temperature), ln(rural pop. supply, 1970), ln(rural income opportunities, 1970), ln(market potential, 1970), and state capital and time dummies.

- b. GMM estimates are from the two-step efficient GMM in the presence of arbitrary heteroskedasticity and arbitrary intra-group (within-state) correlation.

- c. OLS regressions are with robust cluster standard errors. We assume the observations may be correlated within states, but would be independent between states.

- d. Average of Partial R<sup>2</sup> and Partial F’s are for average schooling and Ln(inter-city transport costs). Market potential and gravity measures are almost completely correlated with those in 1970 (Partial R<sup>2</sup> ’s are around .99).

33
are a primary focus of this paper, we want to include as many as possible.

Griffith et al. (2004) assume a negligible rate of depreciation, and Jones and Williams (1998) assume a zero depreciation rate, partly on the grounds that existing studies suggest the best fit occurs without adjustment. Hall (2010) suggests that identifying the appropriate level of depreciation is extremely difficult and sensitive to method of estimation. However, Hall and Mairesse in their work on French and the US firms construct stocks of physical and R&D capital through a perpetual inventory method rather than looking at investment rates and we check to see if our results are robust to both the gross (undepreciated) R&D as well as depreciated (net) series.

# **3.2 Instruments**

The reported returns to R&D in the advanced countries are high and a range of authors (see Griliches, 1987; Barro and Sala-i Martin, 2003; Hall and Mairesse, 1995; Hall and Jones, 1999) argue that the estimates may be biased, perhaps due to R&D’s endogeneity arising from liquidity constraints, or joint output and investment decisions. Finding suitable instruments has proven problematic. Griffith et al. (2004, 2006) at the sector level correctly report the absence of papers that have found reliable external instruments and rely largely on internal instruments.<sup>9</sup> While the weak exogeneity of lagged R&D in particular can be questioned, the literature provides apparently successful examples of their use. For example, Hall and Mairesse (1995), comparing France and the US sequentially test the admissibility of instruments beginning with a three year lag and working toward the contemporaneous values. They find lag 0+ instruments admissible for France and 1+ for the US. Were this generalizable, this suggests that our first approach, instrumenting our investment variables using two lags, or the values of 10 years prior, is plausibly long enough to guarantee weak exogeneity.

> 9Griffith et al. (2006) use the System GMM estimator as one option. We also do this. We find that the GMM parameter for gross RD to be roughly 60% of that of the FE with internal lags, while the net parameter is 80%. Since the instrumented FE is the closest to the IVVC, we report only those results.

11
The range of tasks that may be performed by robots varies across sectors. The industry-specific robotization frontier, relative factor prices and productivity determine the extent of robot use within sectors. Production of each final good variety further requires a composite intermediate good from the same industry. In equilibrium, varieties of intermediate inputs and final goods are sourced from the country that supplies at the lowest price. Thus, there are two layers of competition: (1) between robots and workers in factor markets; and (2) between countries in sector-specific product markets for inputs and outputs. Relative production costs (driven by factor prices and technology) determine country-specific robotization and trade patterns.

A fall in the price of industrial robots initially induces robotization in the North, where the cost of labor is relatively higher to begin with. This shift impacts relative production costs between countries, and therefore trade patterns. Robots replace domestic labor within sectors, leading to lower costs of production in the North, and hence to an increase in exports to the South. The effect of Northern robotization on imports sourced from the South in the same sector is theoretically ambiguous. On the one hand, robotization makes Northern producers more competitive, which implies that some varieties that were previously imported from the South are now sourced domestically. On the other hand, robotization leads to an expansion in the scale of production, which raises the demand for inputs sourced from the South.

Simple OLS regressions using country-industry panel data on trade and industrial robots over the last two decades—including country-pair-year and industry-year fixed effects—point to a robust positive relationship between robot intensity in own production and imports sourced from less developed countries. They also reveal an even stronger association with exports to these countries. Robotization thus leads to a significant reduction in net imports from less developed countries within the same sector. However, there are reasons to be concerned that these relationships reflect various forms of endogeneity. Robotization may in part be a response to changing trade patterns—for instance, trade may alter the incentives of Northern producers to automate their production. To address this possibility, as well as biases associated with omitted variables and measurement error, we implement an instrumental variable strategy motivated by our theory, exploiting variation in the exposure to robotization across countries and sectors. Specifically, we use the triple interaction between pre-determined country-wide labor costs (which govern the incentives to robotize), the share of workers engaged in replaceable tasks in the industry (as a proxy for the feasibility of automation), and the global stock of robots (as a proxy for the price of robots) as instrument for robotization at the country-industry-year level. As an alternative 2
# **Figures**

<!-- Start of picture text --> Rice Wheat<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>Market year<br>Stock to use ratio<br><!-- End of picture text -->

**Figure 1. Wheat and rice stocks in India.** Source: USDA (2012).

48