rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6846

# **Abstract**

The enterprise reforms of the 1990s profoundly changed the structure of the economy in China. With the deepening of market economy, the share of the stateowned and collective enterprises declined. Expansion and contraction, as well as establishment and closure, of firms became a common phenomenon. The level and volatility of firm productivity have become increasingly important aspects of the micro performance of the economy. This paper uses a firm-level data set collected annually by the National Bureau of Statistics of China in 1998–2007 to examine the role of different firm characteristics in productivity volatility. The paper measures productivity volatility at the firm level as the standard deviation of the annual growth rate of productivity. The main objectives are twofold: first, it examines the variation of productivity volatility across firms of different characteristics and their evolution over time; second, it investigates the sources of productivity volatility at the firm level in China. The results suggest that in general, productivity volatility at the firm level has declined over time in China. Among firms with different characteristics, large firms, old firms, foreign firms, and firms located in the coastal provinces are less volatile. Firm size and location are the two major factors that drive changes in productivity volatility, one in a positive way and one in a negative way. Although the gaps of volatility between smaller firms and larger firms declined, the gaps between firms located in the coastal provinces and inland provinces increased.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2014: Risk and Opportunity: Managing Risk for Development_ —is a product of the Development Economics Vice Presidency. The views expressed in this paper are those of the authors and do not reflect the views of the World Bank or its affiliated organizations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at xluo@worldbank.org.

_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
Table 25: IV regressions of ERP Administration variables on ICT use

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Payroll|HR|Accounting|Suppliers|Invoicing|Information|Sales|Inventories|
|∆Share of labor that uses computer|0.00234|0.00511*|0.0119***|0.0186***|0.0191***|0.00629|0.0151***|0.00742**|
||(0.00206)|(0.00292)|(0.00389)|(0.00509)|(0.00526)|(0.00430)|(0.00586)|(0.00360)|
|F- frst stage|14.150|14.150|14.150|14.150|14.150|14.150|14.150|14.150|
|Sargan p-value|0.2070|0.1762|0.1972|0.5515|0.1943|0.3596|0.9469|0.8394|
|_N_|641|641|641|641|641|641|641|641|

Robust standard errors in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.
|Foreign ownership (proportion)|0.031|0.017|-0.244|-0.506*|-0.306|-0.449|-0.672**|-0.468|-0.374|
|---|---|---|---|---|---|---|---|---|---|
||(0.137)|(0.130)|(0.253)|(0.265)|(0.273)|(0.304)|(0.311)|(0.432)|(0.540)|
|Tax rates are a major obstacle|-1.850***|-1.715***|-1.740***|-1.274|-1.910**|-1.706**|-1.713*|0.034|-1.111|
|Y:1 N:0|(0.530)|(0.468)|(0.479)|(0.898)|(0.970)|(0.768)|(0.949)|(1.412)|(1.823)|
|Government support Y:1 N:0|0.573|0.452|1.102**|-0.394|-0.538|-0.809|-1.244|-0.316|1.370|
||(0.398)|(0.377)|(0.502)|(0.681)|(0.667)|(0.672)|(0.964)|(1.395)|(1.063)|
|Capital city Y:1 N:0|-0.131|0.198|0.498|0.053|0.434|0.809|0.893|1.331|2.367|
||(0.584)|(0.456)|(0.614)|(1.288)|(1.287)|(1.070)|(1.289)|(1.490)|(2.112)|
|Obstacles severity|1.543|1.349|0.402|-0.173|0.645|-0.343|-0.477|-4.456|-2.099|
||(1.667)|(1.420)|(1.553)|(2.344)|(2.580)|(2.371)|(2.408)|(3.650)|(3.803)|
|Sole proprietorship Y:1 N:0|0.311|0.160|-0.261|1.244|1.415|1.135|0.353|0.709|4.323|
||(0.769)|(0.728)|(0.852)|(1.116)|(1.204)|(1.129)|(1.188)|(2.736)|(3.446)|
|Shareholding company (non-|-0.654|-0.168|-0.801|-0.538|-1.290|0.308|-0.250|0.981|1.246|
|traded) or Partnership Y:1 N:0|(1.174)|(1.107)|(1.351)|(1.818)|(1.947)|(1.734)|(1.989)|(2.478)|(3.303)|
|R&D and New product/process|0.028|-0.193|-0.688|-1.858**|-0.699|-0.835|-1.161|0.004|2.080|
||(0.630)|(0.570)|(0.706)|(0.894)|(0.825)|(0.757)|(0.827)|(1.069)|(1.332)|
|Constant|18.25***|19.38***|-50.12***|-28.71***|-47.24***|-61.56***|-50.34***|-137.1***|-251.30***|
||(6.778)|(6.691)|(10.360)|(15.075)|(16.037)|(15.960)|(14.276)|(19.296)|(38.197)|
|Observations|5,197|5,197|5,197|5,197|5,197|5,197|5,197|5,197|5,197|

Huber-White robust standard errors clustered on country-industry pairs. *** (1%), ** (5%), * (10%). Without much loss of generality, only the combined contribution of R&D and New product/process development is shown.

59
- 42 -

# Table 11(bi

Impact of a 10% Increase iAn Investment Allowance on Investment in Turkish Chemical and Petroleum Derivatives Industries.

Increase in Investmert = Turkish Lira 157,858 Lost tax Revenues = Turkish Lira 7,542,127 Ratio ce Investment Gain to Revenue Loss = 0.02 Source: Model-based calculations Table 11 (b) reports on the impact of a small increase in the investment allowance for physical capital on investment and government revenues. This table indicates that the investment allowances offered to Turkish industries had little impact on their investments but resulted in a major drain on government revenues. Table (12) reports the effect on the endogenous variables of a small change in the tax allowance for investment in knowledge capital.

Table **(12)** Turkish chemical and petroleum derivatives industries Elasticities of R&D expenditures. phvsical investment, output. labour and materials with respect to a small change in the fraction of R&D which firms are allowed to expense, midpoint of the sample

|elKQK|0.00336|
|---|---|
|enlQK=|0.01031|
|eyQK<br>=|-<br>0.00279|
|eLQK<br>=|-<br>0.00038|
|eMQK<br>=|-<br>0.00069|

The positive sign of erKQK implies that a small change in the tax allowance for 3nowledge capital caused expenditures on R&D to move in the same direction. Investment in physical capital also moved in the same direction as R&D expenditures. This is what one would expect, since new technology is often embodied in new physical capital. Output and therefore the demand for the variable inputs labour and materials moved in the opposite direction in the short run only from changes in expenditures on R&D. This is what theory predicts,
# **Table 8 - Comparison with Bleaney, Gemmell, and Kneller (2001) Table 1 on page 44 (OLS methdology)**

## **_Dependent variable: Growth rate of GDP per capita_**

||Bleaney,<br>Gemmell, and<br>Kneller (2001)<br>Table 1 on page<br>44 (Column 1)<br>(5-year<br>averages)|Our results with<br>initial human<br>capital index<br>and lagged<br>budget<br>variables<br>(3-year<br>averages)||
|---|---|---|---|
|Investment|0.020|-0.004||
||(0.32)|(-3.53)||
|Labor force growth|-0.015<br>(-0.05)|---||
|Initial human capital index|---|0.011<br>(1.09)||
|Taxation||-0.222 <br>(-1.17)|(lagged)|
|Non-tax revenues|-0.101<br>(-0.51)|-0.425<br>(-2.60)||
|Other expenditures|0.301<br>(1.82)|---||
|Budget surplus|0.357<br>(2.17)|0.504<br>(4.51)||
|Distortionary taxation|-0.427<br>(-2.36)|---||
|Productive expenditure|0.273<br>(1.77)|0.481 <br>(2.27)|(lagged)|
|Non-productive expenditure|-0.039<br>(-0.23)|0.223 <br>(0.76)|(lagged)|
|Net lending|0.314<br>(1.32)|---||
|No of observations|98|34||
|Adj-R2|0.574|0.508||

Note: The estimation method is the ordinary least squares for panel data. In our case, the lagged values of some budget variables are used, specified as (lagged). t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

35
analysis for groups of advanced economies such as the EU. This raises at least two problems. First, the empirical results lack the external validity, particularly for developing countries. It is difficult for policy makers to interpret these results. Second, developing economies in Asia are large recipients of FDI, yet it is not clear how beneficial FDI is for recipient countries, especially those least inserted in GVCs.

The early research looked at the link between FDI in a sector and the performance of the recipient firm in that same sector. From a very direct point of view, relative to a counterfactual of no financing, a project is clearly better off with foreign investment. However, not all studies have found positive spillovers once other aspects are controlled for. Haddad & Harrison (1993) reject the hypothesis that FDI accelerates productivity growth using firm-level data in Moroccan manufacturing sectors. Employing panel data of Venezuelan plants, Aitken & Harrison (1999) find that the magnitude of the FDI spillovers is marginal, and the positive correlation between FDI and firm productivity is only robust for small enterprises. Evidence is mixed on the magnitude and even the direction of the FDI spillovers. Using plant-level panel data covering manufacturing firms in the United Kingdom during 1973 – 1992, Haskel et al. (2007) show that there is a robust and significantly positive correlation between the total factor productivity of a domestic plant and the foreign-affiliate’s share in that sector. However, Lu et al. (2017) find negative FDI spillovers among Chinese manufacturing firms. At the same time, FDI may be harmful if local firms lose market share to foreign-backed competitors (Aitken and Harrison 1999). Smeets (2008) and Gorg et.al. (2001) review possible sources of FDI induced spillovers through an evaluation of the empirical evidence on productivity and wages, among others. These surveys note that although theory can identify a range of possible spillovers, the empirical results paint a mixed picture at best.

# **Linking to upstream and downstream sectors, and tax incentives for FDI**

The attention has shifted from the direct FDI spillovers to those taking place through input and output linkages. In addition to the direct impact on labor productivity, FDI also generates crosssector spillovers. As Moran (2001) points out, FDI in the downstream sectors could increase the productivity of the upstream suppliers through technical assistance, management experience, and quality assurance. FDI spillovers could also take place through forward linkages. Intermediates supplied by foreign investment embody more advanced technologies from which domestic firms can learn (Grossman & Helpman, 1991). Javorcik (2004) is the first paper to empirically confirm the FDI spillovers through the backward linkages in the steel sector in the Czech Republic.
**Table 1: Change in emissions intensity (CO2 emissions per capita) at various levels of carbon tax in Annex I countries from the BAU case in 2030**

||C|O2 emissi|onper cap|ita|C|O2 emissi|onper G|DP|
|---|---|---|---|---|---|---|---|---|
||||Car|bon tax ra|te(US$/t|CO2)|||
||10|50|100|250|10|50|100|250|
|Australia and New Zealand|-14.9%|-36.0%|-46.2%|-58.7%|-14.8%|-35.6%|-45.5%|-57.6%|
|Canada|-9.4%|-26.7%|-36.6%|-50.8%|-9.3%|-26.2%|-35.7%|-49.2%|
|Germany|-6.4%|-21.1%|-30.5%|-43.8%|-6.4%|-21.0%|-30.2%|-43.2%|
|Spain|-4.2%|-15.2%|-23.6%|-37.5%|-4.2%|-15.0%|-23.2%|-36.7%|
|<br>France|-2.9%|-10.8%|-17.3%|-29.4%|-2.9%|-10.7%|-17.1%|-29.0%|
|UK|-12.2%|-35.0%|-46.2%|-58.4%|-12.2%|-34.9%|-45.9%|-58.0%|
|Italy|-4.2%|-15.0%|-23.1%|-36.6%|-4.1%|-14.7%|-22.7%|-35.6%|
|Japan|-8.0%|-18.2%|-25.4%|-37.1%|-8.0%|-18.1%|-25.1%|-36.4%|
|EFTA Countries & Rest of European Union|-8.3%|-25.6%|-35.9%|-50.5%|-8.2%|-25.2%|-35.3%|-49.4%|
|Russia|-16.7%|-42.8%|-55.7%|-72.2%|-16.2%|-40.3%|-51.7%|-65.9%|
|United States|-10.8%|-31.6%|-43.1%|-57.8%|-10.7%|-31.3%|-42.5%|-56.9%|
|Non Annex I Europe & Central Asia|0.5%|1.9%|3.2%|4.7%|0.4%|1.8%|2.9%|4.3%|
|Argentina|0.1%|0.3%|0.6%|1.2%|0.1%|0.4%|0.7%|1.4%|
|Brazil|0.4%|1.6%|2.7%|4.9%|0.4%|1.6%|2.7%|5.0%|
|Rest of Latin America & Caribbean|0.3%|1.2%|2.1%|4.1%|0.3%|1.3%|2.3%|4.6%|
|China|0.2%|0.8%|1.3%|2.5%|0.2%|0.6%|1.0%|1.9%|
|Indonesia|0.3%|1.2%|2.0%|3.7%|0.3%|1.2%|2.0%|3.5%|
|Rest of East Asia & Pacific|0.5%|1.8%|2.9%|5.0%|0.4%|1.6%|2.6%|4.4%|
|Malaysia|0.2%|0.8%|1.3%|2.4%|0.2%|0.9%|1.5%|2.9%|
|Thailand|0.3%|1.2%|2.1%|3.7%|0.3%|1.0%|1.8%|3.3%|
|India|0.2%|0.8%|1.2%|1.9%|0.2%|0.5%|0.8%|1.1%|
|Rest of South Asia|0.2%|0.8%|1.4%|2.7%|0.2%|0.7%|1.2%|2.3%|
|Middle East & North Africa|0.0%|0.2%|0.4%|1.0%|0.2%|1.1%|2.0%|4.0%|
|South Africa|0.7%|2.2%|2.9%|3.8%|0.8%|2.2%|2.9%|3.7%|
|Rest of Sub-Saharan Africa|0.2%|0.9%|1.7%|3.4%|0.3%|1.4%|2.6%|5.1%|

EFTA stands for European Free Trade Association and includes 12
Soil quality has a positive and significant yield-increasing effect. The estimates show that, on average, yield on plots of good and fair quality is about 259.84kg/ha and 179.14kg/ha respectively higher than yield on plots of poor soil quality. As mentioned, the “soil quality” variable is a measured by farmers’ subjective judgement (1 = good; 2 = fair; 3 = poor). The estimate should therefore be viewed with his caveat in mind.

The gender and years of education of the plot manager are also significantly correlated with maize yield. Yield on female-managed plots is 113.27kg/ha lower than it is on male-managed plots. In a similar study in which agricultural productivity was measured by value of output per hectare, Kilic et al. (2015) observed that productivity on female-managed plots is 25% lower than on male-managed plots. The authors find that 82% of the gender differential in agricultural productivity is attributable to differences in endowments. It has been shown that closing this gender gap in agricultural productivity can potentially reduce the poverty rate by 2.2% and accordingly lift 23,800 people out of poverty each year in Malawi (World Bank, 2015). The production function of this study also show that a year increase in the education of plot managers will all things being equal increase yield by 10.54 kg/ha.

Maize yield is also positively affected by ownership of agricultural tools and durable assets. A unit increase in the index of agricultural tools and durable assets will all things being equal increase yield by 68.96kg/ha and 93.73kg/ha respectively. The positive relationship between asset ownership and maize yield is expected because farmers with more equipment are more likely to purchase and use fertilizer and other modern inputs in production.

Rainfall has a positive and significant effect on maize yield. A millimeter increase in total annual rainfall increase maize yield by 1kg/ha, all things being equal. This finding suggests that increasing farmers’ access to irrigation facilities in low rainfall years could help boost agricultural productivity.

20
Table 9. Effects of Guerrilla Territorial Control on Main Outcomes, Controlling for Conflict

|_Panel A: Separating Disputed areas from Gov_<br>|_ernment controlle_<br>|_d areas_<br>|
|---|---|---|
|Night Light Arcsine|Wealth Score|Years of Education|
|(2013)|(2007)|(2007)|
|(1)|(2)|(3)|
|Guerrilla control<br>-0.127***|-0.170***|-0.438**|
|(0.0314)|(0.0587)|(0.188)|
|Disputed area<br>0.0851*|-0.0670|-0.230|
|(0.0473)|(0.0678)|(0.207)|
|Observations<br>3,652|3,630|3,637|
|Bandwidth (Km)<br>2.266|2.266|2.266|
|Dependent mean<br>3.536|-0.0160|6.573|
|_Panel B: Doughnut Hole Analysis (400 m)_|||
|Guerrilla control<br>-0.164***|-0.123**|-0.308**|
|(0.0520)|(0.0541)|(0.153)|
|Observations<br>1,564|1,555|1,555|
|Bandwidth (Km)<br>0.981|0.981|0.981|
|Dependent mean<br>3.245|-0.197|5.845|

_Note:_ The table presents results for the main outcomes but under different specifications that help discard the hypothesis that effects were driven by conflict. Panel A shows results when separating the control group between governmentcontrolled areas and areas disputed by guerrillas. Notice that in Panel A, the omitted category concerns segments under pure governmental dominance. Panel B shows results using a doughnut-hole methodology with a hole of 400 m. The unit of observation in all columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

_Recruitment—_ Coercive recruitment has figured prominently in work that ties civil conflict to lower levels of education. Early military experience is a bad substitute for education and labor market experience, and child soldiers lose key formative years of schooling (Blattman and Miguel, 2010). Nonetheless, coercive recruitment is unlikely to be the force behind our results. First, child soldiers were not prominent in the FMLN. Estimates suggest that of the 9,000–12,000 FMLN members, only 2,000 (about 20 percent) were under age 18, while the percentage of underage combatants in 42
# 3.4. <u>Effect on trade and competitiveness</u>

The fact that Burundi has spent and absorbed most of the high aid flows raises concerns about currency appreciation and its impact on competitiveness of the economy and the potential for some Dutch disease effect. In fact, the real effective exchange rate has appreciated slightly during the aid-surge period, by 1.5 percent annually or 5.2 percent in total from 2005 to 2008 (Table 5).<sup>8</sup> The slight deterioration of the terms of trade is another indicator for a possible loss of competitiveness in the aid-surge period.

**Table 5:** Exchange rate and trade indicators

||2001-2004|2005-2008|
|---|---|---|
|Exchange rate and prices (annual percentage change)|||
|Real effective exchange rate|-10.4|1.5|
|Nominal effective exchange rate|-12.9|-5.1|
|Terms of trade|1.6|-2.9|
|Inflation (CPI, average)|6.7|12.1|
|Exports (percent of GDP)|||
|Total exports of goods (fob)|6.1|6.4|
|Coffee exports|3.5|4.3|
|Tea exports|1.6|1.0|
|Other exports|1.0|1.1|
|Exports (annual percentage change)|||
|Total exports of goods (fob)|2.0|15.5|
|Coffee exports|2.1|19.8|
|Tea exports|-3.2|6.6|
|Other exports|55.0|15.4|

_Source:_ Authors’ calculation based on IMF data; minus = depreciation This overall slight loss of competitiveness has, however, been caused primarily by higher inflation compared to the trading partners and not appreciation of the nominal exchange rate.<sup>9</sup> The nominal effective exchange rate depreciated by 5.1 percent per year on average or 20.1 percent overall between 2005 and 2008. The higher inflation has therefore more than offset the depreciation of the nominal exchange rate. The development of these key indicators is shown in Figure 4.

The high inflation rate has partly been caused by natural events and developments in the commodity markets that are exogenous for the government of Burundi and cannot be attributed to the increase in aid flows. For instance, part of the price increases in 2005 was caused by a drought affecting domestic agricultural production while the high inflation in 2008 was due to the global food and petroleum price crises.

The relative price development of tradables and non-tradables confirms that the slight appreciation of the real effective exchange rate was not due to the increase in aid. In the case of Dutch disease, prices of non-tradables increase more than those of tradables, leading to an appreciation of the real effective exchange rate and ultimately to the contraction of the tradables sector. In Burundi, however, the price level of tradables has increased on average by 13 percent between 2005 and 2008 while those of non-tradables increased only by 11 percent on average.<sup>10</sup>

> 8 For annual figures see Table A-3.

> 9 The real effective exchange rate is calculated as product of the nominal effective, i.e. trade weighted, exchange rate and the inflation differential between Burundi and its trade partners.

> 10 Average year-on-year inflation based on monthly data; authors’ calculation based on IMF data.

11
6 Leaving to one side fine questions of data, however, the rural‐urban dichotomy is absolutely central to the positive and normative development discourse. The Lewis (1954) model of dualistic development is a model where the dichotomy is introduced and used to characterize the development process. Lipton (1977) famously coined the term “urban bias”, meaning by this: “(a) an allocation, to persons or organizations located in towns, of shares of resources so large as to be inefficient and inequitable, or (b) a disposition among the powerful to allocate resources in this way.” (Lipton 2005). Thus Lipton was, strictly speaking, putting together secondary towns and big cities on one end of the balance and contrasting this with rural areas at the other end. Presumably he would think of the dichotomy as being sharper and more relevant if the contrast was between big cities on their own and rural areas, and might agree that the very small towns may have more in common with the rural than with the very large cities. We are back then to the question of a continuum, the conceptual value of a dichotomy, and where exactly to draw the line.

Give this development of the literature, let us start conceptually with a continuum from low population density to high density along the lines of the OECD analysis, combined with size criteria to give us units which range from rural, to mid‐sized urban agglomerations which we can label as “secondary towns”, to large cities. Mindful of the many data issues, and without getting too precise at this stage about specific cutoffs which will have to be country specific, let us think of a division of the population into these three categories—rural, secondary or intermediate towns/cities<sup>4</sup> towns, and big cities. Another way to characterize these might be as zero, low and high urbanization. The ultimate policy question is where and how public investment should be directed across these three categories of population agglomerations if the objective is poverty reduction at the national level. In order to answer this question, a first question is how these three categories of urbanization are associated with poverty reduction.

> 4 In what follows, we will use “secondary towns” as shorthand to indicate this middle group.