Introduction

Investment Climate means the general economic conditions affecting the financial markets. A favorable investment climate encourages businesses to improve efficiency and productivity in order to increase revenues and capital available for investment. It also gives investors confidence in the market and encourages them to invest more capital.

Investment Climate

The investment climate in a country is the collective set of incentives which establish the “rules of the game” to which economic actors must adhere. Set by a wide variety of sources, including government policies, cultures of public administration, and institutional, social, and physical infrastructure, the investment climate determines the level and uncertainty of returns expected by economic agents and consequently impacts the quality and quantity of investment and the incentives to productively employ inputs. The investment climate can be broken down into the following three main areas:

(1) Macroeconomic and Trade Policy - The capacity of domestic institutions and economic policy (e.g. fiscal, monetary, trade, and exchange rate policy, administration of customs and ports, security of property rights, strength of rule of law, and political stability) to reduce costs of international trade and finance and ensure a consistent and non-distortionary basis for investment, production and exchange;

(2) Microeconomic Framework - The contribution of microeconomic regulation (e.g. rules governing market entry and exit and factor markets) and enforcing agencies to efficient, expeditious, and predictable processes of production and exchange;

(3) Enabling Infrastructure - The cost, availability, and reliability of key public factors of production and exchange (e.g. credit, electricity, land, knowledge, physical security, skilled employees, transport).

None of these sectors, or the components that comprise them, exist in isolation, however. Indeed, there is a high degree of complementarily across domains of public policy, regulation, and provision of public goods. The systems dynamics of the investment climate can be complicated accordingly. A change in policy in one facet, for instance, may lead firms to heighten their productivity, releasing binding constraints on growth by seemingly unrelated policies. When drawing reform implications from investment climate analysis, it is important therefore that policy makers be aware of these interrelationships and the potential for indirect, even unintended, effects. A given set of reforms may, for instance, deliver quite different results depending on the sequence by which those reforms are implemented. Where political constraints restrict the scope of reform, as is often the case, identifying the most effective sequence of policy changes is often critical to sustaining the political case for reform.

Ai porjonto raju

COMPARING THE INVESTMENT CLIMATE in
Bangladesh

COMPARING THE INVESTMENT CLIMATE in Bangladesh with those in other countries of East and South Asia, might seem to diminish the progress that Bangladesh has made, since these are countries that have performed relatively well in recent decades. But good performers provide more useful benchmarks than poor performers. If Bangladesh is to meet its Millennium Development Goals, recent estimates suggest, it will need to accelerate GDP growth to about 7 percent a year over the next decade (Bangladesh 2003). So comparing the performance of Bangladesh with that of economies that are doing well provides useful information—both on areas where it lags behind and on areas where it is doing well. How well does Bangladesh fare in this comparison with other Asian countries? On some measures, quite well, especially given its lower per capita income. Bangladesh has recorded a relatively strong performance in economic growth and inflation. It has greatly increased school enrollment, and in time the improvements in enrollment should boost other measures of human resources (such as literacy) that remain low. Bangladesh also appears to perform better than other low-income countries in some dimensions of governance, that is, regulatory quality and government efficiency. But Bangladesh performs less well in other areas. It is less integrated with the global economy than other Asian countries, with low trade and foreign direct investment and high formal and informal barriers to trade. It has poor-quality physical infrastructure, especially in the power sector. It does poorly on some measures of governance, with high corruption, a weak rule of law, and a large administrative burden for starting a business. And it performs poorly on technology- related issues, with relatively low spending on research and development and weak basic research.

Bangladesh offers an unparalleled investment climate compared to the other South Asian economies
• Bangladesh is a largely homogenous society with no major internal or external tensions and a population with great resilience in the face of adversity (e.g. natural calamities). Bangladesh is a liberal democracy and mostly a one race and one religion country. The population of this country irrespective of race or religion have been living in total harmony and understanding for thousands of years.
• Broad non-partisan political support for market oriented reform and the most investor-friendly regulatory regime in south Asia.
• Trainable, enthusiastic, hardworking and low-cost (even by regional standards) labor force suitable for any labor-intensive industry.
• Geographic location of the country is ideal for global trades with very convenient access to international sea and air route.
• Bangladesh is endowed with abundant supply of natural gas, water and its soil is very fertile.
• Although Bengali is the official language, but English is generally used as second language. Majority of even moderately educated population can read, write and speak in English.
• As a result of low per capita GDP of only US$ 386, present domestic consumption is not significant. However, it should always be considered that there exists a middle class with some purchasing power. As economic growth picks up, the purchasing power will also grow substantially. And in a country of more than 130 million people, even a small middle class may constitute a significant market. Furthermore, Bangladesh products enjoy duty free and quota free access to almost all the developed countries. This access to the global market is further helped by the fact that policy regime of Bangladesh for foreign direct investment by far the best in South Asia.
• Most Bangladeshi products enjoy complete duty and quota free access to EU, Japan, USA, Australia and most of the developed countries. However, for apparel export to USA, we have certain quota regime which is generally favorable to Bangladesh.
Ai porjonto Mahedi

Investment Climate and Firms Performance

Drawing on recently completed firm-level surveys in Bangladesh, China, India, and Pakistan, this paper investigates the relationship between investment climate and firm performance. These standardized surveys of large, random samples of firms in common sectors reveal that objective measures of the investment climate vary significantly across countries and across locations within these countries. The authors focus primarily on measures of the time or monetary cost of different bottlenecks (e.g., days to clear goods through customs, days to get a telephone line, sales lost to power outages). For many of these costs, the obstacles are lower in China than in Bangladesh or India, which in turn are superior to Pakistan. There is also systematic variation across cities within countries. The authors estimate a production function for garment firms and show that total factor productivity is systematically related to the investment climate indicators. Factor returns (wages for a given quality of human capital and rate of profit) are also higher where investment climate is better. These higher returns then have dynamic effects: accumulation and growth at the firm level is higher where investment climate is good.

The developing world contains both the fastest-growing and slowest-growing locations on earth. If one ranks countries by 1990 per capita GDP and looks at subsequent growth rates, one finds that virtually all rich-country growth rates are in a tight band between 1 and 3, whereas poor-country growth rates vary from highly negative up to China’s spectacular 7 percent per annum (figure 1). Within countries as well there are often large differences in growth rates of different regions or cities. This is the general puzzle with which we are concerned: why some locations in the developing world grow so rapidly, while others stagnate. There is not likely to be a single explanation of this phenomenon. We are going to explore the hypothesis that variations in “investment climate” across locations can explain much of this variation in growth rates.

What we mean by investment climate is the institutional, policy, and regulatory environment in which firms operate – factors that influence the link from sowing to reaping. If the local government is highly bureaucratic and corrupt; if government’s own provision or regulation of infrastructure and financial services is inefficient so that firms cannot get reliable services – then returns on potential investments will be low and uncertain, and one would not expect much accumulation and growth in these environments. On the other hand, in developing locations that create a good governance environment, returns and accumulation should be high.

This idea has been investigated in the cross-country studies noted above using proxies for strength of property rights and government efficiency. This literature is suggestive, but suffers from three problems: (1) there are not that many countries in the world so that the statistical results are not that robust;[1] (2) the proxies used as explanatory variables do not provide much specific guidance about what countries need to do to improve their investment climates; and (3) using national-level data assumes that the investment climate is the same across locations within a country, when in fact there may be interesting variation based on local governance.

Our contribution is to go down to the firm level to collect data on how institutional and policy weaknesses actually affect firms. We have collaborated with in-country partners on large, random surveys of establishments in Bangladesh (924), China (1500), India (1900), and Pakistan (965). These surveys include data on inputs and outputs, as well as on objective aspects of the investment climate. We describe these in more detail below, but in general we are interested in aspects of the environment such as how long it takes to get goods through customs, how long it takes to get a phone line, or how frequent and disruptive are power outages. We developed the questionnaire through pilot testing and with input from firms about the key bottlenecks that they face. Note that the four countries reflect the puzzle that we start with: China, Bangladesh, India, and Pakistan had similar per capita GDP in 1990, but in the subsequent decade their growth rates were 7.2 percent, 2.7 percent, 4.1 percent, and 1.3 percent, respectively. They are also all major exporters of garments and other labor-intensive manufactures. So, it is natural to inquire why their performances have been so diverse.

In the next section we present the analytical framework for this study. We draw on models from empirical IO (industrial organization), trade theory, and growth theory. The story we want to explore is the following: if there are systematic differences in investment climate across locations, then for the plants that exist in these locations, total factor productivity should be related to investment climate. Bureaucratic harassment, power outages, etc. result in less value added being produced from the same capital and labor in different locations. We then turn to trade theory and argue that since these countries are exporting similar products (have the same comparative advantage), poor investment climate locations would have to have lower wages and return to capital in order to compete. To foreshadow our results, if Bangladesh has a worse investment climate than China, it can still export garments, but it will need lower wages and lower return to capital in order to do so at the same prices as Chinese exporters. Turning to growth theory, it is these potential differences in rates of return to accumulation that lead to the prediction that locations with poor institutions and policies will have lower rates of accumulation and slower growth. These models provide a framework for investigating whether objective measures of the investment climate are related to six outcome variables that we obtain from the firm surveys: estimates of total factor productivity, average wages (controlling for human capital), rate of return on fixed assets, and growth rates of output, fixed assets, and employment. We note that there are other, not mutually exclusive hypotheses, that could account for why locations perform so differently – in particular that natural geography and/or agglomeration economies could be important.

In section 3 we describe the firm surveys in more detail and present the main investment climate indicators that we use in the empirical analysis. The surveys are based on stratified random samples of establishments in particular sectors that vary somewhat across the countries. Garments are covered in all of the countries, as well as some other labor-intensive sectors. In this section we establish that there are statistically significant differences across countries and across locations within countries, in many of the indicators that we collect. We can say with a high degree of confidence that customs clearance is faster in China than in Bangladesh, and faster in Bangladesh than in Pakistan. If our prior is that time delays and service breakdowns are bad things, then we can say that China looks the best of the four countries on many of the measures (fast customs clearance, fast access to new phone lines, few power outages). However, one of the interesting things that emerges is that each location tends to have its relative strengths and weaknesses. In China, for example, the state-owned banking system is providing poor services to the firms (largely privately owned) in our sample. Also, China is relatively bureaucratic with a large number of factory inspections per year compared to other countries.

In section 4 we estimate a production function for garments, which we take as a relatively homogeneous product that each of these countries exports. Following the empirical IO literature we address the potential correlation of inputs with the error term in the production function. We then take the residuals of the production function as estimates of TFP and show that across firms TFP is systematically related to the investment climate indicators. Because there is some co linearity among the investment climate indicators, we focus in particular on the joint test for the indicators together; we can reject with great confidence that the coefficients on the investment climate indicators are all zero. We control for a number of geographic factors such as distance from major markets, distance from ports, and population of the city. We also show that the results are robust to including country dummies, so that identification comes only from the variation across locations within a country. The coefficients are economically meaningful. For example, they suggest that the good investment climate indicators in Shanghai give its garment factories a productivity advantage over South Asian cities (advantages of 18 percent versus Bangalore, 43 percent versus Dhaka, 78 percent versus Calcutta, and 81 percent versus Karachi). In this section we also estimate equations for average wages and profits relative to fixed assets, for garment firms. After controlling for firm-specific characteristics of the labor force (schooling, experience), investment climate indicators are related to wages in the intuitive direction. Similarly, investment climate differences are related to the rate of profit at the establishment level.

In section 5 we estimate equations for the other outcome variables of interest. Here we pool the data across industries in order to get the maximum number of observations, and include industry dummies to account for differences in industry price and output cycles. The growth rates of output, capital stock, and employment are calculated for the two years prior to the survey date. In each case we can estimate an equation and find that the investment climate measures are significant determinants of input and output growth, with plausible magnitudes for the coefficients. In some of these equations the geography variables work fairly well also. It seems very likely that there is some truth to the stories that emphasize natural geography and agglomeration economies. We do not find manufacturing plants randomly distributed around rural locations. That said, most of the locations we cover are large cities, and many of them are ports, with potential access to the international market. Nevertheless, locations such as Karachi (Pakistan), Chittagong (Bangladesh), Calcutta (India), and Tianjin (China) are not performing as well as Guangzhou and Shanghai in China. So, while being a big port city is an advantage, the advantage can easily be undone by poor local governance. The fact that the investment climate indicators are highly significant even after controlling for geography, population, and even country dummies, is consistent with the view that local governance is important.

Thus, we conclude in the final section that a number of major cities in China have created quite good investment climates, compared to other locations at similar levels of development ten years ago and with similar good potential for access to the international market. The result of this better investment climate is that there is a strong connection between sowing and reaping. The plants that exist in these good environments produce more value from given capital and labor and thus can pay higher wages and have higher profits. These superior returns then spur greater accumulation, so that the typical plant is rapidly expanding capital stock, employment, and output. Given the large differences in investment climate that we find in our surveys, it is not surprising that growth rates vary so much across these locations.

Ai porjonto Bappi

Analytical Framework

The analytical framework that we have in mind for our investigation of the relationship between investment climate and firm productivity and growth combines elements of theory from industrial organization, trade, and growth.

The empirical IO literature often starts with a simple production function written in logs as:

(1) [pic]

Where, y is gross output, k is capital input, l is labor input, m is material input,[pic] is an unobserved productivity shock, and i and j index firms and locations.

We are working with firms in different countries, but we assume that in a common export sector, such as garments, firms face the same international price for their output. There is a world market price for men’s shirts of a certain quality, and all firms are price-takers. We further assume that the countries allow firms in these export sectors to purchase capital equipment and raw materials at world market prices. So, the dollar value of capital and materials and the dollar value of output can be compared across countries. Wages are going to diverge, as we will see below, so the labor input in the production function needs to be in physical rather than value units (e.g., number of workers, adjusted for years of schooling and experience).

(2) [pic]

Suppose that labor is adjusted relatively easily, and capital not. In this context, a good productivity state leads the firm to use more labor – that is, labor input is correlated with part of the unobserved error. An OLS estimate that does not adjust for this will have biased estimates for the coefficients of both labor and capital. We are going to use the technique introduced by Levinsohn and Petrin (2000) to address this endogeneity problem and arrive at consistent estimates of the coefficients of (2).

Where firms are producing in the same location, it makes sense to think of them facing the same basic investment climate: quality of public infrastructure, efficiency of regulation, degree of corruption, access to financial services. However, we want to explore the idea that these factors vary to a large extent across countries and even to some considerable extent across cities within developing countries. So, we introduce into the production function a term, A, that captures the influence of the investment climate on production of the firm. A, in turn, we will model as a function of observable indicators of the investment climate, X:

(3) [pic] Ai porojnto Kawser

(4) [pic]

If we have consistent estimates of the parameters of the production function, then we can estimate the effect of investment climate indicators as:

(5) [pic]

The left-hand-side here is what is conventionally called total factor productivity. We are in effect decomposing TFP into a part that depends on the local investment climate, plus a firm-specific productivity shock.

Equation (5) is the first one that we are going to estimate across a sample of garment firms from different countries. Now, foreshadowing our results, suppose that we find that the investment climate is better in China than in Bangladesh, so that China’s typical firm is producing more value added than the typical Bangladeshi firm, with the same capital and labor inputs. How then does Bangladesh compete with China? Why would any Bangladeshi firms survive? Here we turn to international trade theory.

Schott (2000) finds that the basic Heckscher-Ohlin model of trade explains global production patterns fairly well, provided that we recognize that countries are in different “cones of diversification.” In particular, he estimates a labor-abundant cone that includes countries with overall capital-labor ratios up to $5,000. It happens that all of the countries in our sample are in this cone. Thus, they have comparative advantage in the same labor-intensive agricultural and manufacturing products, and tend to import more capital- and skill-intensive items. In this model, these countries in the same cone would have the same factor prices, provided that they all have the same As in their production functions. In the trade literature, differences in the “As” are usually interpreted as differences in technology. But the algebra is the same if we interpret these as differences in investment climate. It is straightforward to introduce into the trade model neutral differences in As across countries. In other words, if investment climate varies across countries and affects different industries to the same extent, then the basic predictions of the trade model go through, with one important change: if China is 20% better than Bangladesh at everything, then prices for capital and labor have to be 20% higher in China than in Bangladesh. Firms in China and Bangladesh will choose the same techniques, since they face the same relative factor prices. With 20% lower factor prices, Bangladeshi firms will be able to export at the same prices as Chinese firms. So, from trade theory we derive a second equation to estimate:

(6) [pic][pic][pic]

The average wage will vary across firms based on firm characteristics, Z (eg., average schooling of the firm’s workers, average experience) and the local investment climate indicators. This model only makes sense if there is restricted labor mobility across countries, which is clearly the case for the developing countries we consider. It is also necessary that there be restrictions on outflows of capital (otherwise all of the capital would leave Pakistan) – and in fact all of the countries we consider do limit capital outflows.

Finally, we turn to the issue of growth. Ventura (2000) embeds a Heckscher-Ohlin trade model of the kind above, into a Ramses growth model. In the one-sector Ramses growth model, a difference in investment climate in two countries that are otherwise the same would result in a higher return to capital in the good environment and a higher investment and growth rate initially. As capital is accumulated, however, diminishing returns reduce profitability and the growth rate slows down. In the steady state the two countries would have the same growth rate. Ventura shows that the multi-sector trade model suggests that the transition could be prolonged for the following reason: Imagine that the agricultural sector is the most labor-intensive, and that the “cone” that China, Bangladesh, et al. are in also includes labor-intensive manufactures. The latter are more capital intensive than agriculture, but less capital intensive than other manufactured products.

In this model, if all countries had the same “As” for their production functions, then in general poor countries would grow more rapidly than rich ones. They would have a higher return to capital and would accumulate rapidly. As they did, labor would shift from agriculture to labor-intensive manufactures. As long as the country remains within the “cone,” the return to capital would be stable and one would not have the offset to the high growth rate of diminishing returns. Eventually the capital accumulation leads the country to a more capital-abundant cone with lower return to capital. So, in the long run the model performs like the traditional model – but the model provides a plausible explanation for why the transition is long.

Ai porjonto Sohel

Appendix: Standard
Investment Climate
Tables

Table : Estimating production function for garment firms

| |(1) |(2) |(3) |(4) |(5) |
| |Log(VA) |Log(VA) |Log(VA) |Log(VA) |Levinsohn-Petrin |
|Log (labor) |0.647 |0.653 |0.636 |0.621 |0.59 |
| |(30.29)*** |(29.96)*** |(11.81)*** |(11.73)*** | |
|Log (capital) |0.258 |0.240 |0.250 |0.246 |0.36 |
| |(13.96)*** |(12.56)*** |(9.11)*** |(9.01)*** | |
|Log (av. schooling of workforce) | |0.022 | |0.026 |.023 |
| | |(2.77)*** | |(3.09)*** | |
|China*log(labor) | | |-0.019 |0.134 | |
| | | |(0.19) |(0.86) | |
|India*log(labor) | | |0.106 |0.128 | |
| | | |(1.42) |(1.62) | |
|Pakistan*log(labor) | | |-0.047 |-0.072 | |
| | | |(0.55) |(0.83) | |
|China*log(capital) | | |0.017 |-0.060 | |
| | | |(0.38) |(0.90) | |
|India*log(capital) | | |-0.036 |-0.049 | |
| | | |(1.17) |(1.60) | |
|Pakistan*log(capital) | | |0.025 |0.029 | |
| | | |(0.74) |(0.86) | |
|Constant |6.238 |6.265 |6.354 |6.334 | |
| |(32.36)*** |(28.62)*** |(29.05)*** |(25.86)*** | |
|Observations |2082 |1887 |2082 |1887 |1882 |
|Number of establishments |775 |697 |775 |697 |694 |
|*Absolute value of z statistics in parentheses |
|* significant at 10%; ** significant at 5%; *** significant at 1% |
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Table: Explaining Average Wages Rates in Garment Firms

| |(1) |(2) |(3) |
| |lnwage |lnwage |lnwage |
|Log (age of establishment) |-0.041 |-0.039 |-0.044 |
| |(1.18) |(1.13) |(1.24) |
|Log (labor in initial period) |0.899 |0.908 |0.897 |
| |(34.52)*** |(32.22)*** |(29.36)*** |
|Log (average schooling) |0.029 |0.030 |0.030 |
| |(3.06)*** |(3.15)*** |(3.11)*** |
|Log (av. age of workers) |0.076 |0.074 |0.075 |
| |(4.68)*** |(4.52)*** |(4.52)*** |
|Log (Av.age sq.) |-0.001 |-0.001 |-0.001 |
| |(4.17)*** |(4.07)*** |(4.08)*** |
|Log (customs days-export) |0.125 |0.138 |0.232 |
| |(2.23)** |(2.34)** |(3.27)*** |
|Log (customs days-import) |-0.206 |-0.225 |-0.175 |
| |(1.77)* |(1.76)* |(1.27) |
|Log (power loss) |-0.257 |-0.299 |-0.323 |
| |(3.76)*** |(2.72)*** |(2.57)** |
|Log (phone days) |-0.084 |-0.083 |0.042 |
| |(1.75)* |(1.13) |(0.44) |
|Log (overdraft facility) |-0.057 |-0.075 |-0.039 |
| |(0.91) |(1.04) |(0.49) |
|Log (distance from market) | |0.142 |0.790 |
| | |(0.68) |(2.14)** |
|Log (distance from port) | |-0.002 |-0.006 |
| | |(0.26) |(0.64) |
|Log (population) | |-0.018 |0.006 |
| | |(0.57) |(0.17) |
|Bangladesh | | |-0.136 |
| | | |(0.70) |
|China | | |0.780 |
| | | |(2.17)** |
|India | | |0.006 |
| | | |(0.04) |
|Constant |6.444 |5.576 |-1.022 |
| |(14.64)*** |(3.38)*** |(0.32) |
|Year dummies |Yes |Yes |Yes |
|Observations |1378 |1378 |1378 |
|Number of establishments |709 |709 |709 |
|Chi2 |32.95 |17.42 |14.60 |
|Prob>Chi2 |0.000 |0.0038 |.0122 |

Table : Explaining Output Growth

Dependent variable: Annual growth rate of employment

Table : Explaining Fixed Assets Growth
Dependent variable: Annual growth rate of fixed assets

References

✓ Acemoglu, Daron, Simon Johnson, and James A. Robinson (2001). “The Colonial Origins of Comparative Development: An Empirical Investigation.” American Economic Review 91:5 (December) 1369-1401.

✓ Gallup, John Luke, and Jeffrey D. Sachs, with Andrew Mellinger, (1999). “Geography and Economic Development.” CID Working Paper No. 1, March.

✓ Hall, Robert E., and Charles Jones (1999). “Why Do Some Countries Produce So Much More Output per Worker than Others?” Quarterly Journal of Economics, volume 114 n 1 February, pp. 83-116.

✓ Krugman, Paul and Anthony Venables (1999). “Globalization and the Inequality of Nations.” The Economics of Regional Policy (1999): 129-52. Cheltenham, U.K. and Northampton, Mass.

✓ Levine, Ross, and David Renelt (1992). “A Sensitivity Analysis of Cross-Country Growth Regressions.” American Economic Review, September, 82(4): pp. 942-963.

✓ Limao, Nuno and Anthony Venables (2001). “Infrastructure, Geographical Disadvantage, Transport Costs, and Trade.” World Bank Economic Review, v15, n3 (2001): 451-79.

✓ Levinsohn, James and Amil Petrin (2000). “Estimating Production Functions Using Inputs to Control for Unobservables.” National Bureau of Economic Research Working Paper, No. 7819 (Cambridge MA).

✓ Olley, Steven and Ariel Pakes (1996). “The Dynamics of Productivity in the Telecommunications Equipment Industry.” Econometrica, 64 (6): 1263-1297.

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