rafmacalaba/datause-encoder-data huggingface dataset:
-24-

59. The analysis of India's fiscal problems illustrate the strong linkages between the budget and financial repression. In 1997/98, under the assumption of a 10% real discount rate, or no financial repression, the public-sector in India would have required a primary surplus of not less than 3.1 percent of GDP in order to be solvent and about 1.6% of GDP primary surplus for fiscal sustainability. If the analysis were carried out using the actual government borrowing rate in the domestic markets (about 5%), the amount of fiscal adjustment would be smaller (Table 5). Thus, for example, with a real discount rate of 5%, roughly what the government is paying in the domestic market, the required solvency adjustment in 1997/98 would fall from about 4.8% of GDP .5% (Table 5) and the no adjustment is required for fiscal sustainability (negative value of 1.2 in Table 5). The difference between the first and the other columns of Table 5 reflects the implicit financial repression in the economy. The fiscal adjustment burden for India is clearly higher when the real discount rate is 10 percent than when it is 5 percent. The implication is that if, and when, financial liberalization takes effect, the burden on fiscal adjustment automatically increase sharply and this partly explains the reluctance of policy-makers to increase real interest rates. Thus, measures to liberalize interest rates would need to be accompanied by stringent fiscal polices over and above the explicit budgetary costs of such measures.

# **Table 5: India: Required Solvency and Sustainability Adjustments Under Alternative Discount Rates, 1997198 (percent of GDP)**

|X<br>.<br>0|000-<br>_**tpisDf**|**$oiint**<br>**gte s**||
|---|---|---|---|
|Fiscal Solvency Adjustment|9.0|4.8|.5|
|Fiscal Sustainability Adjustment|7.8|3.3|-1.2|

60. Perhaps even more important than the quantitative fiscal adjustment is the quality of fiscal adjustment. In recent years, India has succeeded in implementing fiscal reform aimed at reducing personal income tax as well as excise and customs duties to spur growth and to improve tax collection. The root cause of fiscal problems appears to lie not on the revenue side but on the expenditure side, particularly in two areas. First, strong political pressures coming from general elections tend to limit the ability of any minority government to deal effectively with the public-enterprise sector and privatization. Second, inter-governmental fiscal relations hamper the extent to which public expenditure can be controlled. The current structure allows states to finance more than 50 percent of their spending with central government transfers and thus dilute their fiscal responsibilities. At the moment, the center does not appear to be in a position to cut these tax transfers to the states, although it could reduce grants and loans allocated to them. Any serious fiscal effort would need to address these two issues.
_COTE D’IVOIRE: Volatility, Shocks and Growth_

# **Figure 20: Cote d’Ivoire’s Share of World Cocoa Production**

Cote d'Ivoire Share of World Cocoa Bean Output (Share of Physical Production in Metric Tons. Source: FAO) <!-- Start of picture text --> 45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Annual Market Share, 1961-2003<br>Cote d'Ivoire's Market Share<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text -->

**Figure 21: World Cocoa Prices**

<!-- Start of picture text --> International Cocoa Prices 1960-2003: Estimated Spot Price,<br>& Price Adusted by US Consumer Price Index (Base 1995)<br> Based on near term futures prices converted to spot equivalents,<br>Average, London & New York price quotations, at London exchange closing<br><!-- End of picture text --> <!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Average Annual Prices, 1960 to 2003<br>US cents/lb. Real price<br>US cents per pound<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> This descriptive data provides justification for our initial hypotheses that: a structural break occurred in or around 1976, that it was cocoa-related, and cocoa is important enough to influence the macro-economy of Cote d’Ivoire.
# **Appendix A: Construction of the panel from the ASIF data**

**Construction of the panel from the ASIF data.** In the dataset, every firm is given a unique firm code. A small number of firms may have changed their firm codes within the sample period but remained in the sample. To address this issue, we follow Brandt et al. (2012) and Yang (2015) to obtain unique firm codes based on the firm’s name, zip code, telephone number, and founding year. We clean the data as follows. First, if the year _t_ observation of a firm cannot be matched to any firm’s observation in year _t+_ 1 based on the firm code, we try to find a firm with the same name in year _t+_ 1, and match them by giving the year _t+_ 1 observation the same firm code as the year _t_ observation. Second, for those firms that cannot be matched by the code or name, we rely on the combinations of the zip code, telephone number and the founding year to match them. We delete firms with missing key information, i.e. assets, fixed assets, sales and employment. Table A1 presents the frequency with which we can link the observations in different years for both SOEs and non-SOEs.

**Table A1. Evolution of the raw panel over time**

|Year|Total firms|Entrants|Incumb|ent,linked using|Exiting (in the|
|---|---|---|---|---|---|
||||NBS ID|Other information|nextyear)|
|1998|164,452||||28,709|
|1999|161,439|25,696|130,863|4,880|27,672|
|2000|162,350|28,583|130,538|3,229|36,395|
|2001|170,780|44,825|117,526|8,429|24,356|
|2002|181,149|34,725|142,950|3,474|28,378|
|2003|196,204|43,433|146,605|6,166|51,295|
|2004|274,750|129,841|137,681|7,228|45,085|
|2005|271,819|42,154|226,675|2,990|25,819|
|2006|301,943|55,943|243,728|2,272|28,485|
|2007|336,742|63,284|271,629|1,829||

Note: Entrants are those that first appear in the sample in the specific year. Exiting means dropping out of the sample in the next year. The ASIF dataset includes all SOEs, and all non-state firms with sales exceeding five million yuan. Thus, a firm's entry year may differ from its establishment year. Similarly, a firm’s exiting year may differ from its death year.

``` 39 ```
# Table 17: Mechanism testing: latrine seepage and quality

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Slab is<br>cleanable|Floor is<br>cleanable|Drophole is<br>only hole|Solid slab|Flooded by<br>water|Strong odor|Feces visible<br>outside pit|Flies always<br>present|Has a squat<br>hole cover in<br>place|
|HWWS|0.026|0.040**|0.026|0.018|0.011|-0.023|-0.008|-0.031|0.005|
||(0.020)|(0.019)|(0.033)|(0.027)|(0.011)|(0.019)|(0.022)|(0.036)|(0.014)|
|TSSM|0.032*|0.037**|-0.025|-0.001|0.009|-0.002|0.013|-0.018|0.052***|
||(0.019)|(0.018)|(0.029)|(0.024)|(0.010)|(0.018)|(0.021)|(0.035)|(0.015)|
|HWWS + TSSM|0.014|0.022|-0.023|0.014|0.005|-0.012|-0.044**|0.019|0.039***|
||(0.020)|(0.021)|(0.030)|(0.024)|(0.007)|(0.020)|(0.021)|(0.037)|(0.014)|
|Observations|2,896|2,896|2,896|2,883|2,887|2,892|2,892|2,974|2,791|
|R-squared|0.196|0.224|0.127|0.269|0.053|0.035|0.055|0.087|0.082|
|Control Mean|0.853|0.863|0.580|0.758|0.013|0.176|0.222|0.537|0.038|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects. Results are conditional on household having a latrine.

53
effect of financial intermediary development on growth volatility beyond its dampening and magnifying effect on the propagation of real and monetary volatility, respectively.

We also run regressions controlling for a separate interaction term of financial intermediary development with both terms of trade and inflation volatility for highincome countries.

where _High_ is a dummy variable taking the value one for countries that are classified by the _World Development Report_ as high-income, and zero otherwise. The overall impact of the interaction of financial intermediary development and real or monetary volatility in high-income countries would then be r/+7 and r2+r4, respectively.

The interaction terms in these regressions are by definition correlated with their components. This gives rise to the problem of multicollinearity. While this does not necessarily bias the estimates, it does increase the size of the estimated variance, and, given the relatively small sample sizes, may cause instability in the parameter estimates. Examination of variance inflation factors<sup>22</sup> reveals that volatility in terms of trade changes is the largest sources of collinearity. In our empirical work, this might lead to the case where the parameter estimates on Private Credit and its interaction with the respective volatility measure are individually insignificant, but jointly significant. We therefore report the joint significance of the individual volatility measures and the interaction terms.

To control for biases introduced by the estimation of panel data, we use two different estimation strategies. The data combine cross-country and time-series, which 20
# **References**

- Angelini, Paolo, and Andrea Generale. 2008. “On the Evolution of Firm Size Distributions.” _American Economic Review_ 98 (1): 426–38.

- Aterido, Reyes, and Mary Hallward-Driemeier. 2010. “The Impact of the Investment Climate on Employment Growth: Does Sub-Saharan Africa Mirror Other Low-Income Regions?” Policy Research Working Paper 5218, World Bank, Washington, DC.

- Aterido, Reyes, Mary Hallward-Driemeier, and Carmen Pagés. 2007. “Investment Climate and Employment Growth: The Impact of Access to Finance, Corruption and Regulations across Firms.” RES Working Paper 4559, Research Department, Inter-American Development Bank, Washington, DC.

- ———. 2009. “Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth across Firms.” Policy Research Working Paper 5032, World Bank, Washington, DC.

- Ayyagari, Meghana, Asli Demirgüç-Kunt, and Vojislav Maksimovic. 2006. “How Important Are Financing Constraints? The Role of Finance in the Business Environment.” Policy Research Working Paper 3820, World Bank, Washington, DC.

- Batra, Geeta, Daniel Kaufmann, and Andrew H. W. Stone. 2003. _Investment Climate around the World: Voices of the Firms from the World Business Environment Survey._ Washington, DC: World Bank.

- Beck, Thorsten, Asli Demirgüç-Kunt, and Vojislav Maksimovic. 2005. “Financial and Legal Constraints to Growth: Does Firm Size Matter?” _Journal of Finance_ 60 (1): 131–77.

- Bigsten, Arne, and Mans Söderbom. 2006. “What Have We Learned from a Decade of Manufacturing Enterprise Surveys in Africa?” _World Bank Research Observer_ 21 (2): 241–65.

- Cabral, Luis M. B., and Jose Mata. 2003. “On the Evolution of the Firm Size Distribution: Facts and Theory.” _American Economic Review_ 93 (4): 1075–90.

- Cooley, Thomas F., and Vincenzo Quadrini. 2001. “Financial Markets and Firm Dynamics.” _American Economic Review_ 91 (5): 1286–310.

- Demirgüç-Kunt, Asli, and Vojislav Maksimovic. 1998. “Law, Finance and Firm Growth.” _Journal of Finance_ 53: 2107–37.

- Desai, Mihir, Paul Gompers, and Josh Lerner. 2003. “Institutions, Capital Constraints and Entrepreneurial Firm Dynamics: Evidence from Europe.” NBER Working Paper 10165, National Bureau of Economic Research, Cambridge, MA.

18
also declines with rising transport costs and rising costs of financial services in furniture making, which is the industry that is most similar to the industries of Tables 6B and 10B in that regard. But according to Tables 11 and 11B productivity does not seem to respond to changes in the size of the local market even in the furniture industry among this group of industries.

# 4. Conclusion

Based on analyses of the panel data on plants in 11 four‐digit ISIC manufacturing industries in Ethiopia, this paper estimates the effects on productivity of local market size, transport costs and license fees. The first two of these variables are important demand‐side drivers of industrial productivity growth. The third is an important item of sunk costs of entry and consequently a supply‐side factor. Identification of the effects relies on the Melitz‐Ottaviano model of productivity and producer turnover under monopolistic competition, in which all three are joint determinants of the average cost of the marginal producer in long run equilibrium.

All three predictions of the model are borne out in the most homogenous of the 11 industries, namely, the production of cinder blocks (ISIC 2695). In that industry, expansion of the local market boosts revenue productivity while increases in transport costs and licensing fees reduce it. The picture is somewhat mixed in the other industries but nonetheless supportive of the model in as far as at least one and, more often, two of the predictions hold up. The most significant result is that local market size is a major factor in aggregate revenue productivity within each of 9 of the 11 industries, the exceptions being wood work (ISIC 2000) and the plastic products industry (ISIC 2520). Rising transport costs also reduce revenue productivity significantly within each of 8 of the 11, the industries where the result does not hold being producing footwear (ISIC 1920), furniture (ISIC 3610), or structural metal products (ISIC 2811). Increasing licensing fees reduce aggregate productivity within all industries excepting the manufacturing of textiles (ISIC 1710), wearing apparel (ISIC 1810) and structural metal products (ISIC 2811).

Looking at the relative weight of the three factors, local market size exerts the greatest influence on aggregate revenue productivity across the 11 industries. This is in the sense that the elasticity 27
- 10 -

services are typically labor-intensive: their ratio of recurrent to investment costs is high, causing high payroll costs for public administrations, especially where job-protection policies are in effect.8' Responsibilities for service provision can be budgetarily fragmented among a variety of public institutions, departmental companies and controlled enterprises: the activities of many different agenci s are not easy to coordinate and this makes it difficult to control their investment and operation costs and to utilize their manpower efficiently.2' In metropolitan areas, this picture may be further complicated by iurisdictional subdivision.<sup>1</sup> 01 These institutional differences are often rooted in historical, cultural, social and political factors, which are not easily removed. Therefore, the rising costs of urban services are not only determined by population growth or by the trends of the local and national economy; in fact, the evidence is that per capita expenditures of local governments tend to rise faster than population and GNPU' (see Table 1.10).

1.11 Reasons for Public Intervention. Residents of urban areas generally interact among themselves more thar people living in rural settings. Their business and household activities engender greater cost and benefit spillovers

Table 1.10: PER CAPITA GNP AND SUBNATIONAL EXPENDITURES -Variation Index, 1975=100 a/

||1975|1976|1977|1978|<br>1979|1980|1981|1982|1983|<br>1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE|||||||||||
|Per Capita Expenditure|<br>100|<br>102|<br>110|128|<br>142|158|<br>156|<br>174|<br>168|<br>143|
|GNP Per capita|100|<br>108|<br>117|132|<br>154|177|<br>197|<br>171|<br>141|<br>139|
|Population|100|103|<br>105|108|<br>110|114|<br>117|<br>119|<br>122|<br>125|

---------------------------------------------------------------------

__------__----------------------------a/ The expenditure and GNP variation index for the individual countries is based on constant national currencies time-series while the variation index for the sample average is based on constrant US dollars.

Source: IMF, Government Finance Statistics Yearbook, various years.

- t/ The Municipality of Buenos Aires employs some 85,000 people, that of Montevideo nearly 14,000 people.

- i/ This point is elaborated in Para 3.4 (Parastatal Enterprises).

- I/ The conurbated area of Mexico City overlaps territories comprised in 5 states, surrounding the Federal District. Greater Buenos Aires includes 50 percent of the urbanized population of Buenos Aires Province, adjoining the Federal Capital. Similar inter-jurisdictional problems (more frequently inter-municipal) occur in most large metropolitan areas.

- II/ Again one must note in Table 1.09 and 1.10 the sudden reversal of trends caused by the 1981 debt crisis.
not lead to a proportional increase in imports. Given the absolute level of poverty in African countries and their immediate needs, it is expected ex ante that consumption, investment, or even reserve accumulation expand along with the increase in total grants (or even more given that the constraint is less binding). This will not be the case, however, if the resources are being applied to other uses (such as repayment of debt).

**Table 5. ODA debt forgiveness: Comparison across country groups** (in US$ millions)

||_Average_<br>_1984-88_||_Average_<br>_1989-93_|_Change_<br>_(percent)_|_Average_<br>_1984-93_||
|---|---|---|---|---|---|---|
|**(A) 31 SPA countries (excl. Nigeria)**|||||||
|Import bill|4,287||7,498|74.9%|5,892||
|ODA forgiveness|13||889|6819%|451||
|Pure grants|3,443||6,092|**77.00/o**|4,767||
|Pure grants/Import Bill (percent)|85.3%||81.6%|-4.3%|83.4%||
|Multilateral net flows|1,263||1,717|36.0%|1,490||
|Bilateral net flows|1,198||974|-18.7%|1,086||
|Total private net flows|-723||-501|-30.7%|-612||
|**(B) Low group (16) countries**|||||||
|Import bill|2,369||3,993|68.6%|3,181||
|ODA forgiveness|3||77|**28190/o**|40||
|Pure grants|1,528||2,559|67.4%|2,043||
|Pure grants/Import Bill (percent)|65.3%||64.5%|-1.2%|64.9%||
|Multilateral net lows|595||945|58.7%|770||
|Bilateral net flows|416||306|-26.5%|361||
|Total private net flows|-164||-148|-9.4%|-156||
|(C) Middle group (8)**countries (excl.**|Nigeria)||||||
|Importbill|1,103||2,318|110.2%|1,710||
|ODA forgiveness|7||248|3233%|128||
|Pure grants|1,070||1,914|79.0%|1,492||
|Pure grants/Import Bill (percent)|73.8%|(1)|84.9%|15.0%|80.7%|(2)|
|Multilateral net flows|228||381|67.6%|304||
|Bilateral net flows|493||294|-40.4%|394||
|Total private net flows|-360||-236|-34.6%|-298||
|**(D) High group**(7) countries|||||||
|Import bill|816||1,186|45.5%|1,001||
|ODA forgiveness|3||564|20303%|283||
|Pure grants|845||1,619|91.6%|1,232||
|Pure grants/import Bill (percent)|114.8%||160.1%|39.5%|137.4%||
|<br>Multilateral net flows|440||391|-11.2%|416||
|Bilateral net flows|288||374|29.9%|331||
|Total private netflows|-198||-117|-41.2%|-158||
|<br>Notes: (1) 1986-88; (2) 1986-93.|||||||

In sum, the evidence presented above suggests that, the increase in ODA debt relief since 1989 has been neutral in the strict and limited sense that it has not affected recipient countries' ability to import (the high group has not outperformed the other groups in terms of improvements in import capacity). Next we analyze the same problem but from an incremental or marginal approach.

13
note, a well-known limitation of this approach is that the AVE is not related to any specific regulatory barrier. In our experience in estimating these AVEs in business services sectors in Russia, Armenia, Georgia, Kenya, Tanzania, Kazakhstan, Belarus and Malaysia,<sup>6</sup> we find that incountry officials are often deeply skeptical or unconvinced of such estimates unless the researcher can point to specific regulatory barriers that explain the ad valorem equivalent in particular sectors; but when specific barriers can be referenced, the story typically becomes convincing. Further, without tying the AVEs and the costs of the barriers to specific regulatory barriers, the analyst is not able to recommend potential policy reforms.

Consequently, we prefer the second approach and our methodology is firmly rooted in it. In addition to the single country studies of many sectors mentioned above, the second approach has been employed in single sectors for many countries by Nguyen-Hong (2000), Warren (2000), Kalirajan et al., (2000), Kang (2000) and Kalirajan (2000).<sup>7</sup> Doove et al., (2001), Dihel and Shephard (2007) and Copenhagen Economics (2005) have estimated AVEs employing this approach for many countries and in multiple sectors. This approach collects information on the regulatory regime in a particular services sector in a country and typically converts that information into an index of restrictiveness.<sup>8</sup> Then the impact of services restrictiveness on a measure of price or cost is estimated econometrically, where a vector of independent variables that impact the price or cost of the service is included in the estimation to control for their impact. This approach is necessarily done on a sector by sector basis. The Australian Productivity Commission has pioneered work along these lines, and many of the studies it supported were collected in Findlay and Warren (2000). Although data on the full vector of variables impacting price or cost may not be known for many countries, if the restrictiveness index is known, estimates of the AVE of services barriers may be obtained using this approach assuming that the estimated coefficient on the restrictiveness index applies in these countries.

Lacking information on the regulatory regimes in services for most countries, the authors above were generally able to estimate ad valorem equivalents of the services barriers in less than

> 6 The first five of these country studies are available at https://drive.google.com/?pli=1#folders/0B0V-t-Bs4- <u>hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2</u>

> 7 Bottini et al., (2011) estimate the impact of barriers in banking and telecommunications on performance variables in four Middle Eastern countries.

> 8 Deardorff and Stern (2006) discuss alternate, but less common, ways to use the information on the regulatory barriers in subsequent regressions. See Dihel and Shephard (2007) for one variation of this approach.

3
New Simple Test.” Oxford Bulletin of Economics and Statistics 61: 631-652.

Maddison, A. (2013). The Maddison-Project. http://www.ggdc.net/maddison/maddisonproject/home.htm, 2013 version.

Ostry, J., A. Berg, and C. Tsangarides (2014). “Redistribution, Inequality, and Growth.” IMF Staff Discussion Note SDN14/02.

Scholl, N. and S. Klasen (2016). “Re-estimating the Relationship between Inequality and Growth.” Courant Research Centre: Poverty, Equity and Growth - Discussion Papers No 205.

- Staiger, D. and J. Stock (1997). “Instrumental Variables Regression with Weak Instruments.” _Econometrica_ 65: 557-586.

- Stock J., and M. Yogo (2005). “Testing for Weak Instruments in Linear IV Regression.” In: Andrews DWK Identification and Inference for Econometric Models. New York: Cambridge University Press; 2005. pp. 80-108.

Panizza, U. (2002). "Income Inequality and Economic Growth: Evidence from American Data." _Journal of Economic Growth_ 7: 25-41.

WDI (2017). World Development Indicators. Available for download at http://data.worldbank.org/ 25