rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Policy Research<br><!-- End of picture text -->

# **Financial Pollcy and Systems**

WPS 1019 This papera product of the Financial Policy and Systems Division, Country Economics Departmentis part of a larger effort in the department to study the effectiveness of directed credit policies. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Raggambi, room N9-033, extension 37664 (November 1992, 30 pages).

Schwarz surveys U.S. experience with directed credit as background for a larger study of the Asian experience. Almost half of net credit lent in the United States annually is directly affected by government policies - half of net credit covering budget deficits, and half falling under various federal credit programs.

But the main difference between U.S. and Asian credit policy is that U.S. credit policy is oriented more toward equity than toward growth. Different sectors are affected differently by U.S. credit policies.

Few empirical studies test how U.S. credit - policies affect growth perhaps partly because of the motives behind those policies. Few empirical studies even test whether the policies effectively increase credit to the target group. Schwarz outlines a method for testing the effectiveness of credit policy, then examines existing empirical work to see how it fits that methodology.

The first common empirical technique examnines credit allocation in the economy. Schwarz finds that for the largest program, housing credit, the effect of credit program on credit allocation is very small and may be negative when crossprogram effects are considered.

The second common empirical technique examines individual sectors. Results here are mixed. In agriculture, much of the credit raises the demand for land, providing a gain for landowners rather than increasing production. In education, less than a third of the students who got government credit would not have gone to college without it. So in both cases, the credit had a positive impact but at a sizable cost.

Schwarz concludes that despite its huge volume, directed credit in the United States has a limited impact on growth. The credit programs have generally succeeded in increasing credit to the targeted group, but not necessarily in increasing investment by that group.

**The** Policy Research Working Paper Series disseminates the findings of work **under** way in the Bank. An objective of the series is to **get** these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by **thd** Policy Research Dissemination Center
# Figure 3. Cumulative Distribution Functions of Price Volatility for Goods with Uninterrupted Price Series

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 1 2 3<br>standard deviations<br>primary commodities all manufactured goods<br>others<br>cumulative distribution functions<br><!-- End of picture text --> Note: The figure shows the cumulative distribution functions of the standard deviations of HodrickPrescott filtered series of individual goods prices. The goods represented are those which prices are available for at least 36 consecutive months. Data are from the Foreign Trade Division of the U.S. Census Bureau.

19
Policy ReseaRch WoRking PaPeR 4791

# **Abstract**

This paper surveys the recent literature which examines the impact of business climate variables on productivity and growth in developing countries using enterprise surveys. Comparable enterprise surveys today cover some 70,000 firms in over 100 countries around the world. The literature that has analyzed this data provides evidence that a good business climate drives growth by encouraging investment and higher productivity. Various infrastructure, finance, security, competition and regulation variables have been shown to significantly impact firm performance. Section 1 of this paper outlines the theoretical framework that underpins the investment climate literature. Section 2 describes the available datasets and surveys the key findings of the empirical literature, first macroeconomic and then microeconomic studies. Particular attention is paid to the robustness of the reported results. Section 3 highlights important econometric issues common to this literature and suggests a research agenda and possible improvements in survey design.

This paper—a product of the Research Support Unit, Development Research Department—is part of a larger effort by the World Bank to use enterprise surveys to identify constraints on productivity and growth in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. For information, contact jdethier@ 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
|ple with dummies<br>**SURE**<br>**(8)**<br>0.037<br>(0.86)|-0.002<br>(-0.399)|-0.119<br>(-2.807)***|0.163<br>(3.298)***|0.063<br>(1.838)*|0.134<br>(2.545)**|-0.023<br>(-2.489)**|0.294<br>(5.116)***|287<br>0.301|a in the<br>therwise.<br>* indicates<br>of zero|
|---|---|---|---|---|---|---|---|---|---|
|Whole sam<br>**OLS**<br>**(7)**<br>0.087<br>(1.678)*|0.001<br>(0.184)|-0.129<br>(-2.492)**|0.159<br>(2.562)**|0.059<br>(1.3)|0.202<br>(2.825)***|-0.017<br>(-1.645)*|0.290<br>(4.609)***|287<br>0.321|n for panel dat<br>untries and 0 o<br>ficance level, *<br>ull hypothesis|
|ple pooled<br>**SURE**<br>**(6)**<br>0.102<br>(2.652)***<br>**d SURE)**|-0.010<br>(-2.183)**|-0.059<br>(-1.503)|0.069<br>(1.896)*<br>...|...|0.071<br>(1.365)|-0.022<br>(-2.47)**|0.299<br>(5.141)***|287<br>0.284|elated regressio<br>fast growing co<br>icates 10% signi<br>of rejecting the n|
|Whole sam<br>**OLS**<br>**(5)**<br>0.143<br>(3.148)***<br>**l OLS an**|-0.006<br>(-1.228)|-0.084<br>(-1.765)*|0.068<br>(1.481)<br>...|...|0.161<br>(2.317)**|-0.023<br>(-2.4)**|0.296<br>(4.666)***|287<br>0.312|n seeminly unr<br>which is 1 for<br>enthesis. * ind<br>he probability|
|n group<br>**SURE**<br>**(4)**<br>0.080<br>(0.817)<br>**itures (pane**|-0.004<br>(-0.476)|0.002<br>(0.028)|0.026<br>(0.448)<br>...|...|0.090<br>(0.816)|-0.018<br>(-1.63)|0.240<br>(3.237)***|167<br>0.072|the cross-sectio<br>dummy variable<br>are given in par<br>al to one minus t|
|Compariso<br>**OLS**<br>**(3)**<br>0.061<br>(0.557)<br>**Expend**|-0.008<br>(-0.732)|-0.064<br>(-0.745)|0.043<br>(0.808)<br>...|...|0.250<br>(2.149)**|-0.009<br>(-0.706)|0.284<br>(3.611)***|167<br>0.095|t column and<br>DFAST is the<br>e. t-statistics<br>evels are equ|
|ng countries<br>**SURE**<br>**(2)**<br>0.115<br>(2.698)***<br>**otal Public**|0.006<br>(0.931)|-0.296<br>(-4.175)***|0.448<br>(4.263)***<br>...|...|0.304<br>(3.942)***|-0.177<br>(-4.908)***|0.378<br>(4.11)***|120<br>0.473|l data in the firs<br>ed one period.<br>p and 0 otherwis<br>e significance l|
|Fast growi<br>**OLS**<br>**(1)**<br>0.107<br>(1.917)*<br>**ts with T**|0.007<br>(0.96)|-0.323<br>(-3.884)***|0.477<br>(3.876)***<br>...|...|0.341<br>(3.668)***|-0.205<br>(-5.65)***|0.406<br>(3.928)***|120<br>0.443|ares for pane<br>variables lagg<br>parison grou<br>ce level. Thes|
|**_Dependent variable: Growth rate of GDP per capita_**<br>Private investment<br>(% of GDP)<br>**Table 3: Resul**|Initial human capital|Total fiscal revenue<br>(% of GDP)|Productive plus unproductive expenditures (-1)<br>(% of GDP)<br>DFAST*(Productive plus unproductive expenditures (-1))<br>(% of GDP)|DCOMP*(Productive plus unproductive expenditures (-1))<br>(% of GDP)|Budget surplus<br>(% of GDP)|Inflation - consumer price index|Growth rate of GDP per capita (-1)|No. of observations<br>Adjusted R2|Note: The estimation method is the ordinary least squ<br>second column. Annual data are used. (-1) indicates<br>DCOMP is the dummy variable which is 1 for the com<br>5% significance level, and *** indicates 1% significan<br>coefficients.|

34
between the farm-gate and wholesale markets<sup>12</sup> . Overall, these costs disproportionally add to total marketing costs at the first two segments of the supply chain, particularly the village to primary markets segment as indicated in table 1. Because of this, the itinerant buyer must minimize these and other costs by collecting as much maize as transport capacity allows. The risk of collecting too little maize after having incurred the costs of travelling to and around the village is significantly minimized in the harvest season when maize is more abundant than it would be in the lean season.

_Table 1_<sup>_13_</sup> _: Marketing costs at various stage of the supply chain_

|**Market segment**|**Cost element**|**US$per ton**|
|---|---|---|
|Farm-gate-primary market|Storage/rental fee<br>Transportation charges<br>Hired labor loading/unloading<br>Council cess<br>**Sub total**|0.80<br>6.40<br>1.92<br>1.60<br>**10.72**|
|Primary-secondary market|Storage/rental fee<br>Transportation charges<br>Hired labor loading/unloading<br>Council cess<br>Drying tent/empty bags<br>**Sub total**|1.20<br>27.00<br>4.00<br>1.60<br>0.5<br>**34.30**|
|Secondary-wholesale market/miller|Storage/rental fee<br>Transportation charges<br>Hired labor loading/unloading<br>Council cess|0.11<br>41.40<br>4.00<br>0.0|
||**Sub total**|**45.51**|
||**Total**|**90.53**|

Search costs are extremely common in many markets. As consumers, most of us have experienced this phenomenon, for instance, when searching for the best price for a new computer online or on the high street. Anderson & Renault (1999) provide another informative example of how search costs function is given by: the tourist trap. Tourist areas are often dominated by low quality, high cost restaurants that offer relatively undifferentiated menus, but that nevertheless have full tables. Their good fortune is caused by the high search costs faced by the average tourist, who is unaware of the local alternatives and that values leisure time highly.

> 12 This refers to transaction costs that are normally measured in surveys, which exclude, amongst others, costs related information, search and bargaining.

> 13 Source: World Bank survey carried out in November-December 2008.

7
and non-tariff barriers gathered by UNCTAD for a cross-section of developing countries in 1987. **2** II. Data and correlations between openness variables.

The empirical specification employed in this paper is derived from a general production function, with output growth (GDP) as a function of capital stock, average years of education, population, labor force, agricultural land, and technological change. Inclusion of openness measures (or other policy variables) in the production function is consequently a test their impact on technological change--growth in output after controlling for increases in resource use. The production function is augmented--through the use of country dummy variables--to allow for unobserved country-specific differences in productivity.

GDP growth is calculated as log differences using national accounts data in 1980 dollars, collected by the World Bank. Physical and human capital stocks were calculated by Bhalla and Lau (1991), extending a method applied by Lau, Jamison, and Louat (1991) on a smtller sample of countries. Bhalla and Lau computed capital stock and years of education from annual capital investment and educational enrollment data, using the perpetual

> 2 See Pritchett (1991) for further details regarding the UNCTAD data. Pritchett (1991) uses cross-section data to compare a number of different openness measures, including the UNCTAD and Leamer measures. Although we considered using the UNCTAD data to estimate the impact of 1987 tariff and non-tariff barriers on consequent groqth, not enough post-1987 data was available to do this exercise.

10
measures.

Next, we validate the size of firms against CDR measures in Table A3. For each firm, we calculate the number of unique subscribers (phone numbers) active from January-March 2014, where we winsorize the top 1% of values to address large outliers. We then compare these subscriber totals to the total firm employment numbers gathered during April and May 2014 as part of the screening survey for the Central Statistic Office’s Integrated Business Enterprise Survey (IBES).<sup>18</sup> We successfully name matched 190 firms to this dataset, and find a robust, positive relationship between these two independent measures of firm size; the cross-sectional correlation is .49 in levels in Column (1) of Panel A ( _p < ._ 01) and .23 in logs in Column (1) of Panel B ( _p < ._ 01). We again extend this exercise comparing our original survey data from spring 2017 with the number of unique subscribers in October-December 2016 and three years earlier in October-December 2013, and find a strong cross-sectional correlation between self-reported employees in 2017 of .57 in levels ( _p < ._ 05) and .23 in logs ( _p < ._ 01). These results suggest that even when discounting the role of firm outliers, active mobile subscribers may provide useful information about firm size. In general, substantial caution is warranted when using subscribers as a proxy for firm size, given that firms may either maintain more phone lines than employees or alternatively may not provide corporate lines for all of their employees.

Third, we validate aggregate economic activity against CDR measures in Table A4. For each province-month, we calculate the number of total corporate calls, and compare this to province level tax revenue records from the Ministry of Finance’s Afghanistan Financial Management Information System (AFMIS). Using currently available data, we have 17 overlapping months for a panel of 34 provinces during 2013 and 2014. We find a positive relationship between calling time and tax revenues: a one standard deviation increase in calls in a province is associated with a .85 standard deviation increase in provincial tax revenues ( _p <_ .01). The relationship is robust to controlling for unobserved time-invariant factors:

> 18The IBES survey sample combined a listing of 4,000 establishments with 10 or more employees (including public and non-profit organizations) and a random area sample of establishments with less than 10 employees

14
our predictions based on industrial composition were positively correlated with ICT abundance. However, once we include both regressors jointly, only the industrial prediction regressor has any explanatory power. In fact, the point estimate does not significantly differ from the estimate in column (A.2) and is in the neighborhood of one, as our model would predict. The results in panel B mirror those of panel A. The pooled regressions in panel C also confirm the results from panels A and B. Notice further that the time effects in panes C.2 and C.3 are insignificant (i.e. statistically speaking zero on average), directly lending support to our expression derived in equation (12).

While these results provide some evidence for the predictions of our model, there are many potentially omitted regressors that might cause both ICT accumulation and industrial composition (e.g., low skill labor) and therefore bias our results. To address this concern, panel D of Table 3 shows an additional regression in which we proxy the the predicted values based solely on industrial composition in 1980. That is, we “fix” the industrial composition within each country prior to the main thrust of the so-called “ICT revolution”. This alternative specification confirms the results from our main estimates in panel C.

Finally, panel E reports one additional specification in which we regress the log ratio of observed and predicted relative ICT/NICT values on log real GDP per capita. Again, this regression also suggests that there is no systematic relation between relative ICT abundance and real income after industrial composition is accounted for. Specifically, notice the substantially lower _R_<sup>2</sup> (and adjusted _R_<sup>2</sup> ) for this final specification.

Given the limited number of countries and years in the GGDC10 database we repeat the above exercise with data from the WDI, which provides many more years and countries but offers a much more coarse classification for the sectors of production. Table 4 shows that our estimates based on WDI data are very close to those based on the GGDC10 database. Specifically, notice that all the time effects in specification (C.1) are highly significant while the bulk of the variation is absorbed by our predictions based on industrial composition in specifications (C.2) - (E). Again, like in our analysis based on GGDC10 data, the coefficient on our industry predictions is close to one in all specifications. Finally, once we divide the observed values of relative ICT abundance by our industry predictions (column E), real income and time effects have virtually no explanatory 25
- **DKFORFGE - Quarterly change** in **U.S. farmer owned reserve of feed grain (computed from KFORFGE)**

- **DLVKFG - U.S. feed and** residual of feed grains (corn, sorghum, oats, **barley!-- million metric tons** _(Feed: Outlook and Situation Report)_

- DMYPIK - **Dummy** variable for **PIK** Program, 1 if third or fourth quarter of 1983, 0 if not

- DRSI - Feed grain program interest rate subsidy (computed from RS and ICCC)

- EXR - Exchange rate, trade weighted index in dollars per unit of foreign currency, 1972 - 1.00

- GNPD = GNP price deflator from the Fair model

- ICCC - Interest rate charged for CCC non-recourse loans in percent _(Feed:_ KCCCFGE = Ending feed grain stocks under CCC loan, million metric tons _(U.S. Agricultural Stabilization and Conservation Service)_

- KFORFGE = U.S. ending farmer owned reserve stocks of feed grains, million metric tons _(U.S. Agricultural Stabilization and Conservation Service)_

- KGOVFGE - Ending government owned feed grain stocks (total CCC inventory), million metric tons (U.S. Agricultural Stabilization and

- Conservation Service) _Outlook & Situation Report)_

- KMKTFGE - U.S. ending market inventory of feed grain (computed from KPRIFGE and KCCCFGE)

- KPRIFGE - U.S. free ending stocks of feed grains, million metric tons (U.S. Agricultural Stabilization and Conservation Service)

- KPRISBE - U.S. ending free soybean stocks, million metric tons **_N-_** Population

- PAFC **-** U.S. average price of corn at farm in dollars per bushel _(Feed: Outlook & Situation Report)_ Q1 - January-March, Q2 = April-May, Q3 = June-September, Q4 = October-December

- PAFCFR - Real difference in the U.S. corn price and the corn support price (computed from PAFC, SPRC, and GNPD)

- PAFS = Soybeans, price at farm, U.S. average in dollars per bushel _(Oil Crops: Outlook & Situation Report)_ Q1 - January-March, Q2 = April-May, Q3 = June-September, Q4 = October-December

- PAFSG = Sorghum, price at farm, U.S. average in dollars per bushel _(Livestock & Poultry: Outlook & Situation Report)_ Ql -

- January-March, Q2 = April-May, Q3 = June-September, Q4 October-December

- PBEEF = Average retail price of choice beef in cents per pound _(Livestock_ **_6_** _Poultry: Outlook 6 Situation Report)_ Q1 - January-March, Q2 April-June, Q3 - July-September, Q4 - October-December

- PBR = Average retail price in 4-regions of broilers in cents per pound _((Livestock & Poultry: Outlook & Situation Report)_ Ql - January-

- March, Q2 - April-June, Q3 = July-September, Q4 - October-December

- PCDBEEF = Per capita disappearance of carcass weight of beef in pounds _(Livestock & Poultry: Outlook 6 Situation Report,_ Supply &

- Utility Table)

- PCDBEEF - Per capita beef consumption (computed from PCDBEEF, N, and POP) PCDBR - Per capita civilian disappearance of young chickens in pounds _(Livestock & Poultry: Outlook & Situation Report)_

- PCDBR - Per capita broiler consumption (computed from PCDBR, N, and POP)

- PCDPORK - Per capita disappearance of carcass weight of pork in pounds _(Livestock & Poultry: Outlook & Situation Report)_

13
namely its impact on endowment differences between a region’s small states and on the benefits from regional bloc formation.

The remainder of the paper is organized as follows. Section 2 presents a general equilibrium model where the formation of a regional cooperation bloc among small states and its welfare implications re examined under various accession rules. Section 3 incorporates SouthSouth migration into the model and Section 4 examines the relationship between migration and trade and shows that it varies with the objective of the negotiations and the nature of the benefits. Section 5 incorporates South-North migration in the analysis and Section 6 concludes.

# **2. Model**

This section presents, in Subsection 2.1, a general equilibrium framework in order to examine the formation of a regional cooperative arrangement or regional bloc between some of the region’s small states. Subsections 2.2 and 2.3 examine the bloc’s equilibrium size and welfare impact under two different rules regarding small states’ accession to the regional bloc. This section abstracts from international migration, which is taken up in Section 3.

## 2.1. Bloc Formation in a General Equilibrium Framework

Assume a Heckscher-Ohlin model for a region with a uniform distribution of small laborabundant developing states<sup>_n_</sup> _i_  2,1[] using capital and labor to produce a labor-intensive exportable ( _X_ ) and a capital-intensive importable ( _Y_ ). Prices, which are normalized to one ( _PX_  _PY_  1 ), are given to each small state and to the region as a whole, and thus so are the normalized factor prices _w = r =_ 1.

The region’s small states form a regional bloc if cooperation between its members raises the benefits obtained from international negotiations. The number of foreign entities with which each state negotiates is denoted by _m_ . Internal solutions are assumed throughout unless stipulated otherwise. Each bloc member’s per-issue payoff or “revenue” _R_ from collective action is 2 2 _R_  _R_ ( _n_ ), _Rn_   _R_ /  _n_  ,0 _Rnn_   _R_ /  _n_  0 . (1) That _R_ ( _n_ ) is an increasing function of bloc size _n_ reflects the fact that a bloc’s bargaining power in international negotiations increases with its size. The latter is particularly important for small 5