rafmacalaba/datause-encoder-data huggingface dataset:
and the state of poverty. For instance, if the mean income of workers in the rural tradable goods sector is below the poverty line, all workers in this sector are considered poor. Likewise, inequality indicators in this framework are based only on the distance between group-specific means. Therefore, withingroup heterogeneity (that is, dispersion around group means) is completely ignored.

However, a common observation is that the contribution of within-group income inequality to overall income inequality is much more important than that of between-group inequality, even if households are disaggregated in relatively small groups and part of the intra-group inequality can be attributed to measurement errors and idiosyncratic, transitory elements of income. If, for instance, Ivorian households are classified in 10 groups according to sector of activity and educational attainment of the household head, more than 80 percent of the variance of household income per capita is within groups. Likewise, in the case of Indonesia a similar classification in 10 groups leaves 74 percent of the total variance unexplained. Or, if we separate Malagasy households in 14 even groups, we still find 76 percent of the total variance within groups.<sup>2</sup> Furthermore, inequality changes within groups may be at least as important as changes between groups. Because, by definition, Macro-RHG models do not account for intra-group heterogeneity, they cannot provide much insight in the analysis of the impact of government policy or exogenous shocks on income distribution.

# 3 Linking Income Survey Data with MacroRHG Models

In what follows we present three approaches to linking Macro-RHG models with information from a Household Income Survey, such as an Integrated Survey (IS) or Living Standard Measurement Survey (LSMS). The first two approaches are so-called ‘micro-accounting’ approaches. This term refers to a special kind of micro-simulation models that work directly with all the observations gathered in a household survey, but do not take explicitly into account the behavior of agents at the micro level. Under the third approach, the Macro-RHG model supplies the household module with group-specific

> 2These estimates are derived from computations by the authors, based on household surveys of the respective countries.

6
- David A. Aschauer. Does public capital crowd out private capital? _Journal of Monetary Economics_ , 24(2):171–188, 1989.

- Marianne Baxter and Robert G. King. Fiscal policy in general equilibrium. _The American Economic Review_ , 83(3):315–334, June 1993.

- Cesar Calderon and Luis Serven. The effects of infrastructure development on growth and income distribution. Vol. 3400, Washington, DC: World Bank., 2004.

- Cesar Calderon, Enrique Moral-Benito, and Luis Serven. Is infrastructure capital productive? a dynamic heterogeneous approach. World Bank Policy Research Working Paper 5682, 2011.

- Eduardo Cavallo and Christian Daude. Public investment in developing countries: A blessing or a curse? _Journal of Comparative Economics_ , 39(1): 65–81, March 2011.

- Walter H. Fisher and Stephen J. Turnovsky. Public investment, congestion, and private capital accumulation. _The Economic Journal_ , 108:399–413, March 1998.

- Edward M. Gramlich. Infrastructure investment: A review essay. _Journal of Economic Literature_ , 32(3):1176–1196, September 1994.

- A.S. Hadi. Identifying multiple outliers in multivariate data. _Journal of the Royal Statistical Society_ , 54:761–771, 1992.

- Aart Kraay. How large is the government spending multiplier? evidence from world bank lending. _Quarterly Journal of Economics_ , 127(2):829– 887, 2012.

- Aart Kraay. Government spending multipliers in developing countries. evidence from lending by official creditors. _American Economic Journal: Macroeconomics_ , 2013. Forthcoming.

28
44 Table 8 (Dependent Variable is Return on Assets, ROA) OLS Results

|Variable|Coefficient|T-Statistics|
|---|---|---|
|C|-0.12324|-1.54|
|MSHARE|0.096954|1.54|
|CA|0.148375|5.44|
|TA|6.412E10|0.85|
|LA|-0.02495|-4.95|
|DT|0.010267|3.26|
|OEA|-0.191380|-3.29|
|MDGR|0.001398|0.50|
|CR5|0.198141|2.66|
|DI|0.87596|2.34|
|R-Squared||0.56|
|F-Statistic||22.04|
sometimes, even to keep them above the poverty line: consider that the basic consumption basket for a family of four in 2011 cost US$6,400 per year, and the poverty line for a family of four was around US$3,300.

# **2.4 Access to finance**

By and large, access to credit for firms in the EMES sample is low. Only 22 percent of firms borrowed funds at their inception, and only 31 percent of them obtained a loan in the last year. This is substantially lower than the share of formal urban firms of all sizes who currently access credit in Ecuador (49 percent) and formal firms with 50 employees or less in all of Latin America (42 percent; LAC Enterprise Survey 2010). Female entrepreneurs tend to be more successful in obtaining loans, and smaller firms are also more likely to obtain a loan either at the start or in the last year, compared to their larger counterparts. This is likely explained by the fact that most of the credit –and thus the liability—is given to the entrepreneur, not to the firm (this was confirmed by focus group participants), which increases the risk and difficulty for larger firms to obtain loans (that are typically also larger). As a result, larger firms are more likely to use their own earnings to finance their operations, although retained earnings alone are not likely to be sufficient to consistently finance business expansion.

**Figure 4: Uses of credit**

<!-- Start of picture text --> Construction<br>Food vendors<br>Restaurants and hotels<br>Auto repair<br>Transport<br>Groceries retail<br>Manufacturing others<br>Textiles manufacturing<br>[11-50]<br>[6-10]<br>[1-5]<br>Males<br>Females<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Machinery & equipment Merchandise Expansion Rent Other expenses Repay debt Other<br>Source: EMES<br>Sector<br>Size<br>Gender<br><!-- End of picture text --> For credit obtained in the past year, loan sizes tend to be small and maturities short. The median loan is about 20 percent of annual sales, which is a small amount even for a micro firm (if median annual sales are US$12,000, the median loan is US$2,400). Consequently, firms use credit mainly for short-term cash flows (e.g., purchase inventory), except in transport and auto repair shops where most of the credit is used to purchase machinery and vehicles (Figure 4). As for duration, most loans are repaid within 1 1⁄2 years, which is significantly shorter than in OECD countries and points to markets characterized by with high information asymmetries and low borrower bargaining power (Kirschenmann and Norden, 2012).<sup>5</sup>

> 5 The median loan maturity for firms of less than 50 employees in OECD is close to five years (See HernándezCánovas and Koëter-Kant, 2011).

6
Clearly it is not necessary that _X kH_ ≠ _X kL_ for us to attribute part of the growth gap to differences in _X k_ . It is sufficient that the rate of return varies between the two groups of states.

We present in table 10, the Oaxaca-Blinder decomposition of the effect of labor regulation, power shortages and access to finance on business growth. All effects in the table relate to businesses that are unconstrained by corruption in the sense having reported that corruption was no obstacle to the growth of their businesses. The first panel of the table relates to the growth gap between quadrant I states and quadrant III states. The second relates to the gap between quadrant II states and quadrant III states.

We see from the table that the partial effect of power shortages is as large as the entire actual growth shortfall of low-income low-growth states against high-growth states. It should be noted that the difference between low-income low-growth states and the two groups of high-growth states in this regard is not that power shortages are more sever in low-income low-growth states, which they are not, but rather that the marginal effect of the shortages is stronger. In other words, power shortages contribute to the performance gap between the two groups of states, not as an endowment effect, but as a rate of return effect.

The partial effect of labor regulation on growth is about 40 % of the actual growth shortfall of low-income low-growth states vis-à-vis high-income high growth states and almost twice the actual shortfall against low-income high-growth states. Again one is dealing with a rate-of-return effect of labor regulation. The labor regulation indicator is actually better in low-income low-growth states, and the growth rate should have been higher in low-income low-growth states than in the other groups of states on this account alone if differences in marginal effects of labor regulation did not work against better performance in the low-income low-growth group.

# **5. Summary and conclusion**

Based on the analysis of data from the FACS survey of the World Bank and the Confederation of Indian Industry, this paper has offered a quantitative assessment of the effects of corruption, labor regulation, access to finance and the quality of power supply on the growth of manufacturing businesses in India. All four institutional factors are highly rated by business managers as obstacles to business operations and business growth almost in every state covered by the survey. The ratings are backed up by data on objective indicators of the incidence of corruption, the intrusiveness of labor regulation, the costliness of power shortages and the quality of access to formal external finance. India’s states vary enormously on all these indicators just as they do in terms of per capita incomes and GDP growth.

Businesses are most productive on average in what we have classified as high-income and high-growth states (or“quadrant-I states”), but least productive in low-income, lowgrowth states (“quadrant-III states’). Businesses in low-income high-growth states (or 16
# **ENDNOTES**

- **1** See Akiyama _et al_ (2001) for a review of the commodity reform experience in Africa.

- **2** This is not unusual. Baffes (2005) reports that following every major restructuring of the Tanzanian cof‐ fee sector, coffee quality suffered initially but it quickly returned to earlier levels.

> **3** The ICO price indicators are daily averages obtained by the International Coffee Organization (ICO). They are defined as: _Arabica,_ ICO indicator price, other mild Arabicas, average New York and Brem‐ men/Humburg markets, ex‐dock. _Robusta,_ ICO indicator price, Robustas, average New York and Le Havre/Marseilles markets, ex‐dock. Both are quoted in $US.

> **4** The key reason behind the exit of most coffee exporters was the lack of hedging. When coffee prices were rising, exporters who did not hedge were in a better position compared to those that hedged, because in addition to buying low and selling high, they did not incur hedging fees. However, when prices declined, they were buying high and selling low, which combined with the absence of hedging drove them out of business.

> **5** Exception to this is a new 1,840‐hectare robusta estate (Kaweri Coffee Plantation LTD, member of the Nuemann Kaffe Gruppe.) The estate, which employs about 2,000 workers, is expected to reach between 80,000 and 100,000 bags of coffee at its peak, to be marketed under the sustainable/traceable trademark. Contrary to what has been reported, however, the estate will not be associated with any outgrower scheme (author’s interview, September 17, 2005). For example, MFPED (2004, p. 24), reported that “the Kaweri Coffee farmers Alliance is to nucleate as out‐growers with the Kaweri Coffee Plantation ... and feed the plantation with red cherries as an out‐grower scheme.”

- **6** See Gibbon (2006) for an overview of the certified organic export sector in Uganda.

- **7** Personal communication (September 22, 2005).

**_— 17 —_**
Policy ReseaRch WoRking PaPeR 4785

# **Abstract**

Using data from a survey of 91 banks in 45 countries, the authors characterize bank financing to small and medium enterprises (SMEs) around the world. They find that banks perceive the SME segment to be highly profitable, but perceive macroeconomic instability in developing countries and competition in developed countries as the main obstacles. To serve SMEs banks have set up dedicated departments and decentralized the sale of products to the branches. However, loan approval, risk management, and loan recovery functions remain centralized. Compared with large firms, banks are less exposed to small enterprises, charge them higher interest rates and fees, and experience more non-performing loans from lending to them. Although there are some differences in SMEs financing across government, private, and foreign-owned banks — with the latter being more likely to engage in arms-length lending — the most significant differences are found between banks in developed and developing countries. Banks in developing countries tend to be less exposed to SMEs, provide a lower share of investment loans, and charge higher fees and interest rates. Overall, the evidence suggests that the lending environment is more important than firm size or bank ownership type in shaping bank financing to SMEs.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand bank financing to SME across countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ademirguckunt@worldbank.org, T.Beck@ uvt.nl and mmartinezperia@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 (G4) Impacts of the Program on Altruism and Trust

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Trust in Syrians|-0.45|(0.46)|[0.37]|4.75|(3.37)|1,102|
|Dictator game, Altruism to Jordanians|-0.51*|(0.28)|[0.37]|1.88|(1.77)|1,102|
|Dictator game, keep|0.30|(0.31)|[0.37]|2.24|(2.03)|1,102|
|Incentivized Dictator game, Altruism to Jordanians|-0.43|(0.53)|[0.37]|1.95|(1.98)|375|
|Incentivized Dictator game, keep|0.97|(0.60)|[0.37]|2.29|(2.26)|375|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: trust in Syrians reflects how much of the total endowment the respondent chooses to share with a Syrian refugee game partner in a hypothetical trust game. The respondent is told they (hypothetically) have 10 JD and can share as much of it or as little of it with their game partner as they want. Anything shared will be doubled, and the partner could chose to share or not share the proceeds with the respondent, so the amount shared with the game partner reveals a measure of trust. The subsequent outcomes correspond to a dictator game that was conducted as follows: the respondent was given 5 JD and told to distribute it between the following categories: keep for self, donate to a charity helping needy Jordanians, or donate to a charity helping needy Syrian refugees in Jordan. 2/3 of the sample did this hypothetically, and 1/3 actually received the 5 JD. “Altruism to Jordanians” reports how much of the 5 JD was allocated to the charity for Jordanians, and “keep" reports how much of the 5 JD was kept for self. Both these outcomes include the entire sample. The final two rows report these outcomes only for the incentivized sample. Altruism to Syrians is reported in table G1

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 4 in the pre-analysis plan.

42
- β: Ratio of Total Wages ( _W_ ) to Total Production Cost ( _Y_ ). - α = 1- β - i / j:  Enterprise and country index, respectively

**Table 3. Firm-Level Total Factor Productivity** _<u>(Country average, in % of country with the most productive firms)</u>_

|**Country***|**Textile**|**Leather**|**Garment**|**Agro**<br>**Processing**|**Metal &**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal &**<br>**Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**South Africa**(2003)|88||**100**|**100**|91|82|**100**|**100**|
|**Brazil(**2003)|**100**|**100**|87|**100**|**100**|**100**|91||
|**_Morocco (_**_2000)_|_80_|_81_|_79_|_79_|_70 _|_90 _||_71 _|
|**Thailand (**2004)|70||90|75|73||78|82|
|**_Morocco (_**_2004)_|_73 _|_64_|_77_|_77_|_70 _|_79_||_80_|
|**_Saudi Arabia_**_(2005)_||||_70 _|_68_||_81_||
|**Ecuador (**2003)|69|74|76|73|75|72|78|64|
|**ElSalvador (**2003)|76|70|66|64|61|69||76|
|**Philippines (**2003)|64||77|65|||||
|**_Algeria (_**_2002)_|_65_|||_44_|_59_|_66_||_76 _|
|**Honduras (**2003)|61||72|55|57|84|50|54|
|**Guatemala (**2003)|65||67|54|62|56|54|73|
|**India (**2000)|67||63||58|58|||
|**China (**2002)|59|58|56||45||||
|**Zambia (**2002)|58|||52|55|52|||
|**Pakistan (**2002)|55|58|56|54||48|||
|**India (**2002)|59|61|49|54|51|50|||
|**Tanzania (**2003)||||55|||53||
|**Sri Lanka (**2004)|41||51|61|51|||56|
|**Bangladesh (**2002)|51|46|57|50||44|||
|**Nicaragua (**2003)|49|51|45|47|42|50|44|52|
|**Ethiopia (**2002)|51|34|46|49|||36||
|**_Lebanon (_**_2006)_|_35_||_39_|_40_|||_37_||
|**_Egypt (_**_2004)_|_41_|_36_|_35_|_39_|_34_|_33_|_36_|_43_|
|**_Egypt (_**_2006)_|_37_|_30_|_33_|_41_|_34_|_34_|_31_|_38_|

_Note_ : * Ranking of countries goes from the ones with the most productive firms to the ones with the least productive firms. _Source:_ Authors’ calculations Table 3 presents the firm-level relative _TFP_ by industry, under the reasonable assumption that a sector-based technology leads to a more homogeneous production function. As for Productivity of Labor, results are presented in percent of the average _TFP_ of the most performing country (detailed calculations are given in _Annex_ 4). Conclusions are quite similar than for Productivity of Labor.

A first conclusion concerns the ranking of the most performing countries. As previously, _South Africa_ and _Brazil_ present, in most industries, the most productive firms. These countries are again followed by _Morocco_ , which firms’ productive performances are quite good in most industries. When compared to _Brazil_ , _Moroccan_ firms show a _TFP_ gap of 10 to 30 percent depending on the sector, what is less than the revenue gap between the two countries (47 %, and 38.5% in _PPP_ respectively). As far as other MENA countries are concerned, ranking is also quite similar than for Productivity of Labor ( _LP_ ). As previously, _Egypt_ and _Lebanon_ rank at the bottom of the sample (with a limited number of enterprises for the latter country), while 11
weaknesses must be traded off against the fact that such indicators are easily, quickly, and cheaply measured relative to household expenditure or consumption data. There are also some potentially significant ways to improve self-reported data. For example, a more disaggregated ordering of self-assessed food security (such as using scales of 0 to 5) might reduce measurement errors. King et al. (2004) also argue for anchoring vignettes to make measurements more comparable across different socioeconomic groups. Another approach might be to ask households to report the frequency of consumption across different groups rather than asking about more subjective feelings of deprivation. Such dietary diversity or food consumption scores have been shown to be strong predictors of household calorie consumption and individual anthropometric outcomes (Wiesmann et al. 2009; Arimond and Ruel 2006) and might capture the fact that reducing dietary diversity is a common means of coping with higher staple food prices (Block et al. 2004). Others have argued for the use of sentinel sites to collect higher frequency measurements of food security and nutrition outcomes (Barrett 2010).

Regardless of the path that is pursued, there are strong grounds for making a large push to improve the measurement of food security. The global food crisis of 2007–08 revealed some significant deficiencies in our capacity to monitor coping strategies and welfare impacts in an acceptable timeframe. Moreover, if strong economic growth had not been prevalent in substantial parts of the developing world prior to and during the food crisis, the impacts of higher food prices might have been far more disastrous. Indeed, predictions of higher food prices and continued price volatility in the next decade or beyond (Headey and Fan 2010) would seem to justify greater investment and experimentation in food security measurement in the near future.

37
**Table 6: Outsourcing by Country - 2002**

|**Million of US**<br>**Country**|**$**<br>**Business Services**|**Country**|**Computer and**<br>**Information**<br>**Services**|
|---|---|---|---|
|U.S.|40929|U.S.|6124|
|Germany|39113|Germany|2002|
|Japan|24714|Japan|2148|
|Netherlands|21038|Netherlands|1586|
|Italy|20370|Spain|1572|

Source: Amiti and Wei (2004) 49
|**TABLE 2A- Persistance of Equality of O**<br>|**pportunit**|**y in Histo**<br>|**ry**<br>|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Regression||Dep|endent Va|riable: Hu|man Oppor|tunityInd|ex - Averag|e Mathem|atics,Rea|ingand Sc|ience Out|comes Lev|el 2 - PISA|2009||
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Index of Knowledge Distribution (1858)|0.782***<br>(0.18)|||||||||||||||
|Index of Knowledge Distribution (1868)||0.774***||||||||||||||
|||(0.17)||||||||||||||
|Index of Knowledge Distribution (1878)|||0.756***<br>(0.16)|||||||||||||
|Index of Knowledge Distribution (1888)||||0.735***<br>(0.16)||||||||||||
|Index of Knowledge Distribution (1898)|||||0.762***|||||||||||
||||||(0.15)|||||||||||
|Index of Knowledge Distribution (1908)||||||0.855***||||||||||
|||||||(0.14)||||||||||
|Index of Knowledge Distribution (1918)|||||||0.890***<br>(0.14)|||||||||
|Index of Knowledge Distribution (1928)||||||||0.891***<br>(0.14)||||||||
|Index of Knowledge Distribution (1938)|||||||||0.881***<br>(0.14)|||||||
|Index of Knowledge Distribution (1948)||||||||||0.632***||||||
|||||||||||(0.14)||||||
|Index of Knowledge Distribution (1958)|||||||||||0.765***|||||
||||||||||||(0.13)|||||
|Index of Knowledge Distribution (1968)||||||||||||0.723***<br>(0.14)||||
|Index of Knowledge Distribution (1978)|||||||||||||0.725**|*||
||||||||||||||(0.15)|||
|Index of Knowledge Distribution (1988)||||||||||||||0.822***<br>(0.15)||
|Index of Knowledge Distribution (1998)|||||||||||||||0.801***|
||||||||||||||||(0.19)|
|Number of Observations|24|26|28|29|29|33|34|39|38|41|44|45|51|51|59|
|R-squared|0.455|0.46|0.463|0.455|0.493|0.55|0.56|0.543|0.52|0.339|0.457|0.397|0.334|0.387|0.242|
|* p<0.05, ** p<0.01, *** p<0.001||||||||||||||||
|*Average Mathematics, Reading and Scien|ce Outcome|s Level 2 -|PISA 2009|||||||||||||

20