rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5. The Robustness Check**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017. This table presents estimates from OLS regressions of investment on Titling Firm dummy variable. For columns (1)-(5), cross-sectional OLS regressions are implemented. The independent variable is the averaged investment rate (capital expenditure divided by PP&E) between 2013 and 2017 net of various benchmark levels. In Column (1), only firms headquartered in Shenzhen are included in the regression sample. In Column (2), only firms headquartered in Beijing, Shanghai, Shenzhen and Guangzhou, four metropolitans are included. In Column (3), city dummies are included. In Column (4), the propensity score matching method is implemented. Four characteristics, size, age, leverage and ownership are used to find the most similar firms in the universe of the control firms. In Column (5), the size of the takeover is controlled. The relative size of the takeover is calculated by the size of firms acquired by the listed firm normalized by the size of the listed firm. In Column (6), the panel data regression (regression 2) is implemented. For each firm, observations from 2008 to 2017 are included. The dependent variable for Column (6) is the annual investment normalized by PP&E. _Post_ is a dummy variable that is 1 if the year is after 2012 and 0 if not. In Column (7), the pre-trend test is implemented. For each year between 2008 and 2017, its interaction with the Titling Firm dummy is added in the panel data regression 2. All variables are winsorized at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||∆(Investme<br>|nt/Total Fix<br>|ed Asset)<br>||Investment/<br>|Total Fixed<br>|
|---|---|---|---|---|---|---|
|Shenzhen<br>firms only|Firms in<br>Metropolitan|<br>City<br>dummy|Propensity score<br>matching|Takeover|Panel data<br>regression|Pre-Trend<br>Test|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Titling Firm<br>0.411**|0.341***|0.352***|0.295*|0.297***|||
|(2.313)|(2.662)|(2.867)|(1.706)|(3.114)|||
|Takeover||||0.000***|||
|||||(3.156)|||
|Titling Firm * Takeover||||0.000|||
|||||(1.004)|||
|Post Dummy|||||0.004||
||||||(0.153)||
|Titling Firm * Post dummy|||||0.276***||
||||||(3.427)||
|Titling Firm * Year 2008||||||-0.057|
|||||||(-0.377)|
|Titling Firm * Year 2009||||||-0.094<br>(-1.377)|
|Titling Firm * Year 2010||||||-0.172<br>(-0.878)|
|Titling Firm * Year 2011||||||-0.120<br>(-1.064)|
|Titling Firm * Year 2012||||||-0.178|
|<br>||||||(-1.475)|

50
guarantees, which can defeat the purpose of a guarantee scheme<sup>10</sup> . Some countries set ceilings for collateral, such as Morocco (maximum collateral of 100%), and more recently Lebanon (50%). However, most other schemes do not impose ceilings.

**Many MENA schemes also impose minimum down-payment rules.** Requiring minimum contributions from borrowers can be an effective way to reduce adverse selection and moral hazard, especially for riskier types of loans, such as start-ups or long term investments. This feature is used in many MENA countries. With a significant down payment the loan amount represents a smaller percentage of the value of collaterized assets, thus improving the (theoretical) recovery expectancy.  Moreover, the down-payment rules seem reasonable, capped at about 20%-30% of the project value.

**Table 12: Collateral and Down-payment Rules in MENA**

||**Down-payment**|**Collateral**|
|---|---|---|
|Egypt|Medium firms: 20%|Allowed, no ceiling|
|Jordan|30% SME loan, 30% industrial loan,<br>Leasing50%|Allowed, no ceiling|
|Lebanon|Kafalat plus: 20% of total investment,<br>30% if start ups|Allowed, Ceiling of 50% of the loan amount<br>(Kafalat Basic)|
|Morocco|Start-up loans: 10%-20% depending on<br>the loan amount|Allowed, Ceiling of 100% of the loan amount|
|Palestine|No|Allowed, no ceiling (in practice, the majority<br>of loans are not secured against collateral)|
|Tunisia|30% of the cost of the investment|Allowed, no ceiling|
|Saudi Arabia|No|Allowed, no ceiling|
|Syria|N/A|N/A|

# _4.7 Operational Mechanisms_

_General guiding principles and international experience_

**Guarantees can be delivered though the individual, portfolio or hybrid approaches** . Under the individual approach, every loan application is assessed and approved by the guarantee scheme. The portfolio approach is more flexible and allows banks to extend guarantees without consulting the guarantee scheme. Each bank receives a guarantee allocation which can be used for eligible firms.  The hybrid approach mixes elements of individual and portfolio approaches: certified lenders can extend guarantees without referring to the guarantee scheme up to a limit; above a certain threshold, the guarantee scheme adopts an individual approach and appraises the loan application before extending the guarantee. Beck et al (2008) report that 72% of schemes surveyed use the individual approach, 14% the portfolio approach, and 9% the hybrid approach. The schemes in our benchmark group adopt either the portfolio approach (Canada, Netherlands, UK, and Chile) or the hybrid approach (France, USA, Taiwan, Hungary, and Korea) as shown in Table 13.

> 10 Lebanon Investment Climate Assessment, World Bank, 2009

20
Policy Research Working Paper 9163

# **Abstract**

This study uses loan-level data on syndicated lending to a large sample of developing countries between 1993 and 2017 to estimate the mobilization effects of multilateral development banks (MDBs), that is, their ability to crowd-in capital from private creditors. Controlling for a large set of fixed effects, the paper shows evidence of positive and significant mobilization effects of multilateral lending on the size of bank inflows. The number of lenders and the average maturity of syndicated loans also increase.

These effects are present not only on impact but last for up to three years and are not offset by a decline in bond financing. There is no evidence of anticipation effects, and the results are robust to numerous tests controlling for the role of confounding factors and unobserved heterogeneity. Finally, the results are economically sizable, indicating that MDBs can mobilize about seven dollars in bank credit over a three-year period for each dollar invested.

This paper is a product of the Knowledge and Strategy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at broccolini.chiara@gmail.com, glotti@iadb.org, alessandrom@iadb.org, apresbitero@imf.org, and rstucchi@iadb.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
1997b). For the 173 countries that have data on under-5 mortality and World Bank atlas method GNP per capita in US dollars (which is not purchasing-power-adjusted), the explanatory power of the regression which includes variables similar to those presented above, is about 90 percent. The coefficient on income in this auxiliary regression is -0.39 which is a smaller effect than we found. This is partly explained by the fact that non purchasing-power-adjusted estimates systematically underestimate the incomes of poorer countries which would bias the estimate towards zero. In addition, random measurement error may cause a bias towards zero. When using purchasing-power-adjusted GDP per capita (from the Penn World Tables database) as an instrument for GNP per capita with the set of countries included in our main analysis, over 92 percent of the variation is explained, and the coefficient rises (in absolute value) to -0.49.

Including the reported public spending on health as a share of GDP produces a point estimate of - 0.07 (t-statistic of .92) for the 1 19 countries for which the relationship can be estimated." _Cost of averting a death: Macro and medical estimates._ Overall, these regressions show that differences in public sector spending on health do not go far in explaining why some 27 The OLS regression has an R-squared on .9095 and estimates (t-statistics) for the sample of 175 countries: ln(u5) = -.387 In(GNPpc) + .008 **Fcmillit** + **.012** Gini - .001 Urb - .003 AcSaWa (13.2) (3.9) (2.4) (.64) (1.9) The regression which includes public sector health spending as a share of GDP has an R-squared of .9234 and estimates (t-statistics) for the samnple of 119 countries: In(uS) = -.412 In(GNPpc) + .008 Femillit + .014 Gini- .001 Urb **-.001** AcSaWa -.073 InPHS (12.0) (3.4) (2.8) (.23) (.54) (.92) The two-stage least squares regression has an R-squared of .9215 and estimates (t-statistics) for the sample of 103 countries that are in our analysis: In(uS) -.492 In(GNPpc) + .008 Femillit + .014 Gini + .002 Urb - .002 AcSaWa (6.6) (2.6) (2.2) (.73) (.65) All of these include dummy variables for when urban and access to safe water are missing, as well as dummies for area.

23
Table 7: Prediction Accuracy of the Probit Model<sup>_a/_</sup> Share of actual _GEit_ predicted by the model _(share in total observation in parenthesis)_

|||actu|al||
|---|---|---|---|---|
|d||GE=1|GE|=0|
|cte|GE=1|78.0%<br>_(9.1%)_|37.7%|_(33.3%)_|
||||||
|red|GE=0|22.0%<br>_(2.5%)_|62.3%|_(55.1%)_|
|P|Total|100.0%<br>_(11.6%)_|100.0%|_(88.4%)_|
|Co|rrectly clas|sified =<br>64.2%|||

_a/_ computation based on coefficients reported in column 4 in table 5 , cutoff =11.6% (value for determining whether an observation has a predicted positive outcome).

_Source: Authors’ computation from table 5, col.4._ We carry out two robustness checks. First, as discussed above, we estimate a logit function (which has fatter tails and may be more appropriate for our sample with many zero values for the dependent variable). Results in columns (5) and (6) of table 5 show that the logit specification does not change the qualitative conclusions based on results in col. (3) and (4). Second, we change the definition of _FEit_ with the dummy that takes the value of 1 at the date of the fiscal event and during the _9_ years following this date (instead of 4). This alternative, which gives more time for the effects of a fiscal event to have an impact on growth, does not alter qualitatively the estimates.

26
- Pitt M., Rosenzweig M.R. and D.M. Gibbons. (1995) “The Determinants and Consequences of the Placement of Government Programs in Indonesia”, in van de Walle D. and N. Kimberly, eds _. Public spending and the poor: Theory and evidence_ pp. 114-49

- REPOA 2004. “Trends in Malnutrition in Tanzania”. Mimeo.

- Rosenzweig, M. R. and W. Kenneth, (1986), “Evaluating the Effects of Optimally Distributed Public Programs: Child Health and Family Planning Interventions”, _American Economic Review_ v76, n3, pp. 470-82

- Quisumbing A. R. (2003). “Food Aid and Child Nutrition in Rural Ethiopia”, _World Development,_ Vol. 31(7): 1309-1324 _._

- Shrimpton R., Victora C., de Onis M., Costa Lima R., Blössner M., and G. Clugston.  2001. _Worldwide Timing of Growth Faltering: Implications for Nutritional Interventions Pediatrics_ , 107(5): e75-81.

- Svedberg, P., (1990) “Undernutrition in Sub-Saharan Africa: Is There a Gender Bias?” _Journal of Development Studies_ , v26, n3 (April 1990): 469-86

- UNICEF 2001. Women and Children in Tanzania, Dar es Salaam, 1990.

- United Republic of Tanzania, National Bureau of Statistics [2002], _Household Budget Survey 2000/01._

- United Republic of Tanzania, President’s Office 2003. _Economic Survey 2003._

- Yamano T, H. Alderman and L. Christaensen, Forthcoming. “Child Growth, Shocks, and Food Aid in Rural Ethiopia”, _American Journal of Agricultural Economics_ .

- Wooldridge, J. _Econometric Analysis of Cross Section and Panel Data_ . Cambridge, MA: MIT Press, 2002.

- WHO (World Health Organization) 1995. _Physical Status: The Use and Interpretation of Anthropometry_ , WHO Technical Report, No.854, Geneva

- WHO (World Health Organization) 1986, Use and Interpretation of anthropometric indicators of nutritional status, Bulletin of the WHO, Geneva.

16
18 production package intensifies, households tend to use less household labor in most farming activities. Specifically, much labor is spent in the traditional production package in land preparation, weeding and harvesting, compared to the other improved production packages. This difference can be attributed to the use of labor-saving technologies in the intensified production packages. Male and female household labor allocated on the plots tends to reduce. However, a slight increase is observed in the amount of household labor spent by children, except for modern production packages. The amount of hired labor applied on the plots also decreases as production processes intensify, but at a slower pace than household labor.

**Table 4: Labor input by agricultural activity**

||All|TP|STP|SMP|MP|P-value|
|---|---|---|---|---|---|---|
|**1. Land preparation**|||||||
|Use of household labor (1=Yes)|0.85|0.93|0.86|0.86|0.47|<0.001|
|Numbers of Person-days/ha (Men)|8.95|25.38|9.04|5.74|1.24|<0.001|
|Numbers of Person-days/ha (Women)|2.85|5.39|2.59|2.72|0.13|0.001|
|Numbers of Person-days/ha (Children)|1.47|0.82|1.48|1.72|0.08|0.407|
|<br>Total Person-days (Household)|13.28|31.58|13.11|10.18|1.45|<0.001|
|<br>Use of hired labor (1=Yes)|0.65|0.86|0.71|0.54|0.82|<0.001|
|Person-days/ha of hired labor|12.88|32.04|19.51|3.65|1.44|<0.001|
|Person-days/ha of total labor|26.16|63.63|32.63|13.82|2.89|<0.001|
|**2. Sowing and transplanting**|||||||
|Use of household labor (1=Yes)|0.91|0.94|0.87|0.94|0.89|0.002|
|Numbers of Person-days/ha (Men)|4.91|14.45|4.81|3.10|1.32|<0.001|
|Numbers of Person-days/ha (Women)|3.68|9.00|3.46|2.84|1.28|<0.001|
|Numbers of Person-days/ha (Children)|1.34|0.83|1.46|1.42|0.59|0.729|
|Total Person-days (Household)|9.93|24.28|9.74|7.36|3.19|<0.001|
|Use of hired labor (1=Yes)|0.56|0.78|0.67|0.42|0.50|<0.001|
|Person-days/ha of hired labor|12.72|19.71|17.83|6.85|10.62|<0.001|
|<br>Person-days/ha of total labor|22.65|43.98|27.57|14.21|13.81|<0.001|
|<br>**3. Fertilizer: Urea NPK**|||||||
|Use of Urea/NPK (1=Yes)|0.90|0.00|1.00|1.00|1.00|<0.001|
|Use of household labor (1=Yes)|0.86|0.00|0.95|0.96|0.97|<0.001|
|Numbers of person-days/ha (Men)|2.55|0.00|3.34|2.50|1.91|<0.001|
|<br>Numbers of person-days/ha (Women)|0.67|0.00|0.72|0.80|0.43|0.001|
|Numbers of person-days/ha (Children)|0.31|0.00|0.41|0.29|0.28|0.397|
|<br>Total person-days (Household)|3.53|0.00|4.47|3.59|2.62|<0.001|
|Use of hired labor (1=Yes)|0.12|0.00|0.13|0.13|0.13|0.001|
|Person-days/ha of hired labor|0.55|0.00|0.71|0.55|0.28|0.510|
|Person-days/ha of total labor|4.07|0.00|5.18|4.13|2.90|<0.001|
|<br>**4. Weeding**|||||||
|Use of household labor (1=Yes)|0.95|0.99|0.95|0.94|0.92|0.123|
use in wheat fields is unaffected by size for commercial farms and is positive, but with a decreasing slope, for smallholders. By contrast, for sesame and sorghum, we find a negative relationship between size and productivity for small farms, approximate neutrality of yields with respect to size for large farms, and a positive relationship between fertilizer application and farm size for commercial farms.

# **5. Conclusion and policy implications**

A key premise of this paper is that, without sustained improvements in the quality and publicity of data on large-scale land-based investment, target countries may struggle to put in place a regulatory framework to increase the likelihood of such demand being translated into sustained local benefits or to attract responsible investors and institutional capital. The case of Ethiopia shows that a modest investment in data collection can have high returns and can incrementally build local data collection and analytical capacity.

Although the descriptive evidence reported here only scratches the surface, it provides an interesting basis for future research. From a research perspective, it will be interesting to link this more directly to ongoing data collection efforts in the smallholder sector, among others, to look at the interaction between large farms and neighboring smallholders; in particular, the nature and size of potential spillover effects and the channels through which they materialize. Among commercial farms, determinants of entry and exit, and more generally the dynamics of firm performance over time would also be of interest.

The usefulness of such data for government decision-making can be improved by linking more closely to data that are—or should be—held by other parts of the administration in at least two respects. First, business plans or licenses held by investment promotion agencies can serve as a basis for enforcement of contractual terms (e.g., fee collection) and meaningful monitoring of compliance with contractual obligations and, if necessary, adoption of remedies to bring about improvements. Second, linking geographic data on land cultivated with parcel boundaries kept by land administration institutions will be useful, as it allows comparison between the area adjudicated and what is actually used, thus possibly providing an opportunity to either cleanse or establish records through a process of negotiation to avoid border disputes.

The fact that, in Ethiopia, the statistical agency is now initiating experiments to use the parcel boundaries as a basis for yield monitoring illustrates that, beyond its immediate usefulness in terms of providing vital information to assess and take regulatory action to manage an important part of the agricultural sector, such investment in data also create opportunities for realizing synergies with other institutions that can help to increase transparency to better assess and manage risk in production or upstream in the value chain. This can in turn be an important input into attracting higher levels of investment to the sector, something that by itself can generate the returns needed to justify such an investment.

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32 Evidence from Madagascar further underscores the importance of weather shocks in comparing poverty over time. Simulations indicated that 75 percent of the predicted change in household economic well-being and poverty incidence could be traced back to the relative change in drought occurrence between 1993 and 1999. The insurance capacity of households against covariate shocks in many parts of Africa is clearly extremely limited.

# IV. **Concluding remarks**

The evidence of the 1 990s gives ground for cautious optimism. In the aggregate at least, episodes of growth have been pro-poor in Africa, and countries which have experienced a recovery in their macro-economic balances and the quality of their institutions have seen the numbers in poverty decline. But there are three serious qualifications. First, experiences have varied enormously. Some countries have enjoyed a decade of sustained growth, and others have had to cope with crisis and decline. In the eight countries covered by the _Poverty Dynamics_ study, four experienced significant declines in poverty (Ethiopia, Ghana, Mauritania and Uganda), two faced sharp increases (Nigeria and Zimbabwe), and in two (Madagascar and Zambia) there was no discernable trend, the outcome depending on the specific circumstances (rainfall, terms of trade) of the years in question.

The second qualification derives from the need to go beyond the averages. While it is true that _overall_ income distributions (evidenced by the Gini ratio) have not changed during African episodes of growth, and that such growth (or recession) can be characterized as pro-poor in this aggregate sense, this can be misleading. Beneath the aggregate numbers exists a variety of experiences. Neglect of this reality by policymakers-and sometimes also academics-has often impeded a constructive and fruitful dialogue with 'civil society' about appropriate poverty reducing policies (Kanbur, 2001). Third, the _Poverty Dynamics_ work highlights the importance of taking different perspectives of poverty. Although trends in human development indicators are generally consistent with economic well-being, their dynamics have been quite different in some countries. The multifaceted nature of poverty (emphasized in the 2000/1 World Development Report) calls for multivariate approaches to tracking and understanding its dynamics.

Focusing on income poverty, our review of the evidence shows that there have been systematic changes in income distributions and poverty in the countries covered. We have identified some of the main contours of these distribution changes, and highlighted four key policy messages: the
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