rafmacalaba/datause-encoder-data huggingface dataset:
# References

Alkire, S., Foster J. (2011). _Counting and Multidimensional Poverty Measurement_ . Journal of Public Economics, 95(7): 476-487.

- Arezki, R., Lederman, D., Abou Harb, A., El-Mallakh, N., Fan, R. Y., Islam, A.,  Zouaidi, M. (2020). _How Transparency Can Help the Middle East and North Africa._ Washington, DC: World Bank. doi:10.1596/978-1-4648-1561-4.

- Atamanov, A., Tandon,S., Lopez-Acevedo, G., Vergara B., Mexico. A., (2020). _Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region. Data Gaps and Different Options to Address Them._ World Bank Policy Research Paper # 9259, May 2020.

- Atkinson, A. B. (2003). _Multidimensional Deprivation: Contrasting Social Welfare and Counting Approaches_ . Journal of Economic Inequality, 1(1): 51-65

- Brackfield, D. (2011). _OECD Work on Measuring Trust in Official Statistics_ . Int Statistical Inst.: Proc 58th World Statistical Congress, Dublin (session STS070).

- Cady, J. (2005). _Does SDDS Subscription Reduce Borrowing Costs for Emerging Market Economies? IMF Staff Papers, 52_ (3), 503-17.

Cameron, G.J., Dang, H.H, Dinc, M., Foster, J., Lokshin, M.M. (2021).

- _Measuring the Statistical Capacity of Nations_ . Oxford Bulletin of Economics and Statistics. Doi: https://doi.org/10.1111/obes.12421

Chakravarty, S.R., D'Ambrosio. C. (2006). _The Measurement of Social Exclusion_ .

- Review of Income and Wealth. 52(3): 377-398.

<mark>Dang, H.H., Pullinger, J., Serajuddin, U., Stacy, B. (2021).</mark>

- _<mark>Statistical Performance Indicators and Index: A New Tool to Measure Country Statistical Capacity</mark>_ <mark>. Policy Research Working Paper; No. 9570. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/35301 License: CC BY 3.0 IGO.</mark>

- <mark>Dang H., Jolliffe D., & Carletto C. (2019). Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data Scarce Environments. Journal of Economic Surveys. 3</mark> 3(3). Pg 757-797 <u>https://doi.org/10.1111/joes.12307</u>

Sauro, J. (2016). SUPR-Q: A Comprehensive Measure of the Quality of the Website User Experience. Journal of Usability Studies. Vol. 10, Issue 2, February 2015, pp. 68-86. - <u>https://uxpajournal.org/wp content/uploads/sites/7/pdf/JUS_Sauro_Feb2015.pdf</u> 32
<u>Table 4: Simultaneous Estimates; “Agriculture”</u> _<u>versus</u>_ “Non agriculture”

|<br>**Dependant variable: Welfare Ratio**||<br>**Dependant variable: "Agriculture" HH-hea**|**d**|
|---|---|---|---|
|Size|-0.056|Size|0.000|
||(0.003)**||(0.002)|
|Number above age 60|-0.011|Number above age 60|0.023|
||(0.033)||(0.017)|
|HH-head Couple without children (D)|0.251<br>(0.030)**|HH-head Couple without children (D)|0.013<br>(0.016)|
|HH-head single parent with children (D)|-0.074<br>(0.028)**|HH-head single parent with children (D)|0.027<br>(0.019)|
|HH-head single people (D)|0.014|HH-head single people (D)|0.004|
||(0.037)||(0.020)|
|Average level of education|0.110|Time to the nearest tarred road|0.154|
|in the household|(0.008)**|in hours|(0.011)**|
|"Agriculture" HH-head (D)|-0.981<br>(0.130)**|||
|"Multi-Active" HH-head (D)|0.021|"Multi-Active" HH-head (D)|-0.016|
||(0.020)||(0.011)|
|Time to the nearest tarred road|-0.208|Male-headed household (D)|0.022|
|in hours|(0.349)||(0.016)|
|Time to the nearest primary school|0.060|Age|0.002|
|in hours|(0.081)||(0.001)**|
|Time to the nearest health center|0.134|Primary instruction (D)|-0.072|
|in hours|(0.139)||(0.014)**|
|Time to the nearest food market|0.122|Secondary (1st cycle) instruction (D)|-0.149|
|in hours|(0.144)||(0.016)**|
|Part of people affected by malaria|-0.333|Secondary (2nd cycle) instruction (D)|-0.230|
|in the cluster|(0.123)**||(0.018)**|
|Average level of education|0.096|Tertiary education (D)|-0.291|
|in the cluster|(0.065)||(0.020)**|
|Rural Forest|0.758|Rural Forest|0.471|
||(0.263)**||(0.050)**|
|Rural Savannah|0.384|Rural Savannah|0.515|
||(0.206)||(0.031)**|
|Rural High Plateaux|0.258|Rural High Plateaux|0.273|
||(0.174)||(0.028)**|
|Constant|-0.186|Constant|-0.425|
||(0.149)||(0.070)**|
|||Mills|Not sign.|
|District Fixed Effects & IV|yes|District Fixed Effects & IV|yes|
|Observations|5405|Observations|5405|
|R Squared|0.2698|R Squared|0.5222|
|RMSE|0.585|RMSE|0 .330|
|(robust se);*:p<5%;**:p<1%||||

23
# II. Patterns of Trade Policy Reform in Early Transition

# A. The CEE Countries'

The rapid reorientation of trade in the CEE countries towards the EU and OECD in the aftermath of the breakup of the Council of Mutual Economic Assistance (CMEA) has been amply documented and discussed elsewhere ( Benton and Gros, 1997, Kaminski et. al. 1996, Kaminski, 1998). The CMEA had created two broad kinds of distortions: First, by being a largely closed system, it permitted specialization and exports by firms and sectors which were totally uncompetitive in world markets.<sup>2</sup> Second, it resulted in more intensive trade among members than would be justified under market conditions as demonstrated by a number of studies, (Biessen, 1991; Havrylyshyn and Pritchett, 1991;Winters and Wang, 1993).

Following the breakdown in the CMEA arrangements during 1990 and the introduction of a market basis for most international trade transactions, two kinds of broad adjustment were needed in the CEE economies: firms had to adjust to international competition both in their own markets and in their export markets in other CEE countries.<sup>3</sup> This was combined with a reduction in aggregate export demand as a consequence of the systemic income and output shocks caused by the transition in other CMEA countries-- especially in the former Soviet Union. The result of these adjustments and the decline in CMEA demand was a rapid shift away from dependence on trade with other CEE countries and the FSU and towards increasing trade with the rest of the world, especially the EU and EFTA.

_]Several countries including the Baltics, Moldova and Ukraine that emerged as independent in the aftermath of the break-up of the Soviet Union are geographically located in Central and Eastern Europe. For analytical purposes however, these are considered in the FSUgroup, while the CEE designation refers to all others in the area_ _2Examples of such sectors abound. My favorite one is the Bulgarian electronic and computer industry which employed more than 100, 000 people and exported perhaps $2 billion to the CMEA in 1987 which almost disappeared completely by 1991 (World Bank, 1991)._ _3 The CMEA was formally dissolved in June 1991._ 3
estimation techniques (Levine, Loazya and Beck, 2000; Christopoulos and Tsionas, 2004; Rousseau and Sylla, 1999). Research also indicates that financial sector development helps economic growth through more efficient resource allocation and productivity growth rather than through the scale of investment or savings mobilization (Beck, Levine and Loayza, 2000). Furthermore, cross-country time-series studies also show that financial liberalization boosts economic growth by improving allocation of resources and the investment rate (Bekaert, Harvey and Lundblad, 2001 and 2005).

To further understand the relationship between financial development and economic growth, researchers have also employed both firm-level and industry-level data across a broad cross-section of countries. These studies better address causality issues and seek to discover the mechanisms through which finance influences economic growth.

Demirguc-Kunt and Maksimovic (1998) use firm level data and a financial planning model to show that more developed financial systems – as proxied by larger banking systems and more liquid stock markets- allow firms to grow faster than rates they can finance internally. Consistent with Demirguc-Kunt and Maksimovic (1998), Love (2003) also uses firm level data and shows that the sensitivity of investment to internal funds is greater in countries in less developed financial systems. Beck, Demirguc-Kunt and Maksimovic (2005) use firm level survey data for a broad set of countries and show that financial development eases the obstacles that firms face to growing faster, and that this effect is stronger particularly for smaller firms. Recent evidence also suggests that access to finance is associated with faster rates of innovation and firm dynamism consistent with the cross-country finding that finance promotes 3
# Bank Competition, Concentration, and Credit Reporting

Miriam Bruhn Subika Farazi Martin Kanz<sup>1</sup> _Keywords_ : Bank competition; bank concentration; credit information.

_JEL codes_ : O16, G21, L1.

> 1 Development Economics Research Group and Financial and Private Sector Development, The World Bank, 1818 H Street NW, Washington, DC 20433, U.S.A. Email: <u>mbruhn@worldbank.org, sfarazi@worldbank.org  and mkanz@worldbank.org. The</u> opinions expressed in this paper do not necessarily represent the views of the World Bank, its Executive Directors or the countries they represent. This paper was prepared as part of the background work for the 2013 Global Financial Development Report.
target populations with a true mean consumption level that is low, and to underestimate the mean consumption level of rich target populations. To see this note that when true consumption is low, the bias - defined here as “truth” minus estimated consumption, is negative – while it is positive when true average consumption is high. However this relationship is not strong. Overall, the average difference between the estimated mean consumption and true consumption is about 1.5 pesos: about 1% of the mean consumption level of the poorest target population. The bias is similarly modest for the headcount (Figure 2), squared poverty gap (Figure 3) and mean log deviation (General Entropy class measure with parameter 0) inequality measure (Figure 4).

The extent of bias in these estimates is related to the degree to which the model specification fails to capture location effects on the basis of census-mean variables or other variables intended to capture locality-level characteristics. As we saw in the preceding section and in Table 1, our model specifications are quite successful in removing the effect of latent community level characteristics, and as a result the bias in our estimates is quite modest. If we produce estimates that omit village-level census means, then the bias is accentuated. Figure 5 illustrates how the slope of the line capturing the extent to which headcount is overestimated in truly non-poor communities and the headcount is underestimated in truly poor communities becomes steeper when estimates are based on a consumption model that fails to capture unobserved location effects. The intuition behind this bias is quite straightforward: if there is a sizeable location effect, and our model fails to capture it, then there will be a tendency for poverty to be over-estimated in communities that are relatively well-off, given the explanatory variables in the model, i.e. that have large positive location effects. Part of the reason that the communities are well-off is likely attributable to community-wide characteristics of the community, and this will not be reflected in estimates based on a model that fails to capture the effect of those characteristics. As a result estimates will tend to overstate poverty of such communities. Conversely, in truly poor communities, part of the reason they are so poor will be due to the broader characteristics of the community. Again, if the consumption model does not capture the impact of those broader characteristics, there will be a tendency for estimated poverty to be an understatement of true poverty in the community. We see, therefore, that not only is there a strong incentive to proxy location 17
2 the medium run, however, it can generate a positive supply effect that increases the level of production.

Each of t'* hypotheses has a straightforward implication for the time series properties of food aid and production -- or imports -- of food. Under the first hypothesis, the previous level of aid should predict current food productton levels. The second hypothesis, on the other hand, implies that there is no causal effect from food aid to food production, and the third argues that the effects go both ways: previous levels of food production also explain current levels of food aid.

> .n the standard terminology on vector auto-regression (VAR) analysis, the issue is whether food aid causes food output (in the sense of Granger causality), or food production affects food aid. This paper applies VAR techniques to data for Sub-Saharan Africa to study food production and food aid. Rather than analyzing a specific country in a structural model of the agricultural sector (see Mann 1967; Hall 1980; Dudley and Sandilands 1975; and Blandford and Von Ploski 1977), I use a typical relationship based on a large set of countries over a long period of time. The empirical framework is based on reduced form equations. Given that such major variables as consumer and producer food prices are not available for Africa on a systematic time series basis, attempts to estimate a structural model for each country or set of countries will be futile. We present, however, some of the analytics of food aid in a general equilibrium model. The comparative static and dynamic analysis of this model can help interprete the VAR results.

Within this empirical framework, we explicitly test the three hypotheses rgarding the relationship between food aid and domestic food production. The
**Table 1: Crisis probability: Panel logit model with random effects**

|**Explanatory**<br>**variables**|**Debt crisis**|**Banking**<br>**crisis**|**Currency**<br>**crisis**|**Any crisis**|
|---|---|---|---|---|
|Inflation shock|-0.012|-1.159|0.344|-0.327|
||[2.502]|[0.780]|[1.131]|[0.984]|
|Reaction shock|0.301|1.245|4.528***|3.012***|
||[2.813]|[1.030]|[1.309]|[1.113]|
|Real shock|-2.386**|-0.146|0.893**|-0.164|
||[1.006]|[0.316]|[0.447]|[0.367]|
|GDP growth (t-1)|-0.214**|-0.041|-0.140***|-0.0545|
||[0.093]|[0.032]|[0.047]|[0.0436]|
|Short-term debt (t-1)|-0.016|0.006|0.017|-0.016|
||[0.048]|[0.014]|[0.019]|[0.018]|
|Debt service (t-1)|-0.004|0.016**|0.001|0.001|
||[0.027]|[0.007]|[0.011]|[0.010]|
|Reserves cover (t-1)|-0.700**|-0.087*|-0.151*|-0.064|
||[0.314]|[0.052]|[0.086]|[0.057]|
|Change in government debt (t-1)|0.007||0.042**|0.011|
||[0.021]||[0.017]|[0.014]|
|Change in private debt (t-1)||0.063**|0.011|0.083**|
|||[0.030]|[0.047]|[0.036]|
|Change in government debt (t-1)|||0.005*|-0.001|
|<br>x Change in private debt (t-1)|||[0.003]|[0.002]|
|Concessional debt (t-1)|-0.123**|||-0.017*|
||[0.061]|||[0.009]|
|Funding ratio (t-1)||0.003**||0.002|
|||[0.001]||[0.002]|
|Currency overvaluation (t-1)|||0.000|0.147***|
||||[0.001]|[0.026]|
|Currency mismatch (t-1)|||-0.001|-0.000|
||||[0.001]|[0.001]|
|FDI (t-1)|||0.001|-0.019|
||||[0.031]|[0.030]|
|Constant|-3.150**|-3.962***|-3.321***|-2.796***|
||[1.532]|[0.362]|[0.643]|[0.625]|
|No. of observations|1,634|2,085|1,325|1,271|
|No. of countries|103|92|88|88|
|Estimated on annual data 1985-2018 ex<br>inflation and reaction shocks reflect per<br>2-year U.S. bond yield driven by a react<br>percent, from 2.4 percent to 6.2 percent.<br>*** p<0.01, ** p<0.05, * p<0.1|cluding 2009. St<br>cent not percenta<br>ion shock raises<br>|andard errors in b<br>ge point. For exam<br>the probability of|rackets. The coeffic<br>ple, a 25-basis-poi<br>a currency crisis by|ients on the real,<br>nt increase in the<br>over 100|

30
_The capital markets_ . As discussed above a main implication is that now the net rates of return to capital in the household-producer sector and enterprise sector are equalized in the long run. That is, in long run equilibrium we have<sup>15</sup> , In addition we have that the household equilibrium allocations (15) and (14’) are still valid which for the sake of clarity we repeat here, Equations (15) and (39) solve for the long run levels of the stock of natural resources and wage rate, * * * _n_ and _w_ and condition (14’) solves for the long-run equilibrium level of _k A_ . Evaluating condition (14’) at the levels _w_ * and _n_ * yields the long run equilibrium level for the stock of capital in the household-producer sector, _k A_ * .

Thus, the household-producer sector follows a path that is akin with the standard Swan-Solow model where output growth ceases over the long run. By contrast, the enterprise sector may reach long run equilibrium with positive output and physical capital growth like in endogenous growth models. The main reason for this asymmetry in dynamic behavior is that the household-producer sector, unlike the enterprise sector, is constrained by the availability of the natural resources which has a limited capacity to regenerate itself. Ultimately, the natural resource is fixed and this puts limits to output growth in that sector. Importantly, this does not mean that the household sector does not benefit out of the ensuing process of economic growth. What happens is that once the

> 15 The net returns are _r_  _k_ for the household-producer sector and    _k_ . Since the depreciation rates are assumed identical in the two sectors we get (14’) below.

37
# **TABLE 21 Withholding Tax Rates as of 1997** (In Percentage)

||---------------------<br>Canada-Mexico|---- Tax Treaties-<br>Canada-U.S.|-------------------<br>U.S-Mexico|
|---|---|---|---|
|Parent/Subsidiary Dividends|10.00|5.00|5.00|
|Portfolio Dividends|15.00|10.00|15.00|
|Interest|15.00|10.00|15.00 - 10.00|
|Royalties|15.00|10.00|10.00|
|Capital Gains|0.00|0.00|0.00|
|SOURCE: Cockfield (1998).||||

# **TABLE 22 Effective Corporate Tax Rate on the Foreign Capital Investment** (In Percentage)

## **1A. Mexico as the host, non-exporters**

||------------<br>Manuf|acturing-----------------<br>---------------------Ser|vices---------------------|
|---|---|---|---|
||U.S.|Canada<br>U.S.|Canada|
|Buildings|9.5|11.8<br>8.7|10.3|
|Machinery|31.2|32.3<br>40.9|41.7|
|Inventory|26.9|28.5<br>26.9|28.1|
|Land|22.9|24.5<br>22.9|24.2|
|**Aggregate**|**25.2**|**26.7**<br>**18.9**|**20.3**|

|**'B. Mexico as the host, fo**<br>---|**r exporters (i.e., w**<br>--------------Manufa|**ith import duty exem**<br>cturing|**ption)**<br>Services (for illustration only)|
|---|---|---|---|
||U.S.|Canada|U.S.<br>Canada|
|Buildings|9.5|11.8|8.7<br>10.3|
|Machinery|17.1|18.9|25.8<br>27.0|
|Inventory|22.9|24.5|22.9<br>24.2|
|Land|22.9|24.5|22.9<br>24.2|
|**Aggregate**|**17.9**|**19.8**|**15.6**<br>**17.0**|
|**2. Canada as the host**<br>---|--------------Manufa|cturing-----------------Se|rvices-|
||Mexico|U.S.|Mexico<br>U.S.|
|Buildings|31.4|23.0|29.5<br>20.9|
|Machinery|25.4|16.3|46.1<br>39.4|
|Inventory|41.0|33.7|45.4<br>38.7|
|Land|32.0|23.7|35.1<br>27.3|
|**Aggregate**|**33.5**|**25.3**|**35.0**<br>**27.1**|
|**3. The U.S. as the host**|**-he.S.as---Man**|**ufacturing--------------**|**---**---------------------Services---------------------|
||Mexico|Canada|Mexico<br>Canada|
|Buildings|23.8|21.7|22.4<br>19.8|
|Machinery|25.4|22.2|35.5<br>32.1|
|Inventory|21.1|19.8|21.1<br>19.2|
|Land|21.1|19.8|21.1<br>19.2|
|**Aggregate**|**23.4**|**21.2**|**23.8**<br>**21.3**|

52
Given the lack of rights to buy and sell land, much of the increase in operational holdings by successful farmers represents formal or informal acquisition of temporary use rights held by others. Such transfers of temporary use rights may be consistent with improvement in efficiency, because they combine idle land with surplus labor and other factors. Such transfers are limited by the extreme population pressure under which the tenure arrangements operate. Landowners fear they will lose long-term rights to their land if they permit others to use it.

# _Synopsis_

The evidence on the farm size-efficiency relationship in South African agriculture is mixed. However, much of the evidence on scale efficiencies in South African agriculture cited above, with only a few exceptions, should be interpreted with extreme care because data were not standardized for differences in land quality or labor productivity, or for the particular commodity mix of farms, and in some cases inappropriate analytical methods and measurement variables were used. **Evidence On Scale Efficiency In The Homelands6** The neglect of agriculture in the former homelands extends to the availability of information. The data for commercial agriculture is on a par with that for developed countries, and includes detailed time series for inputs and outputs dating from the end of the second World War. But for the former homelands there are a general paucity of data, with only sample survey data that is really reliable. Such data, collected for the Development Bank of Southern Africa, are analyzed in this section to establish farm size efficiency in these areas. These data are sufficient for estimation of efficiency of individual farms, which makes it possible to compare regions.

The organization of the rest of this section is as follows: the data is discussed next, while the methodology, including that of separating total from technical and scale efficiency, is discussed in the following sub-section. The last sub-section reports and interprets the results, comparing efficiency levels for the three homelands.

**6** This section builds on analyses conducted by Piesse, Sartorius von Bach, Thirtle and Van Zyl (forthcoming).

15
**Table 4.5a: Effectiveness of Bias Correction**

||**Full ESS**<br>||**Ethiopia**<br>**HFPS(n**<br>|**=3,247)**<br>||**Full IHPS**<br>||**Malawi**<br>**HFPS(n**<br>|**=1,729)**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||**(n=6,774)**|_Adjust_|_ed Weights_<br>|_Unadju _|_sted Weights_<br>|**(n=3,181**|_Adjust_|_ed Weights_<br>|_Unadju _|_sted Weights_<br>|
||_Mean_|_Mean_|_Difference†_<br>_(2) -(1)_|_Mean_|_Difference†_<br>_(4) -(1)_|_Mean_|_Mean_|_Difference†_<br>_(7) -(6)_|_Mean_|_Difference†_<br>_(9) -(6)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|_(9)_|_(10)_|
|Rural|0.675|0.670|-0.006|0.528|-0.147***|0.826|0.774|-0.015|0.797|-0.029|
|Average household size|4.483|4.686|0.203|4.504|0.021|4.904|4.751|0.022|5.097|0.193|
|Dependencyratio|0.955|0.922|-0.033|0.845|-0.111***|1.055|1.000|-0.015|0.986|-0.069|
|Female head(%)|0.263|0.242|-0.021|0.246|-0.016|0.296|0.269|-0.003|0.246|-0.05**|
|Age of head|42.912|42.132|-0.780|41.114|-1.799**|45.855|44.756|-0.488|44.887|-0.968|
|**_Religion of head_**|||||||||||
|Orthodox|0.478|0.490|0.013|0.513|0.035||||||
|Muslim|0.293|0.293|0.000|0.259|-0.034|0.808|0.754|-0.007|0.813|0.005|
|Christian|0.211|0.208|-0.003|0.220|0.009|0.145|0.090|0.007|0.147|0.002|
|**_Marital status of head_**|||||||||||
|Married|0.739|0.753|0.014|0.736|-0.003|0.716|0.688|0.007|0.760|0.044**|
|Formerlymarried|0.189|0.178|-0.011|0.171|-0.018|0.265|0.237|-0.005|0.219|-0.045**|
|Never married|0.072|0.068|-0.003|0.093|0.021*|0.018|0.011|-0.004|0.018|0.000|
|**_Education level of head_**|||||||||||
|Head Literate|0.520|0.536|0.016|0.652|0.132***|0.921|0.907|0.008|0.959|0.038***|
|None|0.523|0.512|-0.011|0.384|-0.139***|0.675|0.632|-0.023|0.573|-0.103***|
|Primary|0.296|0.296|0.001|0.320|0.025|0.207|0.171|0.010|0.247|0.040|
|Secondary|0.102|0.107|0.005|0.159|0.057***|0.083|0.064|0.012|0.132|0.049***|
|Tertiary|0.079|0.084|0.005|0.137|0.058***|0.034|0.020|0.001|0.049|0.015|
|**_Working status of head_**|||||||||||
|Any|0.735|0.741|0.005|0.720|-0.015|0.819|0.794|0.007|0.838|0.019|
|Wage|0.133|0.136|0.004|0.196|0.064***|0.158|0.129|0.024|0.209|0.051**|
|Agriculture|0.551|0.560|0.009|0.444|-0.107***|0.469|0.424|-0.026|0.441|-0.029|
|Nonfarm enterprise|0.115|0.118|0.003|0.152|0.036**|0.193|0.168|0.006|0.225|0.032|
|Ganyu||||||0.248|0.216|-0.014|0.199|-0.05**|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

34