rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Comparison of study sample vs. rural localities with forest coverage vs. all localities in the states.**

|||Experim|ental Sam|ple||Ru|ral Localities|with Fore|st Cov|erage||All localit|ies in stat|es|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||N|Mean or %|SD|Min|Max|N|Mean or %|SD|Min|<br>Max|N|Mean or %|SD|Min<br>Max|
|Total inhabited<br>houses|113|37.96|34.88|5|209|870|20.69|35.69|1|309|12,880|53.73|124.32|1<br>1296|
|Total population|113|172.22|173.38|28|1230|870|92.67|163.25|1|1513|12,880|221.46|501.75|1<br>4939|
|Agriculture as main<br>economic activity<br>(dichotomic)|112|89.29%||||780|91.03%||||11,641|78.76%|||
|With population<br>that speaks<br>indigenous<br>language<br>(dichotomic)|113|76.11%||||794|75.94%||||11,826|61.19%|||
|Without<br>information of<br>subgroups in<br>locality (less than 3<br>households in<br>locality –<br>dichotomic)|113|0.00%||||870|30.57%||||12,880|31.23%|||
|Proportion female|113|0.5|0.04|0.39|0.61|604|0.5|0.07|0|0.78|8,857|0.51|0.06|0<br>1|
|Proportion of|||||||||||||||
|<br>female-headed<br>households|113|0.16|0.12|0|0.5|604|0.15|0.13|0|0.75|8,839|0.20|0.13|0<br>1|
|Proportion that|||||||||||||||
|speaks indigenous<br>language for 3<br>years or older|113|0.62|0.41|0|1|604|0.59|0.4|0|1|8,839|0.45|0.41|0<br>1|
|Average grades of<br>schooling for 15<br>years or older|113|4.89|1.31|1.88|10.56|604|4.7|1.43|0|11.47|8,839|5.09|1.67|0<br>18.23|
|Marginalization<br>index = Very High<br>(dichotomic)|113|29.20%||||604|42.05%||||8,839|28.72%|||
|Marginalization<br>index = High<br>(dichotomic)|113|68.14%||||604|56.62%||||8,839|65.79%|||

9
Table 10. Openness, Technological Innovation and the Demand for Skills: Robustness across Income Groups

||Low Income|Low-Middle Income|Middle Income|
|---|---|---|---|
||(1)|(2)|(3)|
|Exporter|0.00159|-0.115**|-0.0958**|
||[0.0746]|[0.0489]|[0.0474]|
|Foreign Ownership|0.459***|0.0721|0.0785|
||[0.0933]|[0.0512]|[0.0486]|
|Technological Innnovation|0.015|0.297***|0.280***|
||[0.0589]|[0.0395]|[0.0380]|
|Baseline Firm Characteristics?|Yes|Yes|Yes|
|Country-Sector-Size Effects?|Yes|Yes|Yes|
|Observations|1,865|5,077|5,396|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: * significant at 10%; ** significant at 5%; *** significant at 1%. Dependent variable is the logarithm of the share of workforce with more than the secondary education. Columns (1) through (3) take the baseline specification (in column 5 of table 5) but run it separately for low income countries (column 1), low-middle income countries (column 2) and middle income countries (column 3). All the variables are defined in table A1 in the appendix.
<u>Table 8 Monthly earnings differentials between teachers and other wage workers</u>

||<br>All te|<br>achers|<br>Primary<br>teachers|<br>Secondary<br>teachers|<br>Public<br>school<br>teachers|Public school teachers<br>relative to other public<br>sector employees|
|---|---|---|---|---|---|---|
||OLS|Median|||OLS||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Burkina Faso|0.433***|0.616***|0.447***|0.358|0.495***|0.344***|
||(0.104)|(0.090)|(0.111)|(0.238)|(0.113)|(0.132)|
|Côte d'Ivoire|0.241*|0.317**|0.288|0.192|0.616***|0.112|
||(0.131)|(0.129)|(0.175)|(0.177)|(0.156)|(0.156)|
|DR Congo|-0.121***|-0.099***|-0.113***|-0.159**|-0.135***|-0.084**|
||(0.038)|(0.029)|(0.040)|(0.079)|(0.040)|(0.040)|
|The Gambia|-0.184***|-0.182***|-0.343***|0.049|-0.143**|-0.142*|
||(0.056)|(0.050)|(0.069)|(0.081)|(0.069)|(0.077)|
|Ghana|0.051|-0.004|0.022|0.093|0.128***|-0.076|
||(0.045)|(0.045)|(0.055)|(0.063)|(0.050)|(0.056)|
|Liberia|-0.475***|-0.337***|-0.433***|-0.642***|-0.398***|-0.405***|
||(0.084)|(0.083)|(0.092)|(0.172)|(0.098)|(0.105)|
|Malawi|0.018|0.143**|N|A|0.075|-0.008|
||(0.058)|(0.059)||..|(0.063)|(0.072)|
|Namibia|0.381***|0.352***|0.390***|0.365***|0.409***|0.238***|
||(0.058)|(0.072)|(0.068)|(0.087)|(0.059)|(0.055)|
|Niger|-0.156|-0.162*|-0.042|-0.510*|-0.026|-0.275|
||(0.140)|(0.092)|(0.157)|(0.262)|(0.154)|(0.200)|
|Nigeria|-0.250***|-0.276***|-0.297***|-0.166|-0.043|-0.172**|
||(0.071)|(0.072)|(0.082)|(0.109)|(0.078)|(0.071)|
|Senegal|0.247***|0.202***|0.330***|0.132|0.300***|0.155***|
||(0.055)|(0.059)|(0.070)|(0.081)|(0.057)|(0.055)|
|Sierra Leone|-0.586***|-0.286***|-0.660***|-0.480***|-0.541***|-0.517***|
||(0.132)|(0.106)|(0.158)|(0.182)|(0.145)|(0.165)|
|Tanzania|-0.143***|-0.088**|-0.162***|-0.099|-0.139***|-0.245***|
||(0.040)|(0.040)|(0.046)|(0.065)|(0.042)|(0.041)|
|Uganda|-0.188**|-0.301***|-0.240***|0.075|-0.169*|-0.413***|
||(0.078)|(0.087)|(0.081)|(0.148)|(0.092)|(0.102)|
|Zambia|0.522***|0.391***|0.490***|0.608***|0.610***|0.218***|
||(0.045)|(0.058)|(0.051)|(0.076)|(0.048)|(0.042)|
|**Average **|**-0.014**|**0.019**|**-0.023**|**-0.013**|**0.069**|**-0.085**|

Notes: Each column reports the coefficient in the regressions of ln (monthly earnings) on (a dummy for) a type of teachers with controls. The comparison group are other wage workers in columns 1-5 and other public sector employees in column 6. Controls include gender, age, age-squared, urbanicity and (a dummy for) post-secondary education. Sample is restricted to workers with secondary education or more. The survey in Malawi did not differentiate the levels of teachers. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Surveys listed in Table 1.

41
# **I. Introduction**

Consumer price indexes (CPIs) play an important role for our understanding of poverty trends in Africa. This is because international poverty estimates rely on national CPIs to express nominal consumption estimates from household surveys in real terms and the base year of the international poverty line.<sup>1</sup> However, there are concerns that CPIs may not always adequately reflect changes in the cost of living. Recent studies suggest that consumption growth and poverty reduction in Sub-Saharan Africa in the recent past may have been stronger than widely believed (see Kenny 2011; Young 2012; Pinkovskiy and Sala-i-Martin 2014), though others have challenged this notion (see Harttgen, Klasen and Vollmer 2013). Underlying these differing views about levels and trends in poverty and living standards in the region are often concerns about missing or inaccurate data, including concerns about the accuracy of measured inflation (see Beegle et al 2016).

There are several reasons why CPIs may not correctly measure changes in the cost of living. The most well-known causes, which have received considerable attention in the United States and other developed countries, are commodity substitution bias, outlet substitution bias, quality change bias and bias from the introduction of new goods (Schultze and Mackie 2002; Hausman 2003). Additional caveats arise for CPIs in Sub-Saharan Africa, due to weaknesses in country statistical systems, and when CPIs are used specifically for the purpose of measuring poverty trends (Beegle et al 2016). These include concerns about the representativeness of CPI weights for the poor (see Günther and Grimm 2007) and urban bias of the underlying input data (see Gaddis 2016).

The Engel curve method introduced by Costa (2001) and Hamilton (2001) attempts to address some of the above biases, mostly commodity and outlet substitution bias. Engel’s law arises from the observation that the food budget share in household consumption declines with the increase in real household income. This suggests that movements in the food budget share over time can reveal changes in real incomes, conditional on changes in relative prices and household characteristics. As a corollary, among demographically similar households at the same level of real income, differences in food budget shares at different points in time might signal mismeasurement of the true change in cost of living.

> 1 The current international poverty line stands at $1.90 per person per day in 2011 international dollars. See PovcalNet for the latest international poverty estimates: http://iresearch.worldbank.org/PovcalNet/.

2
while varying this threshold. The point estimates on aid disbursements, illustrated by the blue bars in Figure 4, suggest a strong positive correlation between aid-dependence and aid diversion. One the one hand, when we lower the threshold to 1% (sample of 46 countries), the estimate falls to around 1.8%, which is not significantly different from zero. On the other hand, raising the threshold to 3% (sample of 7 countries), the estimate increases to a highly significant 6%. The implied leakage rates, illustrated by the red line in Figure 4, exhibit an even stronger gradient: from a leakage rate of around 4% with a threshold of 1% to more than 15% with a threshold of 3%.<sup>18</sup> The steep gradient in leakage rates has several important implications. First, it suggests that our estimate of leakage out of aid disbursements to the main sample of highly aid-dependent countries is a poor estimate of leakage out of aid disbursements more generally. The 22 highly aid-dependent countries in our main sample account for around 10% of the aid disbursed by the World Bank and the results in Figure 4 suggest that leakage rates are much lower (if not zero) for less aid-dependent countries. Second, it constitutes novel evidence that aid capture may be more salient in underdeveloped and poorly governed countries, which are also most in need of development assistance (Alesina and Weder, 2002). While this association may simply reflect that the combination of poor development and bad governance stimulates foreign aid, it is also consistent with the view that very high levels of aid may foster corruption and institutional erosion (Knack, 2000; Djankov et al., 2008).

# **4.5 Publicly available data**

Up to this point, we have conducted the analysis with a restricted dataset from the BIS that allows us to break down each country’s total foreign deposits, which is public information, into deposits in havens and deposits in non-havens, which is not publicly available. To enhance transparency and to facilitate work by other researchers on aid and foreign deposits, we show that results similar to our main results can be obtained with a publicly available dataset from the BIS. This recently released data includes quarterly data on cross-border deposits at the bilateral level for a selected group of banking centers.

Table 6 summarizes the publicly available information. In our main sample of 22 highly aiddependent countries (Column 1), the average of total foreign deposits taken across all quarters in the sample period 1999-2010 stands at $199 million (corresponding to the sum of Columns 2 and 3 in Table 1). With the public dataset, 29% of these deposits can be assigned to six

> 18Not only does the point estimate on aid increase as we raise the threshold, the ratio of haven deposits to GDP also increases, which implies a higher leakage rate for a given point estimate.

17
# **<u>Table C.8 Descriptive statistics of estimation sample, Iraq</u>**

||**2007**|**2007-2012**<br>**2012**|**diff**|
|---|---|---|---|
|Lo of er caita consumtion|14.22|14.67|0.1***|
|g p p p|(0.01)|(0.01)|(0.0)|
|Head` ae|40.46|44.28|1.7***|
|s g|(0.07)|(0.06)|(0.2)|
|Hd i fl|0.09|0.09|0.0|
|ea s emae|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i i|0.41|0.40|0.0***|
|eas ges eucaon eve s prmary|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i d|0.12|0.08|-0.0|
|eas ges eucaon eve s seconary|(0.00)|(0.00)|(0.0)|
|Hd' hih di ll i i|0.19|0.15|-0.0***|
|eas gest eucaton eve s tertary|(0.00)|(0.00)|(0.0)|
||0.68|0.60|-0.1***|
|Urban area|(0.00)|(0.00)|(0.0)|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. The data are unweighted. Household heads' ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second survey round. The reference groups are household with no primary education and living in rural areas.

86
Tnalo II Simtlate(d FffPctq of Bond Finan ed P'ulblic **Ile** fic it 1 P(rrentit ago e **i Ft in** f trom lknsel i no Sq **Iiit** i **()**

|V'arinhIo|I981|1982|1983|1981|1985|1986|
|---|---|---|---|---|---|---|
|Xn|0.68|1.11|1.42|1.55|LS(|1.18|
|Xi|0.49|0.76|0. 95|0.o95|<br>1.02|11 5|
|X-;|0.00|0.00|0.00|0.0(1|<br>0.0o|0.00|
|Ma|0.63|0.92|0.92|1.04|1.07|0.99|
|M)|0.93|1.14|1.01|1.2i|1.23|1.09|
|**MNJ**|1.70|2.08|1.87|2.17|2.18|1.91|
|Ms|2.05|2.81|3.(0|3.2i|3.33|2.98|
|Qn|1.18|1.41|1.29|1.50|1.51|1.32|
|Qi|0.fi7|0.82|0.73|0.85|0.85|0.75|
|cp|0.21|0.33|0.31|0.32|0.31|0.25|
|Ip|0.74|0.86|0.7.1|0.92|O.87|0.78|
|T|0.84|1.50|1.86|2.19|2.39|2.41|
|Pd|0.01|0.03|0.05|0.07|0.10|0.11|
|Pda|0.01|0.03|0.06|0.08|0.10|0.12|
|Pdi|0.01|0.03|0.06|0.08|0.11|0.13|
|PM|0.00|0.00|0.00|0.00|0.00|0.00|
|Rd|-0.01|0.00|0.02|0.00|0.01|0.01|
|F|-0.02|0.01|0.06|0.01|0.05|0.05|
|Hf|-0.98|-1.26|-1.35|-1.06|-1.03|-0.42|
|_7._|0.50|0.66|0.60|0.68|0.67|0.57|
|DL)|0.51|0.65|0.58|0.67|0.66f|0.60|
|TO|0.29|-0.415|-0.99|-0.26|-0.93|-0.61|
|SI)|0.52|0.65|0.60|0.65|0.62|0.57|
|Fl|0.02|-0.02|-0.17|0.01|-(.16|-0.11|
|L|0.23|-0.15|-0.47|0.00|-0.41|-0.21|
|GBb|0.39|-0.19|-0.56|-0.04|-0.51|-0.19|
|Fa|0.25|-0.14|-0.48|-0.03|-0.4fi|-0.18|
|CAb|0.32|-0.17|-0.50|-0.03|-0.47|-0.18|
|P|-0.82|-0.76|-0.89|-0.21|-0.49|-0.60|
|It|0.35|-0.18|-0.55|-0.04|-0.5()|<br>-0.20|
|CPTl|-1.03|-0.91|-0.98|-0.28|-0.54|-0.80|
|_Gip_|_|_|_|_|_|_|
|lih|-0.52|-0.09|0.03|0.46|0.72|-0.40|
|X|0.62|1.03|1.27|1.35|1.36|1.33|
|XN|0.62|1.01|1.26|1.33|1.31|1.31|
|M|1.15|1.40|1.27|1.46|1.45|1.29|
|MN|1.16|1.41|1.27|1.44|1.44|1.20|
|TBL|2.06|2.21|1.28|1.69|1.8R|-0.12|
|CAI)|2.88|3.50|2.52|3.06|3.51|9.49|
|Y|0.46|0.62|0.5f6|0.67|0.68|0.54|
|Dg|44.36|2.67|2.09|0.94|0.38|-2.98|
|<br>Pdh|0.01|0.03|0.05|0.06|0.08|0.10|
revenue – a ratio that is nearly three times the debt stress threshold of 20 percent, while the debt burden is expected to be nearly 90 percent of GSDP in 2026/27 (Figure 12).

# **Figure 12. Policy Risks in Tamil Nadu**

<!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Baseline Interest rate increase<br>Wage increase Combined policy risks<br>Other expenditure increases<br>2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025<br>Debt as a % of GSDP<br><!-- End of picture text --> Source: Authors’ simulation based on equation (4).

# **_4.4.3 Contingent liabilities_**

Although the maximum contingent liabilities as a share of GSDP are lower in Tamil Nadu than in some other Indian states, they constitute a risk to the state’s long-term fiscal sustainability. In 2002/03, the government’s guaranteed debt was estimated at 8 percent of GSDP.<sup>43</sup> An unexpected realization of these contingent liabilities would not only augment government debt, but could also lead to rising effective borrowing costs, and possibly to a fall in real output.

Suppose state liabilities as a share of GSDP jump by 8 percentage points in 2006/07 as a result of realized contingent liabilities from government guaranteed debt.<sup>44</sup> Suppose that this jump in state liabilities triggers an increase in nominal effective interest rates by 2 percentage points and a 1.5 percent drop in real growth rates.<sup>45</sup> Our simulations suggest that in this case the government will need to run a surplus of 1.2 percent of GSDP in order

> 43 Source: Government of Tamil Nadu.

> 44 We assume in this experiment that the government assumes responsibility for all of its government guaranteed debt.

> 45 Unless it is a part of widespread fiscal deterioration affecting many Indian states, this scenario is unlikely to occur since all states pay the same premium regardless of risk.

29
# **Figure 11: Share of Households that have any bills in arrears by Age Group, 2007**

<!-- Start of picture text --> Austria Belgium Cyprus Czech Republic Denmark<br>.3 .3 .3 .3 .3<br>.2 .2 .2 .2 .2<br>.1 .1 .1 .1 .1<br>0 0 0 0 0<br>Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65<br>35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64<br>Estonia Finland France Greece Hungary<br>.3 .3 .3 .3 .3<br>.2 .2 .2 .2 .2<br>.1 .1 .1 .1 .1<br>0 0 0 0 0<br>Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65<br>35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64<br>Ireland Italy Latvia Lithuania Luxembourg<br>.3 .3 .3 .3 .3<br>.2 .2 .2 .2 .2<br>.1 .1 .1 .1 .1<br>0 0 0 0 0<br>Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65<br>35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64<br>Netherlands Poland Portugal Slovak Republic Slovenia<br>.3 .3 .3 .3 .3<br>.2 .2 .2 .2 .2<br>.1 .1 .1 .1 .1<br>0 0 0 0 0<br>Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65<br>35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64 35-44 55-64<br>Spain Sweden United Kingdom<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>Below 35 45-54 Over 65 Below 35 45-54 Over 65 Below 35 45-54 Over 65<br>35-44 55-64 35-44 55-64 35-44 55-64<br><!-- End of picture text --> Source: SILC - EU Statistics on Income and Living Conditions, 2007 24
syndicated loans, the share of flows captured by Brazil and Mexico is similar for flows within LAC and for flows to the rest of the world. In addition, portfolio investment is the only type of venture in which other countries of the region rather than Brazil and Mexico accounted for the great majority of the investments.

The role of Brazil and Mexico as receivers of flows from the North and South is even more striking, as they accounted for the great majority of the inflows in the four types of investment considered (Figure 10, Panel B). Moreover, flows to Brazil and Mexico have increased at a faster pace than flows to other LAC countries, and therefore they have increased their participation as receivers of flows. For flows within LAC countries, while important, their role was more subdued. Within LAC, other countries of the region such as Argentina, Chile, Colombia, Peru and República Bolivariana de Venezuela capture an important share of the inflows, especially in greenfield investments.

Summing up, although LAC is more integrated with North and South countries, due to an increase of both the extensive and intensive margins, much of this growth seems to be driven by the two largest countries of the region. Within LAC connections, although the role of Brazil and Mexico is still important, other countries of the region do capture an important share of the flows.

# **5. LAC’s Financial Flows and Trade Flows**

The globalization of LAC, which started in the late 1980s and continued strongly during the 1990s, accelerated and intensified in the 2000s. A growing body of evidence suggests that the patterns of financial globalization changed during the 2000s.<sup>18</sup> Aside from size, one aspect of both trade and financial flows that has been changing significantly for LAC (as well as other South countries) is their composition. The sectoral composition of LAC’s connections with other South countries is generally different from the composition of its connections with North countries, in both trade and finance. Hence, an important question is the extent to which financial flows reflect the dynamics of trade connections. This section sheds light on the links between these two types of flows and the importance of the link for LAC.

Here we study the role played by the different sectors in the growth of the financial flows to and from LAC countries. In addition, we study the links between trade and financial flows in LAC at the sectoral level. To do so, the data on foreign investments (both M&A and greenfield) and syndicated

> 18 Given the relatively short time span of the data on gross capital flows explored in this paper, we are not able to clearly disentangle the extent to which changes in the nature of financial integration of LAC countries are inherent to its globalization process or driven by the changes in the global landscape, such as those associated with the rise of the South for instance.

15
Figure 11.A. Global economic activity and financial market developments in global expansions

# A. Output per capita

# B. Trade

<!-- Start of picture text --> Percent Percent<br>2.0 Excluding the initial year of recovery 8 Excluding the initial year of recovery<br>1.5 6<br>1.0 4<br>0.5 2<br>0.0 0<br>1976-81 1983-90 1992-2008 2010-19 1976-81 1983-90 1992-2008 2010-19<br>C. Unemployment rate D. Credit<br>Percent Percent<br>8 Excluding the initial year of recovery 8 Excluding the initial year of recovery<br>6 6<br>4 4<br>2 2<br>0 0<br>1976-81 1983-90 1992-2008 2010-19 1976-81 1983-90 1992-2008 2010-19<br>E. Equity prices F. House prices<br>Percent Percent<br>18 Excluding the initial year of recovery 4<br>Excluding the initial year of recovery<br>12 3<br>6 2<br>0 1<br>-6 0<br>1976-81 1983-90 1992-2008 2010-19 1976-81 1983-90 1992-2008 2010-19<br><!-- End of picture text --> Sources: Feenstra, Inklaar, and Timmer (2015); Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Each bar represents average growth during the periods of global expansions. The growth rates of per capita output, credit, equity prices, and house prices are market-weighted (by GDP in U.S. dollars).

46
15

# **4 Empirical Results**

# **4.1 Results of the Equilibrium Hypothesis**

Table 5 presents the regression results using different techniques, all of which yield similar results. In the demand equation, real industrial production has the expected positive sign while the stock market index has a negative impact on the demand for credit, reflecting its role as a substitute for bank credit. The real lending rate turns out with an unexpected positive sign and statistically significant coefficient.

**Table 5: Demand and Supply of Private Credit (Equilibrium Estimation)**

||S<br>|URE<br>|M<br>|VREG<br>|
|---|---|---|---|---|
||Ln(Cla<br>|im. Priv.)<br>|Ln(Cla<br>|im. Priv.)<br>|
||Supply|Demand|Supply|Demand|
|Real Lending Rate|1.721***|0.892***|1.721***|0.892***|
||(0.327)|(0.212)|(0.343)|(0.222)|
|Real T-bill Rate|-0.958***<br>(0.335)||-0.958***<br>(0.351)||
|Ln(Lagged Lending Cap.)|0.198***<br>(0.0584)||0.198***<br>(0.0612)||
|Ln(Lagged Industrial Prod.)||0.249***<br>(0.0523)||0.249***<br>(0.0548)|
|Ln(Lagged EGX 30)||-0.0397***<br>(0.0121)||-0.0397***<br>(0.0126)|
|Constant|6.165***|6.566***|6.165***|6.566***|
||(0.497)|(0.285)|(0.521)|(0.299)|
|Observations|45|45|45|45|
|R-squared|0.458|0.491|0.458|0.491|
||<br>Ln(Cla|OLS<br>im. Priv.)|2<br>Ln(Cla|SLS<br>im. Priv.)|
||Supply|Demand|Supply|Demand|
|Real Lending Rate|2.509***|0.788***|2.509***|0.788***|
||(0.391)|(0.235)|(0.391)|(0.235)|
|Real T-bill Rate|-1.892***<br>(0.431)||-1.892***<br>(0.431)||
|Ln(Lagged Lending Cap.)|0.241***<br>(0.0658)||0.241***<br>(0.0658)||
|Ln(Lagged Industrial Prod.)||0.378***<br>(0.0622)||0.378***<br>(0.0622)|
|Ln(Lagged EGX 30)||-0.0785***||-0.0785***|
|||(0.0155)||(0.0155)|
|Constant|5.776***|6.044***|5.776***|6.044***|
||(0.560)|(0.328)|(0.560)|(0.328)|
|Observations|45|45|45|45|
|R-squared|0.517|0.560|0.517|0.560|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1