rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Firm-level Earnings Regressions: Entire Sample (1993-2006)**

|||Dependen<br>|t variable:<br>|logfirm-leve<br>|l average wa<br>|ges<br>|||
|---|---|---|---|---|---|---|---|---|
||OLS<br>('93-'06)|FE<br>('93-'06)|OLS<br>('93-'06)|FE<br>('93-'06)|OLS<br>('93-'06)|FE<br>('93-'06)|OLS<br>('95-'97<br>'06)|FE<br>('95-'97<br>'06)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|% Female|-0.569***|-0.209***|-0.534***|-0.203***|-0.131***|-0.121***|-0.168***|-0.181***|
||(0.010)|(0.015)|(0.009)|(0.015)|(0.007)|(0.014)|(0.009)|(0.025)|
|Log Minimum Wage|||0.305***|0.069***|0.070***|0.040**|0.091***|0.130***|
||||(0.022)|(0.020)|(0.017)|(0.018)|(0.032)|(0.044)|
|Log Min.Wage*|||<br>0.097***|<br>0.086***|<br>0.143***|<br>0.129***|<br>0.134***|<br>0.094***|
|<br>*%Female|||(0.018)|(0.017)|(0.014)|(0.016)|(0.022)|(0.031)|
|Foreign-owned|||||0.002|-0.005|0.030**|0.008|
||||||(0.010)|(0.013)|(0.013)|(0.024)|
|Government-owned|||||-0.040***|0.011|-0.019|0.023|
||||||(0.014)|(0.011)|(0.018)|(0.026)|
|Exporter|||||-0.001|0.004|-0.006|0.009|
||||||(0.005)|(0.005)|(0.007)|(0.009)|
|Log Firm Age|||||<br>0.017***|<br>0.072***|<br>0.031***|<br>0.091***|
||||||(0.002)|(0.006)|(0.003)|(0.011)|
|Log L|||||0.033***|0.003|0.015***|-0.017*|
||||||(0.002)|(0.005)|(0.003)|(0.009)|
|Unskilled ratio|||||<br>-0.415***|<br>-0.568***|<br>-0.335***|<br>-0.539***|
||||||(0.015)|(0.019)|(0.019)|(0.033)|
|Log V/L|||||0.315***|0.241***|0.292***|0.233***|
||||||(0.003)|(0.003)|(0.004)|(0.005)|
|% Primary|||||||0.134***|0.019|
||||||||(0.016)|(0.021)|
|% Junior|||||||0.283***|0.099***|
||||||||(0.015)|(0.023)|
|% High+|||||||0.278***|0.220***|
||||||||(0.016)|(0.025)|
|Province Dummies|X||X||X||X||
|Industry Dummies|||||X||X||
|<br>Year Dummies|X|X|X|X|X|X|X|X|
|N|273,444|273,444|273,444|273,444|272,289|272,289|87,785|87,785|
|R<sup>2</sup>|0.210|0.097|0.212|0.097|0.525|0.210|0.513|0.232|

_Note:_ *** p<0.01,** p<0.05, * p<0.1. Standard errors, reported in parentheses, are heteroscedasticity robust and clustered by establishment. Minimum wages, wages and value-added are all measured in real terms. Minimum wage and share of female workers are demeaned.

32
<!-- Start of picture text --> Polley Researh<br>Debt and Intornallonal Finance<br>WPS 1160<br><!-- End of picture text -->

- This paper a product of the Debt and International Finance Division, International Economics - Departnent is part of a larger effort in the department to study the determinants of capital flows to developing countries. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rose Vo, room S8-042, extension 31047. (July 1993, 36 pages).

Chuhan, Claessens, and Mamingi investigate what has motivated the large portfolio flows to several developing countries in recent years.

Using monthly data on U.S. capital flows to nine Latin American and nine Asian countries (instead of monthly reserves data), they analyze the behavior of bond and equity flows to those countries.

Using panel data, **Ley** find that global factors - such as a drop in U.S. interest rates and the slowdown in U.S. industrial production - are important in explaining capital inflows. But country developments are at least as important in detennining those flows, especially for Asia.

They also find that equity flows are more sensitive than bond flows to global factors, but that bond flows are generally more sensitive to a courntry's credit rating and to the secondary market price of debt.

ThePolicy Research Working PaperSeries disseminates thefindings of workunder way in theBank. Anobj-ctiveof the series is to get these flndings out **quickly,** even if presentations are less than fully polished. The findings, **int** rpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Disseminadon Center
sized shock by offsetting the cost of additional specific provisions, but would fail to withstand a severe crisis.

# **Bolivia**

Bolivia introduced a rule-based cyclical provisioning requirement in 2008, which adds on top of the specific provisioning requirement and specifies the required percentages of loans that banks need to maintain as cyclical provision for different types of loans.<sup>59</sup> The provision is built up monthly at a rate of 2.78 percent of total required cyclical provision (that is, over a period of 36 months), with a frontload at the beginning of the implementation in December 2008 ( _n_ =9, or a buildup of 25 percent of the required provision) [see equation (3)]. During a downturn, banks can access the stock of the cyclical provision to cover up to 50 percent of the increase in specific provision in a given month as long as the loan quality, as measured by the actual ratio of specific provision required (RPR), has deteriorated for six months and the cyclical provision has been fully phased in. When credit quality improves, as indicated by the decrease of the six-month moving average of the RPR, banks are required to restart cumulate cyclical provision [see equation (4)].

where is the actual ratio of specific provision in a given loan category **,** is the share of the given loan category in total loans, and A though H are categories by loan classification.

# **Colombia**

The Colombian countercyclical provisioning system differs from all other dynamic provisioning systems in that it is based on specific provision and that it is fully discretionary in supervisory decision regarding the switching of states. The system, which was adopted in 2007, uses two matrices of PDs (by loan type and classification) to calculate ―individual provision‖ (a concept of specific provision) [see equation (5)]. Each period, the regulator decides which of the two matrices applies, and the difference between the two will be the required countercyclical provision. In effect, the required countercyclical provision has two states: on and off. In good times, banks are required to maintain a countercyclical provision to bring total provision to the level implied by the matrix with higher PDs, which can be used in bad times (once the change of the state is declared) to cover losses until the level of total provision reaches the requirement implied by the matrix with lower PDs. The biggest issue in such a discretionary system, of course, is making the call. Supervisory independence and the will to act, as well as access to

> 59 The rates of cyclical provisioning are 1.5 percent for mortgages; 2.3 percent for consumer loans; 1.6 percent for micro credit; and 2.3, 3.2, and 5.5 percent for different categories of commercial loans. See SBEF (2008) for details.

34
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**Table 5: Summary statistics on incumbent firms**

|Year|No. of firms|Medium/Large<br>Firms (>50<br>workers)|Mean no. of<br>workers|Mean change in<br>workers|Mean age of<br>firms|Mean change in<br>revenue<br>(in 1000s)|<br>Mean change in<br>profit<br>(in 1000s)|
|---|---|---|---|---|---|---|---|
|2004|58324|12655|86.9<br>(406.4)|‐4.5<br>(113.7)|7.8<br>(11.4)|390.5<br>(14545)|34.7<br>(2493.1)|
|2005|52453|12013|102<br>(425.6)|1.9<br>(57.5)|8.3<br>(11.7)|252.2<br>(21295.6)|17.1<br>(2475.9)|
|2006|53468|12831|87.5<br>(383.8)|1.5<br>(51.8)|8.6<br>(12.1)|876.6<br>(14902.7)|105.1<br>(3206.9)|
|2007|54882|12754|97.7<br>(411.4)|2.8<br>(58.1)|8.8<br>(12)|1525.7<br>(157723.9)|164.5<br>(15630.5)|
|2008|55118|14447|101.7<br>(398.4)|4<br>(53.8)|9<br>(12.1)|‐1665.9<br>(143070.6)|‐137.4<br>(13890.5)|
|2009|53960|15444|105.4<br>(379)|4.8<br>(121.5)|9.5<br>(12.3)|‐630.9<br>(17797.4)|‐178.3<br>(6076.4)|
|2010|51305|15403|107.8<br>(383.6)|4.6<br>(78.2)|9.9<br>(12.3)|478.6<br>(11134.5)|88.7<br>(3617.4)|

Note: Standard deviation in parentheses. Excludes ‘entering’ firms.

22
on their activities (Goldberg and Grosse, 1994; Focarelli and Pozzolo, 2000; Buch, 2003; Buch and De Long, 2004; Buch and Lipponer, 2004; Galindo et al, 2003; Bertus, Jahera, and Yost, 2008) and lower taxes (Claessens et al., 2000).

Although financial regulation and the restrictions on the entry and activities of foreign are typically taken to be exogenous in the empirical literature, those outcomes are the product of a political process, one that is heavily influenced by external events such as crises. After the Asian financial crisis, for example, governments relaxed entry barriers both in that region (Montgomery, 2003) and more broadly throughout the developing world (Domanski, 2005). Post-crisis policy measures in Argentina, Brazil, and Mexico also led to rapid increases in foreign bank participation (Peek et al., 2000). In Mexico and South Korea foreign banks were brought in specifically to re-capitalize the banking sector post-crisis (Moreno and Villar, 2005). A review of crisis episodes in twelve countries concluded that foreign banks tended not to have substantial presence pre-crisis, but were brought in to act as rehabilitators of weak or failed banks ex-post (Tschoegl, 2005).

# **_Mechanisms that help mitigate information costs_**

Another strand of the literature on the determinants of foreign bank participation examines the costliness of acquiring information on borrowers in a destination market. Data from the top 100 multinational banks link greater foreign bank participation to the existence and quality of the credit reporting agency in the host country (Tsai et al, 2009). Another means of coping with informational asymmetry between lender and borrower is through ex-post enforcement in cases of default. Studies have shown that foreign bank participation levels are higher where there is less corruption and greater adherence to the rule of law (Galindo et al., 2003) and greater judicial efficiency (Focarelli and Pozzolo, 2000).

A final strand of the literature on how information costs affect the level of foreign bank participation emphasizes the roles of cultural similarity and geographic proximity. Cross-country evidence indicates that proximity between home and host country and a common language are associated with higher levels of foreign bank participation and greater likelihood of acquisition by a foreign bank (Buch, 2003; Buch and De Long, 2004). A common legal framework between home and host also coincides with higher levels of foreign bank participation (Galindo et al., 9
processing and use of borrowing history and other information relevant to household and small business lending – credit registries - have been rapidly growing in both the public and private sectors (see Miller, 2003, for an overview). Computer technology has also greatly improved the amount of information that can be analyzed to assess creditworthiness, such as through credit scoring techniques. Governments can play an important role in this process, and while establishment of public credit registries may discourage private entry, in several cases it has actually encouraged private registries to enter in order to provide a wider and deeper range of services. Governments are also important in creating and supporting the legal system needed for conflict resolution and contract enforcement, and strengthening accounting infrastructures to enable financial development.

Empirical results show that the volume of bank credit is significantly higher in countries with more information sharing (Jappelli and Pagano, 2002; and Djankov, McLeish and Shleifer, 2005). Firms also report lower financing obstacles with better credit information (Love and Mylenko, 2003). Detragiache, Gupta and Tressel (2005) find that better access to information and speedier enforcement of contracts are associated with deeper financial systems even in low-income countries. Indeed, compared to high income countries, in lower income countries it is credit information more than legal enforcement that matters (Djankov et al., 2005).

_IIIc. Regulation and Supervision_ For as long as there have been banks, there have also been governments regulating them. While most economists agree that there is a role for government in the regulation and supervision of financial systems, the extent of this involvement is an issue 16
44 Table III: Other Parameters

||Cobb-Do<br>|uglas Coeffc|ients|International trade costs|
|---|---|---|---|---|
|Crop|Fertilizer(_γ_<sup>_f_</sup><br>_k_ <sup>)</sup>|Labor(_γ_<sup>_n_</sup><br>_k_ <sup>)</sup>|Land(_γ_<sup>_l_</sup><br>_k_<sup>)</sup>|_Bk_|
|Cinnamon|0.032|0.429|0.539|2.36|
|Cloves|0.043|0.424|0.533|2.36|
|Groundnuts|0.066|0.414|0.520|2.36|
|Maize|0.177|0.365|0.458|1.07|
|Onions|0.075|0.410|0.515|2.14|
|Potatoes|0.129|0.394|0.477|2.39|
|Rice|0.110|0.469|0.421|1.43|
|Tea|0.109|0.487|0.404|9.40|

_Notes_ : the table displays our estimates of the model parameters that govern agricultural production technology ( _{γk_<sup>_f, γ_</sup> _k_<sup>_n, γ_</sup> _k_<sup>_l}K_</sup> _k_ =1<sup>) and agricultural</sup> international trade costs ( _{Bk}_<sup>_K_</sup> _k_ =1<sup>).See Sections VI.A.2 and VI.A.3 for descriptions of our estimation methodologies.</sup>
# **References**

Barth, James R., Gerard Caprio, and Ross Levine. 2004. “The Regulation and Supervision of Banks around the World: A New Database,” 2003 Version and Update, Washington, DC: World Bank.

- Demirgüç-Kunt, Asli, and Enrica Detragiache. 2002. “Does Deposit Insurance Increase Banking System Stability? An Empirical Investigation,” _Journal of Monetary Economics_ , 49(7): 1373-1406.

- Demirgüç-Kunt, Asli, and Harry Huizinga. 2004. “Market Discipline and Deposit Insurance,” _Journal of Monetary Economics_ , 51(2): 375-399.

Demirgüç-Kunt, Asli, and Edward Kane. 2002. “Deposit Insurance Around the World: Where Does it Work?” _Journal of Economic Perspectives_ 16, 175-195.

- Demirgüç-Kunt, Asli, Edward Kane, and Luc Laeven. 2005. “Determinants of DepositInsurance Adoption and Design,” mimeo, World Bank.

Demirgüç-Kunt, Asli, and Tolga Sobaci. 2001. “A New Development Database: Deposit Insurance around the World,” _World Bank Economic Review_ , 15: 481-490.

Folkerts-Landau, David, and Carl-Johan Lindgren. 1998. “Toward a Framework for Financial Stability,” mimeo, International Monetary Fund.

Garcia, Gillian. 1999. “Deposit Insurance: Actual and Best Practices,” IMF Working Paper 99/54, Washington, DC: International Monetary Fund.

Garcia, Gillian. 2000. “Deposit Insurance: Actual and Good Practices,” Occasional Paper No. 197, Washington, DC: International Monetary Fund.

- Gerdrup, Karsten R. 2003. “Three Episodes of Financial Fragility in Norway since the 1890s,” BIS Working Paper No. 142, Basel: Bank for International Settlements.

- Kyei, Alexander. 1995. “Deposit Protection Arrangements: A Comparative Study,” IMF Working Paper 95/134, Washington, DC: International Monetary Fund.

- Laeven, Luc. 2004. “The Political Economy of Deposit Insurance,” _Journal of Financial Services Research_ 26(3), 201-224.

Talley, Samuel H., and Ignacio Mas. 1990. “Deposit Insurance in Developing Countries,” Policy Research Working Paper, No. 548, Washington, DC: World Bank.

14
**Table 9. Data-generating processes and inequality measures under increase-in-IOO scenarios**

|**Coefficient**|**Ineq**|**uality meas**|**ures**||**R‐s**|**quared b**|**y observed c**|**ircumstance**<br>**Father's**|**Mother’s**|
|---|---|---|---|---|---|---|---|---|---|
|**in Urban**|**Gini**|**MLD**|**IOO**|**All**|**Female**|**Urban**|**Region**|**education**|**education**|
|**indicator**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|_0_|0.143|0.010|0.271|0.285|0.089|0.014|0.172|0.025|0.046|
|_10_|0.153|0.012|0.422|0.438|0.068|0.230|0.197|0.069|0.098|
|_15_|0.163|0.014|0.523|0.540|0.055|0.367|0.201|0.088|0.118|
|_25_|0.191|0.019|0.680|0.699|0.037|0.586|0.191|0.108|0.136|
|_35_|0.222|0.026|0.779|0.799|0.024|0.724|0.179|0.117|0.139|
|_50_|0.268|0.038|0.860|0.880|0.014|0.835|0.164|0.121|0.140|

Notes: Data generating process is presented in equation (3). All six scenarios include a normally distributed error term with mean zero and standard deviation of 10. R‐squared is obtained from an OLS regression using income as the dependent variable and the specified circumstance as regressors. MLD is the mean log deviation, IOO is the ratio of the smoothed distribution MLD and total MLD. All estimates are based on observing the full income distribution (i.e. no truncation).

27
21 adjustment to take place. The economic and political costs associated with current transfers are significant after a permanent shock, and simulations suggest that it may be optimal to start redistribution later in the adjustment period.

# d. Reduced Form Econometrics

A fourth methodology is a "reduced form" econometric analysis by Blejer and Guerrero (1988). Using quarterly survey data and quarterly national income data from 1980-86 for the Philippines, the ratio of the share of income of the lowest three deciles to the share of income of the highest decile is regressed on productivity, underemployment, the real exchange rate, real government spending, a time trend, the real interest rate, and inflation. The results for the Philippines are that underemployrent, inflation and government spending are regressive and productivity gains, the real interest rate and the real exchange rate are progressive.<sup>2 0</sup> The authors use these results to conclude that a larger drop in the real exchange rate would have reduced poverty, and that reducing inflation, increasing real interest rates, and reducing (regressive) government expenditures tend to improve the income distribution.

While this econometric model is useful for estimating the "net" effect of a policy, the link between the policy and resulting incomes is unclear. The coefficients are similar to "multipliers", they measure the total effect of the exogenous variables, but do not provide information about the transmission mechanisms. They do, however, provide knowledge about the magnitude of the tradeoff between policies on income distributicn. For example, underemployment has a slightly stronger negative effect than inflation on the relative income 20The results are the same when the LHS variable is the real share of the lowest quintile (absolute poverty).
**Table T2.CR: Intergenerational Persistence in Schooling in Rural China**

|||**LINEARCEF**||**QU**|**ADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
|||**Dahters**|||**Dahters**||
||**Sons (S)**|**ug**<br>**(D)**|**D-S**|**Sons (S)**|**ug**<br>**(D)**|**D-S**|
|Linear Coefft|**0.27*****|**0.33*****|**0.062*****|**0.27*****|**0.38*****|**0.11***|
||(0.015)|(0.015)|(0.021)|(0.042)|(0.045)|(0.061)|
|Quadratic Coefft.||||**-0.00025**|**-0.0047**|**-0.0045**|
|||||(0.0035)|(0.0037)|(0.0051)|
|Intercept|**7.59*****|**6.78*****|**-0.81*****|**7.59*****|**6.73*****|**-0.86*****|
||(0.11)|(0.12)|(0.17)|(0.12)|(0.13)|(0.18)|
|No. Observations|3955|4085|8040|3955|4085|8040|
|||**Estimate**<br>|**s of Mobilit**|**y fromQuadra**|**tic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.59|6.78||**_IGRC_**|0.27|0.33|
|**_ES5 _**|9.21|8.76|||||
|**_ES10_**|10.02|9.75|||||
|**_ES16_**|11.91|12.06|||||

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
These three steps generate various quantifications of the intuitive features presented (including strength and diversity of contacts) as well as other measures (intensity and distribution of usage over space and time, and mobility). For each feature, we also add an indicator for whether that individual is missing that feature. Altogether, there are approximately 5,500 features with variation.

# **4. PREDICTION AND RESULTS**

A first question is how individual features correlate with default. Table 2 presents the single variable correlation with default.

Characteristics traditionally available to lenders are not very predictive. Demographic features (gender and age) have very low correlation with repayment (magnitudes between 0.04 and 0.07). Having a credit bureau record has a small negative correlation with repayment (-0.02). For individuals with records, the most predictive feature is the summary score (-0.072; lower is better), and the fraction of debt lost (- 0.046). That individual credit bureau features are only slightly predictive suggests that predicting repayment in this setting is a difficult problem.

Individual features derived from mobile phone usage have slightly higher correlations, ranging up to 0.16. But mobile phone usage data are richer, so there are many more features of behavior that can be included in a model. Many features measure similar concepts, so the table presents broad categories, and the correlation of one top feature within that category. Correlated features include the periodicity of usage (top correlation -0.16), slope of usage (0.13), correlations in usage (0.11), and variance (-0.10). The table highlights particular features that perform well in isolation, including the slope of daily calls sent, and the number of important geographical location clusters where the phone is used. Next, we consider using multiple features together to predict repayment.

# **<u>Predicting Repayment</u>**

Our features are predictive even using standard methods common in the machine learning literature.

We estimate two standard machine learning models: random forests, and logistic regressions using a model 12