rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2. Estimating the NPL Elasticities**

We obtain estimates of the elasticities of NPLs to the four macroeconomic variables of interest by means of a dynamic panel data regression, using a panel of 54 high and middle income countries and controlling for the degree of development, financial deepening, and dollarization (euroization).<sup>19</sup> The sample consists of annual data over the period from 1994 to 2004.<sup>20</sup> The parameters are estimated from the following model: The variable _NPLt_ +1, _n_ is measured as the ratio of non-performing loans to total loans.<sup>21</sup> The variables ∆ _yt_ +1, _n_ , _πt_ +1, _n_ , _rt_ +1, _n_ and ∆ _et_ +1, _n_ are real GDP growth, CPI inflation, the (ex post) real interest rate and the change in the nominal US dollar exchange rate for country _n_ at time period _t_ + 1. _Zt_ +1, _n_ is a vector of variables, comprising the log of GDP per capita (constant 2000 US dollars), the credit to GDP ratio, and the share of FX loans in total loans, that control for the degree of development, financial deepening, and dollarization (euroization), respectively. The model is estimated on an unbalanced panel using the GMM 19The list of countries that was used in the computation can be obtained from the authors upon request. One has to point out that the comparability of data on NPLs across countries remains a challenge that has been also recognized by international policy makers and plans for greater harmonization in the near future are being discussed in the international fora.

20 Estimating the NPL regression on a relatively short time sample covering a period during which a country could have gone through major structural changes in the financial sector could be problematic. Similarly, using country specific historical data especially for emerging countries, covers only a rather limited number of crises periods. For these reasons, we find the pooled regression approach preferable for the sake of robustness of the acquired estimates, as lower income countries develop and their own past experience may not be relevant in assessing the impact of possible future crises on their financial system and on the economy. Moreover, if more country specificity is desirable, the pooled regression estimates could be used as priors in a Bayesian estimation of the NPL regression, including also additional country specific variables, possibly also as latent variables (see, Brand, Buncic and Turunen, 2010, on how latent variables can be incorporated in a monetary polciy modeling environment) 21Note here again that we will simply refer to this variable as NPLs (non-performing loans) in the text.

25
**Table 3. Rents paid after adjustment using a hedonic model (in Egyptian pounds)**

||**Rent ‐ OLD**|**Rent – OLD***|**Rent ‐ NEW**|
|---|---|---|---|
|**Metropolitan**|624.00|4143.12|4807|
||(844.98)|(975.67)|(2716.04)|
|**Lower Urban**|805.00|3732.22|4316|
||(914.72)|(743.37)|(1813.84)|
|**Lower Rural**|1419.00|3041.15|2958|
||(848.53)|(636.10)|(1133)|
|**Upper Urban**|944.00|3727.43|4236|
||(936.10)|(731.26)|(2115.07)|
|**Upper Rural**|1328.00|2471.74|2633|
||(1056.70)|(647.27)|(1040.17)|

Source: HIECS, 2012/13 Note: Rent‐OLD shows rents paid for subsidized tenants, Rent‐OLD* shows rent adjusted rents paid for subsidized tenants and Rent‐NEW rents paid for unregulated tenants. Predictions using the hedonic model include the Duan’s (1983) correction.

We also find that the adjusted rental payments form a significant share of the total consumption expenditure in Egyptian households. Moreover, the corrected values are in line with other empirical estimates observed in other countries. Table 4 reports these relative shares for Egyptian households living in dwellings under different rental regulations. We further disaggregate the relative shares based on quintiles of consumption expenditure to show that underestimation is a phenomenon that affected households across the entire distribution. As expected, for households living in regulated dwellings, rental payment is a lower share of the consumption expenditure for richer households as compared to poorer households. Most importantly, we find that using imputed rent changes the shares for subsidized tenants dramatically, by more than three times for most quintiles.<sup>13</sup>

**Table 4. Share of self‐reported rent in total consumption expenditure—by** **<u>quintile</u>**

|**Per capita expenditurequintile**|**Rent ‐ OLD**|**Rent‐OLD***|**Rent ‐ NEW**|**Owner**|**In‐kind/donation**|
|---|---|---|---|---|---|
|**Lowest**|5.9|18.6|17.8|14.4|14.3|
|**2**|4.5|17.3|17.1|14.6|15.3|
|**3**|4.0|16.8|16.1|15.0|16.3|
|**4**|3.4|15.8|17.0|15.6|15.8|
|**Highest**|2.6|15.7|18.0|18.4|16.9|

Source: HIECS, 2012/13. Notes: For Rent‐OLD*, we used the rent adjusted welfare aggregate to calculate quintiles. For the rest, quintiles are based on the CAPMAS’s official welfare aggregate. Rent‐OLD shows shares for subsidized tenants, Rent‐OLD* shows rent adjusted shares for subsidized tenants and Rent‐NEW shows shares for unregulated tenants.

> 13 The hedonic model can also be used to correct for misreporting or over‐reporting of rents. In our data, we observe that reported rent values for 355 tenants under the new law, owners and in‐kind dwellers were exceptionally high. These outlier households were assigned an imputed rent using the hedonic model, along with households living in regulated dwellings.

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Table 10: Detailed Value Added Composition

||value<br>added<br>total<br>economy|gross exports<br>share of<br>total value|value added<br>in exports:<br>forward<br>linkages|value added<br>in exports:<br>backward<br>linkages|
|---|---|---|---|---|
||A|B|C|D|
|primary sectors|0.03|0.01|0.03|0.01|
|processed foods, beverages|0.03|0.04|0.04|0.04|
|textiles and clothing|0.02|0.06|0.04|0.06|
|chemicals, petrochemicals|0.03|0.13|0.08|0.09|
|motor vehicles|0.01|0.07|0.02|0.05|
|other machinery|0.07|0.23|0.15|0.23|
|other manufacturing|0.10|0.18|0.20|0.18|
|utilities, construction|0.12|0.01|0.05|0.01|
|commercial services|0.36|0.28|0.37|0.31|
|public services|0.23|0.01|0.02|0.01|
|total|1.00|1.00|1.00|1.00|

_source: Own estimates from CGE model and database._ we see the implications of productivity gains in services, combined with the tiersector linkages summarized by Table 4. Comparing Tables 4 and 10, we see the drop in value added for commercial services drives the shift shown in Table 9. The efficiency gains in the sector free-up resources, which are then shifted to other sectors. Because of the substantial drop in commercial service input prices for manufacturing firms (Table 8), this also means there is less value added from commercial services embodied in manufactured goods. Focusing on column D in the two tables, the end result is that commercial services, being more competitive, are exported directly rather than indirectly through goods. The goods sectors also are more price competitive, as falling input prices upstream are effectively equivalent to productivity gains downstream. The end result therefore is more direct exports of services and a shift in value added toward manufacturing as resources are freed up in the now more competitive service sector.

There are of course other ways to approach changes in Italian regulatory policy and openness. For example, in models with monopolistic competition in services (e.g. Balistreri, Rutherford and Tarr 2010; Konan and Maskus, 2006; Francois, van Meijl, and van Tongeren, 2005) productivity effects can follow from changes in service-sector variety, again with downstream implications. However, this type of productivity effect is partially ”implicit,” in that national accounts-based MFP measures do not capture variety effects well. They would however reflect firm selection effects found in firm heterogeneity models. Thus, while variety and selection effects can work as an endogenous mechanism linked to _ava_ and/or _ao_ in equations (7) and (6) above, the determinants are inherently different from basic MFP measures.<sup>34</sup>

> 34See Francois, Manchin, and Martin (2012) for a technical discussion on modeling variety effects. Indeed, one strategy is to explicitly link the coefficient _ao_ in equation (6) functionally

33
A number of studies have also attempted to search for evidence of poverty-traps by examining whether there is an S-shaped relationship in income or asset dynamics at the individual or household level. For example, Jyotsna Jalan and Martin Ravallion (2004) find no evidence for a poverty trap using 6 years of income in China and Michael Lokshin and Martin Ravallion (2004) find no evidence of poverty traps over 4 to 6 year intervals in Hungary and Russia. Felix Naschold likewise finds no evidence of multiple equilibria poverty traps over 3 to 5 year periods using panel data from rural Pakistan and Ethiopia. In contrast, Barrett et al. (2006) do find evidence of multiple equilibria in asset dynamics in some of their more remote sites in small samples from rural Kenya and Madagascar, as do Michelle Adato et al. (2006) with asset dynamics over a five year period in South Africa.

However, a general lack of long-term panels in most developing countries, coupled with concerns about how successful empirical efforts to deal with measurement error are limit the persuasiveness of this evidence. One potential solution has been to track cohorts over time using pseudo-panel methods, with Francisca Antman and David McKenzie (2007) finding no evidence for a poverty trap using 58 quarters of repeated cross-sections from Mexico. Ideally administrative data would enable one to track the incomes of individuals and their children over long periods of time with little measurement error, as has been possible in several developed country studies (e.g. Robert Lucas and Sari Pekkala Kerr, 2013), but the fact that most of the poor in developing countries work outside the formal system negates this possibility. Instead, a more promising approach is to investigate the empirical evidence for specific mechanisms through which traps are theorized to occur. We turn to this evidence in the next section.

# **4.  Inspecting the Mechanisms**

The reduced form evidence suggests that poverty traps are not widespread at either the country or individual level, with most countries experiencing some growth and poor individuals not appearing to have dramatically different income dynamics from those who earn more. However, Martin Ravallion (1997) notes that certain types of non-linearities in these dynamics may only appear in situations of crisis or famine, when markets and institutions break down. Christopher Barrett and Michael Carter level of income and others at a higher income level. While this can be interpreted as evidence of a poverty trap (as for example does Dan Ben-David (1998) who presents a model of subsistence consumption which generates this pattern), another interpretation is simply that there are two groups of countries with some countries converging to a lower steady state and others to a higher one, but with no non-convexities in either group of countries. Another piece of reduced-form evidence can be found in Lopez and Serven (2009) who use cross-country growth regressions to document that countries with higher headcount poverty rates grow more slowly.

9
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8 This pattern of rural‐urban disparity is repeated in countless national studies using national poverty lines. There is far less evidence, however, on such a “poverty gradient” between secondary towns and cities. One of the reasons is that sample sizes in household surveys are not large enough to permit such disaggregation within the urban population. In the best known study in this area, Ferre, Ferreira and Lanjouw (2012) use small area poverty estimation methods to examine the poverty gradient, and to assess whether there is in fact a “metropolitan bias” in eight countries: Albania, Brazil, Kazakhstan, Kenya, Mexico, Morocco, Thailand and Sri Lanka. They analyze the gradient for both money metric poverty measures and data on access to public services, and find that: “In all cases, poverty is lowest and service availability is greatest in the largest cities – precisely those where governments, the middle‐classes, opinion‐makers and airports are disproportionately located.” (Ferre, Ferreira and Lanjouw, 2012, p. 353) Leaving to one side the theory implicit in the above statement, which they characterize as “metropolitan bias” to contrast with Lipton’s (1977) “urban bias”, there is still the dynamic question to answer. Levels of poverty may be lower in areas of high urbanization, but what is the role of urban agglomeration in poverty reduction? Ravallion, Chen and Sangraula (2007) conduct some cross‐country regression analysis to try and answer this question, using both the incidence of poverty and a poverty gap measure: “...we regressed urban and rural poverty rates on the urban population share including additive fixed effects (a dummy variable for each region or country): that is, the mean level of poverty at a given urban population share is allowed to vary by region or country....Both poverty measures tend to decline as the urban population share rises, although the effects are generally smaller (but more significant) for the country data.... Among the six regions of the developing world, sub‐Saharan Africa is an exception to our finding that urbanization has been accompanied by falling overall poverty.... All we can reasonably claim from these results is that the data are consistent with the view that the economic
the study villages, a high proportion of the total population was eligible to receive program benefits. Figure 1 presents a histogram of this proportion of eligible households from among all households in the village for the treatment and control villages. Although there is a good degree of dispersion in this saturation measure, some villages have up to 90 percent of the household population eligible to receive benefits. The median village saturation level in the experimental sample is 65 percent, and the mean is 62 percent. This figure also suggests that program saturation was balanced across treatment and control villages: a two-sample Kolmogorov-Smirnov test has a p-value of 0.945, suggesting equality of saturation in the two distributions. Highly saturated villages are equally likely to be found in treatment and control suggesting that the saturation distribution is not significantly different.

# <u>Household Budget Survey Data</u>

We use the 2009, 2012, and 2015 rounds of the Philippine national household budget survey, the Family Income and Expenditure Survey (FIES), which collected detailed consumption data in 8,500 villages, to calculate unit values of food items. There are 93 food items common across all three rounds of FIES; we construct a unit value for each food item, and then aggregate these into 15 food categories provided by FIES.<sup>15</sup> Annex 2 details how we constructed these data. We relate the median village-level unit values for these 15 categories with the village’s _Pantawid_ program saturation in 2009, 2012, and 2015 to study the relationship between _Pantawid_ and food prices.<sup>16</sup>

# <u>The Household Assessment Form and the Balance of Baseline Data</u>

The Household Assessment Form survey used to assess household eligibility for _Pantawid_ also serves as a baseline for our analysis. This information, which is relatively limited in scope, is primarily used to determine the potential program saturation of each village in the RCT sample as well as assess balance across treatment and control villages for the sociodemographic and economic information collected. We explore baseline balance for

> 15 The 15 categories are: regular rice; other rice; roots and tubers; fresh eggs; processed eggs; fresh fish; processed fish; fresh meats; processed meats; fresh fruits; fresh vegetables; processed rice and grains; coffee, cocoa, and tea; sugar; and milk products.

> 16 An important distinction between the analysis of FIES data and our core analysis using the _Pantawid_ survey is that the FIES analysis exploits the staggered rollout of _Pantawid_ throughout the country and relates village level prices with saturation. Unlike our core analysis, which relies on the randomized assignment of villages to treatment, this analysis does not leverage experimental variation.

13
crisis—see also Frankenberg et al. (1999). Of the child health outcomes they study, only motherreported child health status appears to have worsened significantly. On the other hand, there is some evidence that infant mortality spiked up sharply, from about 30 per 1,000 in 1996 to 48 per 1,000 in 1998 (Rukumnuaykit, 2003; see also Simms and Rowson, 2003, who argue that infant mortality increased between 1996 and 1999 in 22 of Indonesia’s 26 provinces).<sup>29</sup> An important issue with regard to the Indonesian crisis is that expenditures on health appear to have been protected from sharp cuts. This point is made in the upper panel of Figure 3, which shows that although Government health expenditures contracted during the crisis, by about 20 percent, much of this shortfall was made up by donor assistance to the health sector, which increased sharply. This pattern of expenditures may help explain why, with the possible exception of infant mortality, child health outcomes do not appear to have suffered much. The lower panel of the figure shows that, by contrast, public health expenditures in Peru fell by more than half, and there is no evidence that these shortfalls were made up from other sources, including donor assistance.

Health outcomes appear to have deteriorated during economic contractions in most other developing countries. In a study using DHS data for India, Bhalotra (2008) analyzes the effects of aggregate income shocks on infant mortality. Methodologically, the approach is similar to that followed by Paxson and Schady (2005) in their analysis for Peru. However, Bhalotra arguably has better data and a more credible identification strategy because she exploits differences across states in GDP growth rates, and in the magnitude and timing of deviations from GDP trends, rather than simply using national averages. Bhalotra concludes that there is no clear pattern of effects in urban areas. In rural areas, however, infant mortality appears to be counter-cyclical— like in Peru, Mexico, and Indonesia. The magnitude of her estimates implies that a negative income shock of median size in the data is associated with an increase in mortality of approximately 2 per 1,000, which is equivalent to approximately two-thirds of the annual linear decline in rural mortality in India over the period she studies. Bhalotra also argues that high-risk women were more likely to select out of fertility during aggregate downturns in India, and she controls for this by using mother fixed effects.

> 29 Even with regard to infant mortality, there is no conclusive agreement. World Bank (2000) concludes that “infant mortality rates seemed to have continued a downward trend (during the crisis)” (cited in Simms and Rowson, p. 1385).

22
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# **Table 12. The Short- and Long-Term Stock Market Reactions**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents the result of the regression analysis of the stock market reaction in the short and long run. The cumulative abnormal returns in the next two trading days are used to measure the short-term stock market reaction. The increase of Tobin’s Q in the next 5 years after the event net of its 2 years growth before the event was used to measure the long-term stock market reaction. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. _SOE_ is a dummy variable that is 1 if the firm is affiliated to the government and 0 if otherwise. All variables are winsorised 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.

|||Stock Market Re<br>|action [0,2]<br>|||Δ Tobin's Q<br>|[-2 Year, 5 Yea<br>|r]<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Titling Firm|0.076***|0.097***|0.068***|0.082***|2.851**|1.952|0.907*|1.423|
||(7.073)|(4.606)|(6.392)|(7.361)|(2.510)|(0.900)|(1.836)|(1.571)|
|SOE||0.002||||0.823*|||
|||(0.762)||||(1.740)|||
|Titling Firm * SOE||-0.030||||1.723*|||
|||(-1.234)||||(1.780)|||
|Bureaucrats on boards|||0.000||||-0.441||
||||(0.057)||||(-0.780)||
|Titling Firm * Bureaucrats on b|oards||0.027||||8.748***||
||||(0.986)||||(3.423)||
|Party Member||||-0.001||||-0.783***|
|||||(-1.156)||||(-5.492)|
|Titling Firm * Party Member||||-0.098||||10.773***|
|||||(-0.956)||||(3.559)|
|Industry FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exchange FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Adj. R<sup>2</sup>|0.031|0.031|0.031|0.031|0.027|0.028|0.029|0.052|
|# of Obs.|1250|1250|1250|1250|1250|1250|1250|1250|

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