rafmacalaba/datause-encoder-data huggingface dataset:
_Services PTAs_ setting, according to the estimation approach described here below.

# **4 Methodology**

## **4.1 Estimation strategy**

We focus on the direct effect of PTAs on services trade and services VA sourcing within a standard structural gravity estimation framework.<sup>11</sup> Hence, we obtain partial effects of PTA provisions on trade that should be interpreted as reduced form estimates.<sup>12</sup> .

The dyadic country-pair-year structure of our trade and value-added datasets, alongside a large number of countries, makes it possible to adopt a state-of-the-art approach based on the following estimable equation: where _Yij,t_ is either the value of directional bilateral services exports<sup>13</sup> between countries _i_ and _j_ in year _t_ , or the VA ratios _sh_ _~~s~~ ervV A_ _~~s~~ ervij,t_ and _sh_ _~~s~~ ervV A_ _~~t~~ ot_ _~~e~~ xpij,t_ described above. **SPolij** _,_ **t** is our key variable of interest and represents time-varying services trade policy at varying levels of aggregation. At the most aggregated level, and thereby connecting back to most of the empirical literature thus far, this variable is simply a indicator for the presence of a services PTA, additionally broken out by type of PTA (bilateral, plurilateral, or EU-bilateral).<sup>14</sup> In other specifications, **SPolij** _,_ **t** represents a vector

> 11We therefore build on the long tradition of research on trade agreements using gravity models; see Lim ̃ao (2016) for a survey of this literature.

> 12General equilibrium effects will be accounted for, and embodied in, the fixed effects structure but we do not currently exploit this information.

> 13Note that country-pairs may belong to more than one PTA (e.g. pairs belonging to the EU, which has several agreements with third parties), and therefore may appear as many times in our data as they have signed PTAs in any given year, although the content of these PTAs will of course be different.

> 14We prefer to keep these agreements separate from the other two categories, because unlike in goods trade, there is no uniform external services trade policy for the EU. Technically most of these agreements

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price elasticities of energy demand. Long run own price elasticities are in the range of -0.5 to - 1.0 for natural gas, -0.7 to _-1.5_ for petroleum products, -0.5 to -1.0 for coal and -0.5 to -1.0 for electricity. Hoeller and Wallin (1991) estimates the long-run price elasticity of carbon demand to -1.04 in a cross sectional study of the OECD countries. These elasticity estimates woulvd be valid for marginal changes in prices only. In cases of high levels of subsidies, as in for example the former Soviet Union, elasticity estimates for marginal price changes can not be applied to arrive at emission reductions but instead much smaller values of elasticities must be considered. Therefore own price elasticities of demand used are in the range of -0.15 to -0.25 in most of these cases and -0.6 in cases of low levels of subsidies. The analysis attempts to include estimation of interfuel substitution and cross price elasticities are determined within the model. Estimates of emission reductions from the removal of subsidies can also be in serious error for countries where supply is completely inelastic with excess demand at low prices as may be the case in particular for natural gas. In Poland, natural gas may be considered supply constrained and therefore an increase in natural gas prices within a certain range may not have any significant effect on natural gas consumption. Excluding emission reductions from Poland on account of natural gas would lead only to a minor revision in the overall estimate for reductions in global carbon emissions. In the case of Soviet Union, the share of natural gas in total energy consumption is as large as that of petroleum products and coal and it is therefore perhaps realistic to assume that price increases of natural gas will lead to reduced natural gas consumption.

Estimates of carbon emission reductions from subsidiy removal are presented by country in Table 6. Removal of subsidies are estimated to result in almost 7% reduction in world carbon 8
21 The modifications did not, however, tackle significantly a key source of the problems, namely, the staff-intensive high recurrent cost structure. High-cost issues were indeed highlighted in Bagchee (1994, e.g., p. 19). The conclusion was, however, only to exercise caution in expanding systems. By the time an influential World Bank Operations Evaluation Study was published (Purcell and Anderson, 1997), it was apparent that fiscal issues were the key challenge, and the various modifications in design offered (and experimented with) in the World Bank’s African T&V extension projects were not sufficient to reduce the costs, as they came after the extension systems were already enhanced and expanded significantly. The flexibility in retrenching public sector employees was limited and thus staff costs could not be changed much. The 1997 OED report listed a number of frequently occurring difficulties in T&V extension projects and suggested that the relatively high (70%) share of projects graded “satisfactory” was probably not a realistic reflection of the actual rate of success due to the imprecise nature of such summary judgments. The study concluded that “the Bank had erred in the extent to which it has promoted the T&V extension management system in relatively uniform packages of investments and extension practices in large state and national programs”. The authors further concluded that the increased flexibility in African T&V projects was not likely to resolve the problems encountered and therefore the T&V design was “unlikely to be the most appropriate approach for improving extension in many African countries” (p. 98).

The 1990s witnessed greater interest in alternative extension concepts, with stronger participatory aspects, greater pluralism, and smaller public organizations. In Africa, in particular, there was donor pressure to transfer some aspects of extension to private or other nongovernment providers (Gemo, Eicher and Teclemariam, 2005). Senior management was not
earnings drop has an impact on UDR and ICR. Utilities do not show any reaction to the earnings drop, while there was a significant impact of the interest rate shocks.

**Table 10.  Simulation of Earnings Shocks**

|Sector|Baseline|UDR<br> <br>Earnings<br>drop<br>25%|Earnings<br>drop<br>50%|Proportion<br>below 0.75<br>Baseline|of firms wit<br> <br>Earnings<br>drop<br>25%|h ICR<br>Earnings<br>drop<br>50%|
|---|---|---|---|---|---|---|
|1. Mining|0.5%|0.5%|0.5%|9%|9%|9%|
|2. Industry|19%|19%|32%|25%|27%|33%|
|3. General|||||||
|Trade/Services|21%|28%|28%|24%|28%|28%|
|4. Construction|27%|27%|52%|22%|22%|33%|
|5. Transportation|3%|3%|3%|30%|30%|30%|
|6. Tourism and Hotels|7%|7%|7%|12.5%|12.5%|12.5%|
|7. Utilities and Energy|0%|0%|0%|0%|0%|0%|
|8. Financial|24%|24%|39%|22%|22%|27%|

Overall, the stress testing analysis shows that the most vulnerable sectors are construction, finance, industry and trade.

# **4.3 Assumptions and Caveats**

As in any empirical exercise, there are a number of assumptions and caveats that must be kept in mind when applying the results for policy analysis. Below we discuss key assumptions that were made in the study and some caveats.

First, financial statement data only available on publicly listed firms. Thus, it is assumed that all firms in the sector behave similarly to publicly listed firms. In fact, smaller companies might behave differently. However, the analysis of ASE listed companies by size shows higher vulnerability in smaller listed companies. Therefore, the results obtained from large listed companies might be conservative estimates of the overall impact (as the impact on smaller companies might be larger).

Second, sectoral classification may not be precise (i.e. how ASE is classifying companies and how banks are classifying the same companies might be different).

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**Table 7. Impact of Gas Price Increase on Gas Consuming Households**

|**Quintile**|**Amount**_(in drams per_|**Impact**_(% of household_|
|---|---|---|
||_month)_|_consumption)_|
|**Armenia**|||
|_Poorest_|1,680|2.4|
|_2_|2,743|2.8|
|_3_|3,047|3.1|
|_4_|3,600|3.0|
|_Richest_|4,274|2.4|
|_All_|3,221|2.8|
|**Yerevan**|||
|_Poorest_|1,782|2.5|
|_2_|2,868|2.6|
|_3_|2,945|2.7|
|_4_|3,515|2.4|
|_Richest_|4,079|1.9|
|_All_|3,148|2.4|
|**Other Urban**|||
|_Poorest_|1,590|2.4|
|_2_|2,576|2.9|
|_3_|2,985|3.3|
|_4_|3,450|3.3|
|_Richest_|4,317|2.8|
|_All_|3,164|3.0|
|**Rural**|||
|_Poorest_|1,624|2.4|
|_2_|2,653|2.7|
|_3_|3,078|3.1|
|_4_|4,252|3.5|
|_Richest_|4,388|2.6|
|_All_|3,384|2.9|

_Source:_ Author’s calculations based on 2008 ILCS Another illuminating approach to examine the poverty impact of the gas price hike is to measure the extent to which the price hike is “impoverishing.” That is, if a household has total consumption expenditures with gas above the national poverty line, but their total spending without gas is below the poverty line, they could be considered to have suffered impoverishment due to gas expenses. This impoverishment due to spending on gas is illustrated graphically in 15
**Table 8: Difference in agricultural risk by household activity**

||**Nearest**<br>**neighbor**<br>**matching**<br>**methodology**|**Gaussian**<br>**kernel**<br>**matching**|**Epanechnikov** <br>**kernel**<br>**matching**|
|---|---|---|---|
|**A. ALL**||||
|**Difference in the predicted probability of**||||
|**having zero or negative farm profits**||||
|Non-farm enterprises (b)_minus_Farm|0.007|0.008*|0.006*|
|enterprise (a)||||
|Non-farm enterprise (b)_minus_farming for|-0.0006|-0.003|-0.001|
|autoconsumption (c)||||
|Non-farm and farm enterprises (d)_minus_|-0.0006|-0.0001|-0.0003|
|Farm enterprise (a)||||
|**B. POOR**||||
|**Difference in the predicted probability of**||||
|**having zero or negative farm profits**||||
|Non-farm enterprises (b)_minus_Farm|0.008|0.015*|0.009*|
|enterprise (a)||||
|Non-farm enterprise (b)_minus_farming for|-0.001|0.001|0.001|
|own consumption (c)||||
|Non-farm and farm enterprises (d)_minus_|0.008|0.0009|0.0005|
|Farm enterprise (a)||||
|**C. NON-POOR**||||
|**Difference in the predicted probability of**||||
|**having zero or negative farm profits**||||
|Non-farm enterprises (b)_minus_Farm|0.007||0.006*|
|enterprise (a)||||
|Non-farm enterprise (b)_minus_farming for|-0.004||-0.002|
|own consumption (c)||||
|Non-farm and farm enterprises (d)_minus_|-0.003||-0.0003|
|Farm enterprise(a)||||

* indicates a statistically significant result at 5% level of significance. Predicted probabilities are of the order of 2 percent.

We also looked at actual difference in profits between the matched pairs. Table 9 indicates the average of the pair wise differences for poor and non-poor households. The results confirm the general conclusions of Table 8. When comparing households with similar characteristics, farm profits between diversified and non-diversified households are not significantly different, except between farm households which sell on the market and those which do not sell on the market but have a non-farm enterprise. Our results in Table 8 and Table 9 suggest that households in Rwanda are not driven into non-farm enterprises by risks in farm output. This is not to suggest that risk is not a motivation for diversification. Even in this context, the fact that we do not observe significant differences in risk as measured through variations in _actual or expected profits_ does not preclude risk differences which we are not able to measure directly. That said, we do not find empirical evidence to suggest that the farm households who diversify into non-farm enterprises are particularly more prone to volatile agricultural income. Overall, it appears that the decision to participate in the non-farm sector is unrelated to profits in the farming activity of the household. This is consistent with previous findings that risk mitigation do not satisfactorily explain participation in the non-farm sector in rural Africa (Barrett et al, 2001).

19
Table A.4: **Effects of Inpres Schools on Relative and Absolute Mobility in Years of Schooling**

|**Panel A: Inte**|**rgeneratio**<br>(1)|**nal Mobility As**<br>(2)|**sociation (I**<br>(3)|**GMA)/ Relative Mobility (Sons)**<br>(4)|
|---|---|---|---|---|
||**Highest**|**Intensity(=1)**<br>**Normalized**||**Mean Intensity(=0.215)**<br>**Normalized**|
|∆_IGMA_0|-0.255<br>(0.086)|-51.77%|-0.055<br>(0.018)|-11.13%|
|∆_IGMA_6|-0.079<br>(0.052)|-16.04%|-0.017<br>(0.011)|-3.45%|
|∆_IGMA_9|0.009<br>(0.070)|1.83%|0.002<br>(0.015)|0.39%|
|∆_IGMA_12|0.097<br>(0.101)|19.69%|0.021<br>(0.022)|4.23%|
|∆_IGMA_16|0.215<br>(0.149)|43.65%|0.046<br>(0.032)|9.38%|

**Panel B: Expected Schooling (ES)/ Absolute Mobility (Sons)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Highest**|**Intensity(=1)**||**Mean Intensity(=0.215)**|
|||**Normalized**||**Normalized**|
|∆_ES_0|1.534<br>(0.380)|17.52%|0.330<br>(0.082)|3.77%|
|∆_ES_6|0.533<br>(0.347)|6.09%|0.115<br>(0.075)|1.31%|
|∆_ES_9|0.428<br>(0.390)|4.89%|0.092<br>(0.084)|1.05%|
|∆_ES_12|0.589<br>(0.515)|6.73%|0.127<br>(0.111)|1.45%|
|∆_ES_16|1.214<br>(0.895)|13.86%|0.261<br>(0.192)|2.98%|

Notes: Robust standard errors are in parentheses, clustered at the district of birth(*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Intergenerational Mobility Association(IGMA) is the slope of Conditional Expectation Function (CEF). Here, ∆ _IGMAy_ = _θ_ 7 + 2 _θ_ 11 _Eiy_<sup>_p× Inpres_, where</sup><sup>_E_</sup> _iy_<sup>_p_represents</sup> father’s years of schooling for _y_ = 0 _,_ 6 _,_ 9 _,_ 12 _,_ 16. Absolute Mobility is measured by expected schooling conditional on father’s schooling. Here, ∆ _ESy_ = _θ_ 4 _× Inpres_ + _θ_ 7 _× Inpres × Eiy_<sup>_p_+</sup> _θ_ 11 _× Inpres ×_ ( _Eiy_<sup>_p_)2, where</sup><sup>_E_</sup> _iy_<sup>_p_represents father’s years of schooling for</sup><sup>_y_=0</sup><sup>_,_6</sup><sup>_,_9</sup><sup>_,_12</sup><sup>_,_16.</sup> The IGMA and ES values are based on coefficients reported in Table 2. The variable Inpres intensity measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The Normalized IGMA is the IGMA value relative to IGMA of children for father’s with average years of education (5.57 years), comparison cohort (born between 1957 to 1962), and zero Inpres intensity for the full sample (combined sample of sons and daughters). Normalized IGMA values are reported in percentage. 56 Data sources: Indonesia’s full count census 2000 and Duflo (2001).
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Figure 4 plots the ratio of government debt to GDP (―debt ratio‖) from 1997 to 2010. It peaked at 106 percent in 2002 after rising by some 23 percentage points of GDP starting in 1997. It then fell by some 20 percentage points between 2002 and 2009.

**Figure 4. Government Debt and Fiscal Deficit (1997-2010)**

<!-- Start of picture text --> ( % of GDP)<br>12 120<br>10 100<br>8 80<br>6 60<br>4 40<br>2 20<br>- 0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Foreign currency debt/GDP Rupee debt/GDP Fiscal Deficit (left axis)<br>Source: CEIC data.<br><!-- End of picture text --> To get a better sense of the underlying factors driving this debt trajectory, we carried out a decomposition exercise aimed at apportioning changes in the debt ratio over the period 19982010 to the primary fiscal deficit, real GDP growth, the real interest rate and the real exchange rate.<sup>6</sup> In addition to these main drivers of debt dynamics, the effect of revenues from privatization ( ̳divestment‘) and other, unidentified factors are shown in the last two rows of Table 1. The way to interpret the numbers in the table is as follows: the first row gives the actual change in the debt ratio for each sub-period in percentage points of GDP per year (the reason for such ―annualization‖ is that the sub-periods are not equal in length). For example, over 19982004, debt increased by an average rate of 2.8 percentage points of GDP per year, while it fell by some 3 percentage points per year over 2006-08. The numbers in the other rows give the average annual impact of each underlying factor for each sub-period in percentage points of GDP: a

> 6 The technical details and derivations can be found in the appendix to Aizenman and Pinto (2005).

12
8, which shows that the introduction of a collateral registry for movable assets increases the likelihood that the firm will have access to finance broadly defined (i.e., access to a loan, line of credit or overdraft), raises the share of fixed assets financed by banks and lengthens the maturity of bank loans. Hence, the introduction of a movable collateral registry has an impact on access to finance over and above the impact of changes in the collateral laws.

# **_Exploring differences across small, medium, and large firms_**

An extensive literature has shown that small and medium sized firms tend to be more financially constrained than their large counterparts (Schiffer and Weder, 2001; Cressy, 2002, IADB, 2004; and Beck et al., 2005, 2006, and 2008). Hence, it is interesting to investigate whether SMEs are more likely to benefit from the introduction of collateral registries for movable assets which effectively allow firms to widen the range of asset that they can pledge in exchange for financing. Table 9 repeats our baseline estimations (i.e., those including all non-reform countries as part of the control group) but incorporating interactions of the registry reform variable with separate dummies that identify small (5-19 employees) and medium sized firms (20-99 employees). The results provide some evidence that small firms benefit more than large firms from the introduction of registries for movable collateral. In particular, the impact of registries is stronger for small firms in the regressions for the likelihood of access to finance, the probability that the firm reports experiencing severe financing constraints, and the share of working capital and of fixed assets financed by banks. Interestingly, the subjective reporting of financing constraints by firms is only significantly reduced for small and medium firms (as it was not significant for the overall sample in our previous regressions).

14
# **Table 3. Distribution of vacancies by regions, April 2020.**

|**Regions, Republic of**<br>**Karakalpakstan, Tashkent city**|**Vacancies**|
|---|---|
|Republic of Karakalpakstan|1210|
|Tashkent city|6223|
|Tashkent|8274|
|Namangan|2184|
|Andijan|468|
|Fergana|2222|
|Jizzakh|589|
|Samarkand|1471|
|Surkhandarya|2151|
|Kashkadarya|785|
|Bukhara|1255|
|Khorezm|2152|
|Syrdarya|1361|
|Navoi|594|

Source: repost.uz based on the data from the Ministry of Employment and Labor Relations (ish.mehnat.uz), 2020 We developed fake resumes based on a review of resumes posted on major job websites and consulted with human resources specialists to ensure their appropriateness. The resulting resumes described average years of job experience with plausible but fictitious private companies. In order to avoid any possibility of over- or underqualification, we designed all resumes to reflect a typical level for these employment positions. For applicant names, we assigned most common first and last names for men and women, forming matching pairs. These names could be easily recognized as male or female applicants. In all other respects the resumes were identical, including the education, working experience, techniques, age, address, family attributes and other aspects of these two fabricated individuals. Each resume was assigned an email address, mobile phone number and regular home address. During the experiment, the gender revealed in the fictitious resume was randomly assigned to the hiring firm. This experimental design completely controls for problems of self-selection and omitted variable bias, producing unbiased and consistent estimates of gender impacts in callback rates.

As a standard approach to these was not prepared in advance, we deliberately excluded applications to job advertisements that required photos in resumes or a requirement to send the resume via messaging apps (such as Telegram or WhatsApp). We created separate cell phone and email accounts for each of the six profiles in each round. We also created fictitious profiles on internet job boards, also identical for male and female, with the person’s name being the sole difference between them.

Online job boards are one of the most important channels through which jobseekers send their resumes to potential employers in Uzbekistan, and particularly so in Tashkent. Most job seekers use private job boards, and we submitted fake resumes to the four largest in the country – Olx.uz, 8