rafmacalaba/datause-encoder-data huggingface dataset:
7 to the reforms in comparison to what was projected.Y The two stabiition indicators ued to do this are: (a) budget deficit to GDP ratio and (b) current account balance to GDP ratio. The four variables selected to assess actual progrs in responding to reforms are: investmen **t** o GDP ratio, savings to GDP ratio, export growth rate and the growth rate of GDP4/. The variables on trends of investmnent te, savings rates, and export growth represents outcomes expected of the reforms in the short- to medium-run. The GDP growth rAte reflects the overall outcome and will largely be influenced by the performance of investments, savings and exports. Box 1 below presents the exact definition of the variables reviewed and records the sources of the data used. One should note here that there are often differences among sources **ini** the actual values of the variables used. The paper however uses the common sources of country data in the Bank.

iIt would be useful to make a distinction between program targets, particularly with regard to stabilization, and projected outcomes. If the targets are not met, outcomes will not be as projected. Since the min focus of this exercise is to judge the degree to which PFP targets and projections were in pracice met, the paper will not differentiate between the program targets and projected outcomes. They will be treated in the same manner in reviewing the observed congruence or deviation between the actual and projected values of the selected variables. One should also note that the realization of program targets and projections would depend on. the implementation of underlying policies. For example, the reduction of a budget deficit would depend upon improved collection of revenue and/or control of public expenditure. A complete analysis of the sources of the discrepancy between targets and actual values will have to include a critical look at the progress of policies. This paper does not get into the sources of the discrepancy.

&/Of course, other variables could have been selected to assess progress In the implementation of policy reforms and outcomes of these reforms. The projected inflation rate, a key variable for progress in stabilization, was also examined for countries for which such projections were ;,vailable. Similarly, debt ratio was also reviewed. The conclusions reached form the analysis of the variables selected would generally hold for other variables not included in the analysis.
# **Table 5: Firms in Georgia during COVID**

||R|ound 1<br>No-||R|ound 2<br>No-||
|---|---|---|---|---|---|---|
||Support|Support|N|Support|Support|N|
|Percentage change of permanent full-time<br>employees since December 2019|-0.300|-0.069*|403|-0.078|-0.129|431|
|Firms that the total number of temporary workers<br>decreased|0.296|0.272|427|0.465|0.419|404|
|Firms that total hours of operation per week|||||||
|decreased|0.702|0.606*|471|0.420|0.360|457|
|Firms that anticipate falling in arrears on<br>outstanding liabilities within next 6 months|0.223|0.306|412|0.330|0.355|363|
|Cash transfers|0.087||497|0.151||463|
|Deferral of payments|0.507||497|0.467||407|
|Access to new credit|0.017||497|0.066||439|
|Fiscal relief|0.229||496|0.695||402|
|Wage subsidies|0.385||496|0.475||411|
|Other support|0.018||497|0.074||420|
|Average change monthly sales compared to one<br>year ago|-0.530|-0.458|433|-0.292|-0.303|396|
|Overdue on obligations to financial institutions|0.112|0.105|481|0.138|0.126|432|
|Adjusting or converting their production or services|0.271|0.271|497|0.429|0.356|416|
|Decreased demand for products or services compar<br>to one year ago|0.714|0.683|469|0.590|0.527|442|
|Decreased liqdt or cash flow avail. since COVID-|||||||
|19 began|0.776|0.673**|486|0.847|0.806|459|
|Started or increased online business activity|0.284|0.238|496|0.337|0.347|456|
|Started or increased delivery of goods, services or<br>carryout|0.249|0.229|496|0.376|0.278|455|
|Started or increased remote work|0.426|0.440|497|0.522|0.476|455|
|Exporting directly (at least 10% of sales)|0.106|0.088|394|0.080|0.069|458|

Note: This table shows the firm performance and government support types in Georgia in Round 1 and Round 2 surveys. Support columns show the respective statistics of firms that have received government support. No-Support columns show the respective statistics of firms that have not received any government support. N denotes the observation number. Means are weighted using the sampling weights from the ES. Asterisks in the No-Support columns are for unconditional t-tests comparing the respective statistic with the same statistic for the firms that received support. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 4 for more details.

38
<!-- Start of picture text --> oS<br>o<br>S8=]Cc<br>Rs—<br>o<br>zo<br>momo)<br>wu<br>Cc<br>§<br>Qo<br>aT<br>Padro)<br>& oON<br>oO<br>% time employed wage<br>HE All quality determinants GE Student characteristics<br>QE Program characteristics SHE! characteristics<br>( Students' admin variables [MJ Peers' admin variables<br>MO Field of study 1) State<br>ME Graduation year<br><!-- End of picture text --> <!-- Start of picture text --> % time employed Wage<br>“Ny |__| |<br>Infrastructure [<br>“ees [ [|<br>— [| [|<br>0246802468<br>%of explained variation % of explained variation<br><!-- End of picture text -->
- [16] de Gortari, A. (2019). “Disentangling Global Value Chains,” NBER Working Paper 25868.

- [17] Diewert, E. (1974). “Applications of Duality Theory”, pp. 106-171 in _Frontiers of Quantitative Economics_ , Volume II, M. Intriligator and D. Kendrick, eds., Amsterdam: North-Holland.

- [18] Diewert, E., Wales, T. (1988). “A Normalized Quadratic Semiflexible Function Form,” _Journal of Econometrics_ 37: 327-42.

- [19] Dhingra, S., Huang, H., Ottaviano, G., Sampson, T., Van Reenen, J., (2016) “The Costs and Benefits of Leaving the EU: Trade Effects,” CEP working paper 1478.

- [20] Feenstra, R. (1995). “Estimating the effects of trade policy,” in _Handbook of International Economics_ , vol. 3 (G. Grossman and K. Rogoff, eds.), Amsterdam: Elsevier.

- [21] Feenstra, R. (2015). _Advanced International Trade: Theory and Evidence, 2e,_ Princeton University Press.

- [22] Fernandes, A., Kee, H. L., Winkler, D. (2022) “Determinants of Global Value Chain Participation: Cross-Country Evidence,” _World Bank Economic Review_ 36(2): 329–360.

- [23] Grinols, E. (1984). “A Thorn in the Lion’s Paw: Has Britain Paid Too Much for Common Market Membership?” _Journal of International Economics_ 16: 271-93.

- [24] Gudgin, G., Coutts, K., Gibson, N., Buchanan, J. (2017). “The Role of Gravity Models in Estimating the Economic Impact of Brexit,” Centre for Business Research, University of Cambridge, Working Paper No. 490.

- [25] Harrigan, J. (1997). “Technology, Factor Supplies, and International Specialization: Estimating the Neoclassical Model,” _American Economic Review_ 87(4): 475-94.

26
The model can be linearized by applying the natural logarithm and estimated as follows: The ratio Pr(Yi=j) is known as the odds ratio or relative risk. Pr(Yi=J)

**The effect of each explanatory variable or factor on the relative risk is called the relative risk ratio (RRR)** It corresponds to the exponential function evaluated in the coefficient associated with the explanatory variable in question. For continuous explanatory variables, the RRR represents the change in the likelihood that the dependent variable is in a particular category relative to the reference category, associated with a one-unit change in the corresponding independent variable. In the case of dichotomous variables, the RRR represents the change in the probability that the dependent variable is in a particular category with respect to the reference category, associated with exposure to the dichotomous variable or factor.

**Assuming that the variable** xik **is a dichotomous variable, the effect of** xik **on the relative probability is shown in Equation 8.** The interpretation of the relative risk ratio is as follows. A RRR of 1 indicates that exposure to the factor does not affect the relative probability. A RRR greater than 1 indicates a positive association and that exposure to the factor increases the relative probability of obtaining the outcome. A RRR of less than 1 indicates a negative association and that exposure to the factor decreases the relative probability of getting the outcome.

# 5.2.2. Empirical strategy and explanatory variables

**The multinomial logistic regression allows analyzing individual factors affecting the propensity to migrate and accounting for different reasons for migration.** Analyzing the migration decision from a micro perspective is essential to understand the differences in individual and household characteristics that determine why some people decide to move while others stay. It also allows understanding the differences in individual and household characteristics that encourage different reasons for internal migration.

**The Census allows for the identification of internal migrants during the period 2015-2020, and it also provides information on the reasons for migration.** The model's dependent variable in the multinomial logistic regression includes four categories based on the reasons for internal migration, and an additional category to account for those who did not migrate, for a total of five categories. The first category corresponds to people who migrated internally for economic reasons, including seeking a job, changing jobs, or having a job offer. The second category corresponds to people who moved internally due to family reunification or because they got married. The third category is people who migrated due to criminal insecurity or violence in the state of origin. Finally, the fourth category corresponds to people who relocated for study, natural disasters, or other non-identified reasons (see Diagram 1).

23
Let _zj_ be the poverty line in period _j_ . We are interested in knowing such quantities as which represents the percentage of households that are poor in the first period but nonpoor in the second period (considered together for two periods), or which represents the percentage of poor households in the first period that escape poverty in the second period.

If panel data are available, we can easily estimate the quantities in (3a) and (3b); otherwise, we can use synthetic panels for this purpose. Assume that the underlying population being sampled in survey rounds 1 and 2 are the same, or more specifically, ��� � ���, and ���|��� and ���|��� have identical distributions (Assumption 1 in Dang et al, 2014). We can rely on the time-invariant variables _xij_ that are collected in both survey rounds to predict the consumptions in period 1 for households interviewed in period 2, and vice versa.<sup>7</sup> In particular, assume that ��� and ��� have a bivariate normal distribution with correlation coefficient  and standard deviations ���and ��� respectively (Assumption 2’). If  is known, Dang et al. propose to estimate quantity (3a) by

> 7 In other words, this assumption implies that households in period 2 that have similar characteristics to those of households in period 1 would have achieved the same consumption levels in period 1 or vice versa.

7
_Table 2.4 Educational Distribution_ _<u>by Economic Sector, 1988, 1996, 2002</u>_

|Educational group and year|Primary<br>incomplete|Primary<br>complete|Lower<br>secondary<br>complete|Upper<br>secondary<br>complete|University<br>complete|
|---|---|---|---|---|---|
|_1988_||||||
|Primary|41.1|21.0|13.3|14.3|10.3|
|Manufacturing|16.2|33.3|27.8|14.7|8.0|
|Not Manufacturing Industry|36.6|28.5|14.7|9.0|11.2|
|Commerce|18.0|28.7|28.8|18.7|5.8|
|Finance Services or Rent|4.8|6.1|19.5|47.1|22.5|
|Transportation or Communication|14.4|35.7|26.0|18.9|5.0|
|Social Services|11.3|17.6|21.7|28.2|21.2|
|Other Services|32.8|36.6|20.2|8.1|2.3|
|Total|18.5|27.7|24.1|18.9|10.7|
|_1996_||||||
|Primary|32.5|27.4|14.9|10.4|14.7|
|Manufacturing|11.3|30.3|32.4|17.0|9.1|
|Not Manufacturing Industry|27.8|29.6|18.2|11.9|12.4|
|Commerce|13.4|24.2|30.2|23.6|8.7|
|Finance Services or Rent|3.5|5.3|14.9|43.0|33.3|
|Transportation or Communication|8.6|27.9|33.2|21.5|8.8|
|Social Services|6.0|14.4|20.0|28.8|30.8|
|Other Services|25.3|36.1|23.4|12.6|2.6|
|Total|12.9|24.4|25.7|21.6|15.4|
|_2002_||||||
|Primary|22.0|23.0|17.8|17.4|19.8|
|Manufacturing|8.0|25.7|37.6|18.2|10.5|
|Not Manufacturing Industry|23.1|30.8|22.5|10.9|12.8|
|Commerce|9.4|20.9|33.8|25.5|10.4|
|Finance Services or Rent|3.6|7.6|15.1|38.2|35.5|
|Transportation or Communication|7.1|25.9|35.6|21.8|9.7|
|Social Services|5.0|12.6|22.0|27.7|32.7|
|Other Services|20.1|32.7|28.7|14.2|4.3|
|Total|10.0|21.9|29.5|21.9|16.7|

_Source: ENEU 3_<sup>_rd_</sup> _quarter._ Another relevant observation is that educational attainment by age group also improved, as the distribution by educational level was higher in 2002 than it was in 1988 (Table 2.5). In an attempt to reach a better understanding of this event, it is interesting to 15
Concerning the empirical part of this paper, the availability of micro data that includes quality aspects would help test the model predictions more thoroughly. For the moment, we have to rely on unit values and have to stretch their use as far as it can go. Future research efforts that aim at collecting and analyzing firm level trade data with quality aspects would certainly be a worthwhile undertaking. As this paper shows, quality has a major role to play in shaping international trade.

# **5. Bibliography**

ALCHIAN, A. A., AND W. R. ALLEN (1964): _University Economics_ . Belmont, CA: Wadsworth.

- BALDWIN, R. (2005): “Heterogeneous Firms and Trade: Testable and Untestable Properties of the Melitz Model,” _NBER Working Paper_ , 11471.

- BALDWIN, R. AND J. HARRIGAN (2007): “Zeros, Quality and Space: Trade Theory and Trade Evidence,” _NBER Working Paper_ , 13214.

- BERNARD, A. B., S. REDDING, AND P. SCHOTT (2005): “Comparative Advantage and Heterogeneous Firms,” _Review of Economic Studies_ , forthcoming _._

- BERNARD, A. B., J. EATON, B. JENSEN, AND S. KORTUM (2003): “Plants and Productivity in International Trade,” _American Economic Review_ , 93(4), pp. 1268-1290.

- CAVES, R. E., AND D. P. GREENE (1996): “Brands' quality levels, prices, and advertising outlays: empirical evidence on signals and information costs,” _International Journal of Industrial Organization_ , 14, pp. 29-52.

- FLAM, H., AND E. HELPMAN (1987): “Vertical Product Differentiation and North-South Trade,” _American Economic Review_ , 77(5), pp. 810-22.

- FALVEY, R. (1981): “Commercial Policy and Intra-Industry Trade,” _Journal of International Economics_ , 11, pp. 495-511.

37
<mark>employment status and type, and how they coped with the COVID-19 crisis. Personally identifiable information, such as name, residential address, or identity card details, were not collected. Enticements were not offered to participate in the survey, and respondents could easily exit the survey if/when they wanted to.</mark>

|**Table**|**3.1: Survey timelines an**|**d sample siz**<br>|**es**<br>|||||
|---|---|---|---|---|---|---|---|
|||**Brazil**|**Egypt,**<br>**Arab**|**Indonesia**|**Kenya**|**Sri**<br>**Lanka**|**Türkiye**|
|**Panel A: Inter**|**net survey**<br>||**Rep.**|||||
|Wave-1|Dec 10<sup>th</sup>to 29<sup>th</sup>, 2020|1,098|1,071|1,198|1,024|1016|1,180|
|Wave-2|April 28<sup>th</sup>to May 9<sup>th</sup>, 2021|1,106|1,031|1,114|1,016|1,049|1,168|
|Wave-3|August 5<sup>th</sup> to23<sup>rd</sup>,2021|1,146|1,032|1,316|1,020|1,009|1,007|
|**Panel B: Glob**<br>|**al Monitoring Data base**|||||||
|Global<br>Monitoring<br>Database<br>**Panel C: Com**|**parative data from country**|Overall:<br>350,749<br>Internet:<br>272,598<br>Year: 2019<br>**labor force s**|Overall:<br>34,733<br>Internet:<br>11,043<br>Year:<br>2015<br>**urveys**|Overall:<br>804,698<br>Internet:<br>80,840<br>Year: 2018|Overall:<br>52,841<br>Internet:<br>12,355<br>Year: 2015|Overall:<br>61,768<br>Internet:<br>Not<br>availabl<br>e<br>Year:<br>2016|<br>Overall:<br>30,731<br>Internet:<br>20,256<br>Year: 2018|
|Sample size||193,000<br>HH/month||793,542<br>HHs and<br>203,464<br>individuals|11,997<br>individuals|25,750<br>HH|58,560 HH|
|Survey name<br>and<br>timeframe||PNAD:<br> Dec 2020;<br>May and,<br>June 2021||National<br>Labor<br>Force<br>Survey –<br>Indonesia;<br>February<br>2021.|KCBS-<br>Kenya;<br>28<sup>th</sup><br>Sep<br>2020 to<br>30<sup>th</sup><br>Nov<br>2020<br>(4<sup>th</sup>quarter<br>of<br>the<br>survey)|LFS<br>–<br>Sri<br>Lanka;<br>Oct<br>-<br>Dec<br>2021|Household<br>labor force<br>survey;<br>Jan - March<br>2021<br>Apr - June<br>2021|

<mark>Notes: Wave-1 data were collected between December 10</mark><sup>th</sup> <mark>and 29</mark><sup>th</sup> <mark>, 2020, wave-2 between April 28</mark><sup>th</sup> <mark>and May 9</mark><sup>th</sup> <mark>, 2021, and wave-3 between August 5</mark><sup>th</sup> <mark>and 23</mark><sup>rd</sup> <mark>, 2021. Each wave had over 1000 “completed” surveys per country. A “completed” survey is one where respondents answered until the eighteenth question, which included all questions related to employment status and characteristics. For the analysis, we use only completed surveys, and ignored partial-response surveys. The sample size for these surveys in each wave is presented in panel A in table 3.1.</mark> <mark>Second, we use the Global Monitoring Database, a probability-based survey, to compare demographic variables and individual attributes with our survey. The GMD is the repository of multitopic income and expenditure household surveys used by the World Bank to monitor global poverty and shared prosperity. The household survey data are typically collected by national</mark> 11
the creditor's go-ernment requires the c3operation of another governmen:). It is, however, difficult to justify the levels of existing debt, which<sup>has</sup> an aggregate market value of hundreds of billions of<sup>dollars despite the</sup> sizable discounts in the secondary markets, with<sup>this belief.4 The threat</sup> of seizing overseas assets will not suffice if the<sup>borrower is a net debtor</sup> and the overseas assets are small, which currently is the case<sup>for many</sup> debtors. The threat of reducing the defaulting<sup>country's gains from trade</sup> is also problematic, since the creditor countries<sup>stand to lose along with</sup> the debtor from such an impediment. Unle3ss -ceditor countries are willing to bear this cost, the banks threat to impose such sanctions<sup>may not be</sup> credible.5 Thus, it appears, that short of military<sup>interventions to</sup> enforce a debt contract, which presumably are<sup>a thing of the past, direct</sup> sanctions are not sufficient.6 The current paper's contribution is a systematic, empirical investigation of the much debated issue of 'relevance<sup>of past defaults' in</sup> credit market access. The findings are important<sup>for providing validity for</sup> reputational approach.7 In this paper we investigate 2184 bank loans made to 70 developing countries during 1968-1981. 27 of these<sup>countries had</sup> sovereign borrowing experience during the former<sup>episodes of lending. The</sup> repayment problems of these countries pertain<sup>to: 1820 through the 1930s</sup> and the post war (1955-1968) period. The primary finding of this paper is that countries with histories of default were charged<sup>higher interest rates</sup> than countries with no default history. Defulaters<sup>paid nearly 2-4 percent</sup> more to private creditors for interest servicing in<sup>the 1970s than they</sup> would had they not defaulted earlier. Second,<sup>we find that the countries</sup> that a,quired sovereignty recently, e.g. many African<sup>countries, paid higher</sup> spreads than countries that had bad repayment records.<sup>These findings are</sup>
# question: To what extent does gifting represent an alternative to legal reform?

I use correlational evidence to shed further light on the potential tradeoff between legal reform and private action in the form of gifting. Table 6 shows the estimated marginal effects from a logit model: as a benchmark, column (1) shows respondents’ stated preferences regarding legal reform. The outcome of column (2) is favoring gifting over reform. This outcome is coded as “1” whenever a respondent is more favorable of gifting than of reform and “0” otherwise. Both models are estimated on the control group. In the first survey wave, 37 percent of the respondents favor gifting over reform. Column (3) uses reported gifts to daughters as an outcome. This outcome was measured in the first endline and is unlikely to have been affected by treatment, which is why I use the whole sample for estimation. As reported in table 3, about 9 percent of the total sample had already made a gift to their daughter. Figure 8 plots the estimated coefficients of interest of model (2) and (3).

Who is most likely to favor gifting over reforming the national law? The best predictor of outcomes 1 and 2 is again underestimating reform support: respondents who do so are 28 percentage points less likely to approve of reform themselves and 25 percentage points more likely to favor gifting. Women are significantly _more_ likely to favor legal reform and significantly _less_ likely to substitute towards gifting than men. Column (3) offers a potential rationalization for this observation: mothers are 6 percentage points less likely to make gifts to their daughters than fathers. Possible reasons may include lower asset and land ownership among women, but also limited bargaining power within the household. It would be desirable to analyze whether gifting is more common among female heads of the household (i.e. widows) but the sample size does not allow a more fine-grained analysis.

Given the gender discriminatory bequest allocation among siblings in Islamic inheritance law, individuals who have a sibling of the opposite sex would be expected to be less willing to embrace legal reform, as it would change the inheritance allocation within their family. The effect should be stronger for men because they would themselves materially suffer from reform as their sister(s) gain equal inheritance right. Consistent with the theory, I find that respondents who have a sibling of the opposite sex are indeed less likely to favor reform (not statistically significant) and 14 percentage points more likely to favor gifting over reform (marginally statistically significant at the 10 percent level). The limited sample size does not allow for analysis by gender.

Higher levels of education are generally associated with more gender equal preferences, as the most recent survey round of the Tunisian Arab Barometer confirms (Arab Barometer; 28