rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 10. Machado and Mata (2005) decomposition of the gender wage gap**

<!-- Start of picture text --> (a) All (b) Young teenagers<br>Coefficients<br>Coefficients<br>Coef 95% confidence intervals<br>Coef 95% confidence intervals<br>OLS OLS<br>Raw gap<br>Characteristics Raw gap<br>Predicted gap Characteristics<br>Predicted gap<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 0 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantile Quantile<br>(c) Young adults (d) Prime aged<br>Coefficients<br>Coefficients<br>Coef 95% confidence intervals<br>Coef 95% confidence intervals<br>OLS OLS<br>Raw gap<br>Raw gap<br>Characteristics<br>Characteristics<br>Predicted gap<br>Predicted gap<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 0 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantile Quantile<br>.2 .5<br>0 0<br>Gap Gap<br>-.2 -.5<br>-.4 -1<br>.2 .2<br>0<br>0<br>Gap Gap<br>-.2<br>-.2<br>-.4<br>-.4<br><!-- End of picture text --> _Note: These figures are based on the STATA command mmsel._ _The conditional gap is based on earnings equations including controls for years of education, potential work experience, search method (friends and relatives), unlimited contract, having had an apprenticeship contract, working in a small-sized firm (5 or less employees), union membership, living in a rural area. With a larger number of variables, the command does not work, due to the lack of sufficient information._ _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 39
private bank, and 10% a foreign bank.

Panel C shows the local market characteristics used to account for differences in the local economy and supply of banking services, using the Indian state as the local market. CMIE provides information on each firm’s “headquarters district,” the main location of firm operations. Sample firms are located in 25 different states. We also use information on the total number of banks in the market from the RBI Branch Banking Statistics (2002). We find that the average local market has 61 banks, ranging from a minimum of 6 for one very small state to a maximum of 68 ( _Number of Banks_ ). In the model for main bank ownership type below, we include a dummy for the presence of at least one foreign bank in the state to control for whether the firm has a convenient choice of a foreign main bank **.** At least one foreign bank operates in 16 out of the 25 states of India represented in our sample. However, because most firms are in those 16 states, the mean for this variable ( _At least 1 foreign bank present_ ) is 0.97. In the regressions where we estimate the likelihood that firms will diversify across bank ownership types, we control for the degree of concentration in the bank ownership types operating in the local market. _Bank ownership concentration_ is defined as the sum of squared shares of the number of banks of our four ownership types. The mean for this variable is 0.3. We use Census India 2001 to collect population density to proxy for rural versus urban areas ( _Population Density_ ). The average population density per square kilometer is 1,441 people, with a wide range from 109 to 9,294 people. All data are for 2001.

# **5. Empirical Methodology**

_Determinants of main bank ownership type_ Our first model tests the effects of firm and local market characteristics in determining the ownership type of the firm’s main bank: _Main bank ownership type = f{Firm characteristics, Local market characteristics} (1)_ The dependent variables are dummies which equal 1 if the main bank is the given ownership type (foreign, SBI, nationalized, or private) and 0 otherwise. The firm characteristics include measures of firm opacity, access to external finance, ownership type, financial performance, location, and industry type. Local market characteristics include a dummy for the presence of at least one foreign bank (so the firm 16
1. World Bank 2008a.

2. In a few instances surveyors and supervisors reported that respondents were somewhat taken aback at the series of questions, given the personal nature of the topic. This concern was particularly relevant in economies with large security risks, such as Mexico and Zimbabwe, and in economies where personal finances are widely regarded as a private matter, such as Cameroon, Italy, and Portugal. There were also reports from the field that the terminology and concepts used in the survey were entirely new to some respondents. Although efforts were made to include simple definitions of such terms as accounts and debit cards, the unfamiliarity and complexity of the topic were still reported to be a hurdle in several economies, including Afghanistan, Cambodia, Chad, and rural Ukraine. Overall, however, the rate of “don’t know” or “refuse” answers was very low. For the core questions (those not filtered by other questions), “don’t know” or “refuse” responses made up less than 1 percent of the total and no more than 2 percent in any region.

3. The Gallup World Poll has been used in previous academic studies. For example, Deaton (2008) uses Gallup World Poll questions on life and health satisfaction and looks at the relationships with national income, age, and life expectancy. Gallup World Poll questions are also used by Stevenson and Wolfers (2008) and Sacks, Stevenson, and Wolfers (2010) as part of their research to analyze relationships between subjective well-being and income; by Clausen, Kraay, and Nyiri (2011) to analyze the relationship between corruption and confidence in public institutions; by Demirguc-Kunt, Klapper, and Zingales (2012) to study changes in trust in banks over the financial crisis; and by Stevenson and Wolfers (2011) to examine trust in institutions over the business cycle.

4. In some economies oversamples are collected in major cities or areas of special interest. In addition, in some large economies, such as China and the Russian Federation, sample sizes of at least 4,000 are collected.

5. The Kish grid is a table of numbers used to select the interviewee. First, the interviewer lists the name, gender, and age of all permanent household members age 15 and above, whether or not they are present, starting with the oldest and ending with the youngest. Second, the interviewer finds the column number of the Kish grid that corresponds to the last digit of the questionnaire number and the row number for the number of eligible household members. The number in the cell where the column and row intersect is the person selected for the interview. In economies where cultural restrictions dictate gender matching, respondents are randomly selected using the Kish grid from among all eligible adults of the interviewer’s gender.

6. In the latest birthday method an interview is attempted with the adult in the household who had the most recent birthday.

MEASURING FINANCIAL INCLUSION

**10**
the fact that they choose to hold short portfolios suggests that the risk they face by going to longer horizons are larger than any extra risk-adjusted return they might obtain.<sup>24</sup> Aside from the price risk, one potential additional risk is that of liquidity. A possible concern related to liquidity is that if prices for long-term bonds are not available, mutual funds and pension funds that need to mark their portfolios to market each day to calculate their net asset values (NAVs) may avoid debt for which market prices are not readily available. Therefore, the need to calculate daily NAVs might lead to a preference for shorter-maturity bonds whose market prices can be better estimated. However, in the Chilean case there exist official price providers who value the different assets in the market on a daily basis, including short- and long-term bonds. Therefore, PFAs and mutual funds can use those prices to mark their portfolios to market. Moreover, the bonds we analyze at different maturities are liquid enough to provide price signals for investors that need to mark to market their portfolios.

Another aspect of liquidity that can matter for the results is related to the fact that bonds of different maturities might have different liquidity. In unreported results, we compute two measures of liquidity, the turnover ratio and the “conventional liquidity ratio” (Gabrielsen et. al, 2011) for Chilean nominal and indexed peso bonds using data obtained directly from the Central Bank of Chile. The results for government bonds suggest that short-term bonds are more liquid than long-term bonds, although the relation is not monotonic. However, according to both of these estimations and market

> 24 In unreported results, we simulate the returns and risks that pension funds would face in the case they took longer portfolios. In each simulation we increase the average maturity of all funds by lowering the shares of instruments with maturity lower than three years and increasing proportionally the shares of the rest. In all cases we do not change the total investment of each fund. The results show that the average returns and standard deviations increase with the average maturity of the portfolio. However, the standard deviations grow faster than the returns. Given the short-term holding period that PFAs seem to have, these results are consistent with those presented in Figure 8.

28
number of Venezuelan over total population in a municipality, has a negative e↵ect on the propensity of a household to receive _Bolsa Familia_ benefits. This might suggest that having the same ethnic network may lower the need for Venezuelans to access social protection or it might also suggest congestion. So, the model is re-estimated separately first focusing on Roraima and Amazonas, where the concentration is the highest and then on rest of Brazil and find that the relationship is still significant and slightly higher for rest of Brazil, suggesting that both factors are important. Table 13 supports this finding by showing that the average coverage gap is higher in Roraima and Amazonas than the other states in Brazil. The model also seems to explain more of the variation in coverage rate in rest of Brazil than in Roraima and Amazonas. The high unexplained gap may partially reflect the selection of the kind of Venezuelans, who register into _Cadastro ’Unico_ , which this paper could not control for due to data limitations. It may also reflect that some Venezuelans do not have the sufficient Portuguese language skill or the knowledge about their rights for registration.

Table 13: Decomposition of the coverage rates

||(1)<br>Brazil|(2)<br>RR & AM|(3)<br>Brazil - RR &AM|
|---|---|---|---|
|Di↵erential||||
|Brazilian|0.549<sup>_⇤⇤⇤_</sup>|0.699<sup>_⇤⇤⇤_</sup>|0.544<sup>_⇤⇤⇤_</sup>|
||(3258.87)|(780.65)|(3179.49)|
|Venezuelan|0.362<sup>_⇤⇤⇤_</sup>|0.370<sup>_⇤⇤⇤_</sup>|0.335<sup>_⇤⇤⇤_</sup>|
||(65.54)|(58.56)|(29.53)|
|Di↵erence|0.187<sup>_⇤⇤⇤_</sup>|0.329<sup>_⇤⇤⇤_</sup>|0.209<sup>_⇤⇤⇤_</sup>|
||(33.87)|(51.58)|(18.45)|
|Decomposition||||
|Explained|0.0456<sup>_⇤⇤⇤_</sup>|0.137<sup>_⇤⇤⇤_</sup>|0.0857<sup>_⇤⇤⇤_</sup>|
||(13.87)|(38.82)|(11.49)|
|Unexplained|0.142<sup>_⇤⇤⇤_</sup>|0.192<sup>_⇤⇤⇤_</sup>|0.124<sup>_⇤⇤⇤_</sup>|
||(24.85)|(28.90)|(10.59)|
|Observations|8729818|268305|8461513|

_t_ statistics in parentheses _⇤ ⇤⇤ ⇤⇤⇤ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001. Oaxaca decomposition conducted by linear probability model. First column uses all data, second column uses data on Roraima (RR) and Amazonas (AM) and the third column shows the results from the rest of Brazil. The analysis is on data from 2019 _Cadastro Unico_ .

Overall, in line with previous results on education and formal labor market, the results on social protection sector suggests that language training programs, provision of information and facilitation of di↵erent document or credential verification and validation may encourage more Venezuelan refugees and migrants to register in _Cadastro ’Unico_ and benefit from receiving _Bolsa Familia_ cash transfers.

43
||[0.064]|[0.040]|[0.146]|[0.105]|[0.030]|[0.016]|[0.139]|[0.112]|
|---|---|---|---|---|---|---|---|---|
|male|0.061|0.112|0.077|0.011|-0.009|-0.019|-0.013|-0.033|
||[0.041]|[0.053]**|[0.061]|[0.066]|[0.015]|[0.019]|[0.094]|[0.081]|
|Adult|0.000|-0.020|0.097|0.079|0.029|0.010|0.144|0.083|
||[0.042]|[0.038]|[0.085]|[0.049]|[0.023]|[0.008]|[0.093]|[0.052]|
|Constant|-0.203|-0.286|-0.141|-0.079|0.010|0.028|-0.050|0.020|
||[0.099]**|[0.128]**|[0.118]|[0.203]|[0.036]|[0.033]|[0.160]|[0.230]|
|Observations|173|289|257|509|257|509|286|626|
|R-squared|0.557|0.413|0.314|0.370|0.245|0.218|0.461|0.475<br>BFA,|
|||||ETH,|ETH|BFA ETH|ETH,|ETH,|
|Cntri|ETH,|ETH,|ETH,M|MWI,|,<br>MWI|, ,<br>MWINGA|MWI,|MWI,|
|oues|NGA|NGA|WI,NGA|NGA,|,<br>NGA|,<br>ZWE|NGA,|NGA,|
|||||ZWE||,|SLE|SLE,|
|||||||||ZWE|

Source: HFPS-Wave 1.

Notes: Urban observations only. Adult defined as 25 years old or more. Omitted categories: Below 2.15 USD-a-day. Robust standard errors clustered at the subnational geographic units between brackets. *** significant at 1%, ** 5%,

* 10%.

To sum up, in the first months of the crisis, urban households, especially those with non-farm enterprises, experienced significant income loss. Some – mostly those with farm income – coped by selling assets but most did not employ any additional coping strategies when incomes fell. Women fared worse than others, both in terms of income loss, and in having access to coping strategies. Adults experience greater income losses than youth but also had more access to assistance from an outside source. Those with different levels of poverty fared similarly, both in terms of income losses and use of coping strategies, though trends suggest that the non-poor were more successful in acquiring help from others while not selling off assets. The next section considers how these trends progressed over the first year of the COVID-19 pandemic.

# **5. Evolving Impacts of the Pandemic on Household Enterprises, Incomes and Use of Coping Strategies**

**Job type, income losses, and use of coping strategies shifted over the first year of the pandemic, with some differences by gender, age, and household welfare** . The results are reported in figures, which show the point estimate (and confidence interval) of the share of household heads who are in a situation indicated by each graph – employment status, income declines, and use of coping strategies.<sup>34</sup> The first set of graphs is disaggregated by gender, followed by disaggregation by age. Finally, we present the disaggregation by household welfare, where we consider three welfare groups: less than $3.65 per capita, $3.65-$6.85, and above $6.85.<sup>35</sup> The household head definition of household enterprises is used in the analysis. For comparison purposes, the evolution of wage earners and of the non-working population is also estimated for

> 34 The full regression results are available from the authors. 35 We combined the bottom two welfare groups so that the estimates would converge. We also estimated the differences by sector, though most of the regressions did not converge.

23
**_Figure 16. Incidence of Main Direct Transfers by Household Market Income Decile_**

**_Table 6. Fiscal and Distributional Impacts of Potential Reforms_**

|70||**Change i**<br>**contri**|**n marginal**<br>**bution***|**Estimated budget**<br>**savings**|
|---|---|---|---|---|
|ure<br>60||Poverty|Inequality|JOD m|
|endit<br>50|GST reform|-0.2|-0.1|120|
|t of Total Exp<br>40<br>30|Electricity<br>residential<br>tariff reform|0.2|0.3|95|
|cen<br>20|Takaful<br>|1.4|0.7|-100|
|Per<br>10<br>0|expansion<br>NAF re-<br>certification|0.4|0.4|0|
||All reforms|1.7|1.3|115|
|NAF<br>Bread Subsidy Compensation|With bread<br>savings**|-0.1|0.5|225|
|NAF (Re-certified)<br>Takaful|||||
|Notes: Deciles of per capita market income. Takaful<br>includes full expansion to 2021.<br>Source: HEIS 2017-18 and World Bank calculations.|*Marginal contri<br>poverty headcou<br>consumable inco<br>Gini/poverty, ne<br>The change is th<br>contribution bef<br>The budget chan<br>percentage chan<br>administrative b<br>**Approximate b<br>Source: HEIS 201|bution is the<br>nt is reduced<br>me; a positiv<br>gative contri<br>e difference<br>ore the refor<br>ge in JOD is<br>ge in the ‘sur<br>udget.<br>read subsidy<br>7-18 and Wo|points which t<br>between mar<br>e contribution<br>bution is an incr<br>with respect to<br>m.<br>estimated by a<br>vey-budget’ to<br>compensation<br>rld Bank calcul|he Gini Index/<br>ket income and<br>is a reduction in<br>ease in Gini/poverty.<br>the marginal<br>pplying the<br>the actual<br>budget for 2020.<br>ations.|

# 6. Conclusions

In this paper we analysed the impact of Jordan’s main fiscal policies on poverty and inequality applying the CEQ methodology to data from the 2018 HEIS. We covered the key fiscal policies: personal income taxes, GST and SST (indirect taxes), direct transfers from NAF, the bread subsidy compensation scheme and the recent Takaful expansion, indirect subsidies to electricity and water and in-kind benefits for education and health. Our results show that the fiscal system in Jordan is only modestly progressive, but more could be achieved to further its poverty reducing and equalising impact.

In the middle of 2020, Jordan faced extraordinary challenges. While swift action by the government avoided the health crisis, the pandemic was wreaking global economic havoc and Jordan was no different. Moreover, Jordan, like many other countries, was under significant fiscal pressure before the crisis. The crisis is in turn exacerbated that pressure, particularly through a lack of fiscal space to run countercyclical fiscal policy as was needed. At the same time, poverty rates in Jordan have been stagnant
**Table 4:  The impact of typhoons on total PCE and different components of PCE (using a binary variable on wind speed)**

||PCE<br>Total<br>(1)|PCE<br>on Food<br>(2)|PCE<br>Non-Food<br>(3)|PCE<br>Protein<br>(4)|PCE<br>Fruit<br>(5)|PCE<br>Cereal<br>(6)|PCE<br>Education<br>(7)|PCE<br>Medical<br>(8)|
|---|---|---|---|---|---|---|---|---|
|W119_hy|-0.0672***|-0.0506***|-0.0781***|-0.101***|-0.0263|-0.00818|-0.0439|-0.0401|
||(-6.66)|(-5.10)|(-6.11)|(-6.00)|(-1.52)|(-1.03)|(-1.38)|(-1.34)|
|adj. R2|0.632|0.504|0.635|0.384|0.305|0.171|0.298|0.227|
|N|412,286|412,268|412,286|410,242|410,149|411,118|255,586|355,402|

Notes: Additional explanatory variables included but not reported here: family size, head is female, age of head, marital status of head, education level of head, head has a job, building type (single house vs, apartment etc.), type of material for roof, type of material for wall, floor area, type of toilet, water source, and separate binary variables for semester, year, and region, along with a complete set of interactions among these binary variables. Standard errors are corrected for clustering at the barangay level.

Table 5 presents estimates including an interaction term of the windspeed binary variable with the household being a beneficiary of the 4Ps conditional cash transfer program. Qualitatively similar patterns are observed when the binary variable signifying the incidence of high wind speeds (W119_hy) is interacted with a household being a beneficiary of the 4Ps program (compare Table 5 with Table 2).

**Table 5:  The impact of typhoons on PCE including interactions (using a binary variable on wind speed)**

||PCE<br>Total<br>(1)|PCE<br>on Food<br>(2)|PCE<br>Non-Food<br>(3)|PCE<br>Protein<br>(4)|PCE<br>Fruit<br>(5)|PCE<br>Cereal<br>(6)|PCE<br>Education<br>(7)|PCE<br>Medical<br>(8)|
|---|---|---|---|---|---|---|---|---|
|W119_hy|-0.064***|-0.054***|-0.066***|-0.125***|-0.0166|-0.00136|0.0352|-0.0403|
||(-4.66)|(-4.04)|(-3.81)|(-5.53)|(-0.73)|(-0.12)|(0.87)|(-0.95)<br>-|
|4Ps|-0.104***|-0.073***|-0.151***|-0.159***|-0.042***|-0.00364|-0.050**|0.105***|
||(-19.27)|(-15.13)|(-21.13)|(-18.79)|(-5.13)|(-0.77)|(-2.70)|(-6.07)|
|4Ps x<br>W119_hy|0.00318|0.0122|-0.00488|0.00569|-0.0119|0.0141|-0.098**|-0.0566|
||(0.38)|(1.55)|(-0.42)|(0.43)|(-0.93)|(1.73)|(-2.85)|(-1.76)|
|adj. R2|0.619|0.473|0.629|0.349|0.279|0.134|0.276|0.244|
|N|167960|167956|167960|167134|167275|167558|100366|144019|

19
countries and eight in developed countries. In recent years, several investors' surveys explored the effectiveness of tax policies on FDI using alternative samples or asking more detailed questions (for example, JETRO (1995), Ernst & Young (1994) or Fortune/Deloitte & Touche (1997)). In general, these surveys have confirmed the conclusions summarized above; that if tax policy matters it is not the most influential factor in the site selection of multinationals.<sup>3</sup>

# B. Econometric Analysis

The available econometric evidence on the effect of taxation used time-series estimation of the responsiveness of FDI to annual variations in tax rates. Most econometric studies have tended to confirm the results of surveys; that investors are mostly influenced in their decision by market and political factors and that tax policy appear to have little effect on the location of FDI.

A selective sample of studies has concluded in that direction: Root and Ahmed (1978), Agodo (1978), Shah and Toye (1978), and Lim (1983). In 1978, Root and Ahmed performed an econometric study with data for 41 developing countries for the period 1966-1970. They classified countries in three categories of unattractive, moderately attractive, and highly attractive according to their average annual per capita inflow of FDI. Forty-four variables were chosen as potentially significant discriminators of the three country groups. Among the six policy related discriminators were three relating to tax levels. Of these, corporate tax rates proved to be an effective discriminator of the three defined country groups; however tax incentives laws and liberality were not found to be effective discriminators. Agodo analyzed a sample of 33 US firms having 46 manufacturing investment in 20 African countries. Tax concessions were found to be insignificant as a determinant of FDI in simple and multiple regressions.

Much of the literature of the period used highly aggregate data, evaluating the correlation between annual changes in FDI flows, and a series of factors, including In the Fortune/Deloitte & Touche's survey, taxes ranked at the 13 t position out of 26 factors.

6