rafmacalaba/datause-encoder-data huggingface dataset:
Therefore, on the basis of these stylized facts, any functional relationship between portfolio flows to Latin America relative to Asia should postulate an inverse relationship. That is, when portfolio flows to Asia increase one should expect to see a decline in the portfolio flows from abroad to Latin American emerging markets, _ceterisparibus._ This is the basic question that is being addressed in this paper.

# 2. DATA AND METHODOLOGY

The econometric analysis in this study is based upon panel data for a sample of eight countries, namely, Argentina, Brazil, Chile India, Indonesia, Mexico, South Korea and Thailand. The data for each country are quarterly from 1989QI to 1992Q2. As shown previously in figures 3-5, the choice of countries in the sample is motivated by the availability of data on gross portfolio flows and the fact that these countries together account for a significant proportion of total portfolio flows to developing countries during the sample period. Given the recent nature of the dataset, the number of observations is not large enough to obtain robust econometric results on an individual country basis. The use of panel data estimation techniques allow for the most efficient way of simultaneously examining cross country relationsbips at any point in time and countryspecific trends in portfolio flows over time. In addition, it addresses the problem of inadequate data on a country-specific level and increases the degrees of fieedom in the econometric tests that are conducted.7 Quarterly data on gross portfolio investment flows for the eight countries in this study are obtained from the World Bank. These figures, in aggregate, are reported in the World Debt Tables (1992-93 and 1993-94). Initially, the econometric analysis is

> 7Chuhan, Claesscns and **Maningi** (1993) have used this methodology as well when evaluating **the** determinants of portfolio flows to **cmerging** markets. For a related study see also CalVo. **Liederman** and Rcinhart (1993).

15
Eritrea, and Haiti. If we exclude oil exporters, and countries in conflict, only Korea and Senegal turn out to be vulnerable countries.

Thus, while the figures of food deficit countries look significant, when looked at closely, many have special reasons for deficits, such as having a conflict or exporting a valuable commodity such as oil. Others have deficits, but these deficits are too small for the potential price increases to have a significant impact on their trade balances. These findings suggest that the debate on food vulnerable countries is not very important, and should not be taken very seriously.

# **V. Sub-Saharan Africa: A Special Case?**

In most of the discussions, it has been argued that Sub-Saharan Africa has many countries that are significant net food importers and global trade reforms will particularly hurt the continent. In this section we repeat the same exercise we undertook for all countries for the SSA countries.

It is clear from table 5 why SSA has been treated as special. Only 12 of the 47 SSA countries are net exporters of raw food. Even if conflict and oil exporting countries are excluded, only 5 of the 28 low-income African countries are net food exporters. The rest are net food importers. These ratios are much lower than other low-income countries shown in table 1.

**Table 5: African Country Classifications by Raw Food Trade**

||No. of Cou<br>|ntries in Raw Food<br>|Trade<br>|
|---|---|---|---|
|CountryGroup|Net Exporter|Net Importer|Total|
|Middle-income **|4|4|8|
|Low-income|8|31|39|
|Oil Exporter|2|4|6|
|Civil Conflict States|1|4|5|
|Other Low-income|5|23|28|
|Sub-Saharan Africa,total|12|35|47|

Notes: Food is defined as raw food in SITC Revision 2, excluding all cash crops, processed food and seafood (see detailed classifications in Appendix Table 1). The classification of country groups is provided in Appendix Table 4.

- ** Gabon is also in oil exporter, we added it to the middle-income group,

- not to create another group for just one country.

Source: Based on partner data from UN COMTRADE Statistics.

12
## **Figure 4: Relative prices (World vs. Ethiopia)**

<!-- Start of picture text --> 0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>relative price (all CPI) relative non-food price relative food price relative cereal price<br>1997M07 1998M02 1998M09 1999M04 1999M11 2000M06 2001M01 2001M08 2002M03 2002M10 2003M05 2003M12 2004M07 2005M02 2005M09 2006M04 2006M11 2007M06 2008M01 2008M08 2009M03 2009M10 2010M05 2010M12 2011M07 2012M02 2012M09 2013M04 2013M11 2014M06 2015M01 2015M08 2016M03 2016M10 2017M05 2017M12 2018M07 2019M02 2019M09 2020M04<br><!-- End of picture text --> _Sources_ : Authors’ construction using data from NBE database and the World Bank’s Commodity Markets database. _Note_ : An increase represents a real appreciation in favor of Ethiopia.

# 4. Modeling inflation in Ethiopia

The modeling approach adopted in this study accounts for structural features of the Ethiopian economy, notably: (i) exposure to agricultural shocks that affect food prices and the general price level; (ii) import dependence and exposure to shocks to international prices, particularly grains, food, and oil prices; and (iii) financial repression with strong pressure from direct and indirect financing of the fiscal deficit. This approach draws from the study by Durevall et al. (2013) regarding modeling and estimation methodology, as well as Nell (2004) in incorporating structural factors of inflation. This section presents the main blocks of the inflation model.

## **_Structural factors of inflation_**

Drawing on the studies by Nell (2004) and Durevall et al. (2013), this analysis models inflation as driven by deviations of output from its trend level due to shocks as well as changes in the prices of imported goods that are transmitted into domestic prices. This can be represented by the following equation: 0 1 2gap (1) MM where π is the domestic inflation rate, tt tt is import price inflation, and tt tt _gap_ is the output gap. ππ = αα + αα ππ + αα + εε<sup>MM</sup> 10
offered quantitative evidence suitable for use in fiscal diagnostics. A number of technical directions should be explored to refine significantly the results presented here.

The simplest solution would be to test the model on a cross-section dataset made up of the latest data available—as we did for health with the WHO data. By doing this, one would obtain a useful snapshot from relatively large datasets. But it would not say much about the extent to which the poorest are catching up and at what speed. If we want to get a sense of the speed at which things are changing or could change, we need dynamic models. To meet that requirement we could use, for the four sectors, a pure output-expansion model in which the inputs would be used in a second stage to explain differences in efficiency. If a weak or negative relation appeared, we would need to test some alternatives—for example, to group a year in windows analysis (for instance, the mean of year 1 and 2 compared with the mean output of year 2 and 3). If this alternative setting were again inconclusive, it is unlikely that much more could be achieved than what has already been done here. Alternative solutions along the same line would be to look for cost drivers or to work with national accounts data to increase cross-country and intrasector consistency, since the omission biases are likely to be less dramatic with those datasets. The challenge there would be to unbundle the public- from the private-sector providers.

In sum, as evidenced by the long list of papers included in the bibliography, some things can be done better than what we presented here, at least for education and possibly for health. Unfortunately, any recommended use of these sorts of assessments for countryspecific policy purposes will have to be presented with extremely heavy words of caution. Ultimately, policy-relevant work should be conducted at the level of the business unit in each country, but this requires a lot more data than are currently available because, historically, monitoring the performance of the government at the sector level has not really been anyone’s top priority. Until the international community begins to recognize that the sectoral allocation of public expenditure matters, today’s data gaps are unlikely to disappear and the monitoring of the performance of the public sector will continue to be only moderate at the aggregate level and superficial at best at the sectoral level.

20
index. Muller reports that at least approximately 20 years of democratic experience are required for the egalitarian effect to occur; countries with less than a generation of democracy are almost as egalitarian, on average, than non-democracies. In other words, democratic institutions, if maintained for a relatively long time, cause some gradual reduction of income inequality, independent of level of economic development. Also, a very strong inverse association is observed between income inequality and the likelihood of stability versus breakdown of democracy, even independent of a country's level of economic development. Thus, if a democratic regime is inaugurated in a country with an extremely inegalitarian distribution of income, high inequality is likely to undermine the legitimacy of the regime and cause democratic institutions to be replaced by authoritarian rule.

Robustness of Muller's findings with respect to the inclusion of a control variable (literacy rates) and an observation (Taiwan) was subsequently questioned in Weede, 1989. Moreover, using an update inequality data set, Weede failed to reproduce these findings altogether. In his response, Muller (1989) reproduces his results using an altemative measure of the length of democratic experience, even controlling for literacy rates.

Summarizing the results of early research on the subject, Sirowy and Inkeles conclude therefore that evidence on the positive effect of democracy on reducing inequality had been ambiguous, although the _length_ of democratic experience (as opposed to the state level of democracy) may have contributed to reduction in inequality.

Some more recent research seems to be more conclusive. A couple of papers done over the past several years have used an improved inequality data set created in the World Bank (the Deininger-Squire) to test the hypothesis that democracy reduces 19
empirically the predictions of their model described above by extending the Knack and Keefer sample using later waves of the WVS that includes a number of developing countries. They corroborate the conclusion that trust affects economic growth. Beugelsdijk et al. (2004) perform a robustness analysis of the relationship between trust and economic growth and conclude that the Zak and Knack results are highly robust in terms of statistical significance of the estimated coefficients and reasonably robust in terms of size of the estimated effects.

Another strand of the literature links social capital with financial development, as it identifies high levels of trust as one of the main determinants of financial depth. Guiso et al. (2004) measure variations in social capital within Italy through (anonymous) blood donation and (non-mandatory) electoral participation. They show that, after controlling for income and wealth, high social capital areas use more checks, invest less in cash and more in stocks, have higher access to formal credit and borrow less from friends or relatives. Nonetheless, as well argued by Sabatini (2006), indicators such as blood donation and electoral participation might be outcomes of social capital rather than a measure of social capital itself. Garretsen et al. (2004) also show that, after controlling for legal norms, societal norms help to explain market capitalization and hence crosscountry differences in financial development. They obtain their indicators for social norms from survey data about the values of people working in local subsidiaries of IBM in more than 50 countries.

In addition, social capital may influence social learning and information diffusion through reducing the cost of information acquisition, lessening the uncertainty regarding the reliability of the information and increasing the willingness to cooperate and share information. Barr (2000) presents a model where networks facilitate knowledge flows between firms and as a result, firm productivity increases which might lead to sustained growth. Her empirical analysis for the manufacturing sector in Ghana shows that entrepreneurs with large and more diverse networks have firms that are more productive because they benefit not only from their direct contacts but also from the networking of their contacts. Katungi and Smale (2006) draw attention to a gender twist in the way that social capital influences information exchanged among rural households in Uganda. Male-headed households participate more in civic engagement and social institutions than 4
used to incorporate macroeconomic and demographic information and address data constraints, such as missing data from non-survey years (ILO 2010). However, labor market forecasts by occupation, training type or educational attainment level are not standard, primarily due to a lack of data and difficulties in consistently integrating data into economic projections (Maier, Monnig and Zika 2015).<sup>18</sup> In this context, the use of online data for “nowcasting”<sup>19</sup> and forecasting offers new ways to analyze labor market trends and skills demand, which can largely overcome the subjectivity and time-lag issues associated with workforce surveys. For example, Askitas and Zimmermann (2009) used Google keyword searches to conduct a predictive analysis of unemployment rates in Germany. In the wake of the 2008 global economic crisis, this innovative method for using online data proved to be a valuable tool to predict economic behavior, and strong correlations were discovered between keyword searches and unemployment rates. More recently, Vicente, Lopez-Menendez and Perez (2015) examined the possibility of using internet search data and business-confidence indicators to predict unemployment trends in Spain.

# **_Application of the Analysis to Babajob Data_**

Online job-portal data can provide a continuous source of up-to-date, objective information on the demand for and supply of skills in different industries, occupations or locations. A simple predictive analysis using Babajob data has the potential to forecast trends in three variables, including the demand for skills as listed in job postings, the supply of skills as reported in job seeker profiles, and the wages being offered for new positions. However, without the presence of relevant and timely external data for cross-checking the composition of skills demand and supply against, nowcasting or forecasting the skills demand and supply in quantity is constrained.<sup>20</sup> The analysis of wage offers is the most robust among the three variables, considering that the wage offers posted on Babajob are influenced by the labor market for its competitiveness.

# **_Analyzing Wage Trends and Forecasting_**

Areias et al. (forthcoming) analyzed the patterns of wage growth and distribution across different locations using 50,000 job advertisements posted in 20 cities with the largest number of advertisements. Wages were deflated using state-level urban consumer price index (CPI) obtained from the Reserve Bank of India.

> 18 Econometric models can be used to forecast economy-wide employment. See, e.g., ILO (2013).

> 19 “Nowcasting” is used to estimate economic conditions in the present.

> 20 Due to absence of national labor statistics since 2012 at the time of conducting analysis, it was not possible to compare post 2012-period of Babajob data (which this paper mostly uses) with the growth trends of national skills demand and supply.

21
on statistical techniques known as “small-area” estimation methods (see, e.g., Bell _et al._ (2007)) to impute poverty numbers at different administrative levels. These methods essentially impute from a household (income or) consumption survey into a population census to provide more spatially disaggregated measures of consumption and poverty for better targeting purposes. Put differently, consumption data are imputed using a data source that is representative at a higher administrative level to another data source that is representative at a lower administrative level. The higher administrative level can be the state or province level, for example, while the lower administrative level can be the district or community level.

This approach has also been widely employed by international organizations such as the World Bank to impute poverty in a developing country context. Since poverty rates are often visually represented on the country map, it is also commonly known as the “poverty mapping” approach. The most commonly used framework developed by Elbers, Lanjouw, and Lanjouw (2003) proposes that we estimate Equation (3) using the household consumption survey. Then these estimated (distributions of the) parameters can be imposed on the same variables in the population census in a similar spirit as with the temporal imputation of poverty. But importantly, techniques differ on how this is implemented. In particular, Elbers _et al._ (2003) suggest a Bayesian estimation method where the distribution of �<sup>�</sup> ��, rather than ���, is employed for the imputation. In fact, various methods have also been employed for the imputation model.<sup>30</sup> However, Tarozzi and Deaton (2007) note that two assumptions are relevant for the implementation of the poverty map method. The first assumption is similar to Assumption IV.1 discussed earlier, and states that the explanatory variables �� are comparable for both the survey

> 30 The Elbers _et al._ method has also been employed to provide other development outcomes such as child malnutrition (Fujii, 2010). See also Rao and Molina (2015) for a text-book treatment of the statistical literature on small area estimation.

28
Table 6. The IV estimates of the effect of the training program on the participants

||Visiting customers<br>(i)|Record keeping<br>(ii)|Record analysis<br>(iii)|Sales revenue<br>(iv)|Value added<br>(v)|Gross profit<br>(vi)|
|---|---|---|---|---|---|---|
|**_Panel A: RE-IV model_**|||||||
|Fabricator ×_Di_×_Tt_|0.222**|0.593***|0.481***|25.34**|18.05**|15.05**|
|_Instrumented_|(2.26)|(4.56)|(3.42)|(2.09)|(2.16)|(1.99)|
|Machinist ×_Di_×_Tt _|0.270**|0.183**|0.292***|-6.008|7.145|2.629|
|_Instrumented_|(2.40)|(2.29)|(3.32)|(-0.18)|(0.33)|(0.14)|
|Machinist|0.094|0.116|0.060|61.46**|51.21***|39.75***|
||(1.13)|(1.34)|(1.18)|(3.22)|(3.57)|(4.19)|
|Age|-0.003|0.003|0.004|-0.531|-0.284|-0.181|
||(-0.92)|(0.59)|(0.92)|(-0.88)|(-0.77)|(-0.69)|
|From Ashanti|-0.039|0.074|0.079|0.028|4.950|3.657|
||(-0.44)|(0.77)|(0.86)|(0.00)|(0.54)|(0.37)|
|Years of schooling|0.008|0.037**|0.022|3.218**|2.041**|1.592**|
||(0.81)|(1.96)|(1.10)|(2.15)|(2.18)|(2.15)|
|Apprentice training|0.066|-0.052|-0.112|19.49|6.190|3.234|
||(0.50)|(-0.30)|(-0.75)|(1.38)|(0.46)|(0.27)|
|Years of operation|-0.002|-0.001|0.003|0.212|0.147|-0.014|
||(-0.54)|(-0.30)|(0.89)|(0.47)|(0.43)|(-0.06)|
|Fabricator ×_Di_|0.107|-0.045|-0.041|-13.25|-9.932|-5.931|
|_Instrumented_|(0.88)|(-0.39)|(-0.36)|(-1.08)|(-1.31)|(-0.81)|
|Machinist ×_Di_|0.094|0.101|0.142|32.84|10.95|14.36|
|_Instrumented_|(0.81)|(0.86)|(1.12)|(0.92)|(0.59)|(1.13)|
|Year 2002|-1.47e-15|-1.95e-16|-4.91e-15|-4.001|-3.639*|-2.681|
||(-0.26)|(-0.03)|(-0.68)|(-1.38)|(-1.65)|(-1.30)|
|Year 2004|-8.90e-16|0.024|0.024|-10.45**|-9.102***|-6.870***|
||(-0.16)|(1.02)|(1.03)|(-2.94)|(-3.24)|(-3.36)|

34
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<!-- Start of picture text --> Mali 2006, urban areas<br>Women 15-49<br>fan)<br>oS ae<br>x oo?”=<br>we"<br>(a)<br>oS = -_<br>ite} vet<br>N _ =<br>oo a r<br>we e gee<br>So Lr<br>& a? Ja<br>“ — - Married, once<br>~ =, mame Married, prev. widow<br>mm Married, prev. div.<br>m= Curr. widow<br>S == Curr. div.<br>(=)<br>N<br>10 20 30 40 50<br>Age<br><!-- End of picture text -->
# **<u>Table 2 Basic specification of the model</u>**

|<br>_Panel a_|||||
|---|---|---|---|---|
|Dependent variable: Log GDP per capita|(1)|(2)|(3)|(4)|
|Intergenerational persistence|1.9437<sup>***</sup><br>(0.7224)||||
|Intergenerational correlation||1.2317|||
|||(0.7810)|||
|Upward mobility gap|||–0.0321<br>||
||||(0.0358)||
|Upward mobility in higher education||||-0.0359|
|||||(0.4997)|
|_R_<sup>2</sup>|0.985|0.985|0.984|0.984|
|Number of observations|1244|1244|1244|1244|
|Number of countries|68|68|68|68|
|_Panel b_|||||
|Dependent variable: Log GDP per capita|(1)|(2)|(3)|(4)|
|Intergenerational persistence|||||
|× High Income Countries|4.0703<sup>***</sup>||||
||(1.3521)||||
|× Eastern Europe and Central Asia|1.7261<br>||||
||(1.4239)||||
|× Latin America and the Caribbean|0.5276||||
||(0.9445)||||
|× Other Developing Countries|0.8207<sup>*</sup><br>||||
||(0.3466)||||
|Intergenerational correlation|||||
|<br>× High Income Countries||1.4584|||
|||(2.3473)|||
|× Eastern Europe and Central Asia||0.3230<br>|||
|||(1.0612)|||
|× Latin America and the Caribbean||3.2639<sup>*</sup><br>(1.9435)|||
|× Other Developing Countries||2.6847<sup>*</sup>|||
|||(1.5037)|||
|Upward mobility gap|||||
|× High Income Countries|||0.0000<br>||
||||(0.0326)||
|× Eastern Europe and Central Asia|||–0.2903<sup>***</sup>||
||||(0.0694)||
|× Latin America and the Caribbean|||0.2516<sup>**</sup><br>||
||||(0.0962)||
|× Other Developing Countries|||0.2555<sup>***</sup>||
||||(0.0953)||
|Upward mobility in higher education|||||
|× High Income Countries||||–0.8294<br>|
|||||(0.6392)|
|× Eastern Europe and Central Asia||||4.3389<sup>***</sup>|
|||||(0.9254)|
|× Latin America and the Caribbean||||3.8166<sup>**</sup>|
|||||(1.7349)|
|× Other Developing Countries||||6.0788<sup>**</sup>|
|||||(2.7205)|
|_R_<sup>2</sup>|0.986|0.985|0.987|0.988|
|Number of observations|1244|1244|1244|1244|
|Numberofcountries|68|68|68|68|

25
could be advertised through centrally designed campaigns to reach out to a large spectrum of SMEs. Headquarters can also use existing databases like business registries to perform data mining and screen SMEs. Furthermore, in the case of international banks, national headquarter offices can obtain information and guidance from the global headquarter offices, who have acquired greater experience in dealing with SMEs worldwide. In this context, branches need to work with headquarters to generate new SME clients, as the relationship manager is not the only person central to the relation with SMEs and her effectiveness is boosted by information and support from headquarters. Moreover, branches do not operate as separate banks (or niche banks) within the bank. Again, in this context, universal banks have an advantage in exploiting economies of scale compared to small, specialized banks.

As they learn to deal with SMEs, banks are reorganizing their credit risk management systems, with a greater degree of sophistication among international banks and the leading, large domestic banks. Figure 14 shows some aspects of how banks are organizing their risk management processes related to SME lending. In most large banks, and with the exception of pure credit scoring, credit risk management is not automated. In most cases, it involves a credit risk analyst. Typically, risk management is a function that is organizationally separated from sales and is done primarily at headquarters. The risk management department is given independence and strong approval and veto powers. (This type of arrangement is not typically found among small, niche` and public banks.) While maintaining independence in judgment, risk analysts and managers work cooperatively with those who sell products and originate loans (i.e., the SME account managers, in countries where business models are more advanced). In effect, risk analysts 32