rafmacalaba/datause-encoder-data huggingface dataset:
12

# III. EMPIRICAL FINDINGS

In this section we will examine empirically the relationship between credit market conditions and private investment behavior for a sample of five developing countries, i.e. Brazil, Colombia, India, Korea, and Turkey, using annual data from 1965 to 1985. The main objective is to test for the partial and the joint influence of credit availability and interest rates (i.e. quantity and price constraints) on the behavior of aggregate, private investment in that sample of countries.

A preliminary plot (Charts I to V) of actual domestic credit and actual private inv..stment (both as percentages of GDP) does not reveal any obvious link. In fact, aggregate private investment seems to exhibit its own independent pattern. It has declined steadily since the second half of the 7Os in the two Latin-American cases, while domestic credit availability has tended to grow. .rivate investment remained flat both in India and Turkey, in the face of growing, albeit fluctuating, domestic credit supply. Korea is indeed the only country in the sample that exhibits an upward trend in aggregate private investment, although at a much slower pace than domestic credit.

Of course, our charts do not control for other factors affecting private investment decisions, most notably growth in real economic activity and expected real interest rate. A more thorough regression analysis is needed. Following our model's conclusions in the previous sections, we will specify a
16 statistically representative for the continent either. At the same time, this overall headline number provides a useful antidote.

However, more important than the overall headline number is the variation across the countries. At 56 percent, the estimated female share of agricultural labor is highest in Uganda, followed by Tanzania (52 percent) and Malawi (52 percent). Taking the female share in the total population as a natural benchmark, these are also three countries where the female share in the population is slightly above half (52, 53 and 51 percent respectively (Table 1, column 2). In contrast, women contribute less than a quarter of the overall amount labor to crop production in Niger (24 percent) and only slightly more in Ethiopia (29 percent).<sup>21, 22</sup> The findings for Nigeria are especially illuminating. On average about 37 percent of labor in crop production is contributed by women. Yet, this reduces to less than a third (32 percent) when looking at northern Nigeria only. When looking at southern Nigeria, the share is similar to the shares found in eastern and southern Africa (51 percent) (Table 1, column 1). This tallies well with expectations. The ability of the data to distinguish these differences within Nigeria provides confidence in the approach. It also underscores the heterogeneity in women’s time allocation in agriculture, even within countries.

Very similar results are obtained in all countries when using the data without application of the cleaning and/or imputation procedures, suggesting that the outliers and missingness tend to affect responses on both male and female agricultural labor inputs similarly.<sup>23</sup> By way of comparison, Table 1 (column 3) also reports the female agricultural labor shares as estimated from

> 21 Adding hired labor does not change the estimated female agricultural labor shares.

> 22 The average _household-level_ female share of agricultural labor, which is the focus of the multivariate analyses featured later this section, is estimated similarly at 59, 55, 56, 32, 34 and 22 percent for Uganda, Tanzania, Malawi, Nigeria, Ethiopia, and Niger, respectively.

> 23 For Nigeria, where 20 percent of the observations were missing (including outlier values that were converted to missing), the estimated share remains at 37 percent even after dropping the outliers. Without any cleaning, the share is estimated at 41 percent (33 percent in the North versus 52 percent in the South).
Based on survey data and applying dynamic analysis and a Markov-chain approach for predictions, Hanousek and Palda (2008) find that in the Czech Republic between 1995 and 2006, the percentage of tax evaders first increased, then leveled off and they suggest that it is now falling along a quadratic path.

**Table 3: Prevalence of undeclared work in the New EU Member States according to the EU Commission**

|1. Estonia|7.3 % of GDP in 2004, a decline by 2.7 percentage points since<br>2000.|
|---|---|
|2. Czech Republic|9-10%ofGDP,no change in recentyears|
|3. Slovakia|13-15% of GDP estimated in 2000 and moderate decline in recent<br>years|
|4. Poland|12-15% of GDP after 14 % in 2003|
|5. Lithuania|6% of total employment. Slight decrease from 8 % of total<br>employment and 15-19% of GDP in 2003|
|6. Slovenia|17%ofGDP,no change in recentyears|
|7. Hungary|18% of GDP,no change in recentyears|
|8.Latvia|18%ofGDP,no change in recentyears|
|9.Romania|16-21%ofGDP,no change in recentyears|
|10. Bulgaria|22-30% of GDP estimated in 2002/2003; survyeys indicate a<br>marked increase in recentyears|
|Undeclared work in selected<br>other EU countries|Sweden: 5% of GDP and 11% of workers<br>Denmark: 3% of GDP<br>Spain: 12.3% of GDP<br>Greece: above 20% of GDP|

_Sources:_ EC 2007; Undeclared Work in an Enlarged Union (Renoy et al., 2004); Statistics Estonia.

# **3. The Role of Taxes According to Opinion Polls**

13. In a survey, launched by the EU Commission, individuals in EU member countries were asked to report on undeclared work, defined as all remunerated activities (in-kind or cash), which are in principle legal but are not declared to tax authorities or social security institutions (EC 2007a). According to this opinion poll, the most frequent reasons for taking part in undeclared work in the new EU member countries are that salaries in the regular sector are too low. Taxes and social security contributions are also often mentioned as being an important motivator for working informally. In Hungary and Lithuania about 30-35 percent of respondants mentioned taxes as the most important reason for carrying out undeclared work, whereas taxes was the most important determinant for less than 10 percent of the respondants in Slovenia, Romania, Slovakia and Bulgaria (Figure 1).

14. The European Employment Observatory (EC 2007a) confirms the importance of taxes for carrying out undeclared work. It finds that that:

- In Hungary, high income taxes together with social security contributions are seen as providing strong incentives for undeclared work;

- In Lithuania, the combination of relatively low salaries and high taxes including social security contributions are regarded as the main drivers for undeclared work;

5
non-normality such as asymmetry and for the exact specification of the tail behaviour of the shocks.

- [3] **Fiscal Reaction Function** . CDO makes inference with regard to the dynamics between primary balances, lagged debt and the output gap by estimating a fiscal reaction function, which is subject to severe estimation and model uncertainty. In this paper we avoid its introduction by inverting equation (4). As a result, we specify a policy which is consistent with fiscal sustainability, such as debt stabilisation, and by drawing repeated shocks from the true error distribution, we make probabilistic inference with regard to the implied balances. This allows us to escape the aforementioned data constraints and for the evaluation of current policy against such a sustainability benchmark.

Other methodological differences introduced here include the amendment of the debt equation in order to account for tradables which are proxied by the export-to-GDP ratio. Furthermore, we do not assume that interest rate shocks affect the entire debt stock, but rather differentiate between its maturity structure such that merely the short term and a uniformly distributed fraction of the medium to long term debt is rolled over each period. Finally, with regard to data, our estimation is based on the fiscal panel and the input variables provided by CDO, except for the definition of the interest rate for foreign denominated debt. Whereas they employ the deflated US T-Bill rates as a proxy for borrowing costs in foreign markets, we incorporate interest rate spreads in the form of the Emerging Markets Bond Index (EMBI) as we believe that this more appropriate for emerging market countries.

# **3. Methodological Refinements**

# _3.1. Markov Switching VAR_

The Markov Switching VAR, as proposed by Hamilton (1989) and Krolzig (1997), differs from its linear counterpart by allowing the structural coefficients and the covariance matrix to be dependent on an unobserved state variable _St_ , which is assumed to follow a _k_ -dimensional Markov chain. This is of importance in our context, as we expect the joint distribution of the shocks to be non-constant across our sample period.

Furthermore, estimation of this model allows for inference with regard to the probabilities of being in the respective states of the world, such that any structural breaks are identified by regime switches.

# _3.2. Bootstrap_

As discussed above, this paper replaces the assumption of normally distributed shocks imposed in CDO by direct inference with regard to their true joint distribution. To this end we employ nonparametric bootstrapping techniques on the stationary sub-periods exhibiting constancy in terms 3
to winsorize the data in somewhat of an arbitrary fashion so as to remove observations whereby the percentage change in headcount poverty is enormous (e.g., Bourguignon, 2003). To further illustrate this issue, in Appendix C, we re-estimate Equations (7)-(10) using an elasticity framework as opposed to a semi-elasticity framework (i.e., we use ∆ _H_ as our left-hand-side variable as opposed to _dH_ ). We show how are results are very sensitive to including observations where headcount poverty is close to 0.

## _III.A Data_

The data used to estimate Equations (7), (8), (9), (10), and (13) are taken from PovcalNet for all countries that have at least two surveys where measurement of our key variables (e.g., headcount poverty rate, Gini coefficient, mean income) remains comparable across the two surveys.<sup>1213</sup> This leaves us with a total of 135 countries with surveys spanning 1974-2018. For each country, we calculate the difference in our key variables across each pair of consecutive surveys for which measurement of these variables remained comparable. This gives us a total of 1,307 observations on changes in our keys variables or, equivalently, 1,307 “growth-spells”. See Table IV in Appendix A for a list of all 135 countries with the range of survey years for each country.

# IV Empirical Results

In this section we present the results from our four empirical models, and, in turn, explore in detail how well our preferred specification does at explaining observed changes in headcount poverty. First, using our entire sample of 135 countries over 1974-2018, we estimate Equations (7), (8), (9), (10), and (13). Results are presented in Columns (1)-(5) 12. PovcalNet is the on-line tool for poverty measurement developed by the Development Research Group of the World Bank; see http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx 13. Section 4 of `http://documents.worldbank.org/curated/en/344401569259571927/pdf/ September-2019-PovcalNet-Update-Whats-New.pdf` discusses comparability of surveys within a country over time. For instance, two surveys for a given country would not be comparable between years if their measurement of welfare switches from being consumption based to income based.

12
# Table A1.3 - Basic Brazilian Census Listing for Grain Mill Products. 1975

|--------------------------------------------------------------<br>Rank Code<br>Product<br>Item<br>of<br>|Unit|__-------------__-------_-<br>Quantity|------_<br>Cruzeiro<br>Value|Cruzeiro<br>Unit|
|---|---|---|---|---|
|Item|||(1000)|Value|
|----------------------------------------------------------------<br>WHEAT MILL PRODUCTS<br>MANUFACTURE OF CORN PRODUCTS, EXCL. OILS<br>MANUFACTURE OF VARIOUS FLOUR PRODUCTS AND<br>|BY-PRODU|__-----------__-----<br>CTS|<br>-----------<br>||
|Total|||7,001.996||1per ton/|
|||||Tp000 literl|
|5 10848 Corn starch and 'pecula"|ton<br>|213.771|<br>409.822|<br>1.917.11|
|10852 Arrowroot starch, flour and "pecula"|||286|<br>2.205|<br>7.709.79|
|10853 Starch and "pecula", n.e.s.|ton|3.651|<br>9.557|<br>2,617.64|
|10938 Corn porridge|kg.|31.816.895|<br>51.754|<br>1.626.62|
|9 10957 Coconut flour|ton|9.168|<br>133.192|<br>14.527.92|
|7 10981 Corn bran|ton|276.230|<br>212.256|<br>768.40|
|6 10997 Wheat bran|kg.|809.795.848|<br>283.955|<br>350.65|
|11005 Rice flour|ton|5.685|<br>24.483|<br>4.306.60|
|11013 Rye flour|kg.|426.150|<br>1.778|<br>4,172.24|
|11016 Barley flour|kg.|885.215|<br>2.049|<br>2,314.69|
|3 11037 Corn flour|ton|446.012|<br>525.335|<br>1.177.85|
|1 11064 Wheat flour<br>|1|3.048.093|4.420.735|<br>1.450.33|
|411072 Oat flour and meal|ton|11.383|<br>41.111|<br>3.611.61|
|11073 Composite flours and "pecula" products|kg.|3.282.208|<br>23.373|<br>7,121.12|
|8 11080 Corn glucose (dextrose)|ton|105.044|<br>191.745|<br>1,825.38|
|11082 Improved grainsorts (rice. corn etc.)|ton|2.383|<br>9.927|<br>4.165.76|
|:1083 Coconut milk<br>|liter|<br>1.826.100|<br>27.521|<br>15.070.92|
|10 11092 Cracked corn|ton|78.964|<br>90.899|<br>1.151.14|
|11106 Corn stalks||1.150|<br>817|<br>710.43|
|11108 Stalks. n.e.s.||355|<br>82|<br>230.99|
|2 11114 Wheat semolina|ton|355.516|<br>539.400|<br>1.517.23|
|TOTAL SPECIFIED|||7.001.996||

Source: 1975 Censo Industrial do Brasil, Producao Fisica:
- 54 -

Figure 2 EFFECT OF PREVIOUS JOB EXPERIENCE <!-- Start of picture text --> 210<br>200<br>190<br>170<br>15<br>C 1/A40 -<br>130<br>E 120<br>Il<br>100 -<br>-<br>90<br>so<br>70<br>110pevosjo a xerec<br>0 10 2030<br><!-- End of picture text --> Years of current job experience

**-** a) no previous job experience + **-** 5 years of previous job experience 0 **-** 10 years of previous job experience
**Figure 4: % of companies reporting on economic indicators, by subsample**

<!-- Start of picture text --> Direct economic value generated Development & impact of<br>and distributed (G4‐EC1) infrastructure investments &<br>services (G4‐EC7)<br>SME 74.0% SME 31.5%<br>Large 83.3% Large 53.4%<br>MNE 47.8%<br>MNE 84.0%<br>All 49.4%<br>All 82.5%<br>Significant indirect economic Proportion of spending on local<br>impacts, incl. the extent of impacts suppliers (G4‐EC9)<br>(G4‐EC8)<br>SME 37.4% SME 38.8%<br>Large 54.2% Large 49.8%<br>MNE 41.4% MNE 50.2%<br>All 48.6% All 48.8%<br><!-- End of picture text -->

**Table 13: Economic indicators, regressions, Probit model**

|**GRI G4**|**GRI Indicator**|**_MNE_**|**p‐value**|**_Large_**|**p‐value**|
|---|---|---|---|---|---|
|**Metric**||||||
|G4‐EC1|Direct economic value generated and distributed|0.176|0.136|0.305|0.127|
|G4‐EC7|Development and impact of infrastructure investments and services supported|0.173|0.106|**0.588*****|<br>**0.000**|
|G4‐EC8|Significant indirect economic impacts, including the extent of impacts|‐0.032|0.763|**0.272***|**0.077**|
|G4‐EC9|Proportion of spendingon local suppliers at significant locations of operation|0.122|0.280|0.210|0.213|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01. Note: Variables refer to 2015 GRI data (see section 2.2). All regressions control for country‐sector fixed effects. Standard errors are robust to heteroscedasticity.

# **4.2 LABOR AND SOCIAL INDICATORS**

Figure 5 shows the share of companies reporting on labor indicators. A high percentage of firms across all types reports the number and composition of employees, amount of training, skills management, and career development. Of multinationals, 78% report the number and rates of new employee hires and employee turnover (LA1) and the average hours of training per year per employee (LA9), while almost 72% of MNEs reveal their composition of governance bodies and breakdown of employees per employee category (LA12). Around two‐thirds of multinationals report information related to regular performance and career development reviews (LA11) (68%) and programs for skills management and lifelong learning (LA10) (64%). For these questions, MNEs and large firms show very similar percentages.

18
Deposit Kte Figure 6. Deposit Rate and Lending Rate in the Philippines (1980-87) <!-- Start of picture text --> 20~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>1.1<br>.s<br>O.1<br>so0 Il loO/3SOI 6 f?o A . t2<br>II} so. A<br>8.L Sore<br>/Fs Lenin<br>a-I~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~Rt<br><!-- End of picture text --> Source: IFS.

Rlate8
they may also pay higher wages.

Finally, I tested for the possibility that the increases in wages are the result of the acquisition itself rather than a result of the foreign acquisition by including in the sample the group of domestic acquisitions of foreign fi rms. If all the fi rms that are acquired have improvements in productivity and, therefore, higher wages, we would expect that including this group of fi rms would reduce the increases in wages following the foreign ownership. I fi nd that, after including these fi rms in the analysis, the wage differentials associated with foreign ownership are very similar to those reported in tables 4 and 5. Therefore, the results suggest that wage increases are not driven by acquisitions but by the foreign ownership itself<sup>36</sup> .

# 5. Conclusion

Cross sectional evidence shows that foreign fi rms have a more educated workforce and pay higher wages than domestic fi rms. These results do not necessarily imply that foreign direct investment translates into higher demand for educated workers or higher wages, however, since foreign investment may be guided by unobservable fi rm characteristics correlated with the demand for educated workers or wages. Using unique fi rm-level data for Portugal, I seek to isolate the effect of foreign direct investment on the demand for educated workers and wages by observing labor demand and wages of different education groups before and after the foreign acquisition. Existing empirical evidence for European countries is scarce and, apart from evidence for the United Kingdom, not much is known about the impact of foreign acquisitions on labor markets. Portugal is an interesting case, as in the late 1980s and 1990s there was a permissive legal framework for the operation of foreign fi rms that translated into generous amounts of FDI.

My three main fi ndings are the following. First, I show that foreign fi rms have a more educated workforce and pay higher wages for all education groups even after accounting for sector and regional composition, as well as other fi rm and worker level characteristics usually

> 36Unfortunatly, with this data I cannot identify the group of acquisitions of domestic fi rms made by national investors. If acquisitions lead to increases in wages, my estimates for the effect of foreign ownership on wages are over estimated.

18
Table B6: Robustness check using the sample of the aged 60 and below—Current salary

|||Log (curr|ent salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.351<sup>_∗∗∗_</sup>|-0.112<sup>_∗∗∗_</sup>|-0.072<sup>_∗∗_</sup>|-0.083<sup>_∗∗∗_</sup>|
||(0.049)|(0.034)|(0.030)|(0.025)|
|Social networks*tenure|0.008|0.005|-0.001|-0.000|
||(0.006)|(0.006)|(0.005)|(0.006)|
|Tenure in years|0.030<sup>_∗∗∗_</sup>|0.073<sup>_∗∗∗_</sup>|0.082<sup>_∗∗∗_</sup>|0.076<sup>_∗∗∗_</sup>|
||(0.003)|(0.016)|(0.014)|(0.016)|
|Abilities||X|X|X|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.223|0.493|0.523|0.407|
|N|3763|3757|3757|3757|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

Table B7: Robustness check using the sample of the aged 60 and below—Promotion

||||Dummy|for havin|g been pr|omoted|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.028|-0.021|-0.016|-0.015|-0.053|-0.075|-0.061|-0.069|
||(0.022)|(0.025)|(0.021)|(0.023)|(0.052)|(0.059)|(0.040)|(0.054)|
|Social networks*tenure|||||0.010|0.022|0.019<sup>_∗_</sup>|0.023|
||||||(0.014)|(0.016)|(0.011)|(0.016)|
|Tenure in years|0.002|0.001|0.015<sup>_∗∗_</sup>|0.016<sup>_∗_</sup>|-0.005|-0.022|-0.018|-0.035|
||(0.005)|(0.006)|(0.007)|(0.008)|(0.014)|(0.016)|(0.014)|(0.024)|
|Abilities||X|X|X||X|X|X|
|Abilities*tenure||||||X|X|X|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.016|0.034|0.045|0.072|0.017|0.050|0.054|0.087|
|N|1745|1744|1744|1744|1745|1744|1744|1744|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

50
Although agriculture is primarily a rural activity in many developing nations, there has also been evidence that urban agricultural production has positive effects on nutrition. In their study of 15 developing countries, Zezza and Tasciotti (2010) find that urban agriculture does appear to be associated with greater dietary diversity and calorie availability after controlling for economic welfare and a set of household characteristics. Using a smaller set of countries Zezza and Tasciotti (2010) also find some evidence of a relationship between participation in urban agriculture and greater calorie consumption. Fruits and vegetables were the food groups more consistently found to contribute to the increase in calorie consumption.

Fewer studies have looked at both the linkage between income and nutrition and that between the diversity of crops produced and nutrition. Food prices, access to markets and credit can influence decisions on what type of crops households grow. For example, smallholder farmers are more likely to grow food crops to ensure food self-sufficiency rather that grow cash crops and thus staple food expenditures have low income elasticity (Fafchamps, 1992). Using a nationally representative household survey of India, Bhagowalia et al. (2012) examine the relationship between agricultural income and nutrition (measured using children’s anthropometric indicators), as well as agricultural production. They find a modest effect of income on nutritional status unless accompanied by improved health and education outcomes. However, they also find strong evidence that agricultural production conditions such as irrigation, crop diversity and ownership of livestock, substantially influence household dietary diversity. Jones et al. (2014) also find a strong positive association between production diversity and household dietary diversity in Malawi.

4