rafmacalaba/datause-encoder-data huggingface dataset:
- 3 -

In addition, the survey design consisted of a rotating panel: each year 50% of households were replaced, and the other 50% were kept in the sample and revisited the next year. Thus, in principle, the survey yields three overlapping two-year panels of 800 households each (1985-86, 1986-87, 1987-88). In practice, the construction of the panels encountered a set of practical problems, mainly due to inadequacies in the household identification numbers, and the resulting panels contain about 700 households. Grootaert and Kanbur (1995) give details of the procedure to construct the panels and assess their representativeness. They conclude that the attrition of households in the construction of the panels does lead to a bias, in the direction of some overestimation of poverty incidence, but that the extent of attrition is not such that it invalidates the analysis of the panel data.

The years covered by the CILSS, 1985-88, are of particular importance in the recent economic history of CMte d'Ivoire. Throughout the eighties C6te d'Ivoire experienced an economic recession, due to the collapse of the world prices of coffee and cocoa--the country's two main export crops--in the late seventies, and due to unsustainable economic policies (see Demery, 1994, for an analysis). Within the decade, 1987-88 was one of the worst periods. GDP per capita fell by 5% in real terms, but private consumption fell by almost 17%, and the poverty rate rose from 35% to 46% (Grootaert, 1993). While previously, investment had absorbed much of falling income, in 1987-88 there appears to have been a change in expectations, whereby people ceased to view the economic recession as a temporary phenomenon and consequently had to adjust their consumption levels downward to match the perceived fall in permanent income. It is thus meaningful to focus on the 1987-88 period, and the CILSS panel which captures these years, to study the dynamics of poverty, and to investigate which households managed to buck the overall trend of immiserization and to escape poverty.

The model we propose to estimate for this purpose is derived directly from the standard household utility maximization model (see for example, Deaton and Muellbauer, 1980, for a formal derivation). It relies on household expenditure as a money metric measure of utility. Such measure needs to take into account welfare differences due to differences in household size and in relative prices, and where different time periods are involved, due to
48 Although there are some measurement problems as far as labour market (in)activity in Russia is concerned (Commander and Yemtsov, 1995) the following facts can be presented with some confidence:

- Registered unemployment has been very small compared to virtually all transition economies in Central Europe (about 2 percent in mid-1 994), but even unemployment data using household surveys indicate a true rate of only 5-6% (Centre for Economic Analysis, 1994).

- Unlike in all other transition economies, registered unemployment has thus far not been a stagnant pool but rather dynamic: with low inflow rates, outflow rates from registered unemployment into regular employment have been large and comparable to the rates in the Czech Republic and in OECD countries while the average duration of unemployment has been short.

- The majority of separations in 1992 and 1993 has been voluntary quits while employment reductions were only the second most important reason for separations (Commander, McHale and Yemtsov, 1995).

- A substantial part of the workforce in Russian industry remains in their primary jobs on extended leave. While primary employment of these persons is mainly in the state sector many of them have secondary employment in the private or semi-private sector. Often the latter employment has short working hours, is higher paid but also more risky than employment in the state sector.

- There is a strong bias in (un)employment legislation against registering as unemployed. For example, persons who received severance pay in the years 1992 and 1993 were not eligible for unemployment benefits and had, therefore, no incentive to register. Also, the probability of receiving benefits when registering is very low, so again there is no incentive to apply to an Emplcyment Centre (EC). Furthermore, especially in rural areas distances to EC's can be considerable, clearly discouraging the unemployed to register. Finally, as EC's have a very poor reputation with firn managers, the former do not offer efficient job brokerage services to the unemployed (cf. Standing and Chetvernina, 1994)

- With very low rates of registered unemployment in the early phase of transition the share of women unemployment has been disproportionally high (about 70%). Analysis of registered unemployment rates at the regional level by Commander, McHale and Yemtsov (1995) show, however, an inverse relationship between
_5_

# III. DATA

Data on the occupational and training histories of workers in selected occupations in Colombia were derived from a series of five surveys conducted between 1979 and 1981 in seven regions of Colombia.<sup>4</sup> These surveys were conducted to evaluate SENA, the couxstry's national, publicly financed training system.<sup>5</sup> Tv- of these surveys were used for our sample. The primary survey focuses exclusively on a comprL hensive sample of SENA trained workers. In addition to items collecting information on workers' background characteristics, occupational sequences, training and education histories, and current job activities and earnings, the survey also identifies the work ;rs' places of employment and their supervisors. These last two items of data permitted the development of the second survey that we used. This consists of workers not trained by SENA who perform the same type of work in the same (public or private) enterprise under the same supervisors as the SENA-trained workers. Since the sample of non SENA-trained workers is located via the workplace of SENA-trained workers, the proportion of non SENA-trained workers identified in the sample cannot reflect their prevalence in the labor force as a whole.

Our initial sample, which was constructed from these two surveys, contains 4,895 workers, comprising about equal numbers of SENA-trained and non SENA-trained workers in Colombia.

Female workers in these surveys were mostly, but not exclusively employed as secretaries, bookkeepers, and seamstresses, and the male workers as bookkeepers, metal workers, fitters, electricians, and plumbers (Table 2). To focus on each of the occupations by gender, the small number of workers employed in non-traditional occupations (from the

**4** A complete description of the five surveys is contained in SENA-Holanda (1982); see also Horn (1987).

**5** SENA's budget is about equal to 10 percent of the entire public education budget; its programs training nearly 500,000 workers each year (Jimenez, Kugler, and Horn, 1989); almost 9 percent of the workforce in Colombia, as surveyed in 1981, received some SENA training (Horn, 1987).
14000000 <!-- Start of picture text --> _ | | i —EnRus.e<br>2000000<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>mIDP(IDMC) ®UNHCR-Assisted IDPs (SSA) TM Refugees (by origin)<br><!-- End of picture text -->
# FIGURE 2: SHARE OF TOTAL SERVICES (EXCLUDING MILITARY) IN VALUE ADDED: USSR. AND OECD

<!-- Start of picture text --> 8 0 % -<br>i<br>DNK USA<br>BLE<br>60%<br>NOR<br>PRT L Ng DEU LUX_ DEN<br>GRC jRC AUTIN DEO<br>DEU<br>40%-TR U<br>40Zo~~~~~~~ ~USSR Uiw<br>U_SR USSR<br>YUG<br>20% L i I I<br>7 8 9 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
20 midwives are far and away the most<sup>frequent facilities across Indonesia. The</sup> former are present in some 79 percent<sup>of villages across Indonesia, and the</sup> latter in nearly 90 percent. It is a bit<sup>alarming to note how few primary</sup> health centers ('ncluding sub-centers),<sup>general practitioners or resident</sup> doctors there are in rural districts<sup>according to this data. As mentioned</sup> earlier, districts outside Java tend to<sup>be large. The average district size</sup> _fur_ all of Indonesia is 541 sq km.<sup>In Kalimantan Timur and Irian Jaya it is</sup> 2978 and 3587 sq km respectively.<sup>Hence, the figures in Table 9 imply that</sup> considerable travel time and costs<sup>are involved in seeking medical care in</sup> many rural areas of Indonesia outside<sup>Java. One immediate consequence is</sup> likely to be the forsaking of preventive<sup>care by individuals living in these</sup> regions. Many observers of medical<sup>facilities in Indonesia have remarked on</sup> the low utilization rates of hospitals<sup>and health centers (for example, see</sup> world Bank, 1991a). Distances appear<sup>to be an important part of the</sup> explanation. This also suggests<sup>that solutions should be sought in the realm</sup> of increased mobile health units<sup>and augmented capacity and role for village</sup> outreach programs such as the posyandu.

# _Household Expenditures on Education_

Table 10 presents monthly per<sup>capita spending by the household on</sup> education related goods and services<sup>as reported in the 1987 SUSENAS</sup> consumption survey. In the Indonesian system,<sup>it is generally true that</sup> ability to spend privately enhances the<sup>benefits from public provision. This</sup> is because the latter does not provide<sup>for essential teaching and learning</sup> aids. It should be noted that Table 10<sup>does not give an exhaustive list of</sup> the costs of education. The SUSENAS omits<sup>to ask separately about spending on</sup> uniforms which are mandatory in Indonesian<sup>schools. But, the more substantial</sup> costs are likely to be those associated<sup>with travel to schools and the</sup> opportunity costs of the student's time.

Not unexpectedly, per capita expenditures<sup>are generally higher for</sup> urban than for rural individuals. They<sup>also tend to increase with per capita</sup>
-18-

TABLE 3 (b): INFLATION, INVENTORY VALUATION, AND TAXABLE PROFIT

|Inv<br>M|entory Ass.<br>ethod|(1) Inventory<br>unchanged|(2) Inventory<br> <br>depletion|(3) Inventory<br>accumulatio|n Comment|
|---|---|---|---|---|---|
|I.|Base case<br>with zero<br>inflation<br>_ f0|Gross Profit-<br> <br> -<br>$500- $100<br>- $400|Gross Profit-<br>-<br>$500- $150<br>- $350|Gross Profit-<br>-<br>$500- $50<br>- $450|Col.1-3<br>are base<br>profit<br>est.|
|II.||20Per|cent Inflation,w|-0.2||
|A.|<br>FIFO|Gross Profit<br>-<br>$600- $100<br>-<br>$500|Gross Profit<br>-<br>$600- $160<br>-<br>$440|Gross Profit<br>-<br>$600- $50<br>-<br>$550|None of<br>col.are<br>i-neutral|
|B.|<br>LIFO|Gross Profit<br>-<br>$600- $120<br>-<br>$480|<br>Gross Profit<br>- $600-<br>$170<br>-<br>$430|Gross Profit<br>-<br>$600-<br>$60<br>-<br>$540|Col.2 is<br>noti-<br>neutral|
|C.|<br>I-FIFO|Gross Profit<br>-<br>$600- $120<br>-<br>$480|<br>Gross Profit<br>-<br>$600- $180<br>-<br>$420|Gross Profit<br>-<br>$600-<br>$70<br>_ $530|Col.3 is<br>notw-<br>neutral|
|D.|<br>I-LIFO|Gross Profit<br>-<br>$600- $120<br>-<br>$480|<br>Gross Profit<br>-<br>$600- $180<br>-<br>$420|Gross Profit<br>-<br>$600- $60<br>-<br>$540|All col.<br>are w-<br>neutral|

Row I (when r-0) provides the base case estimates of gross profit against which to measure the neutrality characteristics of the four inventory valuation methods. If the taxable profit estimate under 20 percent inflation is exactly 20 percent greater than the level in the corresponding base case, the method is neutral to inflation. Notice that in the case where inventory is unchanged (column 1), LIFO, I-FIFO and I-LIFO provide an inflation-neutral estimate of profit. FIFO fails because it matches the older lower priced inventory against current revenues. LIFO however undervalues the ending inventory - this lowers the estimate of net worth of the firm.
- b. _Labor Force Participation by Gender and Education_ :  Labor force participation rates are significantly higher for men (81 percent) than for women (40 percent).  In fact, one of the major puzzles in Timor Leste is that female labor force participation (FLFP) rates seem to be very low (as measured by the TLSS).  In a regime of high poverty, such low FLFP may point to measurement problems on women’s work.  This is discussed in a later section.

- c. _Unemployment_ is higher among the educated than the non-educated, a pattern seen in many developing countries.  In a regime of scarce employment opportunities, those with no schooling are most likely to be in the labor force, probably because they can less afford not to be working (or looking for work) compared to those with more education.  Finally, in terms of the gender gap, one fourth of the women compared to one in seven men are unemployed, so that concerns about female unemployment are an important factor in Timor Leste.

**Figure 5:  Timor-Leste: Proportion of Working-Age and Youth Population to Total (20002050)**

<!-- Start of picture text --> Source: Calculated from International Database, US Census<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2010 2020 2030 2040 2050<br>Year<br>% Working Age (15-60) % Youth (15-30)<br>Percent<br><!-- End of picture text -->

- d. _Large youth cohorts and high urban youth unemployment_ :  Timor-Leste has an increasingly young population and will be seeing higher numbers of young workers entering the workforce in coming years due to its demographic profile.  The ability of the labor market to absorb the large inflows of young workers will be a key social and economic challenge in the short term, but also the medium term (Figure 5).  As of 2001, unemployment rates were highest among the youngest cohorts in urban areas and declined sharply with age. Thus, unemployment rates among urban 15-24 year olds were 43 percent, but declined to 17 percent for the 25-34 years olds, and nine percent of the over-35 year olds.  Thus, a major issue in Timor-Leste’s employment policy will likely be how to control and in time reduce youth unemployment.

- e. _Importance of small firms and self-employment_ :  In 2001, a little less than 4 percent of the employment in Timor-Leste was in the manufacturing sector, and self-employment was the

9
The IB literature has also paid attention to how global and domestic competition affects the performance of firms. For instance, we know from surveys of managers of MNE subsidiaries that global competition and organizational structures interact to produce different learning and innovation outcomes (Veniak _et al._ , 2005). That is, MNEs that encourage organizational learning by subsidiaries tend to respond to competitive pressures through greater innovation. Hence the IB literature also provides ambiguous answers to the question of how market structure affects firm performances, even across MNE subsidiaries, because the outcomes depend on MNE organizational structure.

Hypotheses to be tested in a nested empirical model As discussed above, the existing literatures related to product innovation make various predictions about the probability of product innovation by incumbent firms, which can be summarized in seven hypotheses. The Global Engagement hypotheses are: Hypothesis 1. Importing foreign know-how through licensing, foreign investment and exporting activities are positively correlated with product innovation.

Hypothesis 2. Trade-policy distortions that raise the costs of global engagement deter innovation.

The Information Spillovers hypotheses are: Hypothesis 3. Firms’ R&D and the education of its personnel increase the probability of product innovation. Although these firm-level variables do not capture inter-firm learning spillovers, they do capture firms’ adoptive capacities.

Hypothesis 4. The density of knowledge available to local firms in their local context spurs innovation.

The Market Structure hypotheses are: Hypothesis 5. The size of firms is positively correlated with the probability of product innovation.

Hypothesis 6. The regulatory environment can have either positive or negative effects on the probability of product innovation by incumbent firms.

Hypothesis 7. The degree of market competition characterized by business density can raise or reduce the probability of product innovation by existing firms.

5
EU‐Poverty Map at the NUTS 3 level, the CAP administrative records at the NUTS 3 levels, and the Farm Structure Survey for several years including 2010 and 2011. This paper is structured as follows. In Section 2 we start by briefly motivating the need for additional policies that foster inclusive growth, as suggested by the state of inequality and poverty in the post‐crisis period. Section 3 introduces the main framework, and the results from the analysis of the relationship between poverty and agriculture. Section 4 then briefly introduces the CAP and proceeds to analyze the relationship between poverty and the CAP. Section 5 wraps up the main results, by presenting an integrative analysis taking results from the previous sections, and finally Section 6 concludes.

# **2. Motivation**

_The current state of inequality and poverty in the European Union_ After the global financial crisis of 2008, some of the economic indicators across the European Union have been under recovery, however others still lag behind, one of these being inequality. Thus, the current state of inequality and poverty in the EU points to the need for inclusive policies that promote shared prosperity. In this section we describe the current state of these indicators in the region. Convergence in agricultural income is also introduced and briefly discussed as an important channel for overall income convergence across EU member states.

Inequality has become an important topic in the policy discussion of the EU, especially since the Great Recession. Even though inequality in the EU member states is low compared to other parts of the developed world (OECD, 2017), inequality in the region has become a topic of concern. The recent member state expansion towards countries with lower levels of average income has contributed to an increase of inequality across the EU. In order to further explore this, we perform an analysis by treating the EU as a single country, thus pooling the income of all member countries together and ranking them along the same distribution. The resulting Gini coefficient is higher than the coefficient associated to any single EU member state. This is a high inequality level by international standards.

**Figure 1.** Inequality Across the EU

<!-- Start of picture text --> 0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Gini by Member States and Pooled EU<br><!-- End of picture text --> 3
# **Figure 3: Banking Sector Developments in EMDEs**

- A. Bank assets (% GDP) 2000‐2016

<!-- Start of picture text --> 70<br>25 pctl<br>median<br>60<br>75 pctl<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

- C. Private credit (% GDP) 2000‐2017

<!-- Start of picture text --> 60 25 pctl<br>median<br>50<br>75 pctl<br>40<br>30<br>20<br>10<br>0<br>E. Regulatory capital (% Risk weighted assets)<br>2000‐2017<br>22<br>20<br>18<br>16<br>14<br>25 pctl<br>12 median<br>75 pctl<br>10<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

- B. Bank assets (% GDP) 2016 regions

<!-- Start of picture text --> 100<br>25 pctl<br>90<br>median<br>80 75 pctl<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text -->

- D. Private credit (% GDP) 2017 regions

<!-- Start of picture text --> 100<br>25 pctl<br>90<br>median<br>80 75 pctl<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text -->

- F. Regulatory capital capital (% Risk weighted assets) 2017 regions

<!-- Start of picture text --> 35<br>25 pctl<br>30 median<br>75 pctl<br>25<br>20<br>15<br>10<br>5<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text --> Note: pctl means percentile. Sources: Bank assets, Fitch Connect; Private credit, World Development Indicators, WB; Regulatory capital / Risk weighted assets, Financial Soundness Indicators IMF.

45
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17
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