rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 2: Lorenz Curve Approach: SimSIP Poverty Predicting the Effect of Aggregate Growth on Poverty in Paraguay**

Datt and others (2003) have applied SimSIP Poverty to data for Paraguay in order to study the impact of growth patterns on poverty for a period of five years (from 1997 to 2001). Six cases are considered: (1) each sector (agriculture, industry, services) of the economy grows at 3 percent, (2) a 2 percent growth rate in each sector, (3) a 1 percent growth rate per sector, (4) a 2 per percent growth rate in agriculture and a 3 percent rate elsewhere, (5) a 1 percent growth in agriculture and a 3 percent rate elsewhere, and, finally, (6) a 3 percent growth in agriculture and a 1 percent rate in other sectors. The underlying data include (1) population shares by sectors (rural/urban) and three economic activities (agriculture, industry, and services) and the total national-level population and (2) the mean income per capita corresponding to the population shares.

The impact of different sectoral growth patterns on the poverty headcount is reported below. Holding inequality constant, a 3 percent annual growth in per capita income in every sector for five years would reduce poverty by 3 percentage points, to 28.95 percent. Using the table below, one can compare the contribution of different growth patterns to poverty reduction. Moreover, given that poverty rates are higher in rural areas and in agriculture, any migration out of those sectors is likely to decrease poverty.

The reported results for each scenario vary slightly depending on whether aggregate poverty is computed from the rural/urban perspective or as a weighted average of outcomes in each sector of employment. The exercise illustrates the fact that the analyst can use SimSIP Poverty to assess different patterns of growth.

**Simulations of the Impact of Growth Patterns on Poverty in Paraguay: Some Examples**

|**N**|**ational P**<br>|**overty head**<br>|**count (%)**<br>||
|---|---|---|---|---|
||Period 1|Period 2:<br>National<br>simulation|Period 2: National<br>as weighted<br>average of<br>urban/ruralsectors|Period 2: National as<br>weighted average of<br>employment sectors|
|3% per sector for five years|32.13|27.46|27.48|27.42|
|2% per sector for five years|32.13|28.95|28.97|28.92|
|1% per sector for five years|32.13|50.51|30.53|30.49|
|2% in agriculture, rural sector,<br>3% elsewhere for five years|32.13|–|28.15|27.94|
|1% in agriculture, rural sector,|32.13|–|28.82|28.46|
|3% elsewhere for five years<br>3% in agriculture, rural sector,<br>1% elsewherefor five years|32.13|–|29.06|29.34|
|_Source_:Datt and others2003|.||||

Computations are based on a parameterization of the Lorenz curve. Two parameterizations are provided by the general quadratic and the Beta models. For robust poverty comparisons over time and between sectors, the simulator produces poverty dominance and Lorenz curves. It also computes the Gini coefficient, poverty measures of 12
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<!-- Start of picture text --> Relative share of urban consumption categories over time, %<br>Actual | Forecast’<br>100%<br>Health care<br>Recreation, education<br>Transportation, communication<br>Personal products<br>Household products<br>Housing, utilities<br>Apparel<br>Food<br>1985 1990 1995 2000 2005 2010 2015 2020 2025<br>Discretionary goods, services<br>©) Basic needs<br><!-- End of picture text -->
Table 4: Peer Effects from Random Assignment to Dormitories

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Own HS graduation|0.362|0.332|0.331|0.400|0.373|0.373|
|test score|(0.014)|(0.014)|(0.014)|(0.024)|(0.023)|(0.023)|
|Own HS graduation||||0.137|0.144|0.142|
|test score squared||||(0.017)|(0.017)|(0.017)|
|Mean dorm HS graduation|0.241|0.222|0.220|0.221|0.208|0.316|
|test score|(0.093)|(0.098)|(0.121)|(0.095)|(0.103)|(0.161)|
|Mean dorm HS graduation||||0.306|0.311|-0.159|
|test score squared||||(0.189)|(0.207)|(0.316)|
|Own _×_ mean dorm HS||||-0.129|-0.132|-0.132|
|graduation test score||||(0.073)|(0.069)|(0.069)|
|_p_-value of test against||||0.000|0.000|0.000|
|equivalent linear model|||||||
|Adjusted _R_<sup>2</sup>|0.213|0.236|0.248|0.244|0.270|0.278|
|# students|3068|3068|3068|3068|3068|3068|
|# dormitory-year clusters|30|30|30|30|30|30|

_Notes:_ Table 4 reports results from estimating equations 2 (columns 1-3) and 4 (columns 4-6). Columns 2, 3, 5, and 6 control for students’ gender, language, nationality and race. Columns 3 and 6 include dormitory fixed effects. The sample is all dormitory students in the random assignment period with non-missing high school graduation test scores. Standard errors in parentheses are from 1000 bootstrap replications clustering at the dormitory-year level.

students in each dormitory. This variation is relatively low: the range and variance of dormitory means are approximately 10% of the range and variance of individual scores. Given this limited variation, the results should be interpreted with caution.

I report estimates of equation 2 in table 4, using the sample of all dormitory students in the ˆ random assignment period. I find that _α_ 2 = 0 _._ 22, which is robust to conditioning on student demographics and dormitory fixed effects. Hence, moving a student from the dormitory with the lowest observed mean high school graduation test score to the highest would increase her GPA by 0 _._ 18 standard deviations. These effects are large relative to existing estimates (Sacerdote, 2011). Stinebrickner and Stinebrickner (2006) suggest a possible reason for this pattern. They document that peers’ study time is an important driver of peer effects and that peer effects are larger using a measure that attaches more weight to prior study behavior: high school GPA instead of SAT scores. I measure peer characteristics using scores on a content-based high school graduation test, while SAT scores are a common measure in existing research. However, the coefficient from the dormitory fixed effects regression is fairly imprecisely estimated (90% confidence interval from 0 _._ 02 to 0 _._ 42) so the magnitude should be interpreted with caution.<sup>32</sup> This may reflect the limited variation in _HSd_ .

> 32As a robustness check, I use a wild cluster bootstrap to approximate the distribution of the test statistic under the null hypothesis of zero peer effect. This yields _p_ -values of 0.088 using dormitory-year clusters and

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# **Figure 1  Excess sex ratios at census time by 5 year birth cohorts, China 1920-1995**

<!-- Start of picture text --> 0.2<br>Internal<br>0.18<br>Wars<br>0.16<br>Fertility<br>0.14 Japanese Famine Decline<br>Invasion<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>1920 1924 1928 1932 1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988<br>First year of 5 year period of birth<br><!-- End of picture text --> Note: These estimates are possible because of the high accuracy of age-reporting in East Asian censuses, since people know their animal year of birth in the 12-year cycle of animal years. The excess ratios peak amongst cohorts born just before a war or famine, because those who were young girls at the time of the crisis experienced the maximum excess mortality.

Source: Das Gupta and Li (1999), calculated from data from 1953, 1964, 1982 and 1990 Population Censuses of China, and the 1995 National One Percent Sample Survey.

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Variable #96 | Question #25 | **indprobadevelpissues** | | level of problems with development issues after establishment of RBO | 1 = no response,2 = situation same,3 = situation improved,4 = situation worsened | int

Variable #97 | Question #25 | **indprobaother25** | level of other problems after establishment of RBO | 1 = no response,2 = situation same,3 = situation improved,4 = situation worsened | int

Variable #98 | Question #26 | **iadmblocal** | percentage of water administration decision making at the local level before the creation of RBO| percentage| float

Variable #99 | Question #26 | **iadmbbasin** | percentage of water administration decision making at the basin level before the creation of RBO | percentage| float

Variable #100 | Question #26 | **iadmbstate** | percentage of water administration decision making at the statel level before the creation of RBO | percentage| float

Variable #101 | Question #26 | **iadmbgov** | percentage of water administration decision making at the government level before the creation of RBO | percentage| float

Variable #102 | Question #26 | **ifinblocal** | percentage of infrastructure financing decision making at the local level before the creation of RBO | percentage| float

Variable #103 | Question #26 | **ifinbbasin** | percentage of infrastructure financing decision making at the basin level before the creation of RBO | percentage| float

Variable #104 | Question #26 | **ifinbstate** | percentage of infrastructure financing decision making at the state level before the creation of RBO | percentage| float

Variable #105 | Question #26 | **ifinbgov** | percentage of infrastructure financing decision making at the government level before the creation of RBO | percentage| float

Variable #106 | Question #26 | **ienfblocal** | percentage of water quality enforcement decision making at the local level before the creation of RBO | percentage| float

Variable #107 | Question #26 | **ienfbbasin** | percentage of water quality enforcement decision making at the basin level before the creation of RBO | percentage| float

Variable #108 | Question #26 | **ienfbstate** | percentage of water quality enforcement decision making at the state level before the creation of RBO | percentage| float

59
Table 6: Gender Differences in General Coping Strategies - Social Network Channel

|||House|hold Internal|||External - Fa|mily/Network|
|---|---|---|---|---|---|---|---|
||Reduce Meal<br>(0/1)|Sell Asset<br>(0/1)|ln<br>Asset|Loan<br>(0/1)|ln<br>Loan|Family<br>Transfer (0/1)|Gift/Assistance<br>(0/1)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.037<sup>_∗_</sup>|-0.031<sup>_∗∗_</sup>|-0.095|-0.022|-0.033|0.037<sup>_∗∗_</sup>|0.014|
||(0.019)|(0.013)|(0.102)|(0.014)|(0.138)|(0.014)|(0.011)|
|Female Head (0/1)_×_Network (0/1)|-0.146<sup>_∗∗∗_</sup>|-0.015|-0.326|-0.057|0.206|-0.002|-0.054|
||(0.046)|(0.047)|(0.369)|(0.062)|(0.250)|(0.046)|(0.044)|
|Network (0/1)|0.091<sup>_∗∗∗_</sup>|0.031|0.186|0.073|0.034|0.161<sup>_∗∗∗_</sup>|0.159<sup>_∗∗∗_</sup>|
||(0.027)|(0.028)|(0.146)|(0.045)|(0.183)|(0.030)|(0.030)|
|Household Head Age|0.002<sup>_∗∗∗_</sup>|-0.000|0.001|-0.002<sup>_∗∗∗_</sup>|0.009|0.002<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|
||(0.001)|(0.000)|(0.004)|(0.001)|(0.007)|(0.001)|(0.001)|
|ln Household Size|0.005|0.022|0.191|-0.009|0.197|-0.092<sup>_∗∗∗_</sup>|-0.039<sup>_∗_</sup>|
||(0.028)|(0.015)|(0.117)|(0.020)|(0.179)|(0.020)|(0.023)|
|Child-adult Ratio|0.068<sup>_∗∗∗_</sup>|0.010|-0.210<sup>_∗∗∗_</sup>|0.010|-0.055|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.078)|(0.015)|(0.114)|(0.012)|(0.013)|
|Urban (0/1)|-0.029<sup>_∗∗_</sup>|-0.004|-0.109|-0.013|0.131|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.132)|(0.013)|(0.010)|
|Dirt Floor (0/1)|0.087<sup>_∗∗∗_</sup>|0.008|-0.139|-0.055<sup>_∗∗∗_</sup>|-0.267|0.041<sup>_∗∗∗_</sup>|0.036<sup>_∗∗_</sup>|
||(0.015)|(0.014)|(0.103)|(0.018)|(0.171)|(0.015)|(0.018)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.075|0.027|0.275|0.042|0.179|0.078|0.084|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

could the choice of used the households. as seen shape coping strategies by Firstly, in columns (6) and (7), those households, which have received transfers from social networks prior to the COVID-19 crisis, are more likely to rely on transfers and assistance from family and friends to cope. In addition, when partialling out the effect of the pre-COVID dependency on social network, female-headed households are still 3.7 percentage points more likely to cope using transfers from family members. Secondly, we could observe that poorer households are less likely to take a loan to cope (columns (4)-(5)).

# **4.3 Robustness Checks**

We conduct two sensitivity analyses and check the robustness of the results in Table 1. To begin with, the rapid response phone survey sample consists of Kenyan nationals as well as refugees. Refugees might have different coping strategies from nationals and thus we exclude the refugee sample and cross-check the gender differences in coping among the Kenyan nationals.

14
# Table of Contents

|1.|Intro|duction<br>.1................................................|
|---|---|---|
|II.|Limi|ts to Changes in Foreign Trade Regimes under Central Planning .<br>................<br>3|
|m.|CEE|-5 Exports to the OECD in the 1980's: The Initial Breakdown and Gromwng Marginalization S|
||A.|An Overview of Major Tendencies in Export Performance .S|
||B.|Two Distinct Phases: Breakdown and Precarious Recovery ....<br>......<br>......<br>8|
|IV.|The|Export Upswing: in Defiance of Past Trends ....<br>...............<br>11|
||A.|Chalenging the Projections ....................................<br>11|
||B.|Rfor<br>Make a Difference ............................<br>..<br>. .12|
||C.|Genmany: Locomotive of CEE-5 Export Growth .......................<br>13|
|V.|The|Reversal in CEE-3 Export Performance: Some Preliminary Hypotheses ....<br>.......<br>16|
||A.|The Collapse of the CMEA ....................................<br>17|
||B.|The Redirtion of former CMEA Sales to the OECD? ...................<br>21|
||C.|Impact of the Switch from a Supply- to a Demand-Constrained Economy ....<br>....<br>25|
|VI.|Is the|Export Expansion to the OECD Sustainable? ............<br>...............<br>30|
|VI.|Conc|lusion .32|
|Refe.|ces<br>.3|4|
|Statii|cal App|endx .37|
countries had been stable or decreasing until then.<sup>30</sup> With a roughly stable demand (except in many European countries where cocaine consumption is rising, but is still “low” when compared to the prices observed in the US), lower production estimates and increasing seizures, increasing interception of drug shipments, and destruction of cocaine processing laboratories, cocaine prices would have been expected to rise or remain stable, and not fall as seems, in fact, to have been the case (at least until new information became available recently). Or, as Reuter (2001, p. 18) puts it, “If thorough enforcement did not raise drug prices, then it might still claim success if it lowered availability. But the data, mostly from surveys of high school seniors, show no decrease”. However, in the process of writing this chapter, UNODC released the results of field research done in Colombia where they found a large increase in productivity estimates. Specifically, they found that, on average, the number of kilograms produced by one hectare of coca in one year increased from 4.7 to 7.7 (UNODC, 2006). This corresponds to a 40% increase in the yields per hectare. When this new estimates of productivity are used to calculate potential cocaine production they find that, although there has been a large decrease in the number of coca cultivated hectares in Colombia (of more than 30% between 2001 and 2005), each hectare is now found to be more productive. As a result of these two factors, and as explained before, potential cocaine production in Colombia has not decreased as much as was thought before.

Diagram 1a below summarizes the main changes in the market for cocaine between 1980 and 2005, and Diagram 1b summarizes the two opposing forces that have kept cocaine supply relatively stable during the last five years: first, eradication measures, by destroying coca crops, tend to decrease cocaine supply and, second, increases in productivity in

> 30 A very small increase in cocaine prices at the retail level in consumer countries (at “street purity”) is perceivable in the last few years recorded by UNODC (see Figure 12).

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# **IV. Reforming Latvia’s National Innovation System**

As the benchmarking analyses suggest, it is unlikely that Latvia will achieve its goal of becoming an innovative economy unless Latvian officials take steps to promote the technology upgrading of traditional Latvian industrial sectors and also generate closer links between (i) the domestic enterprise sector and the domestic R&D sector, (ii) the domestic R&D sector and the international R&D sector, (iii) domestic enterprises and global technology markets, and (iv) SMEs and dynamic large enterprises, both inside and outside of Latvia. None of these linkages currently exist, at least not in sufficiently strong form. Even worse, the enterprise sector and R&D sector seem to be developing in total isolation from each other. As a result, on the rare occasion when innovation does occur, it has no connection with the output or technical capacity of the domestic R&D system. And decisions about what R&D to conduct are taken without any regard to the needs of industry. The net effect is an R&D system which is an overhead expense rather than a resource for generating wealth.

# <u>A. Enterprise Sector – Little Innovation and Less R&D</u>

The sad but true situation is that Latvian enterprises are not very innovative. According to a recent survey, only 19% of Latvian enterprises conducted any innovative activity,<sup>30</sup> defined as the introduction (or intended future introduction) of a new product or production process. This is much lower than in EU countries or in NMS’s like Estonia (36%) and Lithuania (27%).

An overwhelming preponderance of the innovative activity that does occur takes place in large firms which are more than four times as likely to innovate as small or medium size enterprises. Moreover, although they comprise only 3% of the total sample, large enterprises accounted for 52% of the total spending on innovation.

**Table 5 Innovative enterprises by enterprise size group, 1999-2001**

||Number of<br>employees|Number of<br>enterprises|Number of innovative<br>enterprises|Share of innovative<br>enterprises (%)|
|---|---|---|---|---|
|Small enterrises|10-19|2113|268|12.7|
|p|20-49|1338|231|17.3|
|Medium enterrises|50-99|495|140|28.3|
|p|100-249|297|118|39.7|
|Large entreprises|≥250|155|90|58.1|
|Total||**4398**|**847**|**19.3**|

_Source_ : Latvia Survey of Innovation Activities, 2002.

> 30  According to the innovation survey (Chapter 3, page 20), an innovation has taken place if a new or significantly improved product (good or service) has been introduced into the market or if a new or significantly improved process has been introduced into the enterprise. The innovation is based on the results of new technological developments, new combinations of the existing technology or other combinations, or utilization of other knowledge acquired by a specified enterprise. The innovation can be new to the specified enterprise but it needs not necessarily be new to the market. Changes of solely aesthetic nature are not considered as innovation.

35
**<u>Table 6. Hedonic Estimates for Imputed Rent (dependent variable: log imputed rent)</u>**

|Variables|(1) Kigali,<br>Rwanda|(2)|Abidjan,<br>Cote d’Ivoire|(3) Dar<br>es<br>Salaam,<br>Tanzania<br>(4) Maputo,<br>Mozambique|
|---|---|---|---|---|
|Rooms|0.291***<br>(0.200)|N/A||N/A<br>0.047<br>(0.065)|
|Homeownership<br>(dummy)|0.103<br>(0.098)|||0.070<br>(0.094)|
|Floor (dummy)|0.154**<br>(0.077)|||0.346<br>(0.238)|
|Overcrowded<br>(dummy)|-0.554**<br>(0.221)|||0.162*<br>(0.096)|
|Roof (dummy)|0.627*<br>(0.352)|||1.411***<br>(0.126)|
|Walls (dummy)|0.303***<br>(0.077)|||0.780***<br>(0.125)|
|Electricity|1.460***|||0.815***|
|(dummy)|(0.086)|||(0.076)|
|Water (dummy)|0.394***<br>(0.080)|||0.031<br>(0.112)|
|Toilet (dummy)|0.329***<br>(0.108)|||0.401***<br>(0.076)|
|Constant<br>|9.550***<br>(0.144)|||7.385***<br>(0.419)|
|R<sup>~~2~~</sup>|0.72|||0.52|
|Observations|622|||910|

Asterisks denote significance at the * 10%, **5%, and ***1% levels.

35