rafmacalaba/datause-encoder-data huggingface dataset:
- 5 -

Th- Human De' lopment Inder is then defined as HDIJ - -D The above attempt at arrivirng at a unidimensional standard of living, while interesting, is problematic to say the least. As Ferroni and Kanbur (1990) point out, the normalization adopted is somewhat disconcertiFor example an improvement in the achievement of the worst country in the sample would decrease the HDI for country j! It is not clear that these are the sorts of externalities that one wants in an index. Moreover, it is not clear why each dimension is given an equal weight. In other words, it is not clear what value judgements underlie the HiDI. In view of these problems, we would have to conclude that the HDI should be treated with caution. Until more work has been done on deriving weights for different dimensions of the standard of living on the basis of cxplicit value judgments in the form of a social welfare function (see Ferroni-Kanbur, 1990), the approach in the World Development Report, of supplementing income data with information on the other achievement, is to be preferred.

All of the above is devoted to arriving at a measure of the standard of living. But we then have the oroblem of choosing a cut off to focus attention on the bottom end of the distribution. For some indicators like literacy, this is straightforward since they are in the form of "zero or one" variables. For other indicators, most particularly income/consumption indicators, we bdve to decide on a "poverty line". This is also a vexed question in the literature, and there is no easy solution (see Kanbur, 1987), not least becau5,e what constitutes poverty is country specific, The World Development Report looks at country-specific poverty lines for thirty-four developing and
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_25_ _Price Support at Any Price?_ Figure 10 shows effects of experiments on the income relation. The income of the MORE PENSIONERS experiment is relatively small, but positive (a scope of the policy is reduced by the age structure of farmers). However, the JOB PROMOTION FOR FARMERS experiment successfully competes with price support in increasing the income relation, although it is only after 2005 that the income relation in both experiments is similar. Moreover, the positive policy effect increases over time: the structural problems that keep the relation low are being _solved,_ not only <!-- Start of picture text --> Alternatives to price support<br>Income relation<br>WM, ft m ft bat nm h %<br>70 - f S nmms<br>60<br>50<br>40 Jobs prondon kw hmer<br>30 , Fat patVouiut reduct<br>20 More pensiors<br>'<br>10 / -<br>--<br>-10 , . 2<br>10 1sb6 i06 A1<br>Figure 10<br><!-- End of picture text --> _counteracted._ This is thanks to the positive effects that the experiment (and, to a smaller degree, also the MORE PENSIONERS experiment) has on accelerating the structural change in the agricultural sector. Table 4.2 presents main indicators of this change.

**Tab.4.2 Alternatives for price supporting Simulation experiments: POLICY EFFECTS**

||Fast path<br>reduc<br>(EU<br>pr.<br>by 200<br>|/output<br>tion<br>0,<br>quotas)<br>l<br>|More pen|sioners|Jobs prom<br>farm<br>|otion for<br>ers<br>|
|---|---|---|---|---|---|---|
|l________________<br>|1<br>2000<br>j|<br>2010|2000|2010|2000|2010|
|Income Relation|||||||
|(farmer's income/wage)|||||||
|<br>index, 91 level=100|**134.1**|**133.9**|**111.4**|**118.2**|**116.3**|**141.6**|
|index, base sol.= 100|123.8|120.3|102.9|106.7|107.1|127.8|
|Number of farmers|||||||
|in millions|3.084|2.651|2.775|2.180|2.519|1.542|
|index, base sol.=100|106.6|109.7|95.9|90.2|87.0|63.8|
|Employment in|||||||
|<br>agriculture as % of total||**17.3%**||**14.2%**||**10.1%**|
|Average farm area|||||||
|<br>in hectares|6.7|7.8|7.5|9.5|8.2|13.4|
|index, base sol. = 100|93.8|91.1|104.3|110.8|114.9|156.7|
|Output per farmer|||||||
|<br>index, 91 level= 100|118.4|164.1|137.6|204.6|151.6|289.1|
|index, base sol.= 100|89.7|88.9|104.3|110.8|114.9|156.6|

_Source: Model POLAGR, simulation Jan.1995_
driven women and unmarried women. by high-skill unemployed unemployed Specifically, we observe a null effect (0.02 with _p >_ 0.10) on travel time and an insignificant effect on time spent on household chores (-0.127 with _p >_ 0.10) for married unemployed women, suggesting the persistence of entrenched gender norms.

# **I Impact on Employment and Job Search**

In the previous section, we documented that the _Pink Slip_ scheme affects the amount of time that employed women devote to work, varying by their skill level: increasing for high-skilled and decreasing for low-skilled women. By definition, time spent on work is not applicable for unemployed women. Yet, they display a similar pattern: highskilled unemployed women increase travel time, whereas low-skilled women decrease it. Considering that a portion of this travel time may involve searching for job, the _Pink Slip_ scheme could potentially impact women’s labor market outcomes. While we are unable to directly measure travel time spent on job searches in the CPHS data, we could infer the extent to which these searches may impact labor market outcomes based on observed changes in participation, employment or unemployment. We now present these results, followed additional evidence on the and distance of travel from the Delhi by purpose Primary Survey.

**Evidence from the CPHS Data** We estimate equation (7) to investigate whether women are more likely to be in the labor market, more likely to be employed, and more likely to switch from being out of the labor market to actively searching for jobs after the _Pink Slip_ scheme. The results are in columns 1 to 3 of Table A7. We no reported find statistically or economically significant impact on being in the labor market or being employed. However, there is a marginal increase in the job search for women. Women who were previously out of the labor market are 1.1% ( _p <_ 0.15) more likely to start searching for jobs after the scheme, which is 15.7% of the control mean. We also present the corresponding event study graph in Figure C3, which shows that this marginal increase persisted in the post-implementation periods. The estimated coefficients for time periods 1 and 2 after the policy are statistically significant at 10% and 15% levels, respectively. In period 3, the magnitude remains unchanged, though it is imprecise. These findings indicate that more women are moving from being out of the labor market to actively looking for jobs, although they are not more likely to find employment in this short-run period.

18
**Internal Migration in Ghana: Determinants and Welfare Impacts**

Charles Ackah<sup>†</sup> and Denis Medvedev<sup>‡</sup> April 6, 2010 † Institute of Statistical Social and Economic Research (ISSER) and University of Ghana, Legon. ‡ Corresponding author: Economic Policy Unit, Latin America and the Caribbean, The World Bank Group, 1818H St NW, Washington, DC, 20433 USA; (T) 202-473-3895, (F) 202-614-3895; dmedvedev@worldbank.org The first draft of this paper was prepared as a background paper for the 2010 World Bank Ghana Poverty Assessment. The authors are grateful to Sebastien Dessus, Francisco Ferreira, Iris Gamil Boutros, Markus Goldstein, Chris Jackson, Hanan Jacoby, Phillippe Leite, Andrew Norton, Joana Silva, Fleur Wouterse, seminar participants at ISSER and conference attendees at the 2010 CSAE conference for helpful comments and productive discussions, and Richard Adams for sharing key data. All remaining errors are our own. The findings and opinions in this paper may not reflect the official views of the World Bank, its Executive Directors, or the countries they represent.
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**Table 5. Effect of Business Environment Obstacles on Employment Growth by Region**

||Dep|endent varia|ble: emplo|yment gro|wth|
|---|---|---|---|---|---|
||**World**|**AFR**|**EAP**|**ECA**|**LAC**|
|**Variable**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Laborsize|-0.022***<br>|-0.026***<br>|-0.029***<br>|-0.018***<br>|-0.019***<br>|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Age|-0.023***|-0.021***|-0.019***|-0.031***|-0.024***|
||(0.001)|(0.002)|(0.004)|(0.003)|(0.002)|
|Multi|0.018***|0.012***|0.010|0.020***|0.022***|
||(0.002)|(0.003)|(0.007)|(0.006)|(0.004)|
|Manuf|0.003|0.012***|0.012**|-0.009**|0.005|
||(0.002)|(0.003)|(0.006)|(0.004)|(0.003)|
|Exporter|0.020***|0.020***|0.018**|0.020***|0.025***|
||(0.002)|(0.004)|(0.007)|(0.004)|(0.004)|
|Foreign|0.011***|0.008**|0.009|0.021***|0.011**|
||(0.002)|(0.003)|(0.006)|(0.006)|(0.005)|
|Govt|-0.017***|-0.013|0.017|-0.022***|-0.020|
||(0.005)|(0.011)|(0.012)|(0.008)|(0.017)|
|Access to finance|-0.004***|-0.002*|-0.008***|-0.004***|-0.005***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Informal sector competition|-0.003***|-0.003***|-0.008***|-0.002*|-0.003***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Inadequate education|0.007***|0.005***|0.008***|0.006***|0.010***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Electricity|0.001*|-0.001|0.004**|0.001|0.002**|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Customs and trade|0.005***|0.004***|0.006**|0.006***|0.003*|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|
|Access to land|0.003***|-0.000|0.004*|0.004***|0.005***|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Political instability|-0.002**|-0.001|-0.002|-0.001|-0.003**|
||(0.001)|(0.001)|(0.003)|(0.001)|(0.002)|
|Courts|-0.001|-0.001|0.001|-0.004**|0.000|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|
|Crime|0.000|0.001|0.002|-0.000|-0.001|
||(0.001)|(0.001)|(0.003)|(0.001)|(0.001)|
|Tax rates|-0.001*|-0.002|-0.000|-0.000|-0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Tax administration|-0.000|0.002*|-0.002|-0.002|-0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Licensing and permits|0.001|-0.001|0.003|0.002|0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Corruption|-0.001*|-0.001|-0.002|0.000|-0.002|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.001)|
|Transport|0.000|-0.002|0.002|0.001|0.001|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Labor regulations|-0.001|-0.001|-0.001|-0.002|-0.001|
||(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Constant|0.176***|0.182***|0.156***|0.184***|0.174***|
||(0.004)|(0.006)|(0.011)|(0.008)|(0.008)|
|Adjusted_R_-squared|0.130|0.129|0.148|0.130|0.112|
|Number of observations|26,574|8,600|3,079|6,596|7,592|
|Number of countries|95|37|10|30|15|

_Note:_ Standard errors (in parentheses) are robust to heteroskedasticity and clustered on countries. Regressions for the Middle East and North Africa and South Asia are excluded because of insufficient data. Model: _EG_ = _b_ 0 + _b_ 1All 15 Obstacles + _b_ 2Firm Characteristics + Region + Country Fixed Effects + _e._ * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on World Bank Enterprise Surveys (2006-10).

25
**Panel B. Summary statistics of the sample before and in the year of privatization**

||Obs.|Mean|Median|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|_Privatizedit_|151,269|0.268|0.000|0.443|0.000|1.000|
|_Age t-1_|151,269|51.822|52.000|4.092|39.000|61.000|
|_Labor prod t-1_|151,269|4.056|4.042|1.383|-0.113|8.001|
|_Leverage t-1_|151,269|0.722|0.696|0.402|0.001|2.669|
|_ROA t-1_|151,269|-0.003|0.000|0.111|-0.426|0.878|
|_GDP per capita t (thousand RMB)_|151,269|7.483|5.103|8.636|1.500|85.829|
|_GDP growth (%)t_|151,269|9.532|10.710|6.286|-17.600|26.300|
|_College t_|151,269|0.881|1.000|0.324|0.000|1.000|
|_Central Experience t_|151,269|0.041|0.000|0.199|0.000|1.000|
|_Provincial Experience t_|151,269|0.551|1.000|0.497|0.000|1.000|

**Panel C. Summary statistics of the sample including** **<u>post-privatization observations</u>**

||Obs.|Mean|Median|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|_TFP_OP_|99,703|1.767|1.872|1.433|-3.377|5.176|
|_TFP_OLS_|125,407|0.820|0.856|1.365|-4.010|4.382|
|_TFP_IN_|125,407|1.324|1.443|1.543|-3.360|5.033|
|_TFP_CS_|169,143|-0.463|-0.388|1.487|-5.943|3.494|
|_Government activism_|119,492|0.407|0.351|0.213|0.095|0.994|

``` 31 ```
# **_<u>________________________________________________________</u>_**

# **<u>Annex I.   Summary of Main Data Sources</u>**

|**Data**|**Coverage**|**Years**|**Variables/Description**|
|---|---|---|---|
||**INV**|**ESTMENT CL**|**IMATE**|
|World Competitiveness<br>Yearbook|59|Since 1989|Competitiveness factors (economic<br>performance, government efficiency,<br>business efficiency, and<br>infrastructure)<sup>iv</sup>.|
|Doing Business<br>Database|155|Annual|Indicators of regulatory costs of<br>business and enforcement|
|Investment Climate<br>Surveys (ICS)|32,000<br>entreprene<br>urs in 62<br>countries.|Expanded<br>since mid<br>1990s|Indicators of firm productivity and<br>performance.|
|EBRD Transition Report|26 Eastern<br>Europe<br>and CIS<br>countries||Quality of business environment|
|World Business<br>Environment Survey<br>(WBES)|80|1999-2000|Corruption, judiciary, quality of<br>business environment, efficiency of<br>customs (including time taken to clear<br>customs), quality and efficiency of<br>public services|
|World Bank :<br>Investment Climate<br>Assessments<br>(ICAs)|18||Indicators on entry regulation,<br>infrastructure indicators, regulatory and<br>administrative delays (including<br>customs clearance)|
|World Economic Forum<br>: Global<br>Competitiveness Report<br>(GCR)|117|annual|Growth Competitiveness Index and<br>other measures of economic<br>performance; Index of perception of<br>roads, railway and air services; Index of|
||||<br>Port efficiency|
|||**Governanc**|**e**|
|Governance database|160|1996-2002|Voice and Accountability, Political<br>Stability, Government Effectiveness,<br>Regulatory Framework, Rule of Law,<br>CorruptionControl|
|Business Environment<br>and Enterprise<br>Performance Survey<br>(BEEPS)|20,000<br>firms in<br>ECA<br>countries|1999, 2002<br>and 2005.|Doing Business, Corruption, Crime,<br>Regulations, Customs, Infrastructure|
|Transparency<br>International|Between<br>90-100<br>countries|1995-2002|Corruption Perception Index|
|||**Trade**||
|Global Monitoring||2003|trade effects ofpolicies onan importing|

62 __________________________________________________________________________________________________________
adequate provisions to cope with the low frequency of large losses and the customary longer delays in settling complex claims.

# **H. Life Insurance**

The life insurance sector is dominated by the life subsidiary of one of the largest American insurance groups. This company has branch operations in Jordan and focuses almost exclusively on personal business, emphasizing the financial protection benefits of life policies. Its success is attributed to the training of a relatively large number of sales agents.

**Table 16: Institutional Structure of Life Insurance, 2002**

|% of<br>Other Premiums|
|---|
|1 Large<br>57.5|
|6 Medium<br>30.6|
|11 Small<br>11.9|
|HHI<br>3489|
|Source: Insurance Commission|

Most other companies have expended little effort in creating a well trained sales force. Even a local company that was created with the specific objective of competing with this foreign company has ended up generating more business in motor and medical insurance than in life business. Many companies offer life business on an ad hoc basis, generating very low levels of annual premiums. The result of all this is a very high index of market concentration (Table 16).

As already noted, growth of life insurance has been modest (Table 17). The 1998 decline in reported statistics is due to the reclassification of medical insurance. Reinsurance is on the high side for life business, exceeding 20 percent of gross premiums. Acquisition costs (including net commissions and administrative expenses) range between 25 and 30 percent of net retained premiums, while net claims fluctuate between 35 and 50 percent of net premiums. Investment and other income is low but is probably understated since changes in reported fair asset values are taken straight into equity and are not included in the annual income statement. This is also reflected in a low implied rate of return on accumulated reserves. In contrast, the annual surplus is large, ranging between 31 and 47 percent of net premiums (average 38 percent). The amount allocated to the life fund ranges from 61 and 74 percent of the annual surplus, averaging 68 percent between 1997 and 2002. This appears low and may be explained by the high level of market concentration.

Several of the larger companies launched various life products that have been successful in other countries but met with limited response in Jordan. The strong liquidity preference of savers and the high level of interest rates traditionally offered on bank deposits may also have depressed the demand for long-term saving instruments. The recent decline of bank deposit rates creates an opportunity for the offer of long-term 18
<mark>supported national longitudinal household survey, there are two potential sources of bias in the resulting data: (i) selection bias associated with not being able to call LSMS-ISA households that do not own mobile phones and (ii) non-response bias associated with not being able to interview households that are targeted for phone interviews. Without correcting for these two sources of bias, results of our analysis are likely to underestimate the negative impacts of COVID-19, since poorer households are less likely</mark> than wealthier households <mark>to have phones, and less likely to respond even if they do have phones.</mark> <mark>In our analysis, we use the phone survey weights that are provided in public use datasets and that are computed in an attempt to address the aforementioned potential sources of bias using wellestablished sampling techniques.</mark><sup>_7,9_</sup> <mark>. The phone survey weights in each country build on the sampling weights for the associated LSMS-ISA-supported survey. They are calibrated to address the selection bias introduced from LSMS-ISA households not owning a mobile phone and nonresponse bias from not answering the phone. This latter issue is overwhelmingly due to nonworking phone numbers or prospective respondents not answering calls (as opposed to refusals). To calculate survey weights for the phone survey, we implement the following steps in Ethiopia, Malawi, and Uganda</mark><sup>_8_</sup> <mark>.</mark> <mark>1. We begin with the existing sampling weight for each household, as computed for the associated LSMS-ISA-supported survey (</mark> wi,pre <mark>), which are available in those datasets.</mark> <mark>2. We calculate the probability of selection into the phone survey for each household (</mark> pi <mark>), as the total number of LSMS-ISA households for which contact was attempted in that household’s region (</mark> mr <mark>) divided by the total number of LSMS-ISA households in that region (M</mark> r).

pi =<sup>mr</sup> Mr <mark>3. We multiply the existing LSMS-ISA sampling weight with the reciprocal of the probability of selection in the phone survey, as calculated in step #2.</mark> 1 wi,phone = wi,pre pi <mark>4. Using the entire LSMS-ISA sample, we run a multivariate logistic regression in which the dependent variable is a binary variable that is equal to 1 for LSMS-ISA households that were successfully interviewed for the phone survey and equal to 0 otherwise. The independent variables included represent a range of household, dwelling, and head of household attributes that predict the likelihood of a completed phone survey interview.</mark> 18
Large countries show the greatest absolute sales losses due to power outages, whereas smaller countries lose a larger fraction of GDP. This is illustrated by Figure 6 which disaggregates the global results into absolute numbers and absolute numbers as a share of GDP and shows the 15 most affected countries according to both measures. Sales lost as percentage of GDP in Figure 6 are significantly lower than share of sales lost as reported by the average firm in Figure 5. The set of countries affected most severely is quite different between the two measures. This is caused by several factors. First, the share of sales lost by the average firm is ignorant of firm size. If small firms experience more losses but larger firms can better cope with outages, the share of sales lost by the average firm will be higher than the total sales lost as percentage of GDP. Second, sales and GDP are quite different measures, as sales are ignorant of the value added a firm provides and thus its contribution to GDP. Third, the Enterprise Surveys primarily focus on the manufacturing and services sectors and thus covers a fraction of GDP that varies between countries. Finally, the fraction of firms answering the question about their sales and thus the degree to which a countries total economic activity is captured can differ among sample countries.

Figure 6: Total (left panel) and relative (right panel, as % of GDP) sales lost due to power outages for sample countries most affected <!-- Start of picture text --> Indonesia Sudan<br>India Malawi<br>Brazil Burundi<br>Egypt Kyrgyz Republic<br>South Africa Mozambique<br>Turkey Egypt<br>Pakistan Indonesia<br>Sudan Uganda<br>China Ethiopia<br>Mexico Zimbabwe<br>Poland Yemen<br>Hungary Pakistan<br>Ethiopia South Africa<br>Vietnam Djibouti<br>Nigeria Lebanon<br>10 20 30 0 1 2 3 4 5<br>Total sales losses (in billions, 2018 USD) Sales losses as a share of GDP (in %)<br><!-- End of picture text --> Evidence from selected countries shows that electricity disruptions have a significant negative effect on the performance of firms (Section 2). To assess this issue systematically and globally we analyze a model that estimates the impact of power outages (measured as total annual blackout duration) on firms’ selfreported sales losses. More specifically, an electricity specific pooled ordinary least squares model is estimated with standard errors clustered at the regional level, and using sales losses relative to total sales as a dependent variable.

14