rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8 Job Tumover by Wage Quartile, 1999-2000**

|Wagequartile|Jobcreation<br>rate|Job destruction<br>rate|Jobturnover<br>rate|Netemployment<br>growth|Excessjob<br>reallocationrate|
|---|---|---|---|---|---|
|First|20.6|18.8|39.4|1.7|37.7|
|Second|15.6|17.9|33.5|-2.3|31.2|
|Third|10.9|16.0|26.9|-5.1|21.8|
|Fourth|8.1|10.9|19.0|-2.9|16.1|

Note: Firms are ranked by the average wage level and then categorized into quartile groups.

_Source Annual survey of wages and salaries, 1999 and 2000. Bank staffcalculations._ In sum, low-wage firms are characterized by high job creation, employment growth and substantial job turnover. In contrast, high-wage firms create relatively few jobs, tend to reduce employment, but are characterized by relatively low job turnover.

# **_Industry_**

This section identifies industries which are expanding and declining as well as those which undergo the most intensive restructuring. A look at correlations between the components ofjob turnover will help identify patterns of industrial restructuring. Finally, it will be determined if job reallocation and thus restructuring takes place mainly between industries or within industries.

Where are jobs being created? Table 9, Panel A presents top 10 industries with the highest rates of job creation. As expected, the highest job creation rates are in the service and trade sectors, which were relatively underdeveloped in Lithuania. Financial intermediation stands out as the biggest job creator, followed by trade and so called "other business activities", which include legal services, accounting, business counseling, marketing, personnel recruitment, etc. These are all activities which form a foundation of a developing market economy. However, high job creation is not limited to the service and trade sectors, but it also occurs in the manufacturing industry, such as in manufacturing of wood products and furniture, and manufacturing of plastic products.

19
# **III. Debt Financing by Market Segment**

## **_Debt Financing to Firms_**

Overall financing to firms has marginally declined between 2000 and 2005, but its quality has improved substantially and there has been a shift from external to domestic funding sources. These three main stylized facts can be seen in figure 17. The improvement in the quality of banks’ commercial credit portfolio is particularly striking since non-performing loans dropped from 4 percent in 2000 to only 0.2 percent of GDP in 2005. Bank commercial credit growth has resumed in 2004, although its pace remains slower than that of consumer and mortgage credit. The relative importance of non-bank financial institutions has remained fairly stable over this period although – as noted previously – the role of development banks has actually increased due to second-tier lending and guarantees. Even though the substitution of foreign for domestic bond issuance can be ascribed to several contributing factors (see later paragraphs), the reduction in cross-border bank credit cannot be easily explained and is counter-intuitive given that most large Mexican banks were acquired by foreign financial institutions over this period.

Figure 17: Evolution of Commercial Financing by Provider (2000Q1-2005Q3) <!-- Start of picture text --> 20%<br>Commercial banks - non-<br>18% performing<br>4.0%<br>16% Foreign debt issuance<br>0.2%<br>14%<br>3.5% Cross-border bank<br>12% 2.5% lending<br>10% 3.9% 2.9% Domestic debt issuance<br>8%<br>1.2% 2.2% Other FIs<br>6% 1.6%<br>1.5%<br>4% 1.0% 0.0% Commercial banks -<br>Restructured performing<br>2% 4.1% 4.7%<br>Commercial banks - non-<br>0% restructured performing<br>1Q00 3Q05<br>% of GDP<br><!-- End of picture text --> Source: BOM.

Note: Equity financing, FIRA and Financiera Rural lending, and asset-backed securities issuance are excluded. Domestic debt issuance only includes bonds issued by non-financial private sector firms. Other FIs (Financial Institutions) includes development banks, Sofoles, OACs and credit unions.

Sluggish economic growth over this period is among the main reasons for stagnant commercial credit. Econometric and anecdotal evidence across countries tends to relate commercial credit growth to actual and expected developments in overall economic performance. As mentioned in the previous section, firms’ demand for credit appears to have been dampened by the sluggish economic growth and investment over this period. It 18
Next, we control for total adult population the city ( _Population_ ). The variable captures differences in labor availability across cities and also income differences since larger cities are known to be richer, endowed with better infrastructure, etc. In our sample, the cities of Mumbai, New Delhi, Chennai, Bangalore, Kolkatta and Hyderabad show highest values of _Population_ . These cities are known to be the richest cities in the country and they are also the main beneficiaries of the ongoing retail boom. Regression results with _Population_ as an additional control are reported in column 1 of Table 3. As expected, _Population_ has a positive effect on employment but this effect is not too significant here (p-value of .174). The estimated coefficient of the labor regulation index equals -5.94 significant at less than 5% compared to -4.16 in the previous specification. There is not much change in the results for the remaining variables.

In the Enterprise survey, stores reported irregular power supply and access to finance as the two biggest problems they faced in running their business. These problems could limit the overall profitability and size of business and also the marginal productivity of labor, lowering employment. This could bias our main results if power supply and access to finance differ systematically by labor laws. We check for this possibility using the following controls taken from the survey: duration of power outage faced by a store per day on an average ( _Outage_ ), a dummy variable equal to 1 if a store uses a generator and 0 otherwise ( _Generator_ ), a dummy equal to 1 if a store has checking/savings account and 0 otherwise ( _Checking_ ), and a dummy equal to 1 if a store reported “no need to borrow externally” in the last fiscal year and 0 otherwise ( _Liquid_ ). Regression results with these additional controls are reported in column 2 of Table 3. The estimated coefficient of the labor regulation index here is higher in magnitude than in the 11
The last column in the first panel of table 1 reports the relative stocks from the U.S. in the manufacturing sector. Again China does not appear as a major host of FCS, with one third of the capital accumulated in Mexico. However, in this sector Chinese stocks are larger than those of Argentina, Chile, and Venezuela, suggesting that China has been relatively more attractive to capital in the manufacturing sector.

China and Hong Kong (China) jointly had accumulated FCS for almost $188 billion, more from the OECD than any Latin American country by 2003. China and Hong Kong (China) are important hosts of U.S. FCS, being considerably bigger than every country from Latin America with the exception of Mexico.

India, on the other hand, is a long way from reaching the FCS levels of the major Latin American countries countries. By 2003, total FCS in Mexico alone were more than 5 times bigger than in India. The OECD data shows that this gap is bigger for the largest countries, but it is smaller for Costa Rica, El Salvador and Guatemala.

The last two columns of the table reveal that India is not a major destination for U.S. capital. Except for Argentina and Chile the difference between Latin American and Indian stocks is larger in the manufacturing sector.

Differences in FCS in Latin American countries and China and India may only reflect differences in country sizes. Thus, we proceed to normalize the FCS by each country GDP and recalculate the relative stocks. The U.S. FCS in the manufacturing sector were normalized with the manufacturing value added in each country<sup>13</sup> . The resulting ratios are reported in Table 2. After controlling for country size, China appears with less FCS than any Latin American country. This is true for the different indicators of

> 13 GDP and Value Added in manufacturing in current U.S. Dollars were taken from WDI. Manufacturing Value Added for China in Yuans was taken from China Statistical Yearbook, and transformed into dollars using the period average exchange rate from IFS.

8
# Table 10A. Workers by occupation (absolute numbers and percentages) (Cont.)

|||TOTAL|Unskilled|Laborers|Techni-<br>cians|Profe-<br>ssionals|All 4 occu-<br>pations|Legislators|TOTAL|Unskilled|Laborers|Techni-<br>cians|Profe-<br>ssionals|All 4 occu-<br>pations|Legislators|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Female|7,643|1,366|5,160|557|373|7,456|185|0.05|0.01|0.03|0.00|0.00|0.04|0.00|
||Male|102,668|9,039|78,344|5,589|3,371|96,343|6,325|0.61|0.05|0.46|0.03|0.02|0.57|0.04|
|Transport & Communication|Female young worker|||||||||||||||
||Male young worker|1,261|1,175|86|||1,261||0.01|0.01|0.00|||0.01||
||All labor|111,572|11,580|83,590|6,146|3,744|105,060|6,510|0.66|0.07|0.49|0.04|0.02|0.62|0.04|
||Female|4,339|658|2,866|0|733|4,257|81|0.03|0.00|0.02||0.00|0.03|0.00|
||Male|22,162|1,494|9,516|4,462|5,255|20,727|1,435|0.13|0.01|0.06|0.03|0.03|0.12|0.01|
|Finance, Insurance &<br>Business|Female young worker|||||||||||||||
||Male young worker|||||||||||||||
||All labor|26,501|2,152|12,382|4,462|5,988|24,984|1,516|0.16|0.01|0.07|0.03|0.04|0.15|0.01|
||Female|283,468|76,231|93,961|95,265|9,600|275,057|8,411|1.68|0.45|0.56|0.56|0.06|1.63|0.05|
||Male|429,324|56,840|154,502|178,335|20,478|410,155|19,170|2.54|0.34|0.91|1.05|0.12|2.42|0.11|
|Personal service|Female young worker|276,404|239,439|36,965|||276,404||1.63|1.42|0.22|||1.63||
||Male young worker|193,455|170,479|22,976|||193,455||1.14|1.01|0.14|||1.14||
||All labor|1,182,651|542,989|308,404|273,600|30,078|1,155,071|27,581|6.99|3.21|1.82|1.62|0.18|6.83|0.16|
||Female|6,910,590|348,450|6,258,076|112,275|13,467|6,732,268|178,319|40.86|2.06|37.00|0.66|0.08|39.80|1.05|
||Male|6,540,499|370,775|5,712,447|233,808|37,635|6,354,665|185,835|38.67|2.19|33.77|1.38|0.22|37.57|1.10|
|COUNTRY TOTAL|Female young worker|1,652,924|343,372|1,302,595|0|0|1,645,967|6,957|9.77|2.03|7.70|||9.73|0.04|
||Male young worker|1,810,792|395,109|1,409,702|0|0|1,804,811|5,981|10.71|2.34|8.33|||10.67|0.04|
||All labor|16,914,805|1,457,706|14,682,820|346,083|51,102|16,537,711|377,092|100.00|8.62|86.80|2.05|0.30|97.77|2.23|

Source: Author's own calculations based on the ILFS (NBS, 2002a). Table 9A.

Note: The 9 categories of occupations from Table 9A have been converted into 4 skill levels following Elias (1997). Since there was no conversion for the occupation “Legislators, administratos and managers” (“Legislator” in the Table) this category has been omitted and wages have been allocated to the four remaining categories following their weight in the total of the four categories. The conversion is as follows: Unskilled (“Elementary occupations”); Laborers (is comprised of five occupations: “Clerks”, “Services and shop workers”, “Skilled agricultural an fishery workers”, “Craft and related workers” and “Plant and machine operators and assemblers”); Technicians (“Technicians and associate Professionals”) and Professionals (“Professionals”).

42
|**Table 10: N**|**on-Agricul**|**tural GDP**||||
|---|---|---|---|---|---|
|**Log-Level**<br>|**s 1/**|**10 Year Growth**<br>|**Rates 2/**|**Annual Growth**<br>|**Rates 2/**|
|**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|
|**Full Sample**||||||
|_Constant_<br>-4.475|0.000|0.036|0.000|0.035|0.000|
|_AoL_<br>1.101|0.000|0.165|0.000|0.050|0.000|
|_R_<br>1.633|0.000|0.255|0.000|0.088|0.000|
|Adjusted R-squared<br>0.824||0.684||0.224||
|F-statistic<br>3,237||19.841||5.309||
|**Full Sample, Excluding Later Years**||||||
|_Constant_||0.033|0.000|0.027|0.000|
|_AoL_||0.184|0.001|0.102|0.000|
|_R_||0.533|0.000|0.173|0.000|
|Adjusted R-squared||0.723||0.241||
|F-statistic||13.124||3.771||
|**Excluding High Income**||||||
|_Constant_||0.040|0.000|0.039|0.000|
|_AoL_||0.222|0.000|0.079|0.000|
|_R_||0.328|0.000|0.154|0.000|
|Adjusted R-squared||0.675||0.214||
|F-statistic||18.454||4.806||
|**Excluding High Income and Later Years**<br>||||||
|_Constant_||0.034|0.000|0.027|0.000|
|_AoL_||0.284|0.001|0.136|0.000|
|_R_||0.712|0.000|0.252|0.000|
|Adjusted R-squared||0.710||0.256||
|F-statistic||12.130||3.883||

1. Panel regression, no fixed effects.

2. Panel regressions with fixed effects across time and space.

3. Adjusted R-squared for constant and fixed effects only.

**<u>Table 11:  Manufacturing Value-Added</u>**

||**Log-Level**|**s 1/**|**10 Year Growth **|**Rates 2/**|**Annual Growth **|**Rates 2/**|
|---|---|---|---|---|---|---|
||**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|
|**FullSample**|||||||
|_Constant_|-7.795|0.000|0.033|0.000|0.029|0.000|
|_AoL_|1.213|0.000|0.203|0.000|0.086|0.000|
|_R_|1.753|0.000|0.400|0.000|0.098|0.004|
|Adjusted R-squared|0.860||0.720||0.214||
|F-statistic|4,223||23.452||5.066||
|**FullSample, Excluding Later Years**|||||||
|_Constant_|||0.033|0.000|0.022|0.000|
|_AoL_|||0.173|0.007|0.142|0.000|
|_R_|||0.635|0.000|0.158|0.004|
|Adjusted R-squared|||0.840||0.188||
|F-statistic|||25.395||3.022||
|**Excluding High Income**|||||||
|_Constant_|||0.036|0.000|0.032|0.000|
|_AoL_|||0.265|0.000|0.114|0.000|
|_R_|||0.464|0.000|0.153|0.002|
|Adjusted R-squared|||0.720||0.188||
|F-statistic|||22.604||4.236||
|**Excluding High Income and Later Years**|||||||
|_Constant_|||0.034|0.000|0.228|0.000|
|_AoL_|||0.255|0.002|0.173|0.000|
|_R_|||0.777|0.000|0.228|0.002|
|Adjusted R-squared|||0.837||0.191||
|F-statistic|||24.408||2.987||

1. Panel regression, no fixed effects.

2. Panel regressions with fixed effects across time and space.

3. Adjusted R-squared for constant and fixed effects only.

The GDP hypothesis cannot be rejected but night-lights data cannot serve as a proxy for GDP. In each case, the elasticities are all significantly positive but none are stable over time. There are, however, some additional results that are interesting to note. The elasticity c2 for _R_ is consistently larger than c1 for _AoL_ and the ten-year elasticities are consistently much larger than those for the annual elasticities. The
<!-- Start of picture text --> 100 100<br>80 80<br>60.6<br>5 an 54.7 z 60<br>ao 448 44.5 i aoe a5<br>40 40<br>20 20 14.4<br>oO 92 a6 79) 10.1 Zz<br>Qi Q2 Q3 Q4 Qs 0 a1 Q2 Q3 Q4 Q5<br>100<br>80 ©<br>wo<br>iQ<br>— 60 ire)<br>Cc nD<br>g ac)<br>55<br>a3<br>40 £3X.<br>Sw<br>or<br>a<br>20 16.5 52<br>12.2<br>n<br>ie) Q1Bi Q26.0 Q384 =Q4 Qs a<br>Apr May Jun Jul Aug Sep Nov Dec Apr<br>—*— Top60% —®— Bottom 40%<br>Note: % of HHs with children engaged in any learning/education activities since school. Standard errors clustered on survey wave<br><!-- End of picture text -->
- 17 -

# **FNIAND**

Finnish Intermational Development Agency (FINNIDA) policy provides support for maternal health and family planning within primary health care programs. There are no projects specifically labelled Safe Motherhood and the agency's statisticai system does not provide information on SM components. Officials agreed to estimate SM components of bilateral, multilateral ana NGO disbursements over the past several years.

Planning and negotiating bilateral projects of any size often requires more than two years. Al would be premature to expect significant change in investments in SM in the two years since Nairobi.

Reflecting the agency's experience in Sri Lanka, FINNIDA notes that external support must often take into account the availability of counterpart financing. Such financing is often part of a country's multi-year plan. The time required for such planning and legislative approval is often a constraint on efforts made by external agencies to accelerate project financing.

As for financing by developing countries, it is FINNIDA's impression that social sector financing in their cooperating countries (for example, in Tanzania) is declining.

In 1985 Finland agreed to try to meet the DAC target of setting aside 0.7 percent of GNP as foreign aid by the end of the decade. In US dollars disbursements have increased from S114.7 million in 1981 to $432 million in 1987. For 1988 the budget was S622.5 million (disbursement statistics for 1988 are not yet available). With this increase in total ODA. the health sector's share increased from _5.1_ percent in 1983 to about 9 percent in 1987. Finland also contributes to UNFPA, UNICEF, and WHO. In absolute terms total bilateral and multilateral health contributions increased from approximately S23 million in 1985 to approximately S54 million in 1987, pointing to a gradual increase in the future. Subject to the pending report from Helsinki, the estimate for SM in 1987 would be $6 million in bilateral aid, $9 million to UNFPA, $6 million to UNICEF and up to $500,000 for WHO -- an estimated 1987 total of S21.5 million. This support for SM components should increase along with support for primary health care bilateral projects, UNICEF, UNFPA, and WHO. For 1988 total budgeted ODA is S622.5 million, a 30 percent increase over 1987. Subject to confirmation, the maternal health component would approximate S28 million in 1988, if it increased at the same rate.

Finnish International Deveiopment Agency (FINNIDA) Ministry of Foreign Affairs Mannerheimintie 15 C SF-00260 Helsinki
# **Table A35: OLS; Effects of refugee presence on household land size ownership by change in main source of income, with analysis at cluster level and the distance weighted refugee index at 50km -** **<u>from the clusters (Panel 2009 2012)</u>**

||<br>|Land size ownership (A<br>|cres)<br>|
|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|
|Log of refugees (50km)|0.2253|0.2451|0.3313|
||(0.3017)<br>|(0.2987)<br>|(0.3342)<br>|
|Log of refugees(50km)#To Commercial farming|0.8288<br>|0.8102<br>|1.2021<br>|
||(0.8192)|(0.7906)|(0.7957)|
|Log of refugees(50km)#To Wage employment|0.3728|0.3744|0.3836|
||(0.2625)|(0.2627)|(0.2724)|
|Log of refugees(50km)#To Non-Agric self-employment|0.1166|0.1264|0.0477|
||(0.2071)|(0.1967)|(0.2630)|
|HH head age||0.1692*<br>|-0.0314<br>|
|||(0.0946)<br>|(0.1361)<br>|
|Age squared||-0.0010<br>|0.0008<br>|
|||(0.0010)|(0.0014)|
|HH head sex (male)||1.2921**|0.3418|
|||(0.5019)|(0.5911)|
|Annual mean temperature||0.8442**|0.6335|
|||(0.4191)|(0.6489)|
|Average 12 months total rainfall||0.0057<br>|0.0278**<br>|
|||(0.0057)|(0.0130)|
|Household size|||0.2879***|
||||(0.0913)|
|Experienced shocks|||0.2432|
||||(0.5866)|
|**_HH head marital status_**||||
|Married monogamously|||5.6866*|
||||(3.3881)|
|Married polygamously|||6.6724*<br>|
||||(3.4656)|
|Separated (divorced/widowed)|||6.7381*<br>|
||||(3.6209)|
|**_HH head education level_**||||
|Completed primary sch|||2.3510***|
||||(0.7655)|
|**_HH main source of income_**||||
|Change to Commercial farming|6.1959***|6.2227***|5.2403**|
||(2.2836)|(2.2195)|(2.3430)|
|Change to Wage employment|-0.4692|-0.3335|-0.0200|
||(0.5446)|(0.5404)|(0.5587)|
|Change to Non-agricultural self-employment|-0.6829*|-0.5843|-0.6626|
||(0.3935)|(0.4095)|(0.5305)|
|**_Other source of HH income_**||||
|Property income and investments|||-0.2513|
||||(1.0703)|
|Transfers and other benefits|||0.0948|
||||(0.5247)|
|**_HH Asset_**||||
|Own > median size of land (2.5 acres)|||3.0484***|
||||(0.5257)|
|Observations|3,174|3,173|2,677|
|R-squared|0.1778|0.1899|0.2358|

55
competition hypothesis that individualistic culture facilitates competition and therefore amplifies female leaders’ disadvantage in productivity.

# **Interactions with the business environment**

We now investigate how the female leadership effects differ by the local business environment. We interact all the elements of the business environment with FemHead. We report the results for the world sample in the left, and those for the African subsample in the right, of Table 14.

We find support for the personal network hypothesis that contexts requiring personal networks put female leaders at a disadvantage. _First_ , the female leader disadvantage is larger where trade credit relationship is more important, as indicated by the significant and negative interaction term of FemHead and the trade credit intensity variable. The mean FemHead disadvantage in log LP is -0.27 at the mean Trade Credit, but -0.34 at one standard deviation above the mean, an increase in magnitude by a quarter. _Second_ , the female leadership disadvantage is weakly larger where the local access to bank finance is greater. The interaction of FemHead and the local bank finance variable is consistently negative, though only marginally significant for LP growth. _Third_ , since access to internet reduces the need for personal network, the hypothesis implies that the female leadership disadvantage is smaller where internet access is better. Indeed, the interaction of FemHead and local internet intensity is consistently positive and significant for both log LP and LP Growth. Increasing local internet intensity by one standard deviation of 0.23 is associated with a reduction of log LP disadvantage of female leaders by 0.06 or 6 percentage points, and of LP Growth by 1.6 percentage points.

The competition hypothesis that female business leaders face stronger disadvantage when facing more competition receives some support. The interaction of FemHead with our proxy of informal competition is negative and significant for TFP. Increasing informal competition by one standard deviation (0.2) is associated with rising female leader TFP advantage by 6 percentage points.

The corruption hypothesis that female business leaders face great disadvantage where corruption is worse does not receive support. The interaction of FemHead and our proxy of corruption is never significant.

22