rafmacalaba/datause-encoder-data huggingface dataset:
36 Table 8: BrazDl - Characteristics of Female-Headed Households (FMI) in Three Metropolitan Areas, 1984 (percent)

||Recife|Sao Paulo|Porto Alegre|
|---|---|---|---|
|Share of Total Households||||
|All FHH|21.8|16.5|18.5|
|FHH w/ minors|11.6|6.2|6.7|
|- no adults|3.7|2.6|3.1|
|Share of Total Minors||||
|All|15.0|9.5|10.8|
|Age 0-6|12.9|7.0|8.8|
|<br>Age 7-9|15.6|10.0|11.5|
|<br>Age 1o-14|17.6|13.6|15.0|
|Per Capita Inacome (mi[mum salaries)||||
|All households|1.3|2.5|2.6|
|FHH|1.1|2.4|2.6|
|FHH w/minors|0.6|1.2|1.3|
|Character|istics of Female-lea|ded Households||
|Characteristics of Heads||||
|Percent economically active||||
|<br>All FHH|44.0|55.0|53.0|
|FHH w/minors|51.0|68.0|64.0|
|Average age||||
|<br>All FHH|51.0|49.0|50.0|
|FHH w/minors|47.0|43.0|43.0|
|Average income (minimum sa!aries)||||
|<br>All FHH|1.7|3.0|3.4|
|FHH w/minors|1.4|2.6|2.9|
|Marital Status||||
|Single|37.6|37.8|40.7|
|<br>Married|0.9|2.3|1.7|
|Widowed|27.0|.20.7|22.0|
|Divorced|34.5|3V.2|35.6|
|Characteristics of Households||||
|Average number of children||||
|<br>All FHH|1.2|0.7|0.7|
|FHH w/minors|2.2|1.9|1.8|
|Dependency ratio||||
|<br>All FHH|0.7|0.5|0.5|
|FHH w/nlinors|1.3|1.2|1.2|

Source: author's tabulations
**Table S1.5.** Predictor Balance for Synthetic Jordan, Polity IV Index

||**Real Jordan**|**Sample means**|**OIC all**|**OIC minus PAK**|**OIC SAL**|
|---|---|---|---|---|---|
|Polity II (1975)|−10.000|2.104|−9.000|−9.000|−9.000|
|Polity II (1985)|−9.000|−1.201|−8.685|−8.685|−8.000|
|Political rights|5.667|4.409|5.646|5.646|5.371|
|Civil liberties|5.600|4.370|6.124|6.124|4.781|
|Exec. constraints|1.467|0.360|1.126|1.126|1.467|
|Presidential|1.000|0.556|1.000|1.000|1.000|
|Parliamentary|0.000|0.271|0.000|0.000|0.000|
|Civil law|1.000|0.757|1.000|1.000|1.000|
|Common law|0.000|0.325|0.000|0.000|0.000|
|Muslim law|1.000|0.222|1.000|1.000|1.000|

_Source_ : Authors’ analysis of the Polity IV index. Additional data come from the Polity IV dataset, Freedom House, the Database of Political Institutions (DPI), and the JuriGlobe database. _Note_ : Table presents pre-treatment predictor variables for Jordan, their corresponding pretreatment sample means, and the weighted averages used to construct Synthetic Jordan. OIC denotes the full Organisation of Islamic Cooperation donor pool; OIC minus PAK denotes the OIC donor without Pakistan; and OIC SAL denotes the donor pool limited to OIC countries that received Structural Adjustment Loans (SALs) from either the World Bank or International Monetary Fund (IMF).

**Table S1.6.** Synthetic Jordan Country Weights, Polity IV Index

||**Weight**|||**Weight**|
|---|---|---|---|---|
|||_Panel A. OIC all_|||
|Mali|0.000||Turkey|0.000|
|Niger|0.000||Egypt|0.000|
|Sierra Leone|0.000||Syria|0.685|
|Nigeria|0.000||Pakistan|0.000|
|Morocco|0.000||Bangladesh|0.000|
|Tunisia|0.315||Malaysia|0.000|
|Iran|0.000||Indonesia|0.000|
|||_Panel B. OIC minus PAK_|||
|Mali|0.000||Turkey|0.000|
|Niger|0.000||Egypt|0.000|
|Sierra Leone|0.000||Syria|0.685|
|Nigeria|0.000||Pakistan|–|
|Morocco|0.000||Bangladesh|0.000|
|Tunisia|0.315||Malaysia|0.000|
|Iran|0.000||Indonesia|0.000|
|||_Panel C. OIC SAL_|||
|Mali|0.000||Turkey|0.000|
|Niger|0.000||Egypt|–|
|Sierra Leone|0.000||Syria|–|
|Nigeria|–||Pakistan|0.000|
|Morocco|0.310||Bangladesh|0.000|
|Tunisia|0.690||Malaysia|–|
|Iran|–||Indonesia|0.000|

_Source_ : Authors’ analysis of the Polity IV index. Additional data come from the Polity IV dataset, Freedom House, the Database of Political Institutions (DPI), and the JuriGlobe database. _Note_ : Estimated country weights to construct Synthetic Jordan based on the Polity IV index as the outcome. OIC denotes the full Organisation of Islamic Cooperation donor pool; OIC minus PAK denotes the OIC donor without Pakistan; and OIC SAL denotes the donor pool limited to OIC countries that received Structural Adjustment Loans (SALs) from either the World Bank or International Monetary Fund (IMF).
# TABLE P

A : MACRO;CONOMiC INDICATORS

|THAILAND|**1U6**|**1917**|3935|193g|391|19D1|19 2|
|---|---|---|---|---|---|---|---|
|AVERAGE INFLATION(%)|1.14|2.47|36.|5.)6|5.93|5.70|4314|
|REALflEFCECIVDEXC"..RATECI5-I00)(1)|101.40|11014|111.50|310.52|113.67|120.5S|124.75|
|RESERVEMONEY(L.a.SMILUONS)|95323|130653|133994|116670|31:790|_310451_|243044|
|-NETDOMESTIC CREDIT|24634|10529|*28613|.105913<br>*|174717|-254562|.2903D9|
|*NOTPOREIONASSETS|70644|106124|162607|26253|1360507|465013|53311)|
|INVESTM<br>rNT(%DP)(2)|21.79%|23.92%|2#.44%|3135%|30.76%|3.1136%|<br>3794%|
|NATIONALSAVINGS1% GDP)(2)<br>|20.33%|23.04%|32.19%|29.77%|29.9%|30.18%|32 f*%|
|*PRtVAT CONSUMTIDNO0CDP)(23|64.42%|6341%|59.77%|59.07%|50.20%|5740b|57.b6%|
|-GOVERN'T<br>CONSUMPTION(4CDP) (2)|13.20|11.76%|10.40%|9.15%|9.711%|9.F3%|470%|
|CURRENT ACCOUNTDEICrTr%GDP) (2)|1.46%|0.113%|0.64%|1.75%|**077%**|11.11`|532%|

||DiJI|ALANCEOFP<br>(in**n**|AYMENTSSELEC<br>**uIlliomp .fdnIIun)**|TED DATA||||
|---|---|---|---|---|---|---|---|
|THAILAND|1936|1907|19U|1939|1"9|1991|1992|
|RESERVES: NET CHNO IN HLDOS(3)|*952|*1230|*2520|*470|.3560|*4700|*2740|
|SHORTTERM CAPITAL|-219|461.3|2499<br>|2323.1|5495.9|6661|53ES|
|LoNGTERMCAPITAL|-144|72|_*263.45_|3059|1330|3327<br>|Isis|
|PORTFOLIOINVESTMENT|.29.5|346|530|1490|-33.1|*.3.1|750|
|NETDIRECT INVESTMENT|261.94|332|103.9|1730.3|2300|3431|1934|
|CAPITALACCOUNTBALANCE|-131|1060|3340|6600|9100|130D|9630|
|NETERRORSANDOMISSIONS|593|248|411|92E|1420|425|*23.2|

S.u..

IMF. Imnmkal **Fhrdnl** Sitdea IW. Babes Pynmm Smltiulca The Wrld Bsnk NMmcs: (1) An hocau In d Index oDs ap- dopen ftb.RR 23 **Cge"nd at ema** polen. (3) Anaplve sumber me. - lonsu Im n -e .

# TABLE _1J:_ MAJOR ECONOMIC MEASURES USED BY THE COUNTRIES IN THE SAMPLE

MOVETOWARDS A MORE FISCAL **SMitLIZATN** STERIWUZATON **RESIRlt** ON UMERALIZATION **OP** CAPITAL OUTFLOW FLOATING **EXCH.** RATE **RESIRAINT** THROUGH OPEN **THROUGH OTHER** CAPlTALINFLOWS **Sasa** THECURRENT LIBERAlIZAllON ddl6nhgafihclm_.1U1ldq (tdJecf MARKET **.** MECHANISMS **S/,e terlhdPl.minimum** ACCOUNTWCifs **daweefr,0** . _prAn_ **mpblick** OPERATlIONS **bbusktecnaselnnnmm** mmrmmm uumm nducilan,c.t go p toabuketeor cIndFsbe ) Inlbm'bpAlshln tenip bm4. ceUinp on km.bckzac nusC. Rblga hnwnwi. elt.) ARCENINA YES YES CHILE YES YES YES YES YES **COLOMNMIA YES YES** YES YES **INDONESIA YES YES YE YES YES** KOREA **YES** YES YES **MALAYSIA YES** YES YES **YES** MEliCO YES YES **YES YES YES PHIUPPINES** YES YES YES **YES YES THAILAND YES YES YES YES YES YES YES Only** Teaary
Figure 2 <!-- Start of picture text --> NET ODA AS RATIO OF CDP<br>CArwiual av.raQe)<br>40<br>35<br>20<br>10<br>m Kny 1981-esh WAl988a-0n0 gad<br>j<br><!-- End of picture text --> Source: IMF and World Bank data, and authors' estimates.

Figure 2 shows average aid flows as a percentage of GDP for the five countries in the two periods under consideration. It reveals that net disbursement of ODA as a percentage of GDP has increased in all five countries in the period of reform. The sharpest increase has been in Tanzania where this percentage went up from 13.5 in the pre-reform period to 34.7 in the period of reform. Both in the pre-reform period and during the period of rei.m, Tanzania has received the largest amount of aid, both in absolute terms, and as a percentage of GDP.
Does the increase in the net value of work by apprentices, or marginal profits, affect average firm profits? Measures of firm sales and profits are notoriously noisy. In the context of the study, we asked firm owners to directly report sales and profits (as recommended by De Mel et al. (2009)). We also asked firm owners to list all their sales from the previous months to obtain a second measure. Based on this listing, we then asked them to report again sales and profits, which provides a third (and our preferred) measure. Moreover, experienced supervisors conducted near-systematic back-check of firm surveys to obtain repeat measures (for 598 out of 677 firms). In total, this means we have six measure _yim, m ∈{_ 1 _, . . . ,_ 6 _}_ of sales and profits for most firms in the sample. We estimate the following regression pooling all the observations together, and including dummies specific to each type of measure: Results are presented in Table A9. As for results on firm workforce, we do not detect any significant effect on average sales or profits, either using the variables themselves or an inverse hyperbolic sine transformation. These results suggest that, while marginal profits increase, no significant impacts are found on average firm profits. This could be due in part to more limited statistical power given the dispersion of the profit variables.

Figure A3 provides additional information on impacts on the distribution of revenues and profits. It shows that the point estimates for program impacts on firm profits and revenues are positive for most of the distribution. However, while these effects are significantly positive in a few parts of the distribution, they are not significant overall. They also tend to be negative at the top of the distribution, driven by a few firms in the control group. The observed patterns are consistent with large variability in the profit and revenue variables. To put results in perspective, the estimated increase in the net value-of-work provided by apprentices (21 380 FCFA per month) amounts to 13 percent of average firm profits in the control group. Overall, the results are suggestive that the increase in the value of work by apprentices (or marginal profits), while strongly significant and positive, are not sufficient to increase average revenues and profits in firms.

29
- 17 -

less conclusive, though.<sup>8</sup> While the coefficient of _HRLOCAL_ is significantly negative, _TCREGION_ has a positive coefficient. The fixed-effects results may also be biased because of the uncontrolled endogeneity issue.

**<u>Table 3. OLS and fixed effects estimation</u>**

|OLS<br>Coef.|Std.Err.|OLS<br>Coef.|Std.Err.|Fixed e<br>Coef.|ffects<br>Std.Err.|Fixed e<br>Coef.|ffects<br>Std.Err.|
|---|---|---|---|---|---|---|---|
|ln_N_t-1<br>0.692|(0.072) ***|0.696|(0.072) ***|0.383|(0.062) ***|0.376|(0.061) ***|
|ln_N_t-2<br>0.155|(0.060) ***|0.153|(0.060) ***|||||
|ln_N_t-3<br>0.133|(0.040) ***|0.131|(0.040) ***|||||
|ln_HRREGION_<br>-0.011|(0.005) **|||0.151|(0.122)|||
|ln_HRLOCAL_<br>-0.032|(0.005) ***|||-0.191|(0.111) *|||
|ln_TCREGION_||-0.004|(0.005)|||0.206|(0.057) ***|
|ln_TCLOCAL_||-0.027|(0.005) ***|||0.038|(0.050)|
|ln_POPD_<br>-0.034|(0.005) ***|-0.031|(0.005) ***|0.105|(0.287)|-0.146|(0.272)|
|_t_=2012||||0.184|(0.032) ***|0.166|(0.031) ***|
|_t_=2013||||0.298|(0.031) ***|0.244|(0.031) ***|
|_t_=2014<br>0.054|(0.029) *|0.052|(0.028) *|0.369|(0.028) ***|0.324|(0.027) ***|
|_t_=2015<br>-0.062|(0.029) **|-0.064|(0.029) **|0.333|(0.030) ***|0.287|(0.029) ***|
|_t_=2016<br>-0.052|(0.027) *|-0.053|(0.027) **|0.431|(0.034) ***|0.383|(0.034) ***|
|_t_=2017<br>0.076|(0.025) ***|0.076|(0.025) ***|0.562|(0.037) ***|0.516|(0.036) ***|
|_t_=2018<br>0.312|(0.034) ***|0.309|(0.035) ***|0.845|(0.050) ***|0.800|(0.047) ***|
|_t_=2019<br>0.105|(0.034) ***|0.093|(0.034) ***|0.625|(0.051) ***|0.591|(0.048) ***|
|_t_=2020<br>0.090|(0.028) ***|0.076|(0.028) ***|0.641|(0.051) ***|0.626|(0.048) ***|
|constant<br>0.327|(0.032)***|0.329|(0.035)***|2.463|(0.824)***|2.715|(0.757)***|
|Obs.<br>1,374||1,374||1,893||1,893||
|R-squared<br>0.9925||0.9924||0.9247||0.7214||
|F stat.<br>16771.57||16728.29||138.56||124.32||
|No. of group dummy variab|les|||261||261||
|Hausman test stat.||||652.67|***|688.06|***|

Note: The dependent variable is ln _N_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

38. To manage the potential endogeneity issue, the system GMM is adopted. The results are shown in **Table 4** . First of all, it is found that the selected instrumental variables are valid according to the Hansen overidentifying restriction test. The test statistics are estimated at 220.2 to 253.3, depending on the specification. For the serial correction, as expected, the first-order autoregressive test AR(1) detects significant correlation in the residuals of the differenced

> 8 In the fixed-effects models, the first lag of the dependent variable is included on the right hand side. It is found that only the first lag has a significant impact on the current value.
this data set display variations of property rights both in cross section and time series reflecting the differential timing of many decentralized reforms. The rich variation in property rights and the size of the data set allow us to examine in detail the determinants of property rights such as management turnover, profit retention rates, production decisions, financing arrangements, and wage control rights.

We find that the property rights configurations were in general consistent with the implications of a principal-agent model in which the government had at least three goals: profitability (or tax revenue), control over firms, and inter-firm equality -- through bailing out firms in financial troubles, and collecting heavier taxes on well-performing firms. The property rights structure also reflected government attempts to deal with the information problem relying on the rationality of SOEs: it designed high-powered incentives -- that is, higher firm-level pay sensitivity -- for efficient firms, which accepted this type of contract because they expected high revenue associated with their high productivity, and it used low-powered incentives and more centralized control for inefficient firms, which chose this type of contract in anticipation of low productivity.

# **II. The Data and the Decentralized Reforms**

This research uses _A Survey of State Enterprises: 1980--1989,_ a retrospective survey conducted by the Chinese Academy of Social Science in 1991.2 The data set is a balanced one: no firms were dropped out during the 10 years. The survey questionnaires were sent out to 800 state enterprises. Valid responses of 769 firms, located in 21 cities in four provinces of China (Sichuan, Jiangsu, Jilin, and Shanxi), were returned. This data set was not designed to be a random sample of the state enterprises: large firms were over2This data set has been used by many other authors, including Groves et al., "Autonomy and Incentives in Chinese State Enterprises," _Quarterly Journal of Economics,_ 109(1) (1994); "China's evolving managerial labor market," in _Journal of Political Economy,_ 103(4) (1995).

2
# **Table 7. Trade Partners whose Exports to Belarus Are Likely to Rise the Most because of Belarus Adopting the CET**

|**Partner**|**Change in Import Value**<br>**(US$, millions)**|**Percentage of Total 2008 Belarus**<br>**Imports from Partner**|
|---|---|---|
|EU-27|-45.4|-0.6|
|_Of which:_|||
|Poland|2.66|0.2|
|Finland|1.57|0.9|
|Italy|1.44|0.2|
|Lithuania|1.14|0.5|
|Austria|0.99|0.5|
|Latvia|0.69|0.5|
|China|7.28|0.5|
|Switzerland|1.47|0.6|
|Uzbekistan|1.38|4.6|
|Thailand|0.85|1.2|
|Indonesia|0.84|1.5|
|Malaysia|0.66|0.8|
|Taiwan, China|0.56||
|Serbia|0.40|1.7|
|Moldova|0.36|0.4|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 34
WPS3717

# **The Doha Trade Round and Mozambique**

by Channing Arndt* Chapter 5 in _Putting Development Back into the Doha Agenda: Poverty Impacts of a WTO Agreement_ , Thomas W. Hertel and L. Alan Winters (eds.) forthcoming from the World Bank, Washington, DC

## World Bank Policy Research Working Paper 3717, September 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Ministry of Planning and Development, Mozambique and Associate Professor Purdue University.
see left part of Tier 4). In the right hand part of Tier 4, a material aggregate ( γ ) is derived from the Cobb-Douglas aggregation of different materials. The energy-material composite ( φ ) is combined with leisure (LS) to give the present consumption ( ζ ) at the second tier of the nested structure. Finally, at the top most tier of the nested structure, households trade off between present consumption and savings (S) while maximizing their utility. The household utility function is expressed as follows<sup>7</sup> : U = ψ [ ς { φ ( ν ( η (CH EL ,CH DG ), φ (CH1,..,CH f )), γ (CH1,..,CH k )), LS},S] (10)

**Figure 3: Nested Structure for the Household Sector to Model the DSM Option**

<!-- Start of picture text --> U=CES ( ζ ,S)<br>Tier 1<br>ζ =CES ( φ , LS) S Tier 2<br>φ =CES ( ν , γ ) LS<br>Tier 3<br>ν =CES ( η , φ ) γ =CD (CH1, ...CHk) Tier 4<br>η =CD(CHEL, CHDG) φ =CES (CH1, ...CHf)<br>Tier 5<br>CHEL CHDG CH1 ..... CHf<br><!-- End of picture text --> The CES functional form for U is given as follows<sup>8</sup> :

- 7 A similar approach has been used in a number of existing general equilibrium models (e.g., Jorgenson and Wilcoxen 1993; Bohringer and Rutherford 1997; Shoven and Whalley 1992 and Ballard et al. 1985).

- 8 The difference in household utilities between the base and counterfactual simulations is used as the measure of the change in economic welfare in this study.

9
of poverty and an increase in overall productivity – even if the jobs created pay little more than a subsistence wage or the employment is in less green industries and skill classes.

Some definitions focus on a subset of industries producing environmentally desirable outputs. Thus some studies, notably by the European Commission’s Environment Directorate, have used the OECD/Eurostat definition of the environmental goods and services industry (OECD, 1999), comprising “activities which produce goods and services to measure, prevent, limit, minimize or correct environmental damage to water, air and soil, as well as problems related to waste, noise and eco-systems. This includes technologies, products and services that reduce environmental risk and minimize pollution and resources.” That covers pollution management (e.g. air pollution control) and resource management (renewable energy plants and water supply). On this basis, green jobs constitute a small but significant share of total employment – 1.7% of total paid employment in Europe (EC, 2007). That is probably a higher fraction than a global estimate along UNEP lines would suggest; as UNEP notes, most of the documented growth in green jobs has so far occurred mostly in developed countries.<sup>1</sup> Jobs in the nuclear power sector are not included, and these are not generally regarded as ‘green’, although they are in a lowcarbon industry. Employees in many jobs might find that their jobs are not counted as ‘green’ despite the nature of the goods and services that they help produce. For example, jobs in the car industry are excluded, even though some may be devoted to developing lowcarbon vehicles.

Some studies have developed their own terminology, using data from detailed employment statistics or detailed company databases. The Pew Center, for example, defines the ‘clean energy economy’ as follows: “The ‘clean energy economy’ generates jobs, businesses and investments while expanding clean energy production, increasing energy efficiency, reducing greenhouse gas emissions, waste and pollution, and conserving water and other natural resources” (Pew, 2009). It comprises five categories: Clean Energy; Energy Efficiency; Environmentally Friendly Production; Conservation and Pollution Mitigation; and Training and Support. Using data about individual companies, Pew estimates the USA’s ‘clean energy economy’ to account for about half a percent of all US jobs.

> 1 At the same time, developed countries are responsible for by far the largest share of the stock of greenhouse gases in the atmosphere.  They have also probably made a disproportionate contribution to long-lived solid waste.  Hence some of the green jobs reflect the unsustainability of developed countries’ economies.

5
countries. Columns 3, 8, 13, and 18 present the differences in characteristics between the successfully contacted sample and the overall HFPS sample, which represents nonresponse bias due to the inability to contact sampled households. This reflects nonresponse due to poor network availability; respondent mobile phones being switched off or non-functioning, respondent’s unwillingness to answer a call from an unknown number, etc. For all four countries there appears to be some substantial bias introduced at this stage. Compared to the HFPS sample, successfully contacted households were more likely to reside in urban areas (for Ethiopia and Malawi), are wealthier (in terms of consumption expenditures, asset ownership, and housing), and have heads that are better educated. For Ethiopia, Malawi, and Uganda, the bias due to noncontact appears to further magnify the coverage bias with many of the same indicators showing significant bias and in the same direction as that for coverage bias. Non-response bias due to non-contact does appear to be less substantial for Uganda with fewer significant differences in Column 18 compared to the other countries’ samples. However, this is largely a product of the much higher successful contact rate in Uganda compared to the others.

When disaggregating between the urban and rural HFPS sample in Ethiopia (shown in Table 4.3b), the extent of the detected bias due to noncontact is reduced for both samples. However, bias due to noncontact is detected across more indicators in the urban sample suggesting that bias due to noncontact could be more prevalent for that sample. However, the direction of the bias (in favor of wealthier households) is consistent across the urban and rural samples in Ethiopia.

Lastly, columns 5, 10, 15, and 20 in Table 4.3a present estimates of bias due to non-interview of _successfully contacted_ households in the four countries’ samples. This represents any bias due to respondent refusal to participate in the survey or a break-off during the interview. For all four countries, the bias introduced at this stage appears to be minimal with no significant difference observed for any indicators. This is unsurprising however since refusal and breakoffs were relatively uncommon in Ethiopia, Malawi, Uganda, and Nigeria (representing only 3%, 1%, 5%, and 1% of successfully contacted households, respectively). Minimal bias due to non-interview was also observed in both the urban and rural HFPS samples in Ethiopia in Table 4.3b.

20