rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Regulatory Transparency Index (RTI) and External Variables (Poisson method)**

|y-variable: Regulatory Transpar<br>|ency Index (R<br>|TI) (0-1)<br><br>||||||
|---|---|---|---|---|---|---|---|
|VARIABLES|(I|)<br>(II)|(III|)<br>(IV)|(V|)<br>(VI|)<br>(XII)|
|Democracy (Polity IV) (0-10)|0.060***<br>[0.015]|||||||
|Autocracy (Polity IV) (0-10)||-0.059***<br>[0.023]||||||
|Polity (Polity IV) (0-10)|||0.033***<br>[0.010]|||||
|Freedom house political rights|index (1-7)|||-0.103***<br>[0.025]||||
|Freedom house civil rights ind|ex (1-7)||||-0.133***<br>[0.030]|||
|Control of corruption (0-100)||||||0.254***<br>[0.066]||
|Internet Usage|||||||0.013***<br>[0.002]|
|GNI pc|0.001***|0.001***|0.001***|0.001***|0.001***|0|0|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Constant|0.044|0.470***|0.245***|0.740***|0.832***|0.488***|0.205***|
||[0.111]|[0.067]|[0.078]|[0.097]|[0.111]|[0.060]|[0.070]|
|Observations|126|126|126|149|149|149|148|

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 We also correlate our new index with the Control of Corruption indicator of the Worldwide Governance Indicators (Kaufman et al. (2010)) and find that our new index is positively correlated with the Control of Corruption variable. However, we also find that our income control variable loses its statistical significance suggesting that the Control of Corruption variable might simply be mirroring an overall income effect. Our findings are consistent with those of other authors who have presented evidence that more access to information is associated with greater control of corruption and better overall governance. For example, Islam (2006) finds that governments that are timelier in the release of important political and macroeconomic data are also governments that rank better on various measures of good governance. Djankov et al. (2009) find that corruption is lower in countries where politicians face more stringent disclosure requirements of their assets and business interests. And, Williams (2009) by means of grangercausality regressions shows that the release of information by governments has a positive shortterm effect on the quality of bureaucracy.

# **IV. 3. Is greater access to regulatory information associated with better regulation?**

After investigating which factors make it more likely that regulatory information is easily accessible we also would like to know if more access to regulatory information goes hand in hand with greater regulatory quality. We would expect that greater regulatory quality is associated with greater accessibility of regulatory information. Our hypothesis is that where information is easily accessible it should also be easier for citizens to demand better regulatory 11
**Table A.10: Robustness of provider effort results to exclusion of dysentery cases**

|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|
|**Repre**|**sentative sa**|**mple**|**Dual**|**practice sa**|**mple**|
||Percentage|||Percentage||
|Time Spent<br>(mins)|<br>of checklist<br>items|IRT score|Time Spent<br>(mins)|<br>of checklist<br>items|IRT score|
|**Panel A: SP and case fixed effects**||||||
|Is a private provider<br>1.531***|6.942**|0.519**|2.200***|12.267***|0.687***|
|(0.306)|(3.307)|(0.209)|(0.419)|(2.376)|(0.194)|
|R-squared<br>0.225|0.152||0.170|0.154||
|Number of observations<br>440|440|233|201|201|138|
|Mean of public<br>2.956|17.540||1.970|17.481||
|Mean of private<br>4.548|24.335||4.043|30.196||
|Mean of sample<br>4.427|23.820||3.011|23.870||
|**Panel B: SP, case and market/district fixed ef**|**fects**|||||
|Is a private provider<br>1.907***|7.593***|0.629**|2.201***|12.271***|0.682***|
|(0.367)|(2.727)|(0.275)|(0.406)|(2.392)|(0.194)|
|R-squared<br>0.341|0.278||0.194|0.164||
|Number of observations<br>440|440|233|201|201||
|**Panel C: SP, case and market/district fixed ef**|**fects**|||||
|Is a private provider<br>1.654***|6.087*|0.543*|2.061***|12.243***|0.729***|
|(0.481)|(3.354)|(0.299)|(0.421)|(2.514)|(0.196)|
|Has MBBS<br>-0.062|6.415*|0.206||||
|(0.847)|(3.805)|(0.254)||||
|Has some qualification<br>-0.159|2.737|0.119||||
|(0.435)|(2.159)|(0.156)||||
|Age of provider<br>-0.002|0.027|0.003|0.018|-0.004|0.003|
|(0.017)|(0.088)|(0.008)|(0.024)|(0.141)|(0.010)|
|Gender of provider (1=Male)<br>1.460*|2.136|0.060|-0.343|-3.130|-0.186|
|(0.789)|(4.284)|(0.325)|(0.605)|(3.609)|(0.315)|
|Patient load during visit<br>-0.188***|-0.333|0.034|-0.132|-0.054|-0.033|
|(0.059)|(0.609)|(0.044)|(0.103)|(0.614)|(0.038)|
|R-squared<br>0.357|0.283||0.218|0.170||
|Number of observations<br>423|423|221|183|183|126|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. All regressions include a constant. Observations are standardized provider-patient interactions, except in IRT score column where each observation is a provider. The score is computed using all cases, plausible values scores are used. Market fixed effects are used for the representative sample, and district fixed effects for dual practice sample.

70
effect may be compounded by employers, who presumably expect less career commitment from their female employees.

The other group of Northern European countries differ from the Scandinavians in that they have lower FLFP levels but also less generous policies to encourage participation. As for the other four country groups, there is a clear correspondence between their macro- and micro-levels results; this suggests the existence of a bifurcation at the level of developed countries; in this case increases in FLFP do not lead to declines in inequality. Existing labor market regulations and the fiscal policies play an important role in this respect. In the case of these countries, the higher returns of top deciles suggest that the _glass ceiling_ is less present, and that women can reach top positions. Less generous pro-participation policies limit women’s access in the lower deciles and lead to a much lower participation level than the Scandinavians. Results hold even when considering surveys 5 years older (see table A4 in the Appendix).

When introducing the PAM index as an additional instrument (Table 5) and restricting the sample, the results do not vary substantially. Only in Ireland the impact of FLFP becomes insignificant while in all other countries the signs, significance and country ordering remain practically the same as in Table 4. Overall in developing countries the impact is pro-inequality while with the exception of the United Kingdom and the Netherlands, the FLFP impact is pro-equality in developed countries.

# 5. Conclusions

Over the past few decades developing countries have seen a rapid increase in the number of women taking an active role in the labor market; however, this picture is highly heterogeneous. For many developing countries, increasing FLFP levels remains a priority (United Nations, 2000; World Bank, 2012), as well as a challenge because, as documented in this paper, increasing FLFP levels in many of these countries can produce undesired distributional effects if it is not adequately combined with measures targeted towards women in the lower deciles of the income distribution.

As a matter of fact, our results confirm that the relationship between FLFP and inequality follows an inverted U-shaped. Therefore, if a country is located on the ascending part of the inverted U- shaped curve, increasing FLFP levels can lead to increasing inequality. This is the case in at least 19
# **Table 2: The Real Exchange Rate and Export Growth**

||**I**|**II**|**III**|**IV**|**V**|**VI**|
|---|---|---|---|---|---|---|
||**Mercha**|**ndise**|**Traditiona**|**l Services**|**Modern S**|**ervices**|
|Log Per Capita Income, Lag|-0.03|-0.03|-0.03|-0.03|0.02|0.01|
||[1.08]|[1.02]|[1.33]|[1.43]|[0.41]|[0.28]|
|RER, Percent Change|0.15***|0.14***|0.14***|0.16***|0.23***|0.26***|
||[4.27]|[3.18]|[4.95]|[4.75]|[3.99]|[3.87]|
|RER, Log||0.01||-0.03||-0.06|
|||[0.23]||[1.14]||[1.04]|
|Country Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Time Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|380|380|380|380|380|380|
|Number of Countries|66|66|66|66|66|66|
|R-square|0.30|0.31|0.35|0.35|0.26|0.26|

Note: *, **, *** indicate that coefficients are significant at 10, 5, and 1 per cent levels respectively. The dependent variable is growth in exports of merchandise, traditional or modern services, (in constant USD). Observations are averages over five-year periods. Data are from 1980-2009. Regressions include country fixed effects and time fixed effects, as indicated. Standard errors are robust and clustered at countries. Log per capita income is lagged by one five year period, per cent change in real exchange rate and log real exchange rate are the average values over the contemporaneous five year period.

In Appendix Table A3 we obtain similar results with alternative measures of the real exchange rate. The coefficient on the real exchange rate is positive and significant for all different exchange rate measures, for the exports of merchandise and different services alike. As before, it is larger for modern services.

Next we include lagged changes in RER in regressions similar to those reported in table 2. We find that the coefficient on the contemporaneous value is positive and significant while the coefficient on the lag is insignificant. Again, the coefficient on the contemporaneous value of the RER is twice as large for modern services as for merchandise and traditional services.

In Table 3 we probe for a differential effect of the RER in developing (low and middle income) and developed (high income) countries, by including the interaction of RER with a dummy for developing countries. There is little evidence to this effect. Interestingly, though the coefficient of the interaction between RER and the dummy for developing countries is largest for modern services.<sup>**14**</sup> 14 The results do not change when we define developing countries as low and low middle income countries; and define developed countries as high middle income and high income countries. We also interact the real exchange rate with lag per capita income rather than with a simple dummy for developing countries in order to see if the effect of the RER on growth differs incrementally with per capita income of the countries. When we include the interaction of the RER and per capita income, the coefficient on the interaction term is not significant, and it also takes away the significance of lag per capita income and RER, perhaps due to multicollinearity.

7
which nine would fall in the low-income category as traditionally defined in the World Development Indicators (WDI).' The MICs are a heterogeneous group comprising a wide range of countries in terms of size, level of development and access to international capital markets. For example, MICs as defined here include Nigeria with a 1999 per capita GNP of $310 at one extreme and South Korea with a per capita GNP of $8,490 at the other. They also include Palau with a population of only 19,000 and China with over 1.2 billion people. In terms of access to international capital markets, the MICs include some investment grade countries such as Chile and Mexico, but the majority of countries in this group are rated below investment grade or simply not rated by major credit rating agencies.

# **Rising Weight of MICs in the Global Economy**

4. The MICs are becoming an increasingly important force in the world economy. Nearly 70 percent of the world's population lives in the MICs. The weight of the MICs in global economic activity has been rising. In the 1990s, these countries accounted for 19 percent, almost one-fifth, of world GDP (Figure 1). According to _Global Economic Prospects_ projections, their share will rise to more than one-fifth in the next decade. In purchasing power parity (PPP) terms, their share in world GDP is almost twice as high (Figure 2). Their share in world exports has also been increasing, rising from 12 percent during the 1970s to 20 percent in the 1 990s. These countries also receive a lion's share96 percent in 1990-98-of net private capital flows to developing countries. This share in private flows remained steady during the debt crises of the 1980s and rose significantly during the 1 990s while their reliance on official flows declined (Figure 3). Private capital flows to MICs rose sharply in the 1990s until the East Asian crisis (Figure 4).

<!-- Start of picture text --> Figure 1. MICs' Rising Share in World GDP Figure 2. MIC Share in World GDP<br>and Exports (PPP dollars)<br>2 *Share in world GDP *Sh-re in ,orld exports<br>20_ 17 f 1 29<br>'5-<br>10~~~~~~~~~~~~~~~~~~~~~~2<br>9<br>1 70s 1980. 19999 1975-79 199089 1990-99<br>Source World Bank staff estimates. Source World Bank staff estimates<br><!-- End of picture text --> A list of the 78 IBRD-eligible borrowers, and their classification by per capita income group, is presented in Annex A. The analysis in this paper includes 77 countries; Slovenia, the only high-income country eligible for IBRD lending, is excluded. The current Country Assistance Strategy for Slovenia sets out a 3-5 year phase-out of World Bank assistance and outlines a new partnership between the Bank and Slovenia.

2
Table 3.6: Earnings Equation for All Workers and Apprentices who Earn: Full-time and Male Dependent Variable In (earnings).

|Variables|Equation (1)|Equation (2)|
|---|---|---|
|Constant|7.3|5.49|
||(3.5)|(22.2)|
|Age|0.06|0.17|
||(5.7)|(15.4)|
|Age<sup>2</sup>|-0.001|-0.002|
||(4.6)|(12.8)|
|Education (in years)|0.02|0.032|
||(3.9)|(5.4)|
|Tenure|0.01|0.02|
||(3.3)|_(5.5)_|
|In Size|0.08|0.09|
||(4.0)|(3.7)|
|Union|0.21|0.25|
||(4.8)|(3.7)|
|Firm Age|-0.009|-0.005|
||(6.2)|(5.7)|
|Profits per employee|0.05|0.05|
||(5.1)|(4.2)|
|In Capital/Labor ratio|-0.02|0.005|
||(1.8)|(0.4)|
|Foreign ownership|0.18|0.17|
||(4.3)|(3.60)|
|State ownership|0.35|0.32|
||(5:0)|(3.1)|
|Wave 2|0.21|0.24|
||(5.2)|(4.96)|
|Wave 3|0.32|0.32|
||(8.3)|(3.9)|
|Adjusted R<sup>3</sup>|<br>0.72|0.57|
|<br>F (D.F.)++<br>|3.9(56,1439)*|2.0(42,1460)|
|**X**<sup>**2** </sup>(White test) (D.F.)|529 (389)|385 (231)|
|Number of<br>observations|1,526|1,526|

* indicates pooling is rejected at the 1% significance level.

+Equations (1) and (2) both control for sector and location effects. Equation (1) also controls for occupation while equation (2) does not.

++ The F test is a test for the validity of pooling the regression.

The absolute value of t statistics are in parenthesis where the standard errors have been corrected by White's method (1980).

_Source: Teal (1996)._ The regression models control not only for the human capital characteristic of workers, but also for a number of variables pertaining to the enterprises themselves. It is seen that the ownership variable is strong and significant in spite of the large number of 17
Table 3: Planned FDI by Percentage of Liberalization

||ATET|ATET|ATET|ATET|ATET|
|---|---|---|---|---|---|
|0% (1vs0) Liberalized|-0.856***<br>(0.0816)|||||
|_>_=25%_<_50% (1vs0) Liberalized||-0.0770***<br>(0.0201)||||
|_>_=50%_<_75% (1vs0) Liberalized|||-0.0702***<br>(0.0228)|||
|_>_=75%_<_100% (1vs0) Liberalized||||-0.0586<br>(0.126)||
|100 % (1vs0) Liberalized|||||0.189***<br>(0.0198)|
|Observations|1,716|1,716|1,716|1,716|1,716|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects. The sample period is January 2019 to March 2022.

in the period after the policy as compared to the period before the policy. At the same time, we do not find that DDI declined in the secondary sectors where FDI increased following the reform. Therefore, this may signal that FDI did not crowd out DDI at the broad sector level.

Table 4: Realized DDI by Sector

||ATET|ATET|ATET|
|---|---|---|---|
|Primary Sector (1vs0)|0.582***<br>(0.107)|||
|Secondary Sector (1vs0)||-0.233<br>(0.213)||
|Tertiary Sector (1vs0)|||0.359***<br>(0.136)|
|Observations|990|990|990|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects. The sample period is January 2019 to September 2022.

Source: Author Estimations

# **_Non-Commodity Sectors by Level of Liberalization_**

When we analyze the effect based on the levels of liberalization, we find suggestive evidence of crowding in in the fully liberalized sectors. We find that for the zero percent liberalized sectors, 16
# **5 Correlates of Project Success in FCS and Non-FCS Projects**

**This section of the analysis focuses on understanding more about correlates of project outcome ratings.** It is important to be careful in interpreting these results to distinguish between correlation and causation. Out of a set of observable project characteristics, such as project length, size, costs, staff time, mission days, etc., it is possible to compare the mean level of these characteristics in projects that obtain satisfactory outcome ratings to those that do not, and thus ascertain the correlation between success, as measured through outcome ratings, and project characteristics. This, however, does not mean that these characteristics determine project success. It could be that other unmeasurable factors influence both project success and project characteristics, such as the complexity or difficulty of a project, and this leads to the correlation that we observe.

**Figure 16 shows the breakdown of outcome ratings for projects in FCS and non-FCS countries, as well as the breakdown of satisfactory outcome ratings by sector board.** As discussed in the previous section there are proportionately fewer projects in FCS countries obtaining moderately unsatisfactory ratings (3) and proportionately more obtaining unsatisfactory ratings (2), relative to projects in non-FCS countries. Across sector boards there are not many systematic differences apart from projects under the FPD and Water (WAT) sector boards obtain slightly weaker outcomes in FCS countries, while Health (HE) projects do relatively less well in non-FCS countries.

**Figure 17 considers the country-level covariates, such as the country’s policy and institutional assement (CPIA), GDP per capita growth and the size of the World Bank country office.** CPIA scores are lower in FCS countries as is expected given the criteria for the FCS list but within FCS and non-FCS countries CPIA is not correlated with project success. However, these samples of countries contain very little variation in CPIA scores, meaning that it is difficult to expect variation in the CPIA scores to have significant correlation with project outcome ratings. Where GDP per capita growth data have been recorded, it appears that FCS countries have stronger growth trends than non-FCS countries and in both groups of countries growth is correlated with stronger outcome ratings. As discussed in the previous section, non-FCS countries have larger country offices than FCS countries. However, country office size does not appear to be linearly correlated with project success in FCS countries and is negatively correlated with project success in non-FCS countries.

Figure 18 considers the project-level covariates related to the design and preparation of projects such as size, length, preparation time and costs. **Project size is positively correlated with project success, while project length, preparation time and preparation costs are negatively correlated with project success in both FCS** 24
<!-- Start of picture text --> 2014 2015 2016<br>1.00 ; 1.00 ; 1.00 ;<br>0.75 0.75 0.75<br>0.50 0.50 0.50<br>0.25 0.25 0.25<br>0.00 i 0.00 0.00<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>2017 2018 2019<br>1.00 ; 1.00 ; 1.00<br>0.75 0.75 , 0.75<br>0.50 0.50 0.50<br>0.25 0.25 0.25<br>0.00 i 0.00 i 0.00 i<br>10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100<br>2020<br>1.00 ;<br>0.75<br>0.50<br>0.25<br>0.00 I<br>10 20 30 40 50 60 70 80 90 100<br><!-- End of picture text -->
# **Table 5b – Random Effects (Probit) Poverty Regressions with Different Sets of Regressors**

||||**Balanc**|**ed panel**||||
|---|---|---|---|---|---|---|---|
|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|**Poor=P+a+b+c+d**|**Poor=P+a+b+c**|**Poor=P+a+b+d**|**Poor=P+a+c+d**|**Poor=P+b+c+d**|**Poor=P+a+b**|**Poor=P+a+c**|**Poor=P+a+d**|
|`-0.132`|`-0.336`|`-0.152`|`0.078`|`-0.156`|`-0.354`|`-0.174`|`0.06`|
|`-1.35`|`(3.66)**`|`-1.54`|`-0.82`|`-1.58`|`(3.83)**`|`-1.96`|`-0.62`|
|**9**|**10**|**11**|**12**|**13**|**14**|**15**|**16**|
|**Poor=P+b+c**|**Poor=P+b+d**|**Poor=P+c+d**|**Poor=P+a**|**Poor=P+b**|**Poor=P+c**|**Poor=P+d**|**Poor=P**|
|`-0.352`|`-0.169`|`0.073`|`-0.19`|`-0.364`|`-0.195`|`0.061`|`-0.205`|
|`(3.83)**`|`-1.71`|`-0.76`|`(2.12)*`|`(3.95)**`|`(2.19)*`|`-0.64`|`(2.30)*`|
||||**Unbalan**|**ced panel**||||
|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|**Poor=P+a+b+c+d**|**Poor=P+a+b+c**|**Poor=P+a+b+d**|**Poor=P+a+c+d**|**Poor=P+b+c+d**|**Poor=P+a+b**|**Poor=P+a+c**|**Poor=P+a+d**|
|`-0.012`|`-0.16`|`-0.023`|`0.181`|`-0.01`|`-0.168`|`0.005`|`0.172`|
|`-0.35`|`(4.67)**`|`-0.66`|`(5.16)**`|`-0.27`|`(4.89)**`|`-0.14`|`(4.92)**`|
|**9**|**10**|**11**|**12**|**13**|**14**|**15**|**16**|
|**Poor=P+b+c**|**Poor=P+b+d**|**Poor=P+c+d**|**Poor=P+a**|**Poor=P+b**|**Poor=P+c**|**Poor=P+d**|**Poor=P**|
|`-0.147`|`-0.01`|`0.19`|`0`|`-0.148`|`-0.011`|`0.194`|`-0.007`|
|`(4.27)**`|`-0.27`|`(5.40)**`|`-0.01`|`(4.30)**`|`-0.34`|`(5.53)**`|`-0.22`|

Legenda: z-stat under coefficients; (*) Significant at 5%; (**) Significant at 1%; poor (1=poor; 0=Non poor); P (1= soc. ben. Recipient; 0=Others); a=hfem, hage30_45, hage46_60, hage60plus, hprim, hsec, htert; b=nchildm dep3_4, dep5plus; c=rural, summer; d=terremprate, growth.

25