rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: ANCOVA Regressions

||FS Index|Ethi<br>Mild|opia<br>Moderate|Severe<br>|FS Index|Malawi<br>Mild|Moderate|FS Index|Nig<br>Mild|eria<br>Moderate|Mild|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Baseline HHI|-0.107|0.014|-0.039|-0.031|-0.101|-0.175*|-0.182**|0.017|-0.019|-0.013|-0.013|
||(0.106)|(0.042)|(0.043)|(0.022)|(0.116)|(0.072)|(0.055)|(0.107)|(0.062)|(0.061)|(0.048)|
|Baseline FS _×_ Baseline HHI|-0.066|0.017|0.084|-0.204***|0.412*|0.087|0.270*|-0.012|0.005|0.051|-0.259|
||(0.126)|(0.067)|(0.119)|(0.042)|(0.182)|(0.082)|(0.113)|(0.095)|(0.074)|(0.084)|(0.220)|
|Baseline Food Security (FS)|0.287***|0.152**|0.103|0.148***|0.176|0.075|0.018|0.189*|0.072|0.108|0.311|
||(0.085)|(0.051)|(0.084)|(0.031)|(0.117)|(0.053)|(0.076)|(0.074)|(0.058)|(0.066)|(0.164)|
|Observations|14,506|14,506|14,506|14,506|14,370|14,370|14,370|5,335|5,335|5,335|5,335|

_Note_ : The table displays regression results from our ANCOVA specification with round and regioncontrols (see Equation 3.3). HHI stands for HerfindahlHirschman Index while FS stands for our standardized index of food insecurity. Note that in the Malawi baseline data no respondent reported being severely food insecure and so we cannot estimate the ANCOVA specification for severe food insecurity in Malawi. Standard errors, clustered at the household, are reported in parentheses (*** p _<_ 0.001, ** p _<_ 0.01, * p _<_ 0.05).
**Figure 1.7: As per Kuznets, structural transformation is driven by**

<!-- Start of picture text --> a rising capital -labor ratio<br>Capital Labor Ratio for Selected Countries<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>China Germany South Korea Malaysia<br>Thailand United States Vietnam<br>Sources: ILO, UNIDO and WDI- authors calculations<br><!-- End of picture text -->

# **Section 1.3:  Industrialization – The key to modern economic growth and catch-up**

The Industrial Revolution drew attention to the critical role that continuous technological innovation and upgrading play in the development of an efficient manufacturing sector. The latter offers new and boundless possibilities for the production of _tradable goods_ including technology itself which enable countries to import what they do not produce. It demonstrated that in the first stage of catch-up (Lall, 2005; Gault and Zhang, 2010),<sup>2</sup> in their role as the primary financiers of R&D for technological innovation, manufacturing firms can also be instrumental in the transfer of new technologies to non-manufacturing sectors of the economy (Shen, Dunn and Shen, 2007). Fittingly, manufacturing is credited for the modernization of the agricultural and mining sectors that provide raw materials for it through backward linkages, as well as for spawning ancillary activities, particularly services, through forward linkages.

There are several other advantages in fostering a manufacturing sector. Manufacturing can serve as an indirect source of demand and spur catch-up. Because there is a tight nexus between it and the services sector, technological progress and growth in manufacturing leads to a larger demand for services and propels overall economic growth.

> 2 Later, as a country becomes more prosperous, the primacy of manufacturing is replaced by the services sectors as noted by Kuznets‘s fact 1.

12
ownership of the mineral resources in oil extraction, gas and coalmining (representing 15 percent of total capital); (3) sector specific capital required for expansion of output in imperfectly competitive domestic firms producing either goods or services (representing 32 percent of the capital in the benchmark); (4) sector specific capital required for expansion of output in imperfectly competitive foreign firms producing either goods or services (representing 5 percent of the capital in the benchmark); and (5) ownership of licenses for monopoly rents in services sectors (representing 2 percent of capital in the benchmark). We do not have data that would allow us to associate specific capital holdings in given sectors with particular households. Thus, we assume that all households that hold capital, hold the different types of capital in the same proportions.

# **1.5 Household Consumer Demand**

We assume each household maximizes a Cobb Douglas utility function of the aggregate 35 goods in our model subject to its budget constraint (which is factor income net of transfers). Factor income shares, aggregated to the decile level, are reported in Table 4.<sup>2</sup> Each of the 35 aggregate commodities is a CES (“Armington”) aggregate of imported goods or services and goods or services produced in Russia. In imperfectly competitive goods sectors, imported and Russian produced goods are Dixit-Stiglitz aggregates of the outputs of foreign or Russian firms.

> **2** Households are modeled endogenously based on the 55,000 households of the Russian Household Budget Survey (HBS). The HBS, which is representative at the regional level, has very detailed information on household consumption expenditures, and information about age, gender, education, and occupation of each member of the household. It also has information about expenditures and savings and by implication household income. The major shortcoming of the HBS for our purposes is that it does not contain information on the sources of income of the households. For sources of household income, we must turn to the Russian Longitudinal Monitoring Survey (RLMS). The RMLS has less than 5,000 observations and is not representative of the population on the regional level. But is has extensive information on individual and household sources of income: wages and profits from first, second, third jobs; pensions and unemployment benefits; profits and dividends from accumulated assets.

> We have employed both small area estimation and Matching techniques to generate sources of income data for all 55,000 plus households in the HBS. We describe our procedures in Rutherford, Tarr and Shepotylo. Results from both techniques yield similar results.

8
- 11 -

**<u>Table 2 Ordinary Least Squares Results without County Fixed Effects</u>**

||<br>_OPD_||<br>_MAL_|<br>_DEL_||<br>_OPD_||<br>_MAL_|_DEL_||
|---|---|---|---|---|---|---|---|---|---|---|
|_RDDEN_|0.348|***|0.042|**<br>0.013|**||||||
||(0.103)||(0.017)|(0.006)|||||||
|_GDRD_||||||0.002||-0.007|0.001||
|||||||(0.052)||(0.010)|(0.002)||
|_POPDEN_|-7.669|***|-0.888|**<br>-0.210|*|-1.607|*|-0.143|0.010||
||(2.095)||(0.359)|(0.121)||(0.868)||(0.160)|(0.043)||
|_POV_|-0.041||0.010|0.000||-0.100||0.000|-0.002||
||(0.057)||(0.012)|(0.003)||(0.067)||(0.013)|(0.003)||
|_BED_|1.225||-0.053|0.232|***|1.690||0.023|0.247|***|
||(1.229)||(0.140)|(0.053)||(1.488)||(0.171)|(0.062)||
|_CENTR_|-2.201||-0.275|-0.142||-1.174||-0.122|-0.107||
||(1.793)||(0.325)|(0.097)||(1.870)||(0.332)|(0.096)||
|Constant|5.408||0.114|0.125||15.560|***|1.561|*<br>0.471|**|
||(4.090)||(0.875)|(0.222)||(4.784)||(0.901)|(0.216)||
|Observations|69||69|69||69||69|69||
|R-squared|0.248||0.121|0.415||0.108||0.043|0.368||
|F-statistic|3.07||1.40|5.49||1.36||0.63|4.27||
|Number of<br>county|||||||||||
|dummies|0||0|0||0||0|0||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

County-specific fixed effects are incorporated into the model to take into account potential differences between facilities. Because some counties have only one hospital or health center in our data, only eight county-specific dummy variables are used. The poverty variable was omitted because it is a county-specific variable in our data.

The results are not different from the above (table 3). Although road density has an important effect on health care access, road quality measured according to the share of good roads does not seem to matter much. The needs for health services are relatively high where population density is low, because there are fewer hospitals and health centers in such areas. Hospital capacity is an important determinant of the number of babies delivered. With more capacity, more patients can be treated.
# **Figure 8. Exchange rates, yield differentials and foreign reserves**

# **A.  Bilateral exchange rate with U.S. dollar**

<!-- Start of picture text --> Percent Change since June 2014<br>20 Change since first NIRP announcement<br>10<br>0<br>-10<br>-20<br>Euro Area Sweden Denmark Switzerland Japan Hungary<br><!-- End of picture text -->

# **C. Real effective exchange rate**

<!-- Start of picture text --> Percent Change since June 2014<br>10 Change since first NIRP announcement<br>5<br>0<br>-5<br>-10<br>E. Yen and yield differential with U.S.<br>Exchange rate Percentage points<br>14 0<br>-0.2<br>12<br>-0.4<br>10 -0.6<br>-0.8<br>8 US$ per 1000 Yen<br>-1<br>Japan-US yield spread (RHS)<br>6 -1.2<br>2012 2013 2014 2015 2016<br>Euro Area Sweden Denmark Switzerland Japan Hungary<br><!-- End of picture text -->

# **B. Nominal effective exchange rate**

<!-- Start of picture text --> Percent Change since June 2014<br>15 Change since first NIRP announcement<br>10<br>5<br>0<br>-5<br>-10<br>D. Euro and yield differential with U.S.<br>Exchange rate Percentage points<br>1.4 0<br>1.3 -0.4<br>1.2 -0.8<br>1.1 -1.2<br>US$ per Euro<br>German-US yield spread (RHS)<br>1 -1.6<br>2012 2013 2014 2015 2016<br>F. Swiss Franc and foreign reserves<br>Exchange rate Billions of Swiss Francs<br>1.00 700<br>Euro per Franc<br>Official reserve assets (RHS)<br>600<br>0.95<br>500<br>0.90<br>400<br>0.85<br>300<br>0.80 200<br>2012 2013 2014 2015 2016<br>Euro Area Sweden Denmark Switzerland Japan Hungary<br><!-- End of picture text --> Sources: J.P. Morgan, Bloomberg, Haver Analytics, World Bank.

Notes: A.B.C. A decline in the exchange rate (negative value) indicates a depreciation. NIRP announcements are June 2014 for euro area, February 2015 for Sweden, December 2014 for Switzerland, September 2014 for Denmark, January 2016 for Japan, and March 2016 for Hungary. Last observation is June 2016.

D. German-US yield spread is the percent difference between 2-year government bond yields in Germany and the United States. Last observation is June 2016.

E. Japan-US yield spread is the percent difference between 2-year government bond yields in Japan and the United States. Last observation is June 2016.

F. Last observation is June 2016.

22
<!-- Start of picture text --> Figure 4.1. Overall Budget Balance excluding privatization irevenues<br>(Percentage of GDP)<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>I<br>I # 9 1995 199_ 8 1999 2000 2001<br>.2<br>.3<br>-4<br>.5<br>- 0-Consolidated Public Sector<br>-6<br>D 31Federal Govemment<br>-7 lb Provincial Govemments<br>-8<br>Source: Ministerio de Economla de la RepOblica ArgenUna and World Bank.<br>Figure 4.2. Consolidated Public Debt and Service<br>(Percentages)<br>70-<br>60_<br>lo Consolidated Public Sector Debt / GDP<br>K11 Interest I Tax Revenue- 0_<br>oitiX~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Source: Intemnational Monetary Fund.<br><!-- End of picture text --> 30
**Table A9** Secular Trends in IPV in ELA Only Communities

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**IPV often **<br>**Non-Goal**|**index**<br>**Goal**|**IPV in last **<br>**Non-Goal**|**year index**<br>**Goal**|
|Post|-0.033<br>|-0.350***|-0.071|-0.296***|
||(0.059)|(0.107)|(0.060)|(0.102)|
|Observations|1,285|458|1,285|458|

_Notes._ Each column in this table presents coefficient estimates of _θ_ 1 from a separate estimation of the following specification: _Yic_ = _α_ + _θ_ 1 _Postt_ + _ψagei_ + _εic_ . In columns 1 and 3, the sample is restricted to non-goal setting participants in ELA only communities. In columns 2 and 4, the sample is restricted to goal setting participants in ELA only communities. The outcome in columns 1 and 2 is the IPV often index and the outcome in Columns 3 and 4 is the IPV in last year index. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel, restricted to females in ELA only communities.

**Table A10** Impact of Treatments on Intimate Partner Violence (Restricted to those who report sometimes or never condom use at baseline)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|IPV often index|||||
|treatment x post|-0.155|-0.034|0.000|760|
||(0.223)|(0.218)|||
|treatment x post x Goal|-0.195<br>(0.362)||||
|IPV in last year index|||||
|treatment x post|-0.333|-0.462*|-0.000|760|
||(0.240)|(0.265)|||
|treatment x post x Goal|0.277<br>(0.378)||||

_Notes._ This table presents coefficient estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. See notes for Table 2 for details on table structure, control variables, and outcome definitions. Indices are centered around females in ELA only communities who report sometimes or never using a condom and were not assigned to the _Goal_ treatment, at baseline and endline separately. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel, restricted to females who report either sometimes or never using a condom at baseline.

68
Policy Research Working Paper 7354

# **Abstract**

This paper uses an original database of 469 politically connected firms under the Mubarak regime in Egypt to explore the economic effects of close state-business relations. Previous research has shown that political connections are lucrative. The paper addresses several questions raised by this research. Do connected firms receive favorable regulatory treatment? They do: connected firms are more likely to benefit from trade protection, energy subsidies, access to land, and regulatory enforcement. Does regulatory capture account for the high value of connected firms? In the sample, regulatory capture as revealed by energy subsidies and trade protection account for the higher profits of politically connected firms. Do politically connected firms hurt aggregate growth? The paper identifies the growth effects of the entry of politically connected firms by comparing detailed 4-digit sectors where they entered, between 1996 and 2006, and sectors that remained unconnected. The entry of connected firms into new, modern, and previously unconnected sectors slows aggregate employment growth and skews the distribution of employment toward less productive, smaller firms.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mschiffbauer@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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37
- The equations all have very low Durbin-Watson statistics which suggest that t- values may be upward biased.  (We explore this further in Section 5.2.2, where we report estimates from error correction models.)

**<u>Table 5:  Static Models for Generation Capacity</u>**

|Dependent Variable =<br>Log(Electricity Generation<br>capacity per capita)|**OLS model **|**Basic FE**<br>**model**|**Age-of -**<br>**regulator**<br>**dummies**|**Quadratic in age**<br>**of regulator**|
|---|---|---|---|---|
|**Explanatory variables**|**1**|**2**|**3**|**4**|
|Constant|-8.286||||
||(-52.162)||||
|Real GDPper capita(log)|0.772|0.805|0.697|0.699|
||(31.071)|(9.970)|(8.343)|(8.522)|
|Debt payments as a<br>proportion of national<br>income|4.14E-12|2.49E-12|-4.65E-13|1.07E-12|
||(0.838)|(0.556)|(-0.104)|(0.244)|
|Industry value added as<br>proportion of GDP|0.024|-0.002|0.000|0.001|
||(7.981)|(-0.607)|(-0.003)|(0.232)|
|Index of regulatory<br>governance 0-4|0.056|0.043|-0.026|-0.011|
||(2.982)|(3.444)|(-1.067)|(-0.638)|
|Regulator under 1year|||0.090||
||||(1.465)||
|Regulator 1-3years|||0.187||
||||(2.398)||
|Regulator aged over 3<br>years|||0.353||
||||(4.370)||
|Age of regulator||||0.055|
|||||(4.132)|
|(Age of regulator)<sup>~~2~~</sup>||||-0.002|
|||||(-2.635)|
|||Fixed|Fixed|Fixed effects|
|Estimation method|OLS|effects|effects||
|Adjusted R-squared|0.764|0.952|0.954|0.954|
|<br>S.E. of regression|0.605|0.272|0.267|0.266|
|F-statistic|465.943|372.079|352.169|365.770|
|Durbin-Watson<br>|0.043<br>|0.161<br>|0.163<br>|0.153|
|No of observations|577|577|577||

_Note: t statistics in parentheses_ 26
# **Box 1. Financial Stability Board and the Evaluation of Reform Impacts**

Evaluating and reporting on the effects of the G20 reforms figures prominently on the FSB’s agenda, including for EMDEs, but with a focus on G20/FSB members.

# _Ex-ante impact studies_

To calibrate key individual reforms, ex-ante impact studies were undertaken to estimate the macro-economic impacts<sup>4</sup> . The estimates suggested net benefits of the reforms when comparing the gains from the reduction of the likelihood and severity of financial crises versus the output costs of higher lending rates.

# _Monitoring the reform effects on EMDEs_

In addition, a report prepared jointly by a task force of the FSB, IMF, and WB was first published in November 2011 highlighting potential challenges to EMDEs from the emerging international financial reforms (FSB, IMF, WB (2011)). The first FSB report on the potential unintended consequences of the G20 reforms in EMDEs was prepared in 2012 in collaboration with the IMF and the World Bank based on a survey to regulators (FSB, June 2012) _._ It was followed up by two FSB monitoring notes (Sep 2013 and Nov 2014), based on inputs from FSB work streams and Regional Consultative Groups (RCGs) as well as the international standard setting bodies (SSBs) and international financial institutions (IFIs) (including the World Bank). Since 2015, the FSB has published an annual report to the G20 on the implementation and effects of reforms that includes a discussion on the impacts of reforms on G20/FSB EMDEs. The FSB has also published a review of the effectiveness and broader effects of the OTC derivatives markets reforms and found that changes in other jurisdictions have affected the markets of some EMDEs, such as the location of trading and the impact of overseas margin requirements (FSB, June 2017).

# _Post-implementation evaluation_

The FSB, in collaboration with sectoral standard setters, is also in the early stages of evaluating the post-implementation effects of G20 reforms, including whether the reforms are working together as intended, and of identifying any regulatory gaps, remaining or emerging risks and potential material unintended consequences (FSB, July 2017). Preliminary post-implementation analysis—which also focuses on G20/FSB economies— indicates that the reforms have enhanced financial sector resilience and hence the system’s ability to absorb shocks and support growth, but much work remains to fully assess the impacts.

> 4 See, for example, Box 1 in Annex 1 of the 2015 FSB annual report (FSB, November 2015).

7
# a) Impact of rainfall shortfalls on prices

<!-- Start of picture text --> 0.60%<br>0.50%<br>0.40%<br>0.30%<br>0.20%<br>0.10%<br>0.00%<br>Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct<br>-0.10%<br>-0.20%<br>-0.30%<br>Before 2005, no PSNP and low road After 2005, no PSNP and low road<br>Before 2005, PSNP and low road After 2005, PSNP and low road<br>Before 2005, no PSNP and high road After 2005, no PSNP and high road<br><!-- End of picture text -->

# b) Change in impact of rainfall shortfall on prices

<!-- Start of picture text --> 0.00%<br>Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct<br>-0.05%<br>-0.10%<br>-0.15%<br>-0.20%<br>-0.25%<br>-0.30%<br>-0.35%<br>-0.40%<br> no PSNP and low road PSNP and low road<br>no PSNP and high road PSNP and high road<br><!-- End of picture text --> Note: estimates using results from Table 4. See footnote to table 4 for details on the estimation.

|**Table 3 Food versus cash transfers inthe PSNP**|
|---|

|Impact on|p-|
|---|---|
|ln(price)|value|

23