rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Maternal Sample Baseline (2006) Characteristics**

||**Trea**|**tment**|**Com**|**parison**|**Difference**|**p-value***|
|---|---|---|---|---|---|---|
||**Mean**|**Std Dev**|**Mean**|**Std Dev**|||
|**Maternal characteristics**|||||||
|Age|30.89|(7.05)|31.22|(6.85)|-0.33|0.594|
|Primary education or more (=1)|0.42|(0.03)|0.35|(0.04)|-0.01|0.889|
|Currently married/union (=1)|0.94|(0.23)|0.91|(0.29)|0.04|0.214|
|Partner present (=1)|0.98|(0.14)|0.97|(0.17)|0.01|0.325|
|Number of pregnancies (Parity)|4.32|(2.46)|4.33|(2.43)|-0.01|0.969|
|Number of living children|3.39|(1.93)|3.51|(2.62)|-0.12|0.490|
|Health insurance (=1)|0.55|(0.50)|0.52|(0.50)|0.04|0.591|
|**Household characteristics**|||||||
|Number of household members|5.15|(1.70)|5.40|(1.94)|-0.25|0.145|
|Own land (=1)|0.93|(0.25)|0.87|(0.33)|0.06|0.126|
|Value of Assets (household, animals|, farm an|d enterprise|equipme|nt)|||
|_Quartile 1_(=1)|0.22|(0.42)|0.22|(0.42)|0.00|0.994|
|_Quartile 2_(=1)|0.25|(0.43)|0.25|(0.43)|0.00|0.885|
|_Quartile 3_(=1)|0.27|(0.45)|0.28|(0.45)|-0.01|0.885|
|_Quartile 4_(=1)|0.25|(0.43)|0.24|(0.43)|0.01|0.772|
|**Maternal care utilization**|||||||
|Any prenatal care (=1)|0.95|(0.21)|0.96|(0.20)|-0.01|0.773|
|Started in first trimester|0.11|(0.32)|0.09|(0.29)|0.02|0.544|
|4 or more prenatal care visits (=1)|0.18|(0.38)|0.11|(0.31)|0.07|0.036|
|Number of prenatal care visits|2.76|(0.84)|2.62|(0.80)|0.14|0.180|
|Delivery in facility (=1)|0.35|(0.48)|0.36|(0.48)|-0.01|0.801|
|**Quality of prenatal care**|||||||
|Total Quality Score|0.46|(0.02)|0.47|(0.02)|-0.01|0.6661|
|Standardized Total Quality Score|-0.13|(0.06)|-0.10|(0.06)|-0.03|0.7348|
|Tetanus vaccine (=1)<br>* P-values are for cluster-adjusted t-test|0.71<br>or chi-sq|(0.45)<br>uared tests|0.67<br>of differe|(0.47)<br>nce.|0.04|0.309|

21
# **What Drives the Volatility of Firm Level Productivity in China?**<sup>***1**</sup>

# **Xubei Luo**

(The World Bank, World Development Report)

# **Nong Zhu**

(INRS-UCS, University of Quebec)

# **Corresponding author:**

**Xubei Luo** Senior Economist World Development Report 2014 The World Bank 1818 H Street N.W., Washington, D.C. 20433, USA Phone +1 202.458.1157 Fax +1 202.522.0056 Email: xluo@worldbank.org Web: http://www.worldbank.org/wdr2014

# **JEL classification:** C21; D21; E23.

**Key words:** Enterprise Reform; Productivity; Volatility; China.

* This paper was prepared as a background paper of the World Development Report 2014: Risk and Opportunity. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of their affiliated organizations.

1 The authors would like to thank Sandra Poncet for sharing the data, Jean-Jacques Dethier, Russell Pittman, Yanrui Wu, and participants at the ninth International Conference on the Chinese Economy, CERDI-IDREC, University of Auvergne, France (October 2013) for valuable comments.
Connectivity, Road Quality, and Jobs: Evidence from Armenia Nino Pkhikidze<sup>_∗_</sup> Keywords: _Roads, infrastructure, rural employment, historical IV._ JEL classification: _H54, J60, C26, Q10, R40._

> _∗_ World Bank, 1818 H St. NW, Washington DC, 20433. npkhkidze@worldbank.org. I would like to thank Prof. Joachim von Braun and Alisher Mirzabaev for their guidance on this work. Gilles Duranton, Chiara Kofol, Lukas Kornher, and the participants of different seminars and workshops at the Center for Development Research (ZEF), the University of Bonn, Caucasus Resource Research Center (CRRC) Armenia, and the Development Impact Group (DIME) at the World Bank for their useful comments on the previous versions of this paper. I would also like to thank World Bank’s Armenia Country Office for their guidance and support. The findings, interpretations, and conclusions expressed in this paper are entirely of the author.
<!-- Start of picture text --> IControls<br>∗<br>Zi = wj Zj ,<br>∑<br>j=1<br><!-- End of picture text --> <mark>then</mark> <!-- Start of picture text --> IControls<br>δit = Yit − wj∗Yjt<br>∑<br>j=1<br><!-- End of picture text --> <mark>is an unbiased estimator of</mark> _<mark>δ</mark> it_ <mark>. For further details on the SCM, we refer the reader to Abadie et al. (2010), as well as Abadie and Gardeazabal (2003).</mark> <mark>Note that in order to limit the risk of interpolation and overfitting, as well as to build controls using structurally similar countries, we restrict the pool from which the synthetic control is constructed on the basis of two variables: GDP per capita and institutional strength, proxied by the Polity V index. To be included in the donor pool for a given synthetic control study, the potential donor country has to fall within one quartile of the distribution of each of these two variables.</mark><sup>16</sup> <mark>To move from one individual event study to the average effect of sovereign defaults on the outcome variables across the vast sample of sovereign defaults in our data, we primarily rely on the aggregation methodology introduced by Cavallo et al. (2013). We extend that methodology in that rather than calculating the unweighted (normalized) average effect, we aim to further account for the fact that not all individual analyses bear the same informational value. Which stems from not all sovereigns that default necessarily having a suitable synthetic control constructable from the donor pool (Abadie et al. (2010, 2015)). To illustrate, imagine an islandstate without any close by neighbors that is structurally different to any other economy in the world. Especially since we restrict the pool of possible samples to reduce the risk of overfitting, one would not be able to construct a suitable synthetic control for such a country.</mark><sup>17</sup> <mark>Hence, as is implied by the very nature of event studies, the results from that individual synthetic control</mark>

> 16 <mark>Given that we also use GDP per capita to match the pre-treatment trend, this pool-restriction is seldomly binding. We thus add the measure of institutional strength, to capture the underlying structural differences of countries from the same income group.</mark>

> 17 <mark>We restrict the donor pool to countries that are structurally similar to the treated unit, by only considering countries within one quartile of the distribution of GDP and institutional strength, proxied by the Polity IV.</mark>

13
_Worldwide Governance Indicators (WGI)_ database by the World Bank. Country level macroeconomic variables, i.e. _GDP, GDP per capita_ , _GDP growth rate_ and _inflation_ , come from the _World Development Indicators (WDI)_ database by the World Bank. Our measure of property rights comes from the _Heritage Foundation_ .

# **B. Variables and Summary Statistics**

We first describe our dependent variables.<sup>8</sup> The survey contains various indicators of obstacles to firm growth. We focus on two key obstacles to firm growth: financial obstacles ( _FinancialObstacle_ ) and corruption obstacles ( _CorruptionObstacle_ ). For additional analyses, we also examine two other firm-level obstacles, i.e. business licensing and permits-related obstacles and tax collection-related obstacles. All these firm-level obstacles take on a value based on the self-reported answers to the following WBES question: _“How problematic is _________ for the current operations of a business?”_ The blank space represents one of the aforementioned growth obstacles. The obstacles are on the scale from 0 – 4: no obstacle (0), minor obstacle (1), moderate obstacle (2), major obstacle (3), and very severe obstacle (4). For robustness checks, we also create dummy variables for these obstacles, _FinancialObstacleDummy, CorruptionObstacleDummy, LicenseDummy,_ and _TaxRateDummy,_ which take a value of 1 if the respective obstacle scores equal 2 (moderate), 3 (major), or 4 (very severe), and 0 otherwise.

We employ two additional, objective measures to proxy a firm’s financial constraints: overdraft ( _Overdraft)_ and line of credit ( _CreditLine_ ). Both are indicator variables. _Overdraft_ equals 1 if a firm has an overdraft facility, and 0 otherwise. _CreditLine_ equals 1 if a firm has a line of credit in a financial institution, and 0 otherwise. Firms with an overdraft facility or line of credit are found to be less financially constrained (Lins, Servaes and Tufano, 2010). As reported in Panel B of Table 1, univariate tests show that audited firms are more likely to have an overdraft facility and line of credit, compared to unaudited firms.

> 8 The details of the variables, their sources and the survey data items are provided in Table A2 in the Appendix.

7
Figure 3 <!-- Start of picture text --> AFPAC<br>PERCENTAGE ACHIVEMENT OF D OIJECTIVES<br>(REVIEW YEA.RS cY7S4U<br>oo .<br>70 .. .70 ..<br>10 .. . . . ..<br>IL7IL _<br>l " [,- -~~ -~ %___ _-<br>40 0~~~~~~~~~~~~~~~~~8 .40<br>30 _...3.**-*<br>X~~~ .. -- I I<br>7v711 W2 838 8&85<br>Su-dw Pr Ngbh<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4<br><!-- End of picture text --> <!-- Start of picture text --> EMENA<br>PERCENTAGE ACH4VENT OF 1D OBJECTI<br>(REViW YEARS CWW"<br>gso I<br>U __- ~ ~ _,<br>Oi_<br>0_ . ..<br>. .............................. It .........--- I<br>78-79 Gl>V2 8395 8<br>J&ba_ Pa - 11ue<br><!-- End of picture text -->
1993, and 83 % in 1998. The first file is expected to cover primary school aged children pretty well. The second group covers all of secondary school and possibly vocational training. Given high enrollment rates and larger changes over time for this second group, we felt that it would be a good idea to look at this extended age group rather than limit it to lower or upper secondary school ages. But, this decision is somewhat arbitrary. The sample includes all children who live in the household (regardless of whether they are siblings or the children of the head), as well as children who do not reside in the household but whose parents do.

For Morocco, the size and cut-off points of the groups are different given the different schooling profile. The two sub-samples are for children aged 7 to 12 (containing 2576 observations in 1991 and 3637 in 1998) and 13 to 15 (1239 observations in 1991 and 1860 in 1998), the ages corresponding to the legal age ranges for attending primary and lower secondary school respectively. In 1991, no child below age 7 attends school in the data and in 1998, only 15 do. We drop them from the sample. In the Morocco database, we include only children of the head of household who also reside in the household as the questionnaire did not collect sufficient information on non-residents.

The schooling indicator we use as the dependent variable is enrollment defined as whether a child is currently in school or not. There are many other potential schooling indicators that could be used. We did repeat all the calculations using years of schooling as a proportion of the maximum possible years, calculated as the number of years of schooling divided by age minus 6 for Morocco, and minus 5 for Vietnam. Results were qualitatively very similar and hence, are not reported.

As noted in Section 2, the explanatory variables (which it will be recalled enter linearly) include individual, household and geographic (or community) characteristics. Child 14
large e¤ects on Bangladeshi demand. It is also worth emphasizing that the increases in exports of Bangladesh would tend to come at the expense of other exporters. Thus, EU preferences to less developed countries, including Bangladesh, may end up hurting possibly even poorer countries in Africa, as Bangladesh is likely to be able to better take advantage of such preferences than some other developing countries.<sup>43</sup> In our experiments below, we distinguish between “endogenous license price”and “exogenous license price”scenarios. We transform the license price for a quota into its ad-valorem equivalent in the model. In the presence of quotas, policy changes will a¤ect the license price and its ad-valorem equivalent. In the “exogenous”scenario, we keep the license price ...xed. In the “endogenous”one, we allow the equilibrium value of the license price to change. For each experiment we ...rst check whether the US quota remains binding or not. If it is no longer binding, we set the new license price to be zero. If it remains binding, we ...nd the license price that equates the demand for quota licenses at the new license price to the ...xed supply of licenses. This keeps the quantity sold by Bangladeshi ...rms to the US constant.

We are now ready to look at some policy questions. Our ...rst experiment deals with a question of considerable policy importance, namely, the costs of preferences. Developed countries typically give preferences to developing ones, but require that exporters meet origin requirements as done by the EU in the EBA. Thus, obtaining preferences can be quite costly. Consequently, such preferences can be much less generous than they seem. We use our model to quantify the impact of making such preferences easier or harder to obtain.<sup>44</sup> We show, for example, that removing the home yarn requirement results in a surge of entry and exports.

The second set of experiments looks at the policy e¤ectiveness of subsidies to ...xed costs. Which subsidies are the most e¤ective in terms of promoting exports? This question is relevant for developing countries for a number of reasons. Foreign exchange may be valuable 43In our tables, we calculate welfare changes directly using indirect utility normalized so that the marginal utility of a dollar of income is unity. We use estimates of $12,465 and $11,855 billion dollars for the EU and US GDP in 2004 taken from the IMF World Economic Outlook Database (April 2012 Edition), tari¤ revenues of $105 and $308 million dollars collected by the EU and US, respectively, and their expenditure shares of 0.00024 and 0.0005 on woven apparel in our calculations.

> 44Mattoo et al. (2003) look at the Africal Growth and Opportunity Act and (based on back of the envelope calculations in a simple competitive model) argue that preferences are undone by restrictive ROOs.

29
(2013) show that the relationship between branch expansion and rural poverty is insignificant over the period 1980 to 2005.

Overall, the government ownership of the banking sector in developing economies has been considered problematic. La Porta et al. (2002) find no evidence that the presence of state-owned banks promotes economic growth or financial development. Other individual country studies on state-owned banks provide evidence of political capture of the lending process where the state-owned banks lend to cronies especially around the time of elections (e.g. Khwaja and Mian (2005), Dinc (2005), Cole (2009), and Sapienza (2004)). Carvalho (2014)) finds that in exchange for government loans, Brazilian manufacturers expand employment and investment in politically attractive regions.

Some state-owned banks are designated as development banks or development financial institutions, to promote socioeconomic goals including fostering SMEs by providing credit, loan guarantees, other financial services and advisory and capacity building programs. Using a large survey of development banks worldwide, De Luna-Martinez and Vicente (2012) report that these banks play a countercyclical role by scaling up lending operations during crisis periods. However, there are few rigorous studies of the effectiveness of development banks. There is some evidence that employing new financial products that are less subject to political subversion could be effective in improving credit access to small firms, such as in the case of reverse factoring developed by the development bank in Mexico (Klapper 2006).

32
**Figure A.2.  NIRP announcements: country-specific changes in selected financial variables over one-day window**

<!-- Start of picture text --> A. Interest rates and bond yields B. Inflation expectations, exchange rates, and<br>equities<br>Basis points Euro Area Sweden Basis points Percent<br>Denmark Switzerland 20 Euro Area Sweden<br>Japan Denmark Switzerland<br>0 15 Japan<br>-5 10<br>-10 5<br>-15 0<br>-20 -5<br>NEER (RHS)<br>(RHS)<br>1-month interbank rate 3-month interbank rate 2-year government bond yields 10-year government bond yields 10-year inflation expectations Equities<br><!-- End of picture text --> Sources: Bloomberg, Haver Analytics, World Bank.

Notes: Basis points or percent change between closing values on the day before the NIRP announcement and closing values on the day of the announcement. NIRP announcements are: four events for the European Central Bank, one for the Bank of Japan, two for the Swiss National Bank, four for the Swedish Riksbank, and six for Danmarks Nationalbank. Variable definitions are the same as in Figure 9.

51
# **Figure 12. Spillovers from the United States and the Euro Area**

## _Weak growth in the United States and the Euro Area has had adverse spillovers on output and investment in EMDEs._ **A. Five‐year ahead growth forecasts for the United States B. Import intensity of demand components, 2014 and Euro Area**

<!-- Start of picture text --> Percent<br>3.0 United States Euro Area<br>2.5<br>2.0<br>1.5<br>1.0<br>2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **C. Spillovers to EMDE output growth from decline in U.S. output growth**

<!-- Start of picture text --> Percentage points<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->

## **E. Spillovers to EMDE investment growth from decline in U.S. output growth**

<!-- Start of picture text --> Percentage points<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>Private Consumption<br>Investment<br>40<br>Export<br>30<br>20<br>10<br>0<br>China US Euro Area<br><!-- End of picture text -->

- **D. Spillovers to EMDE output growth from decline in Euro Area output growth**

<!-- Start of picture text --> Percentage points<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>-2.5<br>-3.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->

- **F. Spillovers to EMDE investment growth from decline in Euro Area output growth**

<!-- Start of picture text --> Percentage points<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>-6.0<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text --> Sources: Consensus Economics, World Bank estimates, World Input‐Output Database. A. Five‐year ahead Consensus Forecasts as of the latest available month in the year denoted.

C.‐F. Cumulative impulse response of weighted average EMDE output growth (C.D.) or investment growth (E.F.) at 1‐8 quarters to a 1 percentage point decline in growth in real GDP in the United States (C.E.) and Euro Area (D.F.). Growth spillovers based on a Bayesian vector autoregression of world GDP growth (excluding the source country of spillovers), output growth in the source country of the shock, the U.S. 10‐year sovereign bond yield, JP Morgan’s EMBI index, investment (E.F.) or output (C.D.) in EMDEs excluding China. The oil price is exogenous. Blue dotted lines denote 16th‐84th percentile confidence intervals, and blue solid lines denote median responses. Sample includes 18 EMDEs (Brazil, Bulgaria, Chile, Costa Rica, Hungary, India, Indonesia, Malaysia, Mexico, Paraguay, Peru, the Philippines, Poland, Romania, Russia, South Africa, Thailand, and Turkey) from 1998Q1‐2016Q2.

34
<!-- Start of picture text --> Figure A2: Monthly precipitation anomalies for 19 meteorological stations in Bolivia, 1948-2008<br>CAMIRI CHARAÑA<br>300 200<br>250<br>150<br>200<br>150 100<br>100<br>50<br>50<br>0<br>0<br>-50 -50<br>-100<br>-100<br>-150<br>-200 -150<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08 M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>COBIJA COCHABAMBA<br>400 250<br>300 200<br>150<br>200<br>100<br>100<br>50<br>0<br>0<br>-100<br>-50<br>-200 -100<br>-300 -150<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08 M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (milimeters) Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to Precipitation Anomalies compared to<br>Precipitation Anomalies compared to Average for 1960-1990 (milimeters) Precipitation Anomalies compared to Average for 1960-1990 (milimeters)<br><!-- End of picture text -->

# **CONCEPCION**

<!-- Start of picture text --> 400<br>300<br>200<br>100<br>0<br>-100<br>-200<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (milimeters)<br>Precipitation Anomalies compared to<br><!-- End of picture text -->

# **LA PAZ/EL ALTO**

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>-150<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Average for 1960-1990 (mlimeters)<br>Precipitation Anomalies compared to<br><!-- End of picture text --> 23
stated in the concession contract granted by the previous administrations. Examples, to mention only a few (see Guasch 2004 for more detailed evidence), include recent popular unrest in Bolivia, which led in January 2005 to the cancellation of the La Paz and El Alto water concession, led by the French multinational Suez Lyonnaise des Eaux. As of 2005, most of the concessions awarded in Argentina prior to the 2001 crisis are still undergoing protracted renegotiation processes. Conflict arose as the government converted the dollar-denominated rates to devalued pesos despite contract clauses that contemplated indexation to the dollar and US inflation, and refused any subsequent significant rate adjustment. Despite 62 firms’ suits before the World Bank’s International Centre for Settlement of Investment Disputes, Argentina has been very slow in responding to those challenges, arguing the need to protect the interest of the Argentinian people. It has also argued that international arbitrage decisions should be reviewed by local judicial courts, despite the country’s agreement to abide to international arbitration, under bilateral investment treaties signed by the government of Carlos Menem in the 1990s<sup>2</sup> . Similarly, the Limeira water concession in Brazil was denied tariffs adjustment that was supposed to be automatic according to the contract. The local mayor argued that the contract, signed by a previous administration, was unfair and compromised the municipality’s long term interests. Similar behavior plagued the Tucuman water and sanitation contract in Argentina. In 1995, a new local government took office and sought to limit previously agreed upon tariff increases. This finally led the concessionaire to abandon the concession in 1996. In the toll road concession in Pernambuco, Brazil, the regional government decided to cut the tariffs unilaterally shortly before elections. Sometimes, lack of commitment shows up at even earlier stages, like in the case of the 1997 Ukranian cellular telephone contract or the 1999 Matarani port concession in Peru, where the rules of the tender were changed unilaterally in the awarding period (introducing an additional annual fee in one case, shortening the duration of the concession from 30 to 15 years in the other one).

In most cases, the social costs of such renegotiations are likely to be high. The knowledge by potential investors that the temptation exists to expropriate investments ex post may discourage investment in the first place or it might require an additional risk premium in the form of bigger tariffs or

> 2See The Economist, March 17, 2005, and IPS-Inter Press Service, April 11, 2005, “Economy-Argentina, the Battle that Lies Ahead” at http://www.ipsnews.net/index.asp.

4