rafmacalaba/datause-encoder-data huggingface dataset:
# **Manufacturing Production Function Estimations**

Table 5 analyzes whether a large unorganized sector is associated with lower performance for Indian manufacturing establishments. Columns 1-5 consider India, and Columns 6-7 consider the United States for comparison. We use the 2000 Indian sample for this exercise. We estimate a very simple production function with log output of each establishment as the dependent variable. We use establishment weights from the surveys to create a population level estimation. We control for the basic plant inputs of log employees, log book values of capital, and log costs of materials. We exclude plants with missing values, and we continue to exclude districts that are very small in size as noted in the prior section. Regressions include industry fixed effects to capture broad differences in production techniques.

Our focus is on the district and district-industry conditions that surround each plant. In Column 1, we include the log manufacturing employment density per square kilometer in the district. This measure captures urbanization premiums commonly highlighted in economic geography literature;<sup>8</sup> we use a per square kilometer normalization as Indian districts vary in spatial size. We also add a simple measure of the concentration of the unorganized sector as the log share of district employment in the unorganized sector.

The second column further considers these two metrics defined by district-industry, similar to industry-specific agglomeration premiums in the economic geography literature. The third column refines the unorganized sector’s role by separating out the share of local workers who are not being paid. Unpaid workers are mostly family members, and the surveys directly collect evidence on the extent of their involvement in establishments. Columns 4 and 5 again split the Indian sample by traditional versus modern sectors. Standard errors are clustered by district.

The U.S. estimations in Columns 5-6 are built on the 1997 Census of Manufacturers. This dataset is a universal survey of U.S. manufacturing establishments conducted every five years. We estimate the local conditions by county and industry. We again normalize the local activity measures by the area of the county. We do not attempt to align U.S. industries with Indian industries, and we recognize that there are substantial differences in survey techniques and measurement quality between the two countries. Likewise, there are more advanced techniques for both production function estimation and urbanization/agglomeration premium estimation. But for our purposes, these two samples and transparent regressions provide an intuitive platform to observe differences between the two countries.

> 8  For example, Ciccone and Hall (1996), Duranton and Puga (2004), and Rosenthal and Strange (2004, 2011). Indian studies include Lall et al. (2004), Lall and Mengistae (2005), Deichmann et al. (2008), and Fernandes and Sharma (2011).

10
Just noting the diversity in institutional design, and attributing it all to social conditions and culture while interesting in itself, does not take the work of policymakers very far. For example, Germany has a very high ratio of professional judges relative to the number of incoming cases in first instance courts. The question is why do the numbers for Germany vary so much from England which has a much lower number? Is one system more efficient (in terms of speed) than the other, or cheaper, and might the countries learn from each others’ experience? Is it because cases are screened before coming to court in England and only some come to these courts and is this a better solution in some circumstances? Are there fees which make the difference—and what economic /social benefit does the structural difference confer?

To bring about change we need to understand what might be the reasons for the diversity (e.g. which social and economic factors prompted the adoption of particular institutional features) and what might be the relative advantages and disadvantages of each system for different people and for society as a whole. Until the existing data on judicial systems and new data collected across countries are analysed in this manner, unless the impact on welfare and the costs to society are analysed, such data will be of little use to those seeking for a better way of doing things.

**Table 1: Inputs Into the Judicial System for Selected Countries, 1995**

_<u>(per 100,000 population)</u>_

|**Country**|**Professional**<br>**judges**|**Other judicial**<br>**staff**|**Incoming cases in first-**<br>**instance courts**|
|---|---|---|---|
|Austria|21|117|29,294<sup>a</sup>|
|England and Wales|5|4|4,718|
|France|10|41|2,242|
|Germany|27|69|2,655|
|Italy|12|60|1,227|
|Netherlands|10|n.a.|2,031|
|Portugal|12|70|3,719|
|Spain|9|83|1,898|

a. Including summary cases. _Source:_ Contini 2000 15
Gregorio Impavido, Alberto R. Musalem, and Dimitri Vittas

**Figure 1: Portfolio allocation of Chilean pension funds**

<!-- Start of picture text --> 100%-<br>90%-<br>80% I<br>70%-<br>90%-<br>30%t_<br>019781 =1992 1983 1984 19`80 19`89 19987 1999 1989 1990 1991 199 199'3 19,94 19,95 198 1997<br>NC.sh & D.po.ilb U-iB. & Bonds 01-oan OSharra mOtherhr|<br><!-- End of picture text -->

**Source:** WB contractual savings database

> Notice that the increasing trend in the share of equities and bills and bonds in total portfolio is not necessarily a characteristics of small financial systems. Also countries

> with more established financial systems display the same trend. For the OECD countries Figure 2 shows how equities and bonds have increased in average from 20 and 31 percent, respectively to 33 and 38 percent respectively between 1982 and 1996.

**Figure 2: Contractual savings portfolio allocation in OECD**

<!-- Start of picture text --> 100%<br>20~~~~~~~~~~~2<br>38% 29 24 27 2201<br>40%1_~~~~~~~~2<br>70%<br>20%31 3<br>10%<br>1992 1984 1 999 1 gS 1990 1992 1994 1 999<br>l UC1 sh1nh deposits Bilbl nd bonds OLoans OShes n *Ohr l<br><!-- End of picture text --> Source: 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.
<!-- Start of picture text --> Figure N3: Percentage of Firms Using E-filing by Legal Form<br>ME filers non-efilers<br>Public Limited Company<br>Private limited company<br>Cooperatives<br>Hospital<br>Partnership<br>Sole proprietorship<br>Education institution<br>0 20 40 60 80 100<br>Percent (%)<br>Source: IFC 2012 [Nepal]<br><!-- End of picture text --> <!-- Start of picture text --> TABLE N1: Taxes by E-filing Usage<br>0) 831 | 732 100 | 756 75<br>Non-Efilers | 0% 84% | 93% 49% | 90% 50%<br>) 159 57 102 84 75<br>Efilers 0% 16% | 7% 51% | 10% 50%<br>*Source: IFC 2012 [Nepal]<br><!-- End of picture text -->
Indian manufacturing sector are available in 1989-90, 1994-95, 2000-01, and 2005-06.<sup>6</sup> This section describes some key features of the data.<sup>7</sup> The entire manufacturing sector is surveyed at regular intervals by the Government of India. The organized manufacturing sector is surveyed by the Central Statistical Organisation’s Ministry of Statistics and Programme Implementation in the “Annual Survey of Industries” (hereafter “ASI”). Factories (establishments) employing 10 or more workers and using power (or 20 or more workers without using power) are required to register under the Factories Act, and these registered establishments comprise the ASI sampling frame. The largest plants, comprising what is known as the “Census scheme”, are surveyed every year. The size threshold for the Census scheme has varied over the years, between 50 and 200 workers (see [Bollard et al. (2013)], Table A.1). The remaining plants (the “sample scheme”) are sampled with state and 4-digit industry stratification based on the universe of registered establishments.

The “unorganized” manufacturing sector (those establishments falling outside the purview of the ASI scheme) has been separately surveyed by the National Sample Survey Organization’s (NSSO) Survey of Unorganized Manufactures (hereafter, “NSS”) in 1989, 1994, 2000 and 2005. The sampling frame of the NSS is constructed to capture all manufacturing enterprises not covered by the ASI, making the two surveys complements in comprising a full snapshot of the entire manufacturing sector in a given year.

Our principal fields of interest are commonly surveyed by both the ASI and NSS. These include output (as measured by sales volume), employment, materials expenditure and fixed capital. Capital is measured by the book value of fixed assets, and employment in our analysis is the typical definition of “persons engaged”-- including working owners, family and casual labor, and wage/salary employees.

We use a wholesale price index series available from India’s Office of the Economic Adviser (Ministry of Commerce & Industry) to deflate output volumes to constant 1993-94 INR. To deflate raw materials values, we use the 1989-90 Input-Output table produced by the Central

> 6 Even though these surveys were undertaken over two fiscal years, for simplicity we refer below to the initial year only.

> 7 [Nataraj (2011)], [Kathuria et al. (2010)] , [Hasan and Jandoc (2010)] , and [Fernandes and Pakes (2010)] provide detailed overviews of similarly constructed databases.

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Table 10: Estimates by region

|_Panel A_|l|n simple t|arifs|
|---|---|---|---|
||LAC|Africa|Asia-Pacifc|
|ln rain_jt_|0.142|-0.058|-0.075|
||[0.095]|[0.127]|[0.142]|
|ln rain_jt ∗_agric_i_|0.345***|-0.030|0.303**|
||[0.076]|[0.81]|[0.115]|
|Observations|15,151|8,248|13,512|
|Number of countries|22|18|23|
|Number of products|65|65|65|
|R-squared|0.528|0.611|0.589|
|_Panel B_|ln|weighted|tarifs|
||LAC|Africa|Asia-Pacifc|
|ln rain_jt_|0.256*|-0.125|-0.113|
||[0.134]|[0.217]|[0.183]|
|ln rain_jt ∗_agric_i_|0.398***|0.274|0.388**|
||[0.128]|[0.316]|[0.155]|
|Observations|14,969|8,069|13,458|
|Number of countries|22|18|23|
|Number of products|65|65|65|
|R-squared|0.331|0.398|0.448|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 Table 11: The role of irrigation

|Dep. variable|ln simpl<br>(1)|e tarifs<br>(2)|ln weight<br>(3)|ed tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.048|0.056|0.013|0.024|
||[0.074]|[0.074]|[0.096]|[0.096]|
|ln rain_jt ∗_agric_i_|0.243***<br>[0.048]|0.132**<br>[0.051]|0.447***<br>[0.074]|0.291***<br>[0.069]|
|ln rain_jt ∗_agric_i ∗_irrig_j_||0.004***<br>[0.001]||0.006***<br>[0.001]|
|Observations|18,812|18,812|18,683|18,683|
|Number of countries|32|32|32|32|
|Number of products|65|65|65|65|
|R-squared|0.586|0.588|0.422|0.423|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 7
or 0.545 standard deviations respectively for the regressions without or with province specific time trends.

Second, in columns 3 and 4, we include the two provinces (Cankuzo and Rutana) that did not experience any fighting and so had no children exposed to the war. These two provinces could be systematically different than those provinces that did experience civil war, which is why they were excluded from the baseline regressions. Including them in the analysis allows us to now take advantage of regional variation between conflict and non-conflict provinces. With these provinces included, we are now evaluating whether children born in conflict provinces in years when war occurred have lower height for age z-scores than their counterparts born after the war ended, relative to those children born in non-conflict provinces in the same year. Results in column 3 and 4 are similar to the baseline regressions. Children exposed to the conflict have 0.512 standard deviations lower height for age z-scores in the regression without the province specific time trend and 0.492 standard deviations lower height for age z-scores in the regression with the province specific time trend.

Third, we include the children from the three provinces (Bubanza, Bujumbura Rural, and Cibitoke) in which all children were exposed to the war plus the children from the two provinces (Cankuzo and Rutana) in which no children were exposed to the war. Results in column 5 and 6 are similar to previous results. Children exposed to the conflict experience 0.472 or 0.521 standard deviations lower height for age z-scores and the coefficients are statistically significant at the one and five percent levels, respectively. Results in Table 6 provide convincing evidence of the validity of the identification strategy and the lack of any province-level selection bias in exploiting only the variation across provinces in the timing and duration of the conflict to calculate the causal impact of civil war on children’s health status.

# **6. Conclusions**

The health status of children in Burundi is poor. The average height for age z-score is 2.28 standard deviations below the average American child and 59 percent of all children are moderately or severely malnourished. In this paper, we combine detailed event data about the timing and location of armed conflict with household survey data to examine the impact of the civil war in Burundi on the health status of young children who were exposed to it. The empirical identification strategy exploits variation in the timing of the war across Burundi’s 14
<!-- Start of picture text --> Gross700<br>inflows600 Portfolio QE1 QE2 QE3<br>($ billion) Loans<br>500 FDI<br>400<br>300<br>200<br>100<br>0<br>-100<br>-200<br>-300<br>-400<br>2000Q1 2001Q3 2003Q1 2004Q3 2006Q1 2007Q3 2009Q1 2010Q3 2012Q1<br>(a) Gross inflows to developing countries, cumulative value<br>Gross20<br>QE1 QE2 QE3<br>inflows/<br>GDP (%)<br>15<br>10<br>5<br>0<br>-5<br>2000Q1 2001Q3 2003Q1 2004Q3 2006Q1 2007Q3 2009Q1 2010Q3 2012Q1<br>(b) Gross inflows to developing countries, as share of GDP<br><!-- End of picture text --> Figure 1: Gross financial inflows to developing countries, by different constituent flows, in cumulative U.S. dollars (left panel) and as share of developing country GDP (right panel). The three quantitative easing episodes are shaded. The sharp contraction in flows following the financial crisis in 2008 is evident. Increases in portfolio and loan flows over the period appear to be equiproportional, but faster than the increase in FDI (left panel). The variations in gross flows as a share of GDP mimic those in absolute terms, although the post-crisis recovery in flows remains below pre-crisis peaks when measured in this manner (right panel).

QE operations are credited as increased reserves on the balance sheets of private banks. Since such reserves are more easily traded in secondary markets than long-term securities, there is a decline in the liquidity premium, which in turn enables previously liquidity-constrained banks to extend credit to investors. This results in an decline in borrowing costs and increases overall bank lending, 7
**Figure 7: % of companies reporting on energy‐related environmental indicators, by subsample**

<!-- Start of picture text --> Energy consumption within the Energy intensity ratio (G4‐EN5)<br>organization (G4‐EN3)<br>SME 64.4% SME 28.8%<br>Large 80.1% Large 53.7%<br>MNE 83.4% MNE 56.2%<br>All 79.4% All 51.7%<br>Reduction of energy consumption Reductions in energy requirements<br>(G4‐EN6) of products and services (G4‐EN7)<br>SME 40.2% SME 18.3%<br>Large 62.4% Large 36.4%<br>MNE 65.7% MNE 39.6%<br>All 61.0% All 35.4%<br>Direct greenhouse gas (GHG)<br>Energy indirect greenhouse gas<br>emissions (Scope 1) (G4‐EN15)<br>(GHG) emissions (Scope 2) (G4‐EN16)<br>SME 42.0%<br>SME 34.7%<br>Large 73.7%<br>Large 69.4%<br>MNE 78.7%<br>MNE 78.4%<br>All 71.7%<br>All 68.3%<br>Other indirect greenhouse gas (GHG) Greenhouse gas (GHG) emissions<br>emissions (Scope 3) (G4‐EN17) intensity (G4‐EN18)<br>SME 22.8% SME 18.7%<br>Large 46.0% Large 49.9%<br>MNE 48.9% MNE 55.6%<br>All 44.4% All 48.2%<br><!-- End of picture text --> 24
Table 3: Regression of food consumption on the RSQ with household-level interactions.

||(1)|(2)|
|---|---|---|
|5-month z-rain|1_._862<sup>_∗∗_</sup>|1_._777<sup>_∗∗_</sup>|
||(0_._946)|(0_._805)|
|5-month z-rain _×_ livestock:1st quartile|0_._905<br>(0_._715)||
|5-month z-rain _×_ livestock:2nd quartile|1_._118<sup>_∗_</sup><br>(0_._630)||
|5-month z-rain _×_ livestock:3rd quartile|1_._988<sup>_∗∗∗_</sup><br>(0_._754)||
|5-month z-rain _×_ livestock:Top quartile|2_._278<sup>_∗_</sup><br>(1_._256)||
|5-month z-rain _×_ inc. source:Livestock||2_._619<sup>_∗∗∗_</sup><br>(0_._950)|
|5-month z-rain _×_ inc. source:Small business||1_._605<sup>_∗∗_</sup><br>(0_._727)|
|5-month z-rain _×_ inc. source:Formal||1_._013<sup>_∗_</sup><br>(0_._549)|
|5-month z-rain _×_ inc. source:Remittances||1_._171<br>(0_._938)|
|Constant|36_._121<sup>_∗∗∗_</sup>|36_._907<sup>_∗∗∗_</sup>|
||(2_._192)|(2_._513)|
|Controls-A|Yes|Yes|
|Controls-B|Yes|Yes|
|region FE|Yes|Yes|
|Observations|10,924|10,924|
|Adjusted R<sup>2</sup>|0.172|0.172|
|_Note:_|_∗_p_<_0.1; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

# **4.1.3 Quantile regressions**

As illustrated in Figure 3, our food insecurity estimation procedure assumes a uniform effect of the RSQ on food consumption along the baseline FCS distribution to infer the FCS distribution under a given RSQ scenario. Standard OLS methods used above provide estimates of the RSQ effect at the mean of the outcome distribution, which we have so far implicitly considered to coincide with the uniform effect applied in the estimation 26
|Home<br>Country|No. E<br>activ<br>EMDE|MDE banks<br>e in host<br>countries|No. host<br>EMDE<br>countries<br>T|otal Assets<br>host cou<br>(US$ mi|in EMDE<br>ntries<br>llion)|Total asse<br>in host c<br>Total A<br>Finan|ts EMDE banks<br>ountries (% of<br>ssets Home<br>cial Sector)|Total l<br>banks in<br>(% of T<br>Home Fi|ending EMDE<br>host countries<br>otal Lending<br>nancial Sector)|Total De<br>ban<br>countrie<br>Depo<br>Finan|posits EMDE<br>ks in host<br>s (% of Total<br>sits Home<br>cial Sector)|Loan to<br>(weigh<br>a|Deposit Ratio<br>ted mean by<br>ssets, %)|Retur<br>(weigh<br>a|n on Assets<br>ted mean by<br>ssets, %)|Regul<br>Ratio (w<br>by|atory Capital<br>eighted mean<br>assets, %)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Change|||Change||Change from||Change from||Change from||Change from||Change from||Change from|
||2013|from 2009<br>(abs.value)|2013|2013|from<br>2009 (%)|2013|2009<br>(percent pts)|2013|2009<br>(percent pts)|2013|2009<br>(percent pts)|2013|2009<br>(percent pts)|2013|2009<br>(percent pts)|2013|2009<br>(percent pts)|
|Middle|East and|North Africa||||||||||||||||
|LBN|10|4|7|18,453.3|224.1|17.5|12.0|34.8|27.8|16.7|11.7|76.0|26.2|1.6|0.4|15.9|-59.8|
|LBY|8|-2|7|18,665.4|-24.8|20.7|-15.3|85.9|-2.9|18.4|-16.5|60.0|15.9|1.6|0.3|30.2|1.5|
|MAR|8|4|6|8,029.8|210.4|6.2|3.6|6.2|3.4|6.6|3.7|74.9|6.5|1.7|-0.1|..|..|
|KWT|6|1|6|14,410.8|64.9|12.5|3.2|10.6|4.0|10.9|2.6|70.9|14.1|1.8|-0.3|16.4|-4.7|
|ARE|6|1|6|13,960.7|518.3|3.9|3.1|2.1|1.6|3.8|3.0|53.1|-13.7|1.3|0.7|17.8|-1.1|
|QAT|3|..|3|45,046.6|425.2|3.6|..|3.5|..|3.4|..|90.1|..|0.8|..|18.7|..|
|BHR|3|0|3|16,107.0|2.9|31.0|10.8|39.0|15.5|31.9|8.6|67.0|-10.7|1.6|-0.6|15.6|-1.3|
|EGY|3|0|3|2,890.0|-5.7|0.7|0.2|0.4|0.2|0.8|0.3|36.9|8.0|1.5|-0.2|25.0|-7.7|
|OMN|2|1|2|2,815.2|57.3|9.8|-1.2|8.0|-0.8|10.0|0.6|65.8|-18.8|-0.5|1.9|11.7|-10.1|
|JOR|2|0|2|6610.5|0.5|7.6|-0.1|9.0|1.8|7.7|-0.4|68.2|-1.2|2.2|1.5|13.1|..|
|South A|sia|||,||||||||||||||
|IND|9|2|7|9,230.8|-53.0|0.2|..|0.1|0.0|0.1|..|55.2|2.7|1.8|-0.8|21.6|-5.2|
|PAK|2|0|2|5,445.7|4.2|0.2|0.0|0.2|0.0|0.2|-0.1|46.8|10.5|2.1|-0.3|20.0|3.0|
|BGD|1|0|1|224.8|49.4|0.3|-0.1|0.3|-0.1|0.3|-0.1|72.2|5.1|3.7|-16.5|12.9|7.3|
|Sub-Sah|aran Afr|ica||||||||||||||||
|ZAF|23|3|15|49,681.2|135.1|6.7|2.5|4.9|2.1|7.4|2.4|55.0|10.5|2.7|-0.1|19.6|-0.3|
|NGA|11|4|6|7,699.8|56.4|2.9|1.0|1.9|0.4|3.1|1.3|31.2|-16.0|1.2|1.4|29.0|-1.3|
|MLI|10|1|10|5,789.7|69.6|191.5|33.4|187.5|40.6|194.1|37.1|64.5|-1.0|1.5|0.2|14.3|-1.7|
|KEN|10|4|3|2,327.8|167.4|9.2|2.9|9.7|3.6|10.1|3.5|67.8|8.1|1.0|0.6|15.6|0.1|
|MUS|5|0|5|40,664.9|34.9|127.9|-5.0|159.9|36.0|153.8|8.0|74.3|15.4|1.5|-1.7|24.5|-5.4|
|MWI|3|2|3|184.6|114.0|17.3|9.2|22.3|16.8|17.8|7.8|55.4|20.9|-4.1|-4.2|32.7|10.7|
|BWA|3|0|3|728.8|104.4|10.6|5.1|9.7|3.3|9.6|4.7|63.4|-5.3|0.4|-0.4|12.8|-0.9|

30
� at time � ;<sup>14</sup> ������ is a dichotomous variable that takes the value of 1 whenever there is a preferential trade agreement, either active or inactive, between two countries at time � ;<sup>15</sup> � s are sets of country‐pair, importer‐time and exporter‐time fixed effects.

# b. Baseline results

Table 2 reports the coefficients of total depth, core depth and PCA depth for the regressions using DVA and FVA as dependent variables. All coefficients of depth are positive and significant. Adding a policy area is associated to an average increase 0.4 percent of total domestic value added and an average increase of 0.26 percent of foreign value added. The coefficients increase substantially when looking at core depth only, suggesting that those policy areas are particularly important as they reduce the governance gap between countries in areas that are relevant for GVC‐related trade.<sup>16</sup> Also, the coefficients of PCA depth show a similar pattern to the one of total number of provisions. Control variables such as BITs have the expected sign, suggesting that signing BITs has a positive impact on domestic and foreign value added in exports.<sup>17</sup> The PTA dummy is non‐significant in most of the estimations. This variable controls for the presence of shallow PTAs and for agreements no longer in force for which we have no information on depth. The lack of statistical significance indicates that it is the depth of PTAs, and not the mere presence of shallow agreements, that matters for domestic and foreign value added in exports.

A concern is that the trade variables in our first set of regressions may be driven by traditional trade in final goods and services rather than by GVC trade. To address this concern, we assess the impact of deep trade agreements on FVA and on DVA exports separately for intermediate and final goods and services. Results presented in Table 3 decompose domestic value added into DVA in final exports (columns 1‐3), DVA in intermediate exports (columns 4‐6) and DVA of re‐ exported intermediates (columns 7‐9). The coefficients capturing PTA depth are only significant for DVA of intermediate exports, suggesting that deep trade agreements are particularly

> 14 Data on BITs come from UNCTAD.

> 15 The variable ������ is taken from Egger and Larch (2008).

> 16 See Baldwin (2011) and WTO (2011).

> 17 The magnitude of the BITs coefficient needs to be interpreted carefully, given that these agreements often focus on specific sectors or areas. Therefore, in our regressions, which are aggregated at the country level, their impact might be positively biased.

11