rafmacalaba/datause-encoder-data huggingface dataset:
to recoup at least 60% of any deficit that the hospital can justify, the providers apparently do not bear the full financial risk of capitation.

While provinces did not report any major opposition to capitation payment, there is some indication that physicians prefer FFS because it puts less pressure on them to follow professional standards or to avoid referrals. And a large number of hospitals experienced deficit in their capitation fund, suggesting that capitation rates are underestimated (Kieu 2013). A recent study of the impact of capitation payments on four district hospitals in Vietnam showed that hospitals that have piloted capitation appeared to become highly cost-conscious (Tran 2012). There is some evidence of a decline in hospitalizations and shortened length of inpatient stay when hospitals are paid on a capitation basis, suggesting a potential for cost saving (Ministry of Health 2013).

# **Data and methods**

## **_Data_**

The main source of data used in this study is a panel of annual hospital inventory surveys covering the period 2005-2011. Information identifying which year the health insurance contract was changed from FFS to capitation comes from administrative records of Vietnam Social Security. Supplementary information on total population, total insured and uninsured for each district was estimated from the Vietnam Household Living Standards Surveys in 2006, 2008, and 2010 using the survey expansion factors (inverse of probability weights).

The hospital inventory survey was initiated in 1996 with technical and financial assistance from the World Bank. A second survey was implemented in 2000, again with external assistance. Subsequently, the Ministry of Health has implemented the inventory by itself on an 12
26 1991 is shown in Figure _3._ which gives managerial bonus as a function of profits before tax minus PPWW. At zero profits. the bonus was zero too: and at S 5 million profits. a manager would receive S2800 for the year. Higher profits than that barely affected the bonus. which asymptotes at S2900 per annum. This weak link creates a pre-disposition towards wage awards: but there is nevertheless ample evidence (see Section 3) of wage restraint.

<!-- Start of picture text --> M:aagerial Bonuses<br>1991<br>35.L<br>CD<br>., ~ ~ -- ~ ~ , ~~ _<br>* T 0-5~~~ --<br>- e I5 - f--<br>* _~~~~~~~~~~~5- i<br>0.0 0G1 0.3 O.S I.9 3.0 t.2 7.6 17.0 28.c 56.8 85.2<br>Profits USO 1 0-6<br><!-- End of picture text -->

**Fi-gure** 3: Managerial Bonuses.

Based on the record of Poland's past failed attempts at reform. managers might have concluded that the hard budget was temporary. and that any large crisis in the enterprise sector would lead to -a reinstatement of tariffs. In the absence of strong positive incentives towards making the firm profitable. decapitalizadon dtrough excessive wage claims was a distinct possibility, as many economists feared in 1990. However, the survey evidence below shows that managers believed that the reforms were indeed permanent and that the hard budget constraint and the end to subsidies were going to last. This obviously reduced the incentive for decapitalizaLion; m case they were thinking of nmining the enterprises down. what would they do next for a living?

Table 15 shows managers' responses to questions on optimism and the credibility of the reforms. These questions were asked in the context of what. in 1992, was believed to be growing optimism arnong SOE managers about their ability to operate in the new system. Managers were asked to rank specific
Haines, 1991). Recent research in economics, epidemiology and public health suggests that open defecation, the practice of defecating in the open without using a toilet or latrine, is an important cause of infant mortality and stunting in both rural and urban settings (Humphrey, 2009a; Fink et al., 2011; Cameron et al., 2013; Spears, 2013, 2012).

In this paper we assess whether the importance of dense settlement for infant mortality and child height is moderated by exposure to local sanitation behavior. We ask: does sanitation interact with population density to produce these child health outcomes? Such an interaction would be consistent with facts and theories in the literature. If open defecation reduces human capital by releasing germs into children’s environments, then it is plausible that the consequences of open defecation would be worse where people live more closely together and are more likely to encounter their neighbor’s germs.

Documenting and measuring the magnitude of the interaction between open defecation and population density is important for several reasons. First, it moves beyond dichotomous rural and urban distinctions and clarifies the circumstances under which population density is positively associated with health, and the circumstances under which poor sanitation is particularly harmful. Second, it contributes to understanding the importance of externalities or “spillover effects” of sanitation: one household’s toilet use or open defecation has consequences for neighboring households’ children. Such externalities are recognized in public economics as a central rationale for policy action. Finally, documenting and measuring such an interaction could guide policy decisions. Open defecation is increasingly concentrated in South Asia, a region where even rural areas are very densely populated.

We present two complementary analyses: the first establishes the broad importance of the interaction between sanitation behavior and population density for predicting infant mortality and child height in developing countries, and the second provides evidence to support the internal validity of this interaction. For the first analysis, we construct a new international dataset from 172 Demographic and Health Surveys (hereafter DHS surveys) collected in 69 developing countries between 1990 and 2012. Child level health data are 3
<!-- Start of picture text --> Variable extraction and cleaning, Merging of variables into frames and Data appended across countries<br>creation of frames harmonization of IDs, data<br>ETH_ESS1.do<br>} emesudo | appended across waves<br>ETH_ESS3.do<br>= —___<br>—><br>e<br>e<br>e<br>%<br>”<br>UGA_UNPS8_S2.do -<br>Se o “<br>|| Examplewith dataofprocesseda user-madefromfile raw "Ys! o<br>LUNPSdateeets na<br><!-- End of picture text -->
housing quantity in the formal sector, more than offset by a decrease in housing quantities in the informal sector. The reverse is true when a decrease in _d_ , that is an increase in _δ_ , is considered. Figure 1 illustrates the argument graphically. We obtain Thus, lowering the destruction rate _d_ (or raising _δ_ ) has the effect of lowering the equilibrium rent, augmenting the total quantity of housing, but lowering the quantity of formal housing. The intuition is simple: as _d_ decreases and _δ_ closes up to one, informal land becomes more and more indistinguishable from formal land. Since the cost differential does not change, investment in informal housing becomes more attractive with respect to investment in formal housing, thus raising the former and lowering the latter. The overall effect turns out to be positive, since the overall return on housing investment has increased.

# **3 The Data**

The empirical analysis is based on data from a survey conducted by the World Bank between August and October 2002 in the city of Pune, India. The respondent sample comprises 2,849 households. The survey documents socio-demographic characteristics of households, characteristics of dwelling units, tenure status, accessibility to infrastructure facilities like water, electricity and transport. In addition, the survey also provides information on the valuation of the units by households themselves, in the form of the two following questions:

- Estimated present market price for a similar unit in this neighborhood,

- Estimated monthly rental value for a similar unit in this neighborhood.

The two questions are asked to all households irrespective of their tenure status. Thus for every dwelling we have estimates of both the rent and the price.

10
|**Table 2. Summary statistics of project playgroup ch**<br>|**aracteristic**<br>|**s**<br>||
|---|---|---|---|
|Year of survey:|2010|2013|Diff|
||Mean<br>(S.D.)|Mean<br>(S.D.)|Mean<br>(S.E.)|
|**1. Finance**(units: million IDR)<br>||||
|Amount from government in 2009-2013|.<br>.|22.25<br>(49.53)|.<br>.|
|Amount from non-government in 2009-2013<br>|.<br>.|0.60<br>(3.44)|.<br>.|
|Amount from parents in 2009-2013|.|4.70|.|
||.|(19.41)|.|
|Proportion of annual expenditure for teacher salaries|0.15|0.18|0.03***|
||(0.07)|(0.08)|(0.01)|
|**2. Quality**||||
|Score from classroom observation (ranges from 1-7)|.<br>.|3.09<br>(0.98)|.<br>.|
|Proportion of teachers with post-secondary degree|0.20<br>|0.23<br>|0.03<br>|
||(0.34)|(0.31)|(0.02)|
|Proportion of teachers with teaching experience|0.31|0.36|0.06*|
||(0.42)|(0.45)|(0.03)|
|**3. Market condition (unit: per 100 children in villa**|**ge)**|||
|Number of kindergartens|0.71|0.50|-0.21**|
||(1.45)|(0.56)|(0.10)|
|Number of other playgroups|0.35|0.28|-0.08|
||(1.45)|(0.54)|(0.10)|
|Distance to village center (km)|1.<br>(3.|38<br>46)|.<br>.|
|Distance to nearest playgroup (km)|2.|14|.|
||(3.|88)|.|
|**4. Supplementary services**<br>||||
|Provides weekly food program (1=Yes)|0.44|0.26|-0.18***|
||(0.48)|(0.44)|(0.03)|
|Provides weekly vitamin (1=Yes)|0.08|0.07|-0.01|
||(0.25)|(0.26)|(0.02)|
|Provides anti-worming medication (1=Yes)|0.46|0.63|0.17***|
||(0.49)|(0.48)|(0.04)|
|**5. Community support**||||
|Parental involvement index (z-score)|0.00|0.13|0.13|
||(1.02)|(1.07)|(0.08)|

* p<0.10,  ** p<0.05,  *** p<0.01

Note: Data from Indonesia ECED Surveys. Sample size is 245 project playgroups in each year. Cells with . indicate that the variable was not collected that year. Difference column is the difference between 2013 and 2010. Robust standard errors clustered by center. Classroom observation is measured using the ECERS-R. Parental involvement is constructed from a principal component analysis of four items: parents are involved in activities with teachers and students in the center; parents are involved in cleaning the center; parents are involved in supplementary food program in the playgroup; parents are involved in acquiring toys and materials in the playgroup. The index is normalized to have mean 0 and standard deviation 1 in 2010. A higher value of the index means more parental involvement.
(7.3%). The chemicals sector is equally important to MNEs (7.2%), but is smaller in the sample for large firms (4%) and SMEs (2.7%). Other relevant sectors for MNEs include telecommunications, real estate, construction materials, computers, and commercial services, with shares exceeding 3%.

**Table 4: Distribution of firms by sector**

||**_Al_**|**_l_**|**_MN_**|**_Es_**|**_Lar_**|**_ge_**|**_SM_**|**_Es_**|
|---|---|---|---|---|---|---|---|---|
|**Sector**|**Obs**|**%**|**Obs**|**%**|**Obs**|<br>**%**|**Obs**|<br>**%**|
|Agriculture|20|1.0%<br>|6|1.0%|8|0.7%<br>|6|2.7%|
|Automotive|52|2.6%<br>|17|2.8%|27|2.3%<br>|8|3.7%|
|Aviation|39|1.9%<br>|5|0.8%|33|2.8%<br>|1|0.5%|
|Chemicals|97|4.8%<br>|43|7.2%|48|4.0%<br>|6|2.7%|
|Commercial Services|49|2.4%<br>|19|3.2%|21|1.8%<br>|9|4.1%|
|Computers|26|1.3%<br>|21|3.5%|5|0.4%||0.0%|
|Conglomerates|51|2.5%<br>|17|2.8%|33|2.8%<br>|1|0.5%|
|Construction|58|2.9%<br>|13|2.2%|40|3.3%<br>|5|2.3%|
|Construction Materials|50|2.5%<br>|22|3.7%|25|2.1%<br>|3|1.4%|
|Consumer Durables|13|0.6%<br>|8|1.3%|5|0.4%||0.0%|
|Energy|145|7.2%<br>|28|4.7%|104|8.7%<br>|13|5.9%|
|Energy Utilities|79|3.9%<br>|8|1.3%|69|5.8%<br>|2|0.9%|
|Equipment|29|1.4%<br>|14|2.3%|13|1.1%<br>|2|0.9%|
|Financial Services|293|14.5%<br>|69|11.5%|204|17.0%<br>|20|9.1%|
|Food and Beverage|||||||||
|Products|134|6.6%<br>|44|7.3%|72|6.0%<br>|18|8.2%|
|Forest and Paper|||||||||
|Products|32|1.6%<br>|3|0.5%|24|2.0%<br>|5|2.3%|
|Health Care Products|31|1.5%<br>|14|2.3%|16|1.3%<br>|1|0.5%|
|Healthcare Services|38|1.9%<br>|7|1.2%|26|2.2%<br>|5|2.3%|
|Household and|||||||||
|Personal Products|18|0.9%<br>|7|1.2%|9|0.8%<br>|2|0.9%|
|Logistics|48|2.4%<br>|13|2.2%|30|2.5%<br>|5|2.3%|
|Media|18|0.9%<br>|5|0.8%|7|0.6%<br>|6|2.7%|
|Metals Products|38|1.9%<br>|12|2.0%|23|1.9%<br>|3|1.4%|
|Mining|77|3.8%<br>|15|2.5%|60|5.0%<br>|2|0.9%|
|Non‐Profit / Services|36|1.8%<br>|1|0.2%|9|0.8%<br>|26|11.9%|
|Other|128|6.3%<br>|50|8.3%|53|4.4%<br>|25|11.4%|
|Public Agency|38|1.9%<br>|2|0.3%|30|2.5%<br>|6|2.7%|
|Railroad|15|0.7%<br>|1|0.2%|14|1.2%||0.0%|
|Real Estate|72|3.6%<br>|23|3.8%|39|3.3%<br>|10|4.6%|
|Retailers|35|1.7%<br>|11|1.8%|20|1.7%<br>|4|1.8%|
|Technology Hardware|71|3.5%<br>|56|9.3%|13|1.1%<br>|2|0.9%|
|Telecommunications|77|3.8%<br>|23|3.8%|53|4.4%<br>|1|0.5%|
|Textiles and Apparel|26|1.3%<br>|7|1.2%|15|1.3%<br>|4|1.8%|
|Tobacco|2|0.1%<br>|1|0.2%|1|0.1%||0.0%|
|Tourism/Leisure|37|1.8%<br>|14|2.3%|20|1.7%<br>|3|1.4%|
|Universities|12|0.6%||0.0%|9|0.8%<br>|3|1.4%|
|Waste Management|19|0.9%<br>|2|0.3%|8|0.7%<br>|9|4.1%|
|Water Utilities|17|0.8%||0.0%|14|1.2%<br>|3|1.4%|
|**Total**|**2,020**|**100.0%**<br>|**601**|**100.0%**|**1,200**|**100.0%**<br>|**219**|**100.0%**|

9
6 the respective roles of natural resources, industrialization, and de-industrialization in urbanization. Sections 4, 5, and 6 study the role of these factors for urban employment, construction, and urban primacy, respectively. Section 7 focuses on urban human capital.

# **1. The Global Sectoral Composition of Cities**

It is not obvious how cities of the same population size and located in countries with similar levels of economic development differ globally in their employment composition. GJV16 find examples of countries where urban areas have high shares of _urban tradables_ , which include manufacturing (MFG) and FIRE (finance, insurance, real estate, and business services). They also find examples of countries where urban areas have high shares of _urban non-tradables_ , which include the (non-tradable) wholesale and retail sector.

Since the work of GVJ16, which is cross-sectional and only focuses on the urban sector as a whole, many countries have added censuses to the IPUMS repository (Minnesota Population Center, 2020) and GIS files corresponding to the second level administrative units in which a household is located.<sup>6</sup> In addition, the Global Human Settlements Layer (GHSL) database provides geocoded polygons of urban extent boundaries for the world c. 2015 (Schiavina et al., 2019). More precisely, this database uses satellite data on built-up area to identify “Functional Urban Areas” (FUAs), i.e. commuting zones of at least 50,000 residents today. Combining the two data sets allows us to obtain the sectoral composition of most world cities. Using a simple methodology presented below, we then classify each city as being a _production city_ – a city with a disproportionately high share of employment in tradables; a _consumption city_ – a city with a disproportionately low share of employment in tradables; or a _neutral city_ – a city in which the shares of employment in tradables is neither disproportionately low nor disproportionately high.<sup>7</sup>

**Data.** For 76 countries and 191 country-years (1960-2015), we have IPUMS census microdata with information on the administrative unit in which the respondent lives, the respondent’s sector, _and_ whether the respondent lives in an “urban” area.

We process the data following several steps. First, we select “urban” observations.<sup>8</sup> Second, we have information on the resident’s second level administrative unit. Such units are typically smaller than FUAs.<sup>9</sup> Third, IPUMS classifies employment into 16

> 6These units correspond to _counties_ in the U.S. and _municipalities_ in most Latin American countries. 7We interchangeably use the words “FUAs”, “agglomerations” and “cities” in the rest of the analysis. 8When the urban identifier is unavailable, we identify urban residents from information on the locality. 9In some cases, information is only available for first level administrative units, which leads to coarser
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Table 7: Summary of data on the stock of old age pensioners (2003/04)**

||**2003/04**|**Civil Servant retirees**|**Teacher retirees**|
|---|---|---|---|
|**t**<br>**0)**|**Men**|12.155|8.297|
|**To**<br>**00**|**Women**|1.367|2.765|
|**(**|**Total**|13.521|11.062|
|**.**<br>**e**|**Men**|64.5|70.1|
|**Av**<br>**Ag**|**Women**|62.6|68.0|
||**Total**|64.3|69.6|
|**.**<br>**s.**<br>**0)**<br>|**Men**|2,301.7|1,945.3|
|**Av**<br>**en**<br>**00**<br>**/1**|**Women**|1,801.1|1,198.3|
|**P**<br>**(**|**Total**|2,248.5|1,758.6|

**Notes** : /1 this includes only annuities from reduced and full pensions. **Source** : MPS.

Age distributions for the stock of old age pensioners derived for the base year of 2003/04 are plotted in Figure 5 and Figure 6.

**Figure 5: Almost 90% of government retirees are men between the ages of 42 and 90**

<!-- Start of picture text --> 0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101<br>Age<br>male female<br>Number of pensioners (000')<br><!-- End of picture text -->

**Notes:** * Age distribution of civil servant retirees (2003/04). **Source** : MPS.

- 33 -
**Table 6 (Continued)**

|||**Log Total Calo**|**rie Cons**|**umption Pe**|**r Capita**|
|---|---|---|---|---|---|
|||IHPS|||FIAS|
||1|2||1|2|
|**Weather Shocks†**||||||
|Weather Shock|-0.022|||0.017||
||(.028)|||(.063)||
|Current Shock||-0.038|||-0.018|
|||(.027)|||(.064)|
|Sequential Shock||-0.075|||-0.114|
|||(.077)|||(.110)|
|**Aid * Shock**||||||
|Aid|-0.028|-0.084||-0.023|-0.082|
||(.087)|(.091)||(.130)|(.135)|
|Aid * Weather Shock|-0.150|||-0.241||
||(.138)|||(.182)||
|Aid * Current Shock||0.041|||-0.053|
|||(.136)|||(.203)|
|Aid * Sequential Shock||-0.106|||-0.051|
|||(.230)|||(.250)|
|**Explicit HH Targeting Characteristics**||||||
|Dummy, Female Head|-0.040|-0.038||-0.118|**<br>-0.115<br>**|
||(.031)|(.031)||(.051)|(.051)|
|Dummy, Children<3|-0.039|**<br>-0.040|**|0.015|0.016|
||(.019)|(.019)||(.032)|(.033)|
|Dummy, All Members>59|-0.157|**<br>-0.154|**|-0.196|-0.207|
||(.063)|(.061)||(.129)|(.129)|
|Dummy, HH Member Disabled|0.012|0.013||0.033|0.028|
||(.022)|(.022)||(.041)|(.041)|
|**Wealth**||||||
|Wealth Index|0.256|***<br>0.254|***|0.259|0.257|
||(.082)|(.082)||(.168)|(.171)|
|# Mobile Phones|0.021|**<br>0.022|**|0.019|0.024|
||(.010)|(.010)||(.019)|(.019)|
|**Constant**|10.199|***<br>10.152|***|10.220|***<br>10.164<br>***|
||(.256)|(.260)||(.470)|(.474)|
|# of Obs.|4412|4412||1999|1999|
|R2 (within)|0.177|0.175||0.181|0.182|
|R2 (between)|0.415|0.415||0.379|0.382|
|R2 (overall)<br>**Notes:*****/**/* denote statistical si<br>**Weather Shock**is a dichotomous varia|0.311<br>gnificance a<br>ble that is e|0.310<br>t 1/5/10 percent re<br>qual to 1 if the hous|<br>spective<br>ehold ha|0.283<br>ly. Standard<br>s been expo|0.285<br>Errors in parentheses.**†**<br>sed to a weather shock in|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **† Weather Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in the current period (irrespective of potential exposure to past weather shocks), and 0 otherwise. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

34
# **Table 3**

Dependent Variable: Employment

|VARIABLES|(1)<br>OLS-FE|(2)<br>OLS-FE|(3)<br>IV-FE|(4)<br>IV-FE|(5)<br>IV-FE|(6)<br>IV-FE|(7)<br>IV-FE|(8)<br>IV-FE|(9)<br>IV-FE|(10)<br>IV-FE|
|---|---|---|---|---|---|---|---|---|---|---|
|POST|0.99*|1.31*|13.62|8.44|4.27**|4.18**|2.75|-2.02|4.25**|4.16**|
||(0.57)|(0.68)|(12.72)|(8.73)|(1.84)|(1.71)|(11.55)|(8.07)|(1.83)|(1.71)|
|Time|0.31***|0.15**|-0.31|-0.19|0.42***|0.14|0.54|0.63|0.42***|0.14|
||(0.07)|(0.07)|(0.94)|(0.69)|(0.12)|(0.13)|(0.87)|(0.67)|(0.13)|(0.13)|
|Timesq|0.04***|0.05***|0.07**|0.08**|0.06***|0.07***|0.06*|0.06**|0.06***|0.07***|
||(0.01)|(0.02)|(0.04)|(0.03)|(0.02)|(0.02)|(0.03)|(0.03)|(0.02)|(0.02)|
|WorkingAgePop||0.00**||0.00*||0.00*||0.00*||0.00*|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|rGDP||-0.00||-0.00||-0.00||-0.00||-0.00|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|LFPRate||0.23||-0.16||-0.18||-0.21||-0.18|
|||(0.22)||(0.56)||(0.54)||(0.56)||(0.54)|
|Observations|1,352|1,023|724|723|724|723|724|723|724|723|
|Number of|84|72|60|60|60|60|60|60|60|60|
|Countries|||||||||||
|R-squared|0.17|0.33|||||||||
|Partial R-squared|||0.02|0.02|0.41|0.41|0.02|0.02|0.41|0.41|
|Hansen J p-value|||0.06|0.06|0.11|0.13|0.54|0.36|0.71|0.50|
|Kleibergen-Paap F<br>statistic|||2.89|2.88|518.30|513.40|3.41|3.52|509.16|504.79|

Note: Robust and clustered standard errors at the country level are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. POST is instrumented with debt-to-GDP ratio, terms of trade, and five-year change in democracy score in columns (3) and (4). In columns (5) and (6) the instruments also include lagged POST. POST is instrumented with external debt-to-GDP ratio interacted with the change in world interest rates, terms of trade, and five-year change in democracy score in columns (7) and (8). In columns (9) and (10) the instruments also include lagged POST.

29
the weak-instrument-robust Anderson-Rubin Wald test in the second-stage regressions. We expect the IV estimates of the coe¢ cients on tari¤s and FDI in‡ows to be larger in magnitude than the LS-BE estimates of those coe¢ cients if measurement errors and political economy forces dominate the confounding reverse causality forces.<sup>32</sup>

# 4 Main Results

## 4.1 Least Squares Between Regressions

Starting with the determinants of backward GVC participation shares, Column (1) of Table 1 shows the baseline LS-BE estimates of Equation (1). Most coe¢ cients have the expected signs and are signi...cant. Factor endowments jointly matter for backward GVC participation, with a strong F-statistic of 6.84, which is signi...cant at the 99% level. Larger land and/or natural resources endowments are linked to lower backward GVC participation shares, while abundance in capital is associated with higher backward GVC participation shares. But geography and domestic industrial capacity also matter. A shorter distance to the GVC hubs is positively correlated with backward GVC participation. Countries with larger domestic industrial capacity (i.e., a potentially larger pool of domestic suppliers) exhibit lower backward GVC participation shares, as domestic inputs may be used to replace imports.

Countries’openness to trade and FDI is also a predictor for the intensity of backward GVC participation. Lower tari¤s and larger FDI in‡ows are associated with higher backward GVC participation shares across countries. Better institutional quality measured by a higher score in the political stability index is linked to higher backward GVC participation. Exchange rate appreciation is unrelated to backward GVC participation, which is not surprising, given that appreciation stimulates imports by reducing its prices, but can also reduce export competitiveness due to higher export prices.<sup>33</sup> The R-squared in Column (1)

> 32We also tried using lagged tari¤s and FDI, as well as neighboring countries tari¤s and FDI as instruments. In these cases, the second-stage results are very similar, with IV estimates of the coe¢ cients on tari¤s and FDI being larger in magnitude than the corresponding LS-BE ones. Reed (2015) shows that lagged endogenous variables are valid IVs if the lagged endogenous variables do not a¤ect current period dependent variable and if the lagged endogenous variables are correlated with the current period endogenous variables. These results are available upon request.

> 33This result is consistent with the ...nding of Amiti et al. (2014) who show that low aggregate exchange rate

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