rafmacalaba/datause-encoder-data huggingface dataset:
country. A score of 0 indicates no restrictions while 100 signals severe implementation of lockdown policies in a country (Hale et al., 2021).

The first lockdown measure in Indonesia was on January 18, 2020, when COVID-19 screening of international travelers was introduced. This was followed by the ban on arrivals from some regions on February 5, 2020. Closure of workplaces, total border closure, closure of public transport, restrictions on internal movement and stay-at-home requirements followed in that order from March 15 and they were all operational by April 1. This is depicted by the consistent rise in the stringency index which remained above 70 until May 20, 2020, when the closure of public transport was temporarily lifted. The index has since fluctuated depending on the severity of the COVID-19 wave. Indonesia had the second highest index among the countries in Figure 1, after Australia, by the end of 2020. All the lockdown measures were in operation in 2021 and the index was relatively stable at around 72. Indonesia had the third highest index, after Vietnam and the Philippines, at the end of the sample period on August 31, 2021.

Among the neighboring countries, Singapore was the first to impose lockdown measures (January 2), followed by Vietnam (January 25), India (January 26), Papua New Guinea (January 27), Malaysia (January 30), the Philippines (January 31), Australia (February 1) and Thailand (March 6). All the countries started with international travel controls and progressively introduced domestic measures. Singapore’s stringency index was the lowest of all Indonesia’s neighbors at the end of August 2021 because of the relaxation of the conditions on internal movements and public transport. The Philippines, Vietnam and Australia had the highest stringency indices by the end of August 2021, respectively.

# **2.2 Trade data**

Our export and import data is a monthly product-country data series obtained from the Indonesia Statistics Authority (BPS _Badan Pusat Statistik_ ). The data ranges from May 2018 to August 2021. Products are classified at the 8-digit Harmonized System (HS) level.

6
32) are located in more specialized districts. At the bottom of the table, we group industries into “traditional” or “modern” and find that the latter is generally located in more specialized areas than the former.<sup>8</sup> The traditional versus modern differences are stable over time. By contrast, and showing more of the distinctions between the specialization and diversity indices, there are no differences evident with respect to the average diversity values of the districts in which these two groups of industries locate.

Table 6 provides a second industry perspective. We count by survey the number of times that each industry is responsible for the specialization value of its district. We also tabulate the average of these counts for an industry across all surveys and the changes in these counts from 1989-1994 to 2000-2010. While modern industries tend to be in very specialized locations, this concentration means that on average for our 429 districts, it is a traditional industry that is responsible for the local specialization value. Roughly two-thirds of districts have their specialization in a traditional industry, and this share is climbing slightly over time. The computer and communication industries, by contrast, form the specialized industry for typically 10-15 districts over the sample period, which is a substantially smaller count than many of the larger traditional industries (average count across industries is 20).

The bottom of Table 6 documents the correlation of these counts between adjacent surveys. While the patterns across the 1989 to 2005 surveys are quite stable, more substantial differences emerge at the industry level for the 2010 survey compared to earlier years. This is likely due to the change from the NIC-2004 industry codes in the 2005 data to the NIC-2008 industry codes in the 2010 data. This uncertainty is why we take the 2005 levels in Table 1a for comparison to the United States. Most of the remaining analyses only employ the 2010 data to calculate growth rates at the district level and do not depend upon industry definitions, thus minimizing the importance of this issue.

> 8 These groupings follow Ghani et al. (2013a) and simply classify an industry as being modern if its unorganized share is the less than the unweighted average of the unorganized share across industries in the manufacturing sector in 2000. Specific values are documented in the notes to Table 5.

15
# **ACKNOWLEDGMENTS**

The funding for the design, implementation and analysis of CVIP: Methodological Experiment on Measuring Cassava Production, Productivity, and Variety Identification originated from (1) the World Bank Living Standards Measurement Study (LSMS) “Minding the (Agricultural) Data Gap” methodological research program, funded by UK Aid; (2) the Global Strategy for Improving Agricultural and Rural Statistics, led by the Food and Agriculture Organization of the United Nations; and (3) the CGIAR Standing Panel on Impact Assessment (SPIA). The authors would like to thank Bob Baulch and Sydney Gourlay for their comments on the earlier versions of this paper.

21
outsourcing destinations, as well as structural factors such as a lower intense relationship between trade and output itself (Timmer et al. 2016; Constantinescu et al. 2015).

Figure 5: Increased services import (input) intensity in India after reforms took place (1990-2017) <!-- Start of picture text --> 1990 1995 2000 2005 2010 2015<br>year<br>Automobile Industry Electronic Industry<br>Textiles and Apparel Chemicals and Pharmaceuticals<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: Prowess; Own calculations. Note: Service import input intensity is measured as share of imported services on total service inputs

# 3. Data and Empirical Strategy at the Firm Level

To systematically investigate the link between the use of service inputs in the production and a firms’ involvement in GVCs, we use detailed firm-level information from the Prowess database collected by the Centre for the Monitoring of the Indian Economy (CMIE). Prowess is a panel of large and medium Indian firms and has information primarily from income statements and annual reports of publicly listed companies in India. We use data from 1990 to 2017. This data set has been used in several papers (see Goldberg et al., 2010a, 2010b) and covers approximately around 70 percent of economic activity in India’s formal industrial sector. Besides financial information for each firm, the data contains information on trade activities (exports and imports of goods and services), ownership, revenue, expenditure on various inputs including the different service inputs. The wage bill is measured in relative to the size of a firm. Access to finance is proxied by financial leverage measured as the ratio of external liabilities to equity. Productivity is estimated using Ackerberg, Caves and Frazer (2015). Table 1 reports average values for some key variables.

Table 1: Summary Statistics

|Variable|Average|Unit|
|---|---|---|
|Output|2.157|in million Indian Rupees (logged and deflated)|
|Wage|0.544|in million Indian Rupees (logged and deflated)|
|Age|24.839|years|
|Leverage|1.473|liabilities to equity ratio|
|Productivity|0.002|total factor productivity|
|Service Intensity|15.230|relative to total expenditures|
|IT related service intensity|1.360|relative to total expenditures|
|Complex service inputs|6.590|relative to total expenditures|

7
_Experience of Investing Coalition._ We construct an investor experience measure by (1) calculating for each individual investor<sup>15</sup> the cumulative number of years that it has been involved in each of its projects for each period, (2) summing each investor’s cumulative experience across all projects in a given country for each period, (3) multiplying the resulting figure for each investor by its equity stake in a given project, and (4) summing the resulting figure for all investors in a given investor coalition. Our investor experience variable is thus an equity-weighted measure of the country-specific experience of each investing coalition.

_Other Independent Variables_ . We also include the _per capita_ income of the country to measure a country’s level of economic development. We expect that, independent of our arguments about legitimacy, the material interests of consumers and private investors in poorer countries are more divergent, leading to a greater incidence of policy changes and disputes. We also include a variable measuring the size of a generating facility (in megawatts) to capture its visibility or political salience. Both of these variables are logarithmically transformed to take into account their extreme skewness. Table 5 provides descriptive statistics and a correlation matrix.

# **_4.5 Modeling Procedure_**

We estimate the effects of independent variables on the occurrence of a dispute or wholesale policy change using an event history analysis, a technique that assesses the influence of a set of covariates on the incidence of an event using a longitudinal record of events in a sample from a population. In our model, each investor coalition is at risk of an adverse event in each country in which it has a stake in a power plant in each time period or until an adverse event occurs. This technique models the rate of a transition from an origin state to a destination state

> 15 Investor names were carefully screened to eliminate multiple names for the same firm as well as to take into account mergers, acquisitions and divestitures. In the latter cases, organizational memory, ties and influence were presumed to survive a change in ownership.

23
_Fiscal Crisis in Central Europe_ _15_ do not have conceptually much in common with their market counterparts, but are rather determined, to a large extent, by the structure of relative prices, which is set administratively. Implicitly, sectors - and therefore enterprises - are divided by the central planner between "bad" and "good" ones, and relative prices are chosen so as to penalize the former and artificially promote the financial performance of the latter. The resulting inflated profit tax revenues received from "good" enterprises are then used to "cover the losses" of the discriminated-against enterprises, through cross-subsidization. In a market economy - without any cross-subsidization - gross profits are relatively smaller, but the need for compensatory subsidies for loss-makers is correspondingly reduced. These considerations suggest that any meaningful comparison across time and economic regimes of the overall enterprise sector performance needs to focus on profits _and_ (producer) subsidies together, that is, on the overall _net_ direct contribution to fiscal revenues.

Only in this way we can adequately capture the evolution of the budget-enterprises fiscal relations, at a time when the intermediation role of the state comes to an end.

The traditional view of the fiscal crisis cannot be faulted for attributing a large share of responsibility to the SOE performance. On the face of it, profit tax revenues collapsed in Poland from 11 percent of GDP in 1986 to 4.5 percent in 1992, in Hungary from 11 percent of GDP to 2 percent, and in Czechoslovakia from 18 percent to 12 percent (figures are rounded to unity; see Appendix Tables A.1, A.2 and A.3).<sup>14</sup> The point that is not adequately stressed in these figures, however, is that the fall in profitability is in itself largely attributable to the fall in subsidies. The reduction in subsidies in the three countries considered has indeed been dramatic (see Tables A. 1-3 in the Appendix). They decreased in Poland from about 16 percent of GDP in 1986 to 3.6 percent of GDP in 1992, in Hungary from 17 percent of GDP to 4 percent, and in the former Czechoslovakia from 25 percent to 7 percent. Even more strikingly, virtually all the cuts took place in only three years in Poland (1989-1991) and two in Czechoslovakia (1990-1991), showing the early determination of these

> '4 Data on profit tax and tumover tax refer to, respectively, Enterprise Income Tax and VAT, for the countries/years in which they have been replaced by the latter. See Tables in the Appendix for more detail.
When elections are far, fiscal policy is less disciplined and when countries are close to executive elections, governments tend to groom electors by performing more disciplined policies in order to guarantee their reelection. GDP per capita is negatively associated with cyclicality coefficients. This means that more developed countries tend to perform counter-cyclical fiscal policies. Furthermore, we find that the volatility of output yields pro-cyclical policy as emphasized by Talvi and Vegh (2005).

|Dependent vari|able: Cyclical|ity of fscal p|olicy<br>ˆ<br>_βit_||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|FRD|-0.058***|-0.016***|0.060|0.144|0.197|0.197|0.180|0.069|0.300|0.083|
||(0.032)|(0.033)|(0.250)|(0.244)|(0.241)|(0.239)|(0.239)|(0.249)|(0.593)|(0.490)|
|BQ|-0.194***|-0.183***|-0.091***|-0.111***|-0.032***|-0.034***|-0.132***|-0.021***|0.020***|-0.020***|
||(0.033)|(0.033)|(0.050)|(0.059)|(0.061)|(0.060)|(0.063)|(0.073)|(0.070)|(0.071)|
|ERR|0.005***|0.004***|0.009***|0.012***|0.007***|0.007***|0.006***|0.008***|0.008***|0.009***|
||(0.004)|(0.004)|(0.050)|(0.012)|(0.011)|(0.011)|(0.011)|(0.010)|(0.010)|(0.014)|
|FRD X BQ|||||||||-0.107***<br>(0.070)||
|FRD X ERR||||||||||-0.002***<br>(0.015)|
|IT||-0.253***|-0.441***|-0.618***|-0.586***|-0.585***|-0.528***|-0.447***|-0.456***|-0.447***|
|||(0.051)|(0.086)|(0.106)|(0.107)|(0.106)|(0.129)|(0.053)|(0.052)|(0.055)|
|ELECTIONS|||0.003***|0.002***|0.001***|0.001***|0.000***|0.004***|0.004***|0.004***|
||||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|POLARIZ||||-0.149***|-0.148***|-0.147** *|-0.177***|-0.099***|-0.101***|-0.099***|
|||||(0.014)|(0.015)|(0.015)|(0.016)|(0.010)|(0.010)|(0.010)|
|GDPPC|||||-0.274***|-0.276***|-0.236***|-0.193***|-0.172***|-0.191***|
||||||(0.025)|(0.024)|(0.022)|(0.024)|(0.030)|(0.024)|
|OV||||||5.606**|15.211***|19.987|20.875|19.953|
|||||||(3.763)|(3.414)|(6.854)|(7.231)|(7.183)|
|RENT|||||||-0.023***|-0.029**|-0.028***|-0.029***|
||||||||(0.003)|(0.003)|(0.003)|(0.003)|
|DEBT||||||||0.001<br>(0.001)|0.001<br>(0.001)|0.001<br>(0.001)|
|Obs|2264|2264|1288|1021|1004|1004|985|912|912|912|
|Countries|120.000|120.000|72.000|71.000|70.000|70.000|69.000|68.000|68.000|68.000|
|Hansen J|0.696|0.698|0.662|0.711|0.703|0.674|0.690|0.732|0.735|0.736|

Bootstrapped standard errors in parenthesis (100 replications) * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Table 1: Empirical results: basic panel regression 16
<!-- Start of picture text --> Table 7: Maternal mortality ratio<br>(1) (2) (3) (4) (5)<br>Maternal mortality Births attended by Births attended by Average annual rate Average annual<br>per 100,000 live skilled health skilled health of change 1990-99 rate of change<br>births 1995 personnel 1990 personnel 1999 (%) needed to reach<br>90% attendance<br>____________________ (1999-2015)<br>All developing<br>countries 430<br>East Asia and<br>Pacific 140 50 _ 61 a 2.2 2.4<br>Europe and Central<br>Asia 60 _<br>Latin American and<br>Caribbean 190 72 77 _ 0.7 1.0<br>Middle East and<br>North Africa 240 50 61 2.2 2.4<br>South Asia 430 . _<br>Sub-Saharan Africa 1,120 49 46 _ -0.7 4.2<br><!-- End of picture text --> a. Includes all Asia except India and China. Source: WHO / UNICEF Because of the lack of comparable time-series data, it is difficult to assess progress toward this goal. The last global estimates of maternal mortality for 1995 concluded that about 500,000 women died during pregnancy and childbirth, most of them in developing countries. Data on the proportion of birth attended by skilled health personnel are indicative and are presented in Table 7 above. In Latin America, where the proportion of births attended by skilled health care workers is high, maternal mortality is relatively low, while very high maternal mortality occurs in Africa, where skilled attendants are not readily available. Significant progress in reducing maternal mortality will, however, require more than increasing the number of skilled birth attendants: deaths in childbirth often involve complications such as hemorrhage that require fully equipped medical facilities. The maternal mortality ratio is thus an indication of the capacity of the health care system to meet the needs of the entire population.

# _Goal 6 - HIVIAIDS, malaria, and other diseases_

The Millennium Development Goals also target HIV/AIDS, malaria, tuberculosis and other major epidemic diseases, which pose significant threats to economic and social progress in developing countries. Unfortunately, the data on these illnesses, including AIDS, is often incomplete or collected on an irregular basis. In many countries only one reliable estimate has been made for the HIV/AIDS infection rate, so it is impossible to accurately determine rates of change. The targets for epidemic diseases are still under discussion and have not yet been fully quantified.

In 2000, 34.7 million adults and 1.4 million children were living with HIV/AIDS, and over 95 percent of them are in developing countries (see Table 8 below). The most devastated region is Sub-Saharan Africa (70 percent of cases) followed by South and South-East Asia (16 percent). For the first time, however, HIV incidence has fallen slightly in Sub-Saharan Africa in 2000, partly because successful prevention programs have reduced infection rates, particularly in Uganda, and also because the epidemic has already affected many people in the sexually active population. In Eastern Europe and Central Asia, the number of adults and children living with 10
# **_4. Potential Intraregional Exports in Central America_**

One of the main benefits of the GTM is that it allows projecting the "potential level" of exports based on a specific set of coefficients. In turn, the ratio between the observed and the potential export flows provides a measure of the closeness ─ or _proximity index_ ─ of a specific country or country-pair to its expected exports level ─ _expectations frontier_ . Following Benedictis and Vicarelli (2005), this closeness measure �<sup>�</sup> can be expressed as: Where ���� captures the observed export flows from the exporter country � to the importer country � and ����� are the expected exports flows generated by the gravity equation. To calculate the potential export flows, four different sets of coefficients were used for calculating �����, resulting in four different scenarios or “proximity indices.” The first scenario is based on the coefficients for the CA intra-regional trade model (Figure 6). The second one projects the exports flows in CA from the estimated coefficients obtained for the EU15 intra-regional model (Figure 3). A third scenario is obtained by applying the highest country-pair adjacency coefficient, obtained from the disaggregated GTM for Central America (with results illustrated in Figures 7 and 10),<sup>16</sup> while the fourth scenario applies the adjacency coefficient resulting from the EU15 intra-regional model (Figure 3).

Figure 13 below illustrates the results obtained from the four different scenarios, in this case, comparing Costa Rica with Honduras. In Costa Rica’s case, the observed-to-projected exports are close to the unconstrained _Expectations Frontier_ (i.e. a ratio equivalent to 1), except for when the country’s currently observed exports are compared to what its exports would be expected to be under the assumption of an “EU15-like” behavior of all the independent trade variables (distance, adjacency, and the GDP of both the exporting and the importing country). In the case of Honduras, on the other hand, the ratio is far from 1 in all of the scenarios, including when the country’s observed exports are compared to what they would be expected to be assuming an adjacency effect equivalent to the adjacency effect of the best performer in Central America.

> 16 For Grains (in volume and value terms) and Overall Trade (in value terms) the best country-pair adjacency effect corresponds to the Costa Rica- Nicaragua; for Steel and Processed Fruit & Vegetable exports, even the best “Adjacency Performance” across the country couples is 0.

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||(1)<br>trade|(2)<br>trade|(3)<br>trade|
|---|---|---|---|
|RTA|0.10<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|
||(12.70)|(11.97)|(11.97)|
|RTA_×_ SAR||0.11<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|
|||(6.88)|(6.00)|
|RTA_×_ SAR-intra|||0.20<sup>_∗∗∗_</sup><br>(5.70)|
|_N_|933660|933660|933660|

**Note:** all regression include domestic trade flows and control for border effects. The variable RTA measures the world average effect of trade agreements. The SAR effect is measured as an addition to the average, and SAR-intra as an addition to the SAR effect. _t_ statistics in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

Table 2: South Asia manufacturing results 9
# **Table 5(3): Robustness checks, with more control variables**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Full|Diff|Non-diff|Full|Diff|Non-diff|
|(t-r)|0.811**|1.686***|0.277|0.806**|1.703***|-0.130|
||(0.373)|(0.514)|(0.521)|(0.365)|(0.539)|(0.552)|
|prc_sh|-7.844***|-5.875***|-11.446***|-7.849***|-5.876***|-11.692***|
||(1.588)|(2.064)|(2.636)|(1.588)|(2.064)|(2.655)|
|collpriv_sh|-14.064***|-17.927***|-9.855**|-14.063***|-17.937***|-9.612**|
||(2.601)|(3.569)|(4.010)|(2.601)|(3.571)|(4.003)|
|for_sh|-47.351***|-49.948***|-44.224***|-47.347***|-49.952***|-43.877***|
||(1.979)|(2.594)|(3.276)|(1.979)|(2.595)|(3.278)|
|Ad-R&D-Sales|-9.777**|-2.613|-5.880|-9.787**|-2.642|-5.896|
||(3.825)|(5.593)|(6.899)|(3.828)|(5.605)|(6.916)|
|sigma|0.026|0.023|0.025|0.026|0.023|0.025|
||(0.019)|(0.024)|(0.029)|(0.019)|(0.024)|(0.029)|
|primary|14.456**|-1.344|10.450*|14.494**|-1.341|11.931**|
||(6.376)|(9.977)|(5.778)|(6.372)|(9.978)|(5.272)|
|resource|2.301|4.118|1.428|2.299|4.119|1.311|
||(2.902)|(4.123)|(4.155)|(2.903)|(4.125)|(4.159)|
|advanced-tech|11.302*|7.299|2.074|11.323*|7.308|2.127|
||(5.953)|(5.887)|(12.126)|(5.957)|(5.890)|(12.156)|
|zero/full rebate||||0.297|0.468|11.045|
|||||(2.895)|(3.285)|(7.135)|
|HS4 fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|5,315|3,332|1,806|5,315|3,332|1,806|
|R-squared|0.490|0.520|0.460|0.490|0.520|0.461|

Notes: The regressions are at HS8 level. The dependent variable is the share of indirect exports (conservative measure), ranging from 0 to 100. The export shares by trade regime and firm ownership type variables range from 0 to 1. The omitted trade regime is normal trade. The omitted firm type is SOE. Ad-R&D-Sales refers to the advertising and R&D intensity. Sigma refers to elasticity of substitution. Primary refers to the primary product dummy. Resource refers to the resource based product dummy. Advanced-tech refers to the advanced technology products dummy. Zero/full rebate dummy controls for the products with either a zero or a full rebate rate. “Diff” refers to differentiated products according to Rauch’s classification. “Non-diff” products include both homogenous and referenced products. *** p<0.01, ** p<0.05, * p<0.1.

39