rafmacalaba/datause-encoder-data huggingface dataset:
with price changes less than 3 percent and overestimate the quantity changes for firms with growth in prices over 3 percent. This would imply that productivity change is underestimated for firms that experience a change in price of less than 3 percent and overestimated for firms that experience a change in price greater than 3 percent.

Table 2: Price Changes in Transport Equipment Industry

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Price Dec|line|Price Incr|ease|Firm Data|WPI|
|Year|% of Firms|Mean|% of Firms|Mean|Std. Dev.|Industry Level|
|1990|6.06|-0.21|93.94|0.13|0.11|0.11|
|1991|13.64|-0.07|86.36|0.11|0.10|0.09|
|1992|6.06|-0.22|93.94|0.18|0.15|0.11|
|1993|7.77|-0.07|92.23|0.12|0.12|0.07|
|1994|32.52|-0.07|67.48|0.09|0.12|0.03|
|1995|27.52|-0.12|72.48|0.13|0.17|0.07|
|1996|24.54|-0.11|75.46|0.13|0.15|0.08|
|1997|25.00|-0.11|75.00|0.11|0.14|0.06|
|1998|43.03|-0.10|56.97|0.09|0.14|0.04|
|1999|44.68|-0.09|55.32|0.09|0.14|0.03|
|2000|37.08|-0.09|62.92|0.10|0.13|0.03|
|2001|36.86|-0.10|63.14|0.11|0.16|0.06|
|2002|52.80|-0.09|47.20|0.11|0.15|0.02|
|2003|43.80|-0.10|56.20|0.09|0.14|0.00|
|2004|42.69|-0.10|57.31|0.12|0.16|0.00|
|2005|32.02|-0.10|67.98|0.14|0.18|0.05|

Notes: Column (6) shows price change for the for Transport Equipment Industry using the industry price deflator from the Government of India Price Statistics (WPI) from the 1993-94 price series. Columns (1)-(5) are constructed from firm level price data.

Information on prices allows me to estimate (25). I construct firm specific price deflators, which vary across firms within an industry, unlike industry level deflators that are common to all firms in an industry. I thus recover estimates of the production function coefficients and _ωit_ that do not suffer from the shortcomings discussed above.

# **4 Estimation Strategy**

The empirical strategy involves two steps. First, I recover the parameters of the production function and estimates of total productivity ( _α_ � _χχit_ + _ω_ � _it_ ). Next, I use difference-in-differences propensity score matching to examine the impact of starting to export on efficiency.

13
separates the importer types between general and producer.<sup>9</sup> The data covers the universe of all Indonesian exports in the period, except for those from export processing zones. Thus our data accounts for more than 90% of overall exports. The data contains a total of 69,383 firms, of which 37,260 are importers and 44,363 are exporters. One of the appealing features of the data is the connection between importing and exporting activities at a highly disaggregated level (10-digit HS code). The data contains the monthly dollar value and quantities of imports and exports at the firm-month-HS-10 product level along with information on the country of origin for imports and the destination for exports, whether the importer is a producer or a pure trader, and the customs’ treatment (e.g. privilege customs status). Since we are interested in how NTMs applied on imports affect exports when there is a demand shock, we only use data on the population of Indonesian exporters which also import directly.

A total of 12,735 firms, representing about 18% of all exporters, both import and export directly during 2014-2018. Despite of being fewer than a fifth of exporters, these firms account for over two-thirds of Indonesian export value (figure 2). The other 37% of exports came from firms that do not import directly but may purchase imported inputs through third parties. This is consistent with stylized facts globally where importing exporters tend to dominate trade (World Bank, 2020). The share of importer exporters, though dominant in the export market, has been declining over the recent years, as evident in figure 2 b. During the same time, the share of exporters who face at least one NTM has been increasing, as discussed in section 3.2 below.

# **3.2 Indonesian NTM and data**

NTM-related regulations have been spread out among a total of 13 government institutions (ministries, agencies and some are issued as a presidential decree) during our period of

> 9An API-P holder may import interchangeable manufactured goods such as complementary goods, market test goods and after-sales goods in order to develop its business and investment. Additional approval from the Ministry of Trade needs to be obtained, but such goods may not be used in the manufacturing process.

10
**<u>Table 2: The fiscal impacts of higher oil prices and lower production in 2007</u>**

|||_Projec_|_ted_|_Outco_|_me_|_Change_|
|---|---|---|---|---|---|---|
|||Units|% GDP|Units|% GDP|%|
|Indonesian crude|US$/barrel|63.00||69.69||16.20%|
|Production|m barrel/day|0.950||0.899||-5.40%|
|Exchange rate|Rp/US$|9,050||9,130||0.90%|
|Government oil and gas<br>revenues|Rp billion|181,134|4.8|168,784|4.3%|-7%|
|Tax|Rp billion|41,242|1.1|44,000|1.1%|7%|
|Non-tax|Rp billion|139,893|3.7|124,784|3.2%|-11%|
|Government energy subsidies|Rp billion|87,676|2.3|116,890|3.0%|33%|
|DAU (26% of APBN<br>projected oil and gas revenue)|Rp billion|40,236|1.1|40,236|1.0%|0%|
|Oil and gas revenue sharing<br>transfers toregions|Rp billion|26,382|0.7|23,394|0.6%|-11%|
|Oiland gas'balance'|Rpbillion|26,840|0.7|-11,736|-0.3%|-144%|
|_Source: Ministry of Finance._|||||||

This analysis of recent oil and gas-related fiscal flows enables simulations of the likely impact of higher oil prices on Indonesia’s public finances. Figure 1 and Figure 2 bring together the simulated estimates of the impacts of movements in oil and gas prices on revenues, expenditures on energy subsidies plus subnational transfers, and the overall government deficit. Energy-related expenditures rise faster than revenues due to higher energy consumption. Even with the reduced fuel subsidies post-May 2008, the central government budget balance will become negative if oil prices stay substantially above US$ 100 per barrel (figure 1). Due to the more rapid increase of expenditures, the central government budget deficit increases with rising oil prices – and declines with falling prices (figure 2).

**Figure 1.  The impact of oil prices on Indonesia’s revenues and expenditures** _Billions of US dollars_

**Figure 2. The impact of oil prices and energy subsidies on the central government budget balance** _Percent of GDP_

<!-- Start of picture text --> US$50 %1<br>bln. Pre-May Post May 2008 Further 30% fuel<br>2008 fuel prices fuel prices price increase<br>40 0<br>30 -1<br>Further<br>Oil & gas revenue 30% fuel<br>(income; solid line)<br>price increase<br>20 -2<br>Pre-May<br>Post May 2008 2008 fuel prices<br>fuel prices<br>10 -3<br>Oil & gas subsidy and subnational transfers<br>(outlays; dashed lines)<br>0 -4<br>Oil $60 $80 $100 $120 $140 $160 Oil $60 $80 $100 $120 $140 $160<br>price: price:<br>Source: World Bank staff estimations. Source: World Bank staff estimations.<br><!-- End of picture text --> This paper estimates the impact of higher international oil prices on the government’s fiscal position in 2008 and beyond. It does this by describing the link between the government’s revenues and expenditures, international oil prices, and domestic subsidized product prices and inter-governmental transfers. Section 2 overviews the process by which extracted oil and gas is converted into government revenue, and simulates the effects of an increase in international oil prices. It lays out the main 4
|**Table OA4. Characteristics of eligible households, b**|**aseline vs. addition**<br>Data<br>source|**al sample**<br>Total|Baseline<br>sample|Additional<br>sample|<br>P-value (3)<br>= (4)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Head of household is male (0/1)|Follow-up|0.520|0.553|0.491|_0.061_|
|Age of household head|Follow-up|51.2|50.9|51.4|_0.615_|
|Head of household is married (0/1)|Follow-up|0.637|0.651|0.625|_0.408_|
|Head of household can read and write (0/1)|Follow-up|0.728|0.747|0.712|_0.233_|
|House has concrete floor (0/1)|Follow-up|0.474|0.471|0.477|_0.857_|
|Number of Observations||920|460|460||
|P-value ofjoint F-test that all coefficients are 0|||||_0.469_|

Note: This table shows descriptive statistics using individual level data from the household survey. Column 1 describes when the data were collected, either at baseline or follow-up. Column 2 reports averages for the full sample. Column 3 reports the averages for the baseline sample while column 4 reports the averages for the additional sample interviewed at endline. In Column 5 we report the p-values of the mean comparison between the baseline and additional sample. P-value of joint F-test corresponds to the p-value of a joint F-test that all coefficients of a regression of having baseline data (being in the baseline sample) against characteristics are zero. All p-values are computed using the t-asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008). Clustering was done at the village level. Range of values for categorical variables are given in parenthesis. (0/1) indicates dummy variables, equal to one if the response is yes. See Appendix Table 1 for definition of variables.
Table 6 (Continued) Nominal Transport Costs for Siallar Goods Exported By Carlbbean and Other Countries to the United States In 1986

||Caribbean E|xports by a Sin.le|Transport|NoId(air orvessel<br>|)<br>1/<br>Caribb|ean Exports Partly|Airand Par|tlyVessel<br>2/|
|---|---|---|---|---|---|---|---|---|
||Air<br>|Shipents|Vess|el Shipments|Air|Shipments|Ves|sel<br>Shipmnts|
|Exporter|Number<br>of<br>Items|Average Nominal<br>Frelaht Rate|Number<br>of<br>Items|AvrageNominal<br>Freight<br>Rate|_Numbr_of<br>Items|AverepNominal<br>Freight<br>Rat|Nuerof<br>Items|Average<br>Nminal<br>Freight Rate|
|Haiti<br>|||||||||
|Haiti<br>|442<br>|8.46<br>|421<br>|6.26|292|10.02|292|5.02|
|Other Exporters|442|16.13|421|7.19|292|17.58|292|5.93|
|Jamaica<br>|||||||||
|Jamaica<br>|251<br>|9.93<br>|273<br>|9.50<br>|127<br>|12.40|127|8.30|
|OtherExporters|251|18.14|273|10.32|127|12.15|127|9.58|
|St.Kitts-NWvis<br>|||||||||
|St. Kitts-Nevis<br>|44<br>|3.32|28|3.39|10|6.14|10|7.67|
|Other Exporters|44|8.88|28|5.32|10|9.34|10|4.48|
|St. Lucia<br>|||||||||
|St. Lucia<br>|46<br>|4.68<br>|19<br>|9.02<br>|11<br>|8.03|11|3.16|
|Other Exporters|46|9.93|19|6.11|11|10.23|11|3.67|
|St. Vincent<br>|||||||||
|St. Vincent<br>|12<br>|7.86<br>|9<br>|22.13<br>|2|22.00|2|12.60|
|Other Exporters|12|11.24|9|29.47|2|26.99|2|39.04|
|Suriname<br>|||||||||
|Surlneme<br>|30<br>|7.12<br>|25<br>|14.67<br>|1<br>|10.58|1|2.86|
|Other Exporters|30|10.10|25|9.91|1|14.55|1|4.38|
|Trinidad&Tobago<br>|||||||||
|Trinidad&Tobago<br>Other Exporters|21<br>21|14.81<br>28.11|71<br>71|9.26<br>10.56|7<br>7|22.74<br>42.69|7<br>7|9.10<br>8.48|

I/ Entries in these columns show: (1) the number and average nominal freight rates for all products exported by each Caribbean country to the U.S. by vossel; and (2) similar Information on shipmnts by air. The Caribbean tariff lne level products for which this Inforration Is recorded totalled at least 510,000 In U.S. exports In 1986, 21 Entries In these columns show transport cost Information for Caribbean exports for tariff line products where sao portion of the annual exports were shipped by air and the remainder by vessel In 1986. The Caribbean talff line 1evl produts for which this Information Is recorded totalled at least 510,000 In U.S. exports In 1986.

Note: Mexico and Canada are excluded from the average statistics on lothers" exports to the United States.
26 improvements vis-à-vis budgets. Our empirical work investigates whether the relationships described above represent causal effects from the introduction of different provider payment methods across countries or are mere correlations reflecting selection effects.

# _5.3 Covariates in the estimating equation_

We follow most of the previous empirical literature (see Gerdtham and Jonsson 2000) and include as covariates in our models GDP per capita (measured in constant 2000 dollars and purchasing power adjusted), the share of the population aged 65 or above, and the urban population as a fraction of the total. Data on these three covariates come from the WDI database. Following Wagstaff and Moreno-Serra (2009), who found that the introduction of social health insurance (SHI) in many countries of the ECA region during the 1990s had impacts on some of the outcomes examined in this study, we include in the covariates set a dummy variable for whether the country has a SHI system in place in a given year.<sup>28</sup> As described in Section 2, a number of ECA countries reformed also the way primary health care providers are paid, in addition to payment reforms in inpatient care. In some countries, payment reforms for primary care providers and hospitals were implemented simultaneously, whilst in most instances (two-thirds) they took place at different points in time between 1990 and 2004. It seems important to isolate the impacts of payment reforms in primary care for the ECA countries not only because such reforms have occurred in budget, FFS and PBP systems alike, but especially because, where hospital and primary care payment reforms were coupled,

> 28 Tax-financed system is the reference category. For the subset of SHI countries, switches between hospital payment methods did not occur in the same year as SHI adoption in 60% of the cases. For more details on the data sources, construction of the SHI dummy and the resulting classification of countries as tax-financed or SHI systems, see Wagstaff and Moreno-Serra (2009).
in operation).<sup>21</sup> In order to calculate aggregate changes in manufacturing productivity, we weight each sector by their share of total manufacturing output.<sup>22</sup> The BHC productivity decomposition results from 1987-2001 for the manufacturing sector as a whole are shown in Figures 4a (labor productivity) and 4b (TFP) together with real growth rates of the manufacturing sector. The TFP and labour productivity decompositions appear remarkably consistent with each other.<sup>23</sup> The _within effect_ is generally negative in both decompositions. This suggests that surviving firms have generally made a small negative contribution to productivity growth, which may be reflective of the impact of various business climate constraints analysed in Mengistae and Thompson (2006). The effect however is trending upward; incumbent firms are improving their technical efficiency over time. The _between effect_ , which provides a measure of allocative efficiency is negative in the first half of the sample, then generally positive after 1994. This suggests that market share reallocation of less productive firms to more productive firms is one of the key mechanisms underlying aggregate productivity changes in Moroccan manufacturing. Focusing on firm turnover, we see that net entry actually falls during the period, with its contribution largest (and positive) prior to 1994.

The past literature has found that the importance of the various productivity components varies significantly throughout the business cycle. Baily et al. (1992) for example, find a cyclical pattern to the role of net entry in driving productivity growth in the United States. More specifically, they find that firm turnover has a positive impact on

> 21 In all of the productivity decompositions in this section, the entry and exit terms are combined into a net entry component to facilitate interpretation.

> 22 The same sector weights are used in both the TFP and labor productivity regressions to ensure that any differences between these alternative techniques are driven by the productivity measure and not the weighting procedure employed.

> 23 Previous studies for OECD countries have found the _within_ effect to be comparatively stronger using labour productivity and the net entry effect to be stronger using TFP data (OECD, 2001).

15
# **Exhibit 3: Comparisons between SITP and SEZ Act implementation**

|**Metric**|**SEZ Act**|**SITP**|
|---|---|---|
|Official Start Date|February2006|July2005|
|Number ofprojects formallyapproved byend 2011|580|40|
|Number ofprojects notified byend 2011|380|36|
|Conversion ratio 1: notified/approved|66%|90%|
|Operationalprojects byend 2011|124|24|
|Conversion ratio 2: operational/approved|21%|60%|
|Of which non-IT|44|24|

## **Notes**

Formally approved means, for SEZs, having put in an application - the same definition is used for SITP Notified means having secured necessary land and being ready to start building - the same definition is used for SITP Operational means being at least in the first stage of production - the same definition is used for SITP

## **Sources**

- <u>http://www.sezindia.nic.in/writereaddata/pdf/List_of322notifiedSEZsunderSEZAct2005as19.6.09 masterlist.pdf</u>

World Bank (2012), “Learning from SEZs in India”, Unpublished 44
10 using LPG as the primary cooking fuel were retained for the analysis. If the 20 percent mark was exceeded nationally, the national data were used. Burkina Faso, Kenya, and Nigeria did not meet the 20 percent mark nationally but met it in urban areas, and hence in these three countries only urban households were analyzed. Although use of gasoline or diesel for back-up power generation is polluting, most household survey questionnaires did not distinguish between automotive and electricity-generation uses of these two fuels, and hence they were not included among the polluting household fuels.

LPG users are categorized into three types: (1) those who use LPG as the primary cooking fuel and have abandoned polluting fuels altogether; (2) those who use LPG as the primary cooking fuel and continue to use other fuels, including polluting fuels; and (3) those who use LPG but not as the primary cooking fuel. The households in the last category were identified by searching for those who reported non-zero expenditures on LPG and did not name it as the primary cooking fuel.

# **Patterns of Primary Household Energy Choice for Cooking**

Comparison of primary energy sources for cooking showed significant differences from country to country. The share of people living in households citing clean sources of energy ranged from 99 percent in Mauritius to less than 1 percent in The Gambia, Liberia, and Uganda. Only five countries had more than half of the entire population living in households using clean cooking energy, whereas 16 countries had more than half using wood as the primary cooking fuel. When wood, charcoal, and coal were combined into a single category, the country count increased to 19 (Figure 2). There was a broad correlation between income (represented by national average annual expenditures per capita) and the share of people living in households citing clean energy as their primary sources for cooking.

**Figure 2  Percentage of people living in households citing different primary energy sources for cooking and national annual expenditures per capita**

<!-- Start of picture text --> Charcoal/coal<br><!-- End of picture text --> <!-- Start of picture text --> Kerosene<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data _Note_ : The Botswana survey combines firewood and charcoal under wood. Expenditures per capita are annual. Clean = LPG, electricity, biogas, and solar; others = mostly agricultural residues and dung.
# **Woreda estimates of undernutrition**

Before presenting the SAE results, Figure 3 shows stunting and underweight at the regional level based on EMDHS. Figure 3 shows that the urban-rural divide is an important indicator of undernutrition levels. Child stunting is 16 percentage points higher in urban (27.0%) than rural (42.6%) areas. The urban divide is also reflected in regional variation with urban-dominated regions as Addis Ababa, Dire Dawa, Harari, and Gambella (regions sorted by share of population living in urban areas) having significantly lower rates of stunting, compared to other regions. For underweight, regional differences are smaller, though the south and eastern regions Afar and Somali have significantly higher underweight rates, while capital Addis Ababa has significantly lower rates (Figure 3). Though stunting and underweight levels vary across regions, many are not significantly different from each other, as observed by the 95 percent confidence intervals (Figure 3).

**Fig 3. Share of children stunted and underweight with 95 percent confidence intervals by region.**

<!-- Start of picture text --> Upper 95/Lower 95 Mean stunting rate Upper 95/Lower 95 Mean underweight rate<br>GambelaAddis AbabaDire DawaHarariSomaliBenishangul GumuzOromiya AmharaSNNPTigray AfarUrban avgRural avg Addis AbabaHarariGambelaDire DawaOromiyaBenishangul GumuzSNNP AmharaTigraySomali AfarUrban avgRural avg<br>.6 .6<br>d 95 CI<br>.5 .5<br>.4 an an .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 erweight mend 0<br>U<br><!-- End of picture text --> A first observation, based on the SAE-obtained woreda level estimates, is that only a few regions are cohesive with similar levels of undernutrition across woredas (Figure 4). Most regions are not cohesive and have large variations in levels of underweight and stunting across woredas. This is illustrated in Figure 4 by the wide ranges of levels across woredas within each region. In almost all regions, woredas with higher levels have more than twice the level of those with lower levels. Though the variation in number of woredas in each region influences the graph (from a low of 13 in Gambella to a high of 277 in Oromia), it indicates that there is scope for spatial targeting within most regions.

13
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Lundberg, S. (2010) The Sexual Division of Labour. In The Shape of the Division of Labour: Nations, Industries and Households, edited by Robert M. Solow and Jean-Philippe Touffut, pp. 122-48. Cheltenham, UK: Edward Elgar.

Mammen, K. and Paxson, C. (2000) Wormen’s Work and Economic Development, _Journal of Economic Perspectives_ 14 (4), pp. 141-164 Olivetti, C. (2013) “The Female Labor Force and Long-run Development: The American Experience in Comparative Perspective,” in Human Capital in _History: The American Record_ edited by L. Platt Boustan, C. Frydman and R.A. Margo, University of Chicago Press, forthcoming.

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Serajuddin, U, and Verme, P. (2013) Who is deprived? Who feels deprived? Labor Deprivation, youth and gender in Morocco, _Review of Income and Wealth_ , forthcoming, DOI: 10.1111/roiw.12080 Tam, H (2011) U-Shaped female labor participation with economic development: Some panel data evidence. _Economics Letters_ 110, pp. 140-142.

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Verme, P., Gadiry Barry, A., Guennouni, J. and Taamouti (2013) Economic Shocks, Labor Mobility and Jobless Growth in Morocco: Evidence from panel data, _World Bank Policy Research Working Papers_ , No. 6795 World Bank (2013) Opening Doors. Gender Equality and Development in the Middle East and North Africa. MENA Development Report, World Bank, Washington DC.

21