rafmacalaba/datause-encoder-data huggingface dataset:
_Private participation in infrastructure_ PPI can expand access and quality of infrastructure services, with attendant increases in labor and service productivity (Ramamurti, 1996; Agénor and Moreno-Dodson, 2006; ILO, 2002), support efficiency improvements and deployment of new technology in utilities, introduce alternative mechanisms to finance infrastructure and free scare resources to channel towards growth and other development policy priorities (Besant-Jones, 2006; ADB, JBIC, World Bank, 2005; Harris, 2003) – all of which may positively impact growth. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon prevalence of PPI. In this paper, the dummy variable “private” summarizes the information on the timing of private participation in specific infrastructure subsectors in a country. The variable takes the value 0 before the first year of private entry and the value 1 from that year on. Overall, private investors have invested approximately US$ 224 billion since 1990 (World Bank PPI Database) in East Asia’s infrastructure subsectors across 18 countries<sup>20</sup> .

**Table 6: Augmented Solow model with infrastructure variables and the effect of privatization**

||Telec|oms|Elect|ricity|Ro|ads|Sani|tation|W|ater|
|---|---|---|---|---|---|---|---|---|---|---|
||Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|
|Education|1.24|.53|2.49|.26|.73|.55|1.23|.33|1.02|.37|
|t-stat|3.65|1.68|4.14|1.17|1.40|1.33|2.73|.83|2.21|.79|
|Elast(β)|1.24|.00|2.49|.00|.73|.00|1.23|.00|1.02|.00|
|Total Invst|.03|.62|.36|−.07|.72|1.08|−. 39|−.20|−. 11|−.70|
|t-stat|.08|2.99|.61|−.53|1.40|2.85|−. 65|−.75|−. 16|−1.27|
|Elast(α)|.00|.38|.00|.00|.00|.52|.00|.00|.00|.00|
|No private|||||||||||
|Infrast||.29||.99||.13||1.59||2.16|
|t-stat||1.89||12.29||.92||3.96||2.62|
|Elast (γ)||.18||.99||.00||1.59||2.16|
|Private|||||||||||
|Infrast||.48||.99||.13||1.75||2.16|
|t-stat||2.17||12.29||.92||1.86||2.62|
|Elast (γ)||.30||.99||.00||1.75||2.16|
|R <sup>2</sup>|.29|.57|.41|.93|.24|.33|.20|.58|.14|.33|
|No. Obs|42|42|23|23|28|28|23|23|25|25|
|Difference||YES||NO||NO||YES*||NO|

_Note_ : “*” means result significant only at 10% probability level.

Table 6 shows that PPI, when measured as a dummy variable, did not influence East Asia’s growth, except for telecom and sanitation infrastructure. Specifically, the elasticity of GDP with

> 20 Electricity, roads, water supply and sanitation, and telecom accounted, respectively, for 34%, 18%, 16% and 7% of the number of PPI projects in the region. However, the distribution of projects has been skewed across East Asian countries, with over 77% of water and sanitation, 68% of roads, and 42% of electricity projects located in China alone.

21
Table 3. Transition Probabilities in Bosnia and Herzegovina: All, 2001-2004 (In percent)

||||Employment<br>All Employed|Status in 2004<br>Employed<br>Employed in|Employed in||
|---|---|---|---|---|---|---|
|||||<br>the Formal|<br>the Informal||
||Inactive<br>(1)|Unemployed<br>(2)|(3) = (3A) + (3B)|Sector<br>(3A)|Sector<br>(3B)|All<br>(4) = (1) + (2) + (3)|
|Employment Status in 2001|||||||
|_All_|||||||
|Inactive|63.3|<br>15.3|21.3|8.1|13.2|100.0|
|Unemployed|29.9|<br>27.4|42.7|20.0|22.7|100.0|
|All employed|13.0|<br>8.3|78.7|54.4|24.3|100.0|
|Employed in the formal sector|9.5|<br>5.7|84.9|73.2|11.7|100.0|
|Employed in the informal sector|19.6|<br>13.3|67.2|19.1|48.1|100.0|
|_Women_|||||||
|Inactive|72.1|<br>11.6|16.3|5.3|11.0|100.0|
|Unemployed|46.7|<br>18.7|34.6|18.5|16.1|100.0|
|All employed|19.7|<br>6.3|74.0|54.3|19.7|100.0|
|Employed in the formal sector|12.2|<br>5.6|82.3|74.2|8.1|100.0|
|Employed in the informal sector|33.9|<br>7.7|58.4|16.8|41.7|100.0|
|_Men_|||||||
|Inactive|46.5|<br>22.5|31.0|13.6|17.4|100.0|
|Unemployed|19.6|<br>32.7|47.7|21.0|26.7|100.0|
|All employed|9.6|<br>9.3|81.1|54.4|26.7|100.0|
|Employed in the formal sector|8.1|<br>5.7|86.2|72.7|13.5|100.0|
|Employed in the informal sector|12.3|<br>16.1|71.6|20.2|51.4|100.0|

Source: LSMS and staff estimates. Note: Includes working age population only.

Table 3 summarizes the flows among employment (further disaggregated into formal and informal sector employment), unemployment and leaving the labor force. The first column on the left hand side indicates the labor force status of workers in 2001 while all the other columns indicate the distribution of these workers across various labor market states in 2004. For example, of those who were out of the labor force in 2001, 63 percent remained inactive, 15 percent were unemployed in 2004, while the rest found employment either in the formal sector or more likely in the informal sector. These proportions can be interpreted as the probability of moving from one labor market state to another over the four year period; the annual transition probabilities (not shown but available on request) are broadly consistent with these flows.

Transition probabilities in the BH labor market are broadly comparable with those of Central and Eastern European countries, Western European countries, and a sample of middle-income countries in Latin America (see Appendix Tables 1 and 2). In particular, the flows from employment to unemployment or from unemployment to jobs in BH are similar to other transition economies. Flows from inactivity to either unemployment or employment have been somewhat larger than most countries, underpinning the rapid increase in labor force participation in recent years.

10
Table 6: General Equilibrium Effects, improving connectivity of the African Region

|||Roa|ds, land bo|rders an|d ports|
|---|---|---|---|---|---|
|||Wi<br>|thout<br>|W<br>|ith<br>|
|||Mariti|me Pref.|Mariti|me Pref.|
|Country|Baseline|∆_Xi_<br>|∆_GDPi_<br>|∆_Xi_<br>|∆_GDPi_<br>|
|||(1)|(2)|(3)|(4)|
|Algeria|2018|6.63|0.93|11.10|1.76|
|Botswana|2018|2.98|11.81|2.86|10.36|
|Burkina Faso|2014|38.37|2.01|38.38|1.99|
|Burundi|2018|37.07|1.35|43.22|1.45|
|Cabo Verde|2014|0.12|0.01|2.55|0.17|
|Cameroon|2014|17.46|2.70|9.47|1.89|
|Congo, Rep|2014|5.24|4.60|8.43|6.24|
|Cˆote d’Ivoire<br>|2018|9.39|1.41|15.01|2.17|
|Egypt, Arab Rep.|2018|8.43|0.99|7.36|1.01|
|Ethiopia<br>|2014<br>|14.39<br>|0.59<br>|8.92<br>|0.36<br>|
|Ghana|2018|5.88|0.99|5.12|0.90|
|Kenya|2018|12.74|0.94|24.83|1.70|
|Lesotho|2018|0.60|-0.25|-0.16|-0.51|
|Madagascar|2014|0.12|0.01|0.74|0.09|
|Malawi|2014|25.22|1.99|17.07|1.36|
|Mauritius|2018|0.00|-0.03|-0.03|-0.02|
|Morocco|2018|2.20|0.59|2.09|0.61|
|Mozambique|2018|2.90|0.67|2.20|0.55|
|Namibia|2018|3.22|7.62|3.43|7.10|
|Niger|2018|4.28|4.15|1.59|4.51|
|Nigeria|2018|7.50|0.58|13.59|1.38|
|Rwanda|2018|34.36|1.60|36.46|1.50|
|Senegal|2018|9.84|1.05|13.98|1.48|
|South Africa|2018|4.77|0.58|3.89|0.57|
|Eswatini|2018|1.78|0.37|2.94|1.24|
|Tunisia|2018|2.90|1.19|3.05|1.36|
|Uganda|2018|8.97|1.06|14.49|1.72|
|Tanzania|2018|12.79|1.68|20.83|2.78|
|Zambia|2014|19.74|6.63|14.14|4.86|
|Zimbabwe|2014|11.81|18.85|10.02|15.76|
|Overall||7.56|1.15|9.61|1.46|

_Note_ : percent change compared to the baseline in total exports and GDP of participating countries. We assume _σ_ = 6. The reference country for the normalization is New Zealand.

26
13 selection into ownership type on the basis of unobservable firm efficiency (to the econometrician but not the investor).

# **III.  Data**

The data for this study are based on the universe of manufacturing firms existing in Slovenia between 1994 and 2001. The primary information on firms comes from three data sources. The official financial records of the firm, submitted annually under uniform accounting procedures to the government of Slovenia, provide information on the firm’s capital stock, material inputs, and revenues from domestic and foreign sales. The Slovenian Business Register includes information on the four-digit industries that describe each firm’s product line(s), the year the firm initiated production, and the firm’s ownership structure. The Public Pension Fund data includes information on each employee in the firm including information on education level. These three data sets can be integrated using a common firm identification number used in all three series. The variable definitions and sample means are reported in Table 1.

The employment information includes the number of two- or four-year college graduates, the number if high school graduates, and the number of primary educated workers in the firm. This employment information is in real terms by construction. However, the accounting data on firm output and capital and material inputs are reported in nominal terms. We convert the nominal data into real data, using industry input and output price deflators reported for all years 1994-2001. The material input price deflator is a weighted sum of sectoral prices where the weights are sectoral input shares generated from an input–output matrix of the Slovenian economy. Output price deflators are reported for each industry. There is a single capital price series that was applied to all firms. Using these input and output price series, we generate series for real output, capital and material inputs for each firm and for each year.
service providers (who also own some of the smaller brands): Globe, with a 30.7% market share and PLDT with 69.3% of the market. Globe owns the Innove and Bayantel brands, while PLDT owns the Digitel brand. Based on the PLDT disclosure letter to the Filipino Stock Exchange of January 12, 2017, we know the top 100 stockholders.<sup>31</sup> Globe reports its ownership structure on its website.<sup>32</sup> Ownership shares by nationality in each company were weighted by the company shares in the overall market.

In the case of insurance, for the shares of foreign versus domestic, we took the share of the sum of premiums for life insurance, other insurance and reinsurance for 2015 from the Insurance Commission of The Philippines.<sup>33</sup> The results of all these calculations are in table 1.

# **5. Results: RCEP and Its Key Components: Comparing Market Structures**

We evaluate RCEP and its components in three model classes: Armington, Krugman and Melitz. In our Krugman model, aggregate exports increase by 1.1 percent as a result of the reduction in import non-tariff barriers. To achieve the same percentage change in aggregate exports in the Armington model, all of the GTAP values of the Armington elasticities of substitution were adjusted equi-proportionately, (multiplied by 1.24 as reported in tables 5-7). For the Melitz model, we retain the same Dixit-Stiglitz elasticities in both the Krugman and Melitz models (so as to not directly impact the variety externality); we achieve the equivalent increase in aggregate exports by adjusting the shape parameter in the Pareto distribution in all IRTS goods and services sectors. We scale down the shape parameter in our application by 0.98 (to a value of 4.49), which is a slightly more heterogeneous distribution than the distribution of Balistreri, Hillberry and Rutherford (2011) who had a preferred value of 4.58. The value of 4.49 mutes the trade response so that it is the same as in the Krugman model.

In our Krugman model, we observe an increase in FDI output in The Philippines of 26.8 percent as a result of the decrease in barriers against foreign direct investors in services. We take the FDI response from the Krugman model as our central response and adjust elasticities in the Melitz and Armington model for consistency of the FDI response. For the FDI response, we use

> 31 http://www.pldt.com/docs/default-source/shareholders-information/top-stockholders/2016/pse_pldt-list-of-top100-stockholders_dec-31-2016.pdf?sfvrsn=0

> 32 <u>http://corporate-governance.globe.com.ph/shareholding-structure.html. Singtel is from Singapore and Ayala is</u> Filipino. We assigned the balance to Filipino owners.

> 33 _Annual Report of the Insurance Commission of The Philippines_ , 2015, pages 18, 25 and 29. Available at: <u>https://www.insurance.gov.ph/statistics/ic-annual-report/.</u>

26
Table 1: Differences in Actual and Expected Service Employment Sham: 1970s ad 1980s a/

||Trans oitandC<br>|ommunication<br>|T<br>|rade<br>|Ser<br>|vices<br>|To<br>|al_<br>|
|---|---|---|---|---|---|---|---|---|
||_1970sL&1980|Shifl in1980s|1970s& 1980s|Shift in190s|970s &1980s|Shift in1980s|1970s & 1980s|Shift in 1980s|
|USSR<br>|1.8<br>|1.0<br>|-6.8<br>|-0.3|-9.8|-3.1|-10.6|-0.1|
|Estonia<br>|2.7<br>|_0.5_<br>|-6.7|-0.2|-10.5|-2.4|-11.0|0.3|
|Latvia<br>|2.4<br>|0.6<br>|-6.7<br>|0.1|-11.1|-2.4|-11.3|0.4|
|Russia<br>|2.3<br>|1.0<br>|-6.8<br>|-0.4|-10.0|-3.7|-9.1|-0.5|
|Belaus<br>|_0.5_<br>|1.5<br>|-7.5<br>|-0.1|-11.1|-2.9|-16.7|1.2|
|Lithuania<br>|0.7<br>|0.9<br>|-8.0<br>|-0.7|-11.7|-1.7|-16.5|2.4|
|Ukaine<br>|1.2<br>|0.7<br>|-6.7<br>|-0.4|-11.7|-2.7|-13.5|0.4|
|Moldova<br>|-0.9<br>|1.4<br>|-7.6<br>|0.4<br>|-11.2|-1.7|-19.0|2.0|
|Armenia<br>|0.3<br>|0.2<br>|-6.8<br>|-1.2|-7.1|-3.3|-8.9|-2.2|
|Georgia<br>|1.1<br>|1.0<br>|-7.3<br>|-0.6<br>|-7.6|-2.2|-10.0|1.0|
|K&zWkhstan<br>|4.0<br>|1.2<br>|-5.9<br>|-0.5|-6.8|-3.3|-5.2|-1.0|
|Azejbain<br>|1.7<br>|0.9<br>|-6.1<br>|-1.0|-5.S|-3.71|-6.o|-2.5|
|Turamenitan<br>|0.2<br>|1.1<br>|-5.1<br>|-1.0|-6.5|-3.6|-7.7|-2.9|
|Kyrgysan<br>|0.9<br>|0.4<br>|-6.1<br>|-0.4<br>|-5.9|-3.1|-8.6|-1.3|
|Uzidisan<br>|-0.2<br>|0.9<br>|-5.9<br>|-0.4|-6.8|-1.9|-10.7|.0.1|
|Tadzhikistan|-0.3|0.8|-6.8|-0.3|-6.7|-2.9|-1 1.5|-1.2|

a/ Residuals from regression of sevice anployment shares on income per capita. Republics ordered by income per capita in 1990.
Howes et al. 2022; World Bank 2023).

Combined, the official contribution of the resource sector to overall GDP is high and has been growing over time. The figure was just above 10 percent in 1980, and steadily increased over time to nearly 30 percent of GDP by 2018 (e.g., Howes et al. 2022; World Bank 2023; etc.). The share of GDP contributed by agriculture is also high at approximately one-quarter of GDP, but the sector has grown at the same rate as overall GDP and agriculture’s contribution to GDP has remained relatively stable between 1980 and 2018 (e.g., Howes et al. 2022). As described in the Introduction, the vast majority of employment in the country is in subsistence agriculture, with the most recent estimates illustrating that roughly one-half of households have at least one member primarily employed in agriculture (e.g., World Bank 2024). Thus, any gains that might accrue to the population from growth in the resource sector are likely either through spillovers from the sector or through government transfers redistributing some of the economic gains from the activity (e.g., World Bank 2023).

Economic growth and government revenues are strongly tied to global commodity prices given the country’s resource intensity (e.g., World Bank 2023). The country has had several resource booms associated with spikes in commodity prices since independence that have led to high levels of growth and high levels of revenue collection. The most recent resource boom began in the early 2000s and continued to roughly 2014 (e.g., Fox and Shroder 2018; Howes et al. 2022; etc.).

As discussed in the Introduction, we estimate the change in well-being between two nationally representative surveys conducted during the last resource boom. Specifically, we estimate the change in monetary poverty and access to several essential services using the 2009 Household Income and Expenditure Survey (HIES) and the 2016-2018 DHS. Figure 1 reports both the GDP per capita in current US $ over this time period and the real per capita GDP growth rates in each year.<sup>5</sup> The figure illustrates that per capita GDP in constant US $ increased by over 30 percent between 2010 and 2017 and that the average annual growth in real per capita GDP was 3.3 percentage points. And in a subset

> 5The GDP figures are obtained from the World Development Indicators; and the real per capita growth rates are obtained from the World Bank’s 2023 Macroeconomic and Poverty Outlook.

6
Table 7: Technological sophistication and establishment characteristics

||(1)<br>_MAXj_|(2)<br>_MOSTj_|
|---|---|---|
|_Hj_|0.40<sup>_∗∗∗_</sup>|0.24<sup>_∗∗∗_</sup>|
||(0.02)|(0.01)|
|Management (Z-Score)|0.13<sup>_∗∗∗_</sup>|0.11<sup>_∗∗∗_</sup>|
||(0.00)|(0.00)|
|Size: Medium|0.31<sup>_∗∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|Size: Large|0.66<sup>_∗∗∗_</sup>|0.46<sup>_∗∗∗_</sup>|
||(0.02)|(0.02)|
|Age: 6 to 10|0.09<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|Age: 11 to 15|0.03<sup>_∗∗_</sup>|0.14<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|Age: 16+|0.00|0.03<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|Foreign owned|0.25<sup>_∗∗∗_</sup>|0.25<sup>_∗∗∗_</sup>|
||(0.02)|(0.01)|
|Exporter|0.19<sup>_∗∗∗_</sup>|0.15<sup>_∗∗∗_</sup>|
||(0.02)|(0.01)|
|Multi-establishment|0.27<sup>_∗∗∗_</sup>|0.18<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|
|N|17161|17161|
|R-squared|0.46|0.38|
|2-Dig. Sector FE|Yes|Yes|
|Country FE|Yes|Yes|

Notes : Estimates of _MAXj_ and _MOSTj_ on establishment characteristics using establishment-level sampling weights. The base categories are Size: Small, and Age: _≤_ 5 Years. *, ** and *** denote 10%, 5% and 1% significance respectively.

53
We make several contributions in this paper. On the conceptual front, we offer a review of methods that have been employed to provide estimates of welfare in the absence of consumption data, with a particular focus on comparing trends of poverty outcomes over time. Since the existing studies are presented in a variety of formats and technical approaches, we attempt to consistently present these various methods, some new and some more established, under a common framework. For this purpose, we provide some rather simple, but new theoretical results to help fix ideas for better discussion and also to highlight the nuanced differences between the methods that appears to have received scant, if any, attention in previous studies. For example, most development practitioners would likely implement a full-fledged household consumption survey—if given this choice—to produce poverty estimates, rather than attempt to do so using household assets alone. But to our knowledge, there has been no formal discussion to provide insights into this choice, and possibly other similar choices as well. Our paper attempts to fill in this gap in the literature by offering a review on poverty imputation methods.

Furthermore, we focus on providing the intuition behind each estimation method in an effort to make these techniques accessible to a larger audience. For this objective, we refer to the most important theoretical results in the text, and offer a more detailed technical discussion of these results in Appendix 1. In addition, we mostly review the economics literature in this paper, but will also refer to the statistics literature where it is relevant.

On the empirical front, we provide illustrations using real household survey data from Vietnam. We also offer a practical guide in an appendix offering detailed instructions on computer programs (mostly in Stata) that can be used to implement the reviewed techniques. In an effort to make these methods more accessible, and where there is not yet an established literature, we also aim to provide more specific—and somewhat prescriptive—discussion of best-practices methods.

6
mechanism is particularly relevant in fast growing and volatile industries, and in those with high income pledgeability. Liquidity demand and investment composition in these industries are therefore predicted to be more sensitive to variations in the rate of inflation.

In the second part of the paper, we test these predictions with firm-level data. Examining the microeconomic mechanism and the specific hypotheses of our model requires information on inflation episodes, firms’ investment in advanced technologies (that is, their innovation activities), and the sources of finance available to firms for the working capital expenditure associated with such investment. Empirical work using this information is prone to suffer from small sample problems since inflation only varies at the country-year level and harmonized detailed firm-level panel data are typically only available for few countries or years. This lack of appropriate data has so far limited researchers in studying the impact of inflation on corporate activity, constraining their ability to scrutinize relevant macroeconomic transmission mechanisms on the basis of microeconomic data.

We use novel data from the World Bank Enterprise Surveys (WBES) to address this problem. The WBES provide representative establishment-level data for more than 100,000 establishments that are harmonized across 139 developing countries from 2006-2016. The harmonized WBES data provide substantial variation in firms’ behavior across countries and time periods spanning a significant range of inflation, making it possible to empirically identify the relation between inflation and firms’ innovation and financing activities. The data also include detailed information on establishments’ innovation activity as well as sources to finance working capital and are thus well-suited to test the specific microeconomic mechanism underlying our model. A potential limitation is that the WBES do not provide direct balance sheet information on firms’ short-term liquidity holdings, which we proxy with the extent of working capital finance via internal funds or retained earnings. As a robustness check, we therefore extend our analysis with detailed balance sheet data for U.S. firms from Compustat which include a direct measure of corporate liquidity holdings and also have meaningful variation in inflation rates over the period 1960-2016.

On the basis of the WBES data, we find that inflation reduces the probability of product and process innovation in developing countries but has no effect on total investment. Hence, inflation influences the quality but not the quantity of investment. The impact of inflation on 3
- 71 -

# **Table** A.1 5: **ROge#Obn** CoeffWlsnts for Estimathg **hcome** Multllers State Data

||AGY|AR|AP|AY|R<sup>2</sup>|Income<br>Multiplier|
|---|---|---|---|---|---|---|
|Rural Areas<br>F|.537.<br>_(4.0)_|0.0036<br>(2.1)|-1.53.4<br>(.04)|-4.17.4<br>(1.8)|.81|.478|
|a'|-. 035<br>(.7)|.0029<br>(4.2)|3.2.7.<br>(.5)|2.0.4<br>(.2)|.73|.222|
|Rural Towns<br>at|.286<br>(3.2)|.0003<br>(.2)|-1.12*.<br>(.4)|5.245<br>(.3)|.67|.652|
|Rural Areas Plus<br>|||||||
|RuralTowns<br>F'|.823<br>(4.1)|.0039<br>(1.5)|-1.27.7<br>(.2)|3.6655<br>(.9)|.80|.794|
|Y'|.321<br>|.0003<br>|-1.95.7<br>|7.264|.84|.538|
||(3.6)|(2.8)|(.7)|**(.4)**|||

Notes AGY = total agricultural wage earnings in each state, AR - AGY*road denity. AP = AGY*population density. AY AGY'dady agricultural wage rate (used as an alternative to agricuitural income per agricultural population). Loft side Rural nonfarm income. All equations were estimated using ordinary Ieast squares. Heteroskedasticity was not a problem in the state data, so no adjustment was necessary. There were 17 observations.

The t-ratios are listed in parentheses underneath regression parameters.

a/ Nonfarm income estimated as nonfarm employment * average statewide nonfarm income per nonfarm worker.

bl Rural nonfarm income estimated as employment * average agricultural income per farm laborer.