rafmacalaba/datause-encoder-data huggingface dataset:
the crisis. In Bangladesh, the financial crisis caused a 20 percent decline in inward FDI, but it rebounded in 2011 and signs point to further recovery in 2012. Among some countries, FDI tends to be less correlated to the global business cycle, but can be subject to large discrete investments, such as mining projects in Afghanistan. Both Bhutan and Nepal have relatively low FDI levels, and what they receive is linked to large public-sector investments from India. Bhutan’s FDI has recently averaged about US$15 million a year since 2009. Nepal’s FDI has progressively increased from US$1 million as recently as 2008 to over US$95 million in 2011. Maldives receives the largest share of FDI in GDP, due to the small size of its economy and large inflows of FDI into the tourism sector.

**Figure 6: South Asia: FDI Inflows** (as percent of GDP)

<!-- Start of picture text --> Afghanistan, I.R. of Bangladesh Bhutan India<br>Sri Lanka Pakistan Nepal Maldives<br>9 25<br>8<br>7 20<br>Maldives (right axis)<br>6<br>5 15<br>4<br>3 10<br>2<br>1 5<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>-1 0<br>Source: UNCTAD<br><!-- End of picture text -->

**Table 1: South Asia Foreign Direct Investment Inflows, 2000-11** (FDI in million USD)

|**Country**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Afghanistan|0.2|0.7|50.0|57.8|186.9|271.0|238.0|188.7|94.4|75.7|211.3|83.4|
|Bangladesh|<br>578.6|354.5|335.5|350.3|460.4|845.3|792.5|666.4|1,086.3|700.2|913.3|1,136.4|
|Bhutan|0.0|0.0|2.1|2.5|3.5|9.1|72.2|3.0|7.2|18.3|16.3|13.9|
|India|3,588.0|5,477.6|5,629.7|4,321.1|5,777.8|7,621.8|20,327.8|25,505.6|43,406.3|35,595.9|24,159.2|31,554.0|
|Maldives|<br>22.3|20.5|24.7|31.8|52.9|73.2|95.2|126.5|174.2|152.1|211.8|281.6|
|Nepal|-0.5|20.9|-6.0|14.8|-0.4|2.5|-6.6|5.9|1.0|38.6|86.7|95.5|
|Pakistan|309.0|383.0|823.0|534.0|1,118.0|2,201.0|4,273.0|5,590.0|5,438.0|2,338.0|2,022.0|1,327.0|
|Sri Lanka|173.0|171.8|196.5|228.7|233.0|272.0|480.0|603.4|752.2|404.0|477.6|300.0|

Source: UNCTAD 11
15 government, would be self defeating. 1<sup>3</sup> The 90% subsidy simulation shows that states subsidies are more effective than federal subsidies. But it also shows, that it could be a revenue disaster for the states. This is confirmed by the 50% subsidy simulation.

Table 2: Importance of the Subsidy Level for its Effectiveness (expressed as % of value of a variable without government intervention unless otherwise specified)

||_State_<br>_L_|_Subsidy_<br>_evel_||_Feder_<br>|_al_<br>_Su_<br>_Level_|_bsidy_|
|---|---|---|---|---|---|---|
||_25%_|_50%_|_90%_|_25%_|_50%_|_9t_|
|Social Welfare Level|105.7%|110.5%|<br>121.1%|<br>102.5|% 100.7%|10XA|
|Firm's**Optimal Behavior**|||||||
|Abatement Investment|||||||
|as % of Initial Capital|13.8%|15.9%|<br>4.6%|11.0%|<br>7.6%|<br>0A|
|Output Level|95.2%|93.7%|99.3%|96.8%|98.4|100%|
|Pollution Level|49.5%|20.4%|<br>3.8%|59.7%|61.8%|100%|
|**SEPA's Optimal Behavior**|||||||
|Minimum Monitoring Rate Requi|red||||||
|for Abatement to Start|4.1%|<br>5.5%|20.3%|4.1%|5.5%|20.3%|
|Optimal Monitoring Rate|52.4%|80.7%|<br>100%|42.7%|41.6%|8.7%|
|Nominal Pollution Tax Rate|1.8%|<br>1.0%|<br>0.1%|1.8%|1.0%|0.0%|
|% cut in VAT Revenue|4.8%|<br>6.3%|<br>0.7%|3.2%|1.6%|0.0%|
|Pollution Tax Revenue|||||||
|as % of VAT Revenue|33.7%|11.6%|<br>0.2%|33.0%|18.2%|0.0%|
|Net Revenue|112.2%|52.0%|-33.8%|127.9%|114.6%|100%|

The 90% subsidy simulations also provide some additional insights. They show that when the firms are asked to contribute even, very modestly, 13 The mod, can actually not predict the firm optimal behavior under full subsidization because there is a time inconsistency problem, not handled in its design. In practice, if the FEPA notices that the firms do not take advantage of the subsidy offer, it knows it will it has to improve monitoring. The analysis of this better use of information requires a dynamic framework for mechanism design with incentive compatibility.
**Table 3: Dynamic Analysis, Real GDP**

||**Base**|**Reform**|**Reform &**<br>**Prices**|**Reform,**<br>**Prices &**<br>**Phase out**|
|---|---|---|---|---|
|Real GDP|Annu|al % Change|2013/14 - 2024|/25|
|**GDP at Market Prices**|4.2%|4.4%|4.7%|5.2%|
|**Consumption**|3.9%|4.0%|3.9%|4.1%|
|**Investment**|5.5%|6.6%|8.2%|10.2%|
|**Exports**|3.9%|4.1%|4.3%|5.0%|
|**Imports**|3.6%|3.9%|3.8%|4.5%|

_Source: Authors estimates_

# **Investment, Consumption and Trade**

In this framework, consumption growth remains relatively unchanged, but investment increases sharply under each scenario. It is 1.1% higher each year with the July reforms alone; 2.7% higher when international energy prices fall; and 4.7% higher when energy subsidies are phased out. The trade balance would gradually improve under all of the scenarios as exports grow at slightly higher rates than imports.

# **Public Finances**

Government revenues, as a share of GDP, would be expected to fall over the ten-year period under each of the scenarios. The extent of the reduction is in line with the growth of GDP: the higher the growth rate, the lower will be the share of revenues by the end of the period (Table 4.) (Note that the analysis suggests that nominal revenues will increase.) The budgetary cost of energy subsidies would, as expected, fall with the reductions in energy subsidy rates. Without the July 2014 reforms (i.e., under the base case), subsidies would actually increase as a share of GDP from 9.2% to 10.3%. With the July 2014 reforms alone, there would only a modest decline over ten years, to 8.5%, although this would be 1.8% of GDP less than the base case. The impact of falling international energy prices is striking, with subsidy costs falling by more than one-half the current base case level.

The budget deficit would also significantly decline under each of these scenarios. Other current expenditures would remain relatively constant, but transfers to households would be 14
As this illustrates, the required information is limited to when an action was taken, on which tariff lines. It does not include the volume or value of imports on which an additional duty was levied, nor on the magnitude of the additional duty levied.

Some members provide additional information; e.g., the US and Korean reports provide the volume of imports on which volume-based and price-based SSG actions have been taken.

US notifications indicate that all shipments are screened for the possibility of applying a price-based additional duty: United States price-based safeguards are invoked automatically on a shipment-by-shipment basis. Importers who enter goods under an overquota tariff line are required to declare which pre-established price range is applicable to their product. If there is a safeguard duty associated with that price range, the additional charge is assessed.<sup>64</sup> US notifications on volume-based actions state the time period over which a volume-based additional duty is applied as well as the volumes of imports on which SSG action has been taken.

Though the EEC has posted an up-front schedule of trigger prices, EEC reports list the products for which the special safeguard system ‘has been made operational’ in each marketing year; e.g., ‘In marketing year 2002/2003, the price-based special safeguard system has been made operational for the following products:’<sup>65</sup> There follows in the EEC notifications a list of products, followed by the statement, ‘An additional import duty may be imposed if the import prices for above-mentioned products fall below the trigger prices, which have been notified to the WTO (G/AG/N/EEC/2).’<sup>66</sup> It appears thus that the EEC employs a two stage monitoring process. There are posted trigger prices for a spectrum of agricultural products, the system is ‘made operational’ each year for a specified list of these products, but the EEC does not specify on which, if any, of these products SSG duties have been levied.

The EEC likewise notifies a list of products on which the volume-based special safeguard system has been made operational each year. The EEC has also reported year-by-year that volume based safeguard action has not been invoked. (There is no parallel statement for price-based action.)

> 64 G/AG/N/USA/61 (17 December 2007) p. 1. 65 G/AG/N/EEC/54 (14 February 2008) p. 1. 66 G/AG/N/EEC/54 (14 February 2008) p. 1.

47
**Table D.6:** Health Service Delivery

||(1)<br>|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Control<br>Mean|Pooled|CM|NFA|N|
|**Health service delivery index**(SUR)||0.021<br>|0.037<br>|0.015<br>|2877|
|||(0.029)|(0.035)|(0.031)||
|||[0.355]|[0.254]|[0.463]||
|(1) Absenteeism index|0.000|0.075|_−_0.017|0.109|2874|
||(1.000)|(0.057)|(0.070)|(0.063)<sup>_∗_</sup>||
|||[0.887]|[1.000]|[0.710]||
|(2) Paid for treatment|0.416|_−_0.002|_−_0.036|0.011|2872|
||(0.493)|(0.023)|(0.033)|(0.024)||
|||[1.000]|[1.000]|[0.786]||
|(3) Amount paid|8520.158|553.416|_−_1472.904|1321.751|2843|
||(31895.827)|(1409.161)|(1725.193)|(1496.821)||
|||[0.948]|[1.000]|[0.772]||
|(4) Any problem|0.063|_−_0.000|_−_0.002|0.001|2869|
||(0.242)|(0.016)|(0.018)|(0.017)||
|||[1.000]|[1.000]|[1.000]||
|(5) Staff not present|0.019|0.005|0.001|0.006|2869|
||(0.137)|(0.008)|(0.010)|(0.009)||
|||[0.887]|[1.000]|[0.772]||
|(6) Drugs not available|0.031|_−_0.005|_−_0.007|_−_0.004|2869|
||(0.174)|(0.009)|(0.010)|(0.010)||
|||[0.887]|[1.000]|[0.786]||
|(7) Facility not clean|0.003|_−_0.002|_−_0.003|_−_0.002|2869|
||(0.058)|(0.001)|(0.002)|(0.002)||
|||[0.887]|[1.000]|[0.710]||
|(8) Unpleasant staff behavior|0.022|_−_0.010|_−_0.005|_−_0.013|2869|
||(0.148)|(0.008)|(0.011)|(0.008)||
|||[0.887]|[1.000]|[0.710]||
|(9) Medicine always in stock|0.952|0.006|_−_0.001|0.008|2478|
||(0.214)|(0.009)|(0.013)|(0.010)||
|||[0.887]|[1.000]|[0.772]||
|(10) Individual satisfaction with care|3.645|0.039|0.069|0.028|2863|
||(0.703)|(0.039)|(0.048)|(0.041)||
|||[0.887]|[1.000]|[0.772]||
|(11) Would return to clinic|0.969|0.013|0.018|0.011|2853|
||(0.173)|(0.007)<sup>_∗_</sup>|(0.010)<sup>_∗_</sup>|(0.007)||
|||[0.887]|[1.000]|[0.710]||
|(12) Staff attitude|3.735|0.043|_−_0.011|0.063|2845|
||(0.580)|(0.029)|(0.038)|(0.031)<sup>_∗∗_</sup>||
|||[0.887]|[1.000]|[0.710]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets.The absenteeism index is composed of an indicator of whether patients had ever found no staff present when visiting the clinic and the waiting time at the last visit. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A19
paradigm in the IC space, posed by de Soto (1989), is that informal firm owners would like to be formal, but costly regulations and bureaucracy prevent them from doing so, causing a productivity loss for these firms. The de Soto paradigm has motivated the World Bank Group Flagship Report, _Doing Business_ , that measures the time, cost, and processes involved in 10 or so dimensions of a business life-cycle involving starting a business, its operation, and ultimately its exit. This paradigm is also empirically apparent in a number of informality surveys of the World Bank. World Bank Group Enterprise Surveys of informal firms in a number of countries have cited as some of the main challenges to formalization are the lack of access to information about how to register a business, the amount of time it takes to register a business, and tax policies (Figure 2).

**Figure 2: Main Challenges to Formalization Reported by Informal Firms**

_Source: Based on the data from the World Bank Informality Surveys, multiple countries, multiple years Note:_ Average based on Informality surveys in: Angola, Argentina, Botswana, Burkina Faso, Cote d’ Ivoire, Cameroon, Cape Verde, Democratic Republic of Congo, Guatemala, Madagascar, Mali, Mauritius, Nepal, Peru, and Rwanda.

Comparison between informal and formal firms demonstrates that the informal firms are worse off in terms productivity, profitability, and access to credit facilities. Regulatory constraints around business entry process are key factors preventing the formalization of informal firms. Therefore, development policy makers and practitioners have an obvious interest in finding ways to encourage informal firms to formalize by redesigning the business entry process. Making it easier to formally register a business has received enormous attention from 12
# Liberalizing Basic Telecommunications: The Asian Experience

Carsten Fink, Aaditya Mattoo, Randeep Rathindran* _JEL Classification:_ F13, L12, L51,L96 _Keywords:_ Services Trade Policy, Regulation, Telecommunications, GATS Development Research Group, World Bank, 1818 H Street, NW, Washington, DC, 20433, USA. The views expressed here are those of the authors and should not be attributed to the World Bank. This paper was prepared for the conference "Trade, Investment and Competition Policies in the Global Economy: The Case of the International Telecommunications Regime," Hamburg, Germany, January 18-19, 2001. We are especially grateful to Simon Evenett for valuable advice, to Christine Zhen-Wei Qiang for providing access to some of the data used and to conference participants for useful comments. The United Kingdom's Department for International Development provided financial support for the services trade database used in this paper.
grown at the national level; being a household-level survey, large-scale farming operations which grow commercial crops are not included.

Figure 19: Popularity of Crops Grown in Sudan, 2009 and 2014/15

- (a) Crop Grown as a Proportion of Total Agricultural Households, 2009

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>sorghum millet groundnuts tea Vegetables wheat coffee beans roots & other<br>tubers crops*<br><!-- End of picture text --> _Note:_ * Other crops include cotton, maize, and rice.

- (b) Crop Grown as a Proportion of Total Agricultural Households, 2014/15

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>sorghum millet seasam groundnutvegetables beans wheat roselle fruit dates other<br>crops*<br><!-- End of picture text --> _Source:_ Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

_Note_ : * Other crops include cotton, maize, sunflower, potato, gum arabic, and spices.

There are key differences between crops grown by poor and non-poor households, with subsistence crops more likely to be grown by the poor (Figure 20). While sorghum is grown almost equally by poor and non-poor households, millet is grown more predominantly by the poor; sesame and groundnut are grown more predominantly by the non-poor. Apart from roselle, non-poor households predominantly grow almost all the cash crops listed in the survey. This is driven by the extent to which production constraints, such as access to land, input and output markets, and credit and insurance markets, are binding for poor and non-poor households. As a result, poor households resort to low-risk, low-return subsistence crops such as millet. Increasing the diversity of crops grown by the poor, by creating opportunities to market profitable crops is vital to increase the farm revenues of poor agrarian households.

22
<mark>QUANTIFYING ECONOMIC IMPACTS</mark> The economic performance of politically-connected firms in Ukraine is significantly different from that of their non-connected peers. Econometric analysis, covering Ukraine’s firm-level data for 2006-2015, and using both approaches for the identification of politically-connected firms, found statistically significant differences in their economic outcomes compared to the other firms controlling for sector, size and age of firms.<sup>16</sup> Annex 3 details these estimations. It is important to note that results presented here are only descriptive and identify correlations, but do not prove causality. The results for politically-connected firms identified using the second approach (ownership and influence) show stronger performance differentials between politically-connected and non-connected firms, than firms identified using the first approach (ownership). However, both results are statistically robust.

Politically-connected firms are larger than their non-connected peers. Politically-connected firms have higher turnover and employ more labor than non-connected firms. Politically-connected firms identified using Approach 1 have 8.9 percent higher turnover and employ 11.1 percent more labor than non-connected firms.<sup>17</sup> The gap is even larger for politically-connected firms identified using Approach 2, with 28.8 percent higher turnover and 23.2 percent more labor.

Politically-connected firms pay a lower effective tax rate. Politically-connected firms identified using Approach 1 have a 0.4 percentage point lower effective tax rate, those identified using Approach 2 have a 2.9 percentage points lower tax rate.

Politically-connected firms are less productive. Politically-connected firms have a negative Total Factor Productivity (TFP) gap compared to non-connected firms. The gap ranges from negative 43.7 percent for firms identified using Approach 1 to negative 65 percent for firms identified using Approach 2. In addition, the analysis shows that TFP is likely to be lower in companies with a higher market share and in more concentrated industries. This indicates that there could be a potentially large pay-off from policies that promote competition.

> 16 RUSLANA data set covering the period of 2006-2015 gives around 2 million observations. Random effects estimation has been selected, since the dummy variable for the connected companies implies no variation in it. There are single observations on both owners and PEPs, so a firm is assumed to be connected for the whole analyzed period (in other words, a firm does not change its status from non-connected to connected and vice versa during the period 2006-2015).

9
Table A3: T-Test comparison of village head and PMT targeting methods – Across the full sample

||RMG-eligible<br>obs.|Random<br>Sample<br>obs.|RMG-eligible<br>mean|Random<br>Sample mean|Diff. (RMG-<br>eligible –<br>Random<br>Sample)|PMT poor<br>obs.|PMT nonpoor<br>obs.|PMT poor<br>mean|PMT nonpoor<br>mean|<br>Diff. (PMT<br>poor -<br>nonpoor)|
|---|---|---|---|---|---|---|---|---|---|---|
|Household composition|1146|742|-0.0151|0.0233|-0.0384**|1187|701|-0.0039|0.0066|-0.0106|
|Exposure to shocks|1146|742|0.0039|-0.0059|0.0098|1187|701|-0.0047|0.0080|-0.0128|
|Land and buildings<br>ownership|1146|742|-0.0312|0.0482|-0.0794**|1187|701|-0.0259|0.0438|-0.0696*|
|Nutrition|1146|742|0.0097|-0.0149|0.0246*|1187|701|-0.0423|0.0716|-0.1140***|
|Livestock ownership|1146|742|-0.0449|0.0693|-0.1142***|1187|701|-0.0535|0.0906|-0.1442***|
|Housing conditions|1146|742|-0.0207|0.0320|-0.0527***|1187|701|-0.0640|0.1084|-0.1725***|
|Durables ownership|1146|742|-0.0345|0.0533|-0.0879***|1187|701|-0.0646|0.1093|-0.1739***|
|Total income per HH<br>member|1146|742|-0.0560|0.0865|-0.1426***|1187|701|-0.3049|0.5164|-0.8213***|

Note: Variables are rendered as composite z-scores, computed by excluding variables used in the creation of the PMT index, and by estimating means and standard deviations for the whole sample of 1,888 observations.

* p<0.1 ** p<0.05 *** p<0.01

30
# **1. Introduction**

A large literature on the determinants of poverty finds a strong association with regional and country-specific factors, like the vulnerability to flooding or tropical diseases, remoteness, quality of governance, and property rights (Dollar et al., 2016). A second strand of the literature finds within-country factors, such as the availability of infrastructure (roads, water, electricity) and services (health, education), proximity to markets, and social relationships, to matter as much (Kraay and McKenzie, 2014 for example). A third set of studies investigates household characteristics, such as household size, age structure, dependency ratio, gender of the head of household, employment status, hours worked, property owned, nutritional status and educational attainment, and correlates those to the poverty headcount (Banerjee and Duflo, 2011).

In this paper, we disentangle the link between business regulations and their enforcement and the poverty headcount, expanding the evidence on country-level determinants of poverty. In particularlar, we use business regulations on starting a business, acquiring licenses, getting credit and contract enforcement, as well as an overall regulatory ranking as indicators of property rights. The data are constructed from the World Bank’s _Doing Business_ project and cover 189 economies from 2005 to 2013.

We find that business-friendly regulations are correlated with lower poverty headcount, as is higher government expenditure, a country’s income per capita, and the regional dummies for East Asia and Eastern Europe. We suggest that the conduit for poverty reduction is business creation, both as a source of new jobs and as a manifestation of thriving entrepreneurship.

The rest of the paper is organized as follows. Section 2 describes the data sources. Section 3 details the empirical strategy and presents the main results. Section 4 concludes.

# **2. The data**

This section describes the sources of the dependent variables, and the sources and construction of independent variables.

The main dependent variable used in previous studies is the poverty headcount ratio at $1.90 a day (at 2011 purchasing power parity) as a percent of the population. As an alternative variable, the poverty headcount ratio at $3.10 a day is sometimes used too. We use both indicators, the latter for robustness purposes. The Democratic Republic of Congo is the country with largest share of population living below $1.90 a day: 85.56%, followed by Madagascar and Burundi with 77.88% and 77.65%, respectively. Looking at the alternative definition of the poverty headcount ratio of $3.10 a day, these percentages increase significantly. The Democratic Republic of Congo has nearly its entire population (94.52%) living on less than $3.10 a day, Burundi — 92.17% and Madagascar — 91.08%. The correlation between the two variables is high, at 93.73%.

2