rafmacalaba/datause-encoder-data huggingface dataset:
and/or physical investment,<sup>3</sup> although some papers argue that this requires the destination economy to satisfy certain conditions,<sup>4</sup> and yet others find no significant impact of FDI on investment or growth.<sup>5</sup> There are two major difficulties with the interpretation of many of these results, however. First, both micro and macro studies face problems of bi-directional causality: high-productivity and high-growth firms and countries are more likely to attract FDI than the rest, so that the empirical association between growth and FDI could well reflect reverse causation from the former to the latter. To the extent that high investment itself also reflects high anticipated returns, the same argument would apply to its close association with FDI often found in empirical studies (e.g., Bosworth and Collins 1999).<sup>6</sup> The other difficulty concerns the lack of distinction between greenfield FDI and M&A. Since the former involves mainly (although not only) new capital assets, while the latter is just a transfer of existing ones, greenfield FDI would seem more likely to affect growth -- if at all -- via increased physical investment, while M&A FDI would be more likely to do so via enhanced productivity growth. In fact, the increased importance of M&A in total FDI flows in recent years has been singled out as the likely cause of an observed weakening in the empirical FDIinvestment link in the 1990s (World Bank 2001). Thus, failure to distinguish between the two

> 3 See World Bank (2001) and the references listed therein.

> 4 For example, Borensztein, de Gregorio, and Lee (1998) find that the investment and growth impact of FDI is significant only when the recipient economy possesses high levels of human capital. A similar argument in relation to the importance of financial development is made by Alfaro _et al._ (2002).  In turn, Blomstrom, Lipsey and Zejan (1996) conclude that FDI has a stronger positive impact on growth in high-income destination economies, while Nair-Reichert and Weinhold (2001) find that this positive effect takes place only in open economies.

> 5 See for example Carkovic and Levine (2002).

> 6 Some micro and macro studies do control for simultaneity; see, e.g., Aitken and Harrison (1999) and Carkovic and Levine (2002). Both studies find no significant growth effects of FDI, so that the association between the two variables would mainly reflect causation from growth to FDI. Indeed, as shown by Rangvid (2001) using a sample of industrial and developing countries, growth and investment returns are very closely associated. Thus anticipations of higher growth should attract increased domestic and foreign investment. This line of argument is empirically pursued by Calderón, Loayza and Servén (2001) to explain international capital flows.

3
Table 3: Summary Statistics for the Firm-Level Data

||Estimated<br>population|Sampled<br>frms|Panel|
|---|---|---|---|
|Firm-years|1,410,341|580,122|413,758|
|Firms per year, mean|82,961|34,124|24,338|
|Census frm-years|276,278|276,278|246,881|
|Census frms per year, mean|16,251|16,251|14,522|
|Unique frm series|||147,838|
|Output, mean (million Rs.)|17.9|30.2|39.2|
|Output, median (million Rs.)|2.6|3.6|5.3|
|Capital, mean (million Rs.)|6.0|10.9|14.4|
|Capital, median (million Rs.)|0.4|0.5|0.8|
|Materials, mean (million Rs.)|12.0|19.9|25.7|
|Materials, median (million Rs.)|1.9|2.6|3.9|
|Labor, mean (no. employees)|73|131|168|
|Labor, median (no. employees)|21|31|43|
|In panel, as fraction of total in sa|mpled popula|tion:||
|Output|||0.93|
|Capital|||0.94|
|Labor|||0.92|
|Firm-years_>_100 employees|||0.94|
|Firm-years_>_200 employees|||0.96|
|Firm-years|||0.71|
|Census frm-years|||0.89|

Summary statistics for the estimated population (using sampling weights), for the sampled population (not using sampling weights), and for firms that appear for two or more years in the panel. Only open firms with positive values of key variables are included. “Firm-years” indicates the total number of observations, while “Census firm-years” indicates the number of observations in the census sector. Mean and median values are averages across all years used in the analysis (1985-1994 and 1998-2004). Output, material inputs and capital have been deflated to 1985 values and are expressed in millions of rupees. Fractions of output, capital, etc. that appear in the panel are given in relation to the sampled (rather than the estimated) population. Source: Authors’ calculations based on Annual Survey of Industries data.

35
regulatory system that will limit the use of information or skills. This can be achieved by setting a maximum price, informed by the cost information available and international benchmarking. This price cap gives a strong incentive to the operators to minimize costs since the difference between the price cap and the actual cost defines their profit margin. And while doing so, it buys time for the regulator to learn more about these actual costs.

If instead, the main weakness is an excessive risk of political interference or of corruption, price caps may be risky since the initial price cap may be manipulated. In this case, it may be better to use a costplus approach that limits explicitly the rate of return by defining the allowed maximum mark-up over audited costs and to complement it with an international cost benchmarking.<sup>14</sup> When both concerns apply equally, offering a menu of choices may be the best solution. One of the choices links output or investments with a set fixed price cap (i.e. fixed price regulation) and the other links it with rate of return over investment or a set profit margin over costs (flexible price regulation or cost of service regulation) with corresponding bidding rules that can help in selecting the better performer, even if this comes at the cost of sacrificing efficiency (intuitively it helps reveal information about costs).

In practice, the experience suggests that the actual choice between the various regulatory regimes is mostly skewed by a lack of access to much needed cost accounting data. Regulatory reforms continue to be victims of the lack of desire for accountability of many of the key actors. Data on operational and technical performance are relatively easy to obtain. But data on the financial and commercial management of regulated industries are much more difficult to access. Few developing countries have adopted minimal standards of a regulatory accounting system that would allow this accountability on cost, prices and financing strategies. As long as this is remains unaddressed, the scope for improvements in accountability will be limited.

# **_2.4 What happens when the fiscal efficiency is limited?_**

For the water sector in general, fiscal constraints tend to be quite binding and since the private sector has not delivered as much financing as initially expected in the poorest countries, it is easy to see that progress in meeting the needs of the poorest was inevitably to be slow. Fiscal constraints can indeed be partially credited with the limited progress achieved in reaching the desired access rate for the lower income groups. Fiscal constraints can also be blamed for the failure to ensure reasonable affordability since the margin to provide connection or consumption subsidies is limited.

> 14 See for instance Auriol and Blanc (2009).

10
Syria.<sup>18</sup> This is in line with the evidence above that fewer firms in Hama reported raising their own prices than in the rest of Syria.

# **Box 2: Case example of the garment sector**

The garment sector has suffered a dramatic decline in the crisis. The evidence suggests production, not just exports, has largely collapsed _._ Prior to the crisis in 2011 Syria exported $278 million of finished garments. This made Syria a net exporter of finished garments with net exports of $172 million. Exports of finished garments collapsed to just $38 million in 2015 while imports of final garments had increased by about 50% from $106 million to $159 million. This turned Syria into a net importer of finished garments to the tune of $121 million. Imports of intermediate inputs (yarn and fabric) for garment manufacturers also declined dramatically from $944 million to $282 million suggesting major reductions in production for the domestic market too. In addition, the export of raw materials (cotton and to a lesser extent wool) has actually increased from $31 million to $98 million in 2015 suggesting that surplus raw materials are now exported rather than consumed by the domestic textile and garment industry. This is despite Syria’s cotton producing areas shrinking significantly during the crisis and a large share being under ISIS control according to the FAO.<sup>1</sup> The decline in exports of finished products, reductions in imports of intermediate goods, and increases in exports of raw materials are all consistent with overall production collapse in the garments sector. See Figure B2.1 for an overview.

# **Figure B2.1: Net exports in the garment sector, USD millions**

<!-- Start of picture text --> 400<br>200<br>0<br>Raw materials Yarn and fabrics Final Products<br>‐200<br>‐400<br>‐600<br>2011 2015<br><!-- End of picture text --> Source: UN COMTRADE data The decline in Syria’s garment exports is also consistent with local damage estimates. Aleppo was a key city for the Syrian textile and garment sector with a high concentration of textile and garment factories. Damage to industrial areas in Aleppo is extensive. Satellite imagery analysis commissioned by the World Bank suggests that 81 percent of the Shaykh Najjar Industrial Zone in Aleppo has suffered damage along with 70 percent of Ash‐Shaqif Zone. Jibreen Industrial Zone also suffered damage to about 18 percent of buildings. In Shaykh Najjar Industrial Zone a breakdown of damage based on satellite imagery suggests 932 textile facilities were damaged since 2011, making it the single most affected sector in the zone.<sup>1</sup> Source: This box is taken from Chapter 3 of the Syrian Economic and Social Impact Assessment by the World Bank in 2017.

> 18 Statistically significant at the 5% level.

12
found 33 percent of youth said lack of experience was their main obstacle to finding a job, 10 percent said lack of skills, and 14 percent said difficulty searching for jobs.<sup>5</sup> While employers can often fill jobs with “a” worker quickly, many employers say that difficulty finding workers with the right skills is an issue they face, and many employers appear to feel this is more the case today than in the past. Manpower Group has been surveying public and private employers in multiple countries annually and reports that 77 percent of employers in 41 countries surveyed in 2023 report difficulty finding talent with the skills they need, up from only 31 percent in 2010. Figure 2 shows high levels of firms saying they struggle to find talent in all of the developing countries the survey covers, with the percentage of firms reporting difficulties similar to that in the U.S. and Germany. Employers report both difficulty finding workers with the right technical skills such as information technology, data skills, and sales and marketing skills, as well as with the right soft skills such as reliability, creativity, critical thinking, and resilience. Public policy efforts can then try to help jobseekers develop these skills, as well as helping workers with these skills to signal in a credible way that they have these skills as they search for jobs and match with employers.

> 5 Data from México Piloto de Inclusion Laboral de Jóvenes Baseline survey Phase 1 (2018), used in Abel et al. (2022). A further 21 percent said their age, gender, ethnicity, or socioeconomic background was the main obstacle, which could reflect employer discrimination, social norms, or difficulties they face paying for training or job search.

10
# _Model Calibration and Validation_

The models were calibrated and validated with historical observed data at various locations. The water flow model was calibrated with hourly data on water level time series from November 2011 to June 2012 and hourly data on water discharge time series for one spring and one neap tidal cycle during February 2012 and March 2012. The salinity model was calibrated with the salinity time series (data were monitored twice a day on alternate days during high and low tides) from November 2011 to June 2012. The models were validated against the corresponding time series of measured data from November 2010 to April 2011. The match between the model-generated estimates and the measured data was reasonably good (see Table 2).

**Table 2 Degree of Agreement between Model-Generated Results and Measured Data**

|Location|R<sup>~~2~~</sup>|
|---|---|
|_Comparison of water flow_||
|Baleswar River at Char Doani|0.95|
|Buriswar River at Amtali|0.87|
|Pussur River at Akram Point|0.94|
|Pussur River at Mongla|0.91|
|_Comparison of water level_||
|Marichbunia River|0.84|
|Pussur-Kazibacha River|0.95|
|_Comparison of salinity_||
|Baleswar River at Char Duani|0.73|
|Pussur River at Mongla|0.99|
|Rupsa River at Khulna|0.83|
|Sibsa River at Nalian|0.97|

# _Likely Effects of Climate Change_

The effects of climate change on river salinity in the coastal area of Bangladesh are expected to manifest in several ways. Changes in rainfall, temperature, freshwater flow from rivers in the 15
Table 6: Households - Changes in means across waves III

|||Means b|y groups||Diference in|means (t-test)|
|---|---|---|---|---|---|---|
||(1)<br>ULGSP<br>Round 1<br>Mean (se)|(2)<br>ULGSP<br>Round 2<br>Mean (se)|(3)<br>Control<br>Round 1<br>Mean (se)|(4)<br>Control<br>Round 2<br>Mean (se)|(5)<br>ULGSP<br>Dif<br>Dif (p-value)|(6)<br>Control<br>Dif<br>Dif (p-value)|
|Citizens should pay taxes so local govt develops|0.51|0.62|0.42|0.50|0.11|0.08|
||_(0.03)_|_(0.03)_|_(0.03)_|_(0.03)_|_(0.00)_|_(0.05)_|
|Better to pay high taxes to get more services|0.53|0.55|0.46|0.50|0.02|0.04|
||_(0.03)_|_(0.03)_|_(0.02)_|_(0.03)_|_(0.66)_|_(0.31)_|
|Not paying taxes is wrong and punishable|0.52|0.50|0.39|0.40|-0.02|0.02|
||_(0.02)_|_(0.03)_|_(0.03)_|_(0.03)_|_(0.56)_|_(0.69)_|
|Not paying electricity is wrong and punishable|0.76|0.83|0.64|0.73|0.06|0.09|
||_(0.03)_|_(0.02)_|_(0.03)_|_(0.02)_|_(0.11)_|_(0.03)_|
|Likely to be punished if not paying taxes|0.79|0.81|0.69|0.76|0.02|0.07|
||_(0.02)_|_(0.03)_|_(0.02)_|_(0.03)_|_(0.50)_|_(0.03)_|
|Tax code and collection is fair|0.54|0.71|0.48|0.65|0.18|0.16|
||_(0.02)_|_(0.02)_|_(0.02)_|_(0.02)_|_(0.00)_|_(0.00)_|
|Prefers beneft to malaria (count)|0.69|0.54|0.79|0.64|-0.16|-0.15|
||_(0.05)_|_(0.05)_|_(0.05)_|_(0.05)_|_(0.03)_|_(0.03)_|
|Prefers beneft to road (count)|0.69|0.65|0.74|0.65|-0.04|-0.10|
||_(0.06)_|_(0.06)_|_(0.06)_|_(0.05)_|_(0.67)_|_(0.15)_|
|Governments should control prices|0.94|0.91|0.94|0.90|-0.03|-0.03|
||_(0.01)_|_(0.01)_|_(0.02)_|_(0.01)_|_(0.11)_|_(0.09)_|
|Governments should provide employment|0.95|0.96|0.96|0.95|0.00|-0.02|
||_(0.01)_|_(0.01)_|_(0.01)_|_(0.01)_|_(0.67)_|_(0.08)_|

_Note:_ This table presents group averages and differences in means for households matched across the two waves of the panel data. Columns (1)-(4) present average and s.d. for each variable described for ULGSP LGAs and comparator, for each of the survey waves. Columns (5)-(6) present differences in means for the specific groups and the p-value of a t-test for equality of means (clustering at LGA-level) 48
Table 12: Actual and Predicted Poverty Rates at $5.50 Per Day

||**2004-05**|**2009-10**|**2011-12**|**2014-15***|
|---|---|---|---|---|
|**National**|95.7|94.3|89.7|85.6|
|**Urban**|95.4|92.1|84.5|74.9|
|**Rural**|95.8|95.4|92.1|90.8|

Sources: India National Sample Survey Office (NSSO) Surveys. *Preferred model predictions at $1.90 per day line 28
|||||(0.552)|(0.590)|(0.588)|(0.588)|
|---|---|---|---|---|---|---|---|
|Power Outages (days)|||||-0.108|-0.084|-0.087|
||||||(0.078)|(0.075)|(0.074)|
|Courts Obstacle (cell<br>|||||-0.707|-1.390*|-1.336|
|average)||||||||
||||||(0.850)|(0.811)|(0.813)|
|Crime Losses Y:1 N:0|||||-0.737|-0.809|-0.831|
||||||(0.699)|(0.701)|(0.704)|
|Firm Inspected Y:1|||||-1.273*|-1.325**|-1.327**|
|N:0||||||||
||||||(0.656)|(0.658)|(0.659)|
|How Much Of An|||||-0.290|-0.351|-0.345|
|Obstacle: Tax Rates||||||||
||||||(0.258)|(0.255)|(0.255)|
|How Much Of An|||||0.034|0.075|0.080|
|Obstacle: Labor||||||||
|Regulations?|||||(0.330)|(0.329)|(0.329)|
|Population (logs)||||||-46.382***|-46.717***|
|||||||(11.098)|(11.036)|
|Urbanization (%)||||||1.378***|1.418***|
|||||||(0.456)|(0.457)|
|WBL Index||||||28.689***|28.240***|
|||||||(8.905)|(8.822)|
|Women can work the||||||1.375|1.508|
|same night hours as||||||||
|men Y:1 N:0||||||||
|||||||(3.206)|(3.190)|
|Women can work in<br>||||||-2.446|-1.892|
|the same industries as<br>men Y:1 N:0||||||(2.184)|(2.189)|
|Ratio of Female to||||||2.956***|3.113***|
|Male Population||||||||
|||||||(0.715)|(0.706)|
|Fertility Rate||||||-15.366***|-14.999***|
|||||||(4.758)|(4.767)|
|Starting a Business||||||0.070*|0.074*|
|(DTF)||||||||
|||||||(0.040)|(0.039)|
|Inflation (%)||||||0.054|0.051|
|||||||(0.065)|(0.065)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>fixed effects||||||||

62
Table A-1. Explicit and Implicit Subsidies Provided To Real Sector By Enlarged Budget and State Energy Monopolies, Percent of GDP

||_Subsidy types_|**1994**|**1995**|**1996**|**1997**|**1998**|
|---|---|---|---|---|---|---|
|_1.Explicit budget subsidies_||_10.2_|_8.6_|_7.9_|_8.6_|_5.9_|
|i. Federal||3.0|2.5|1.7|1.8|0.7|
|ii.Subnational||7.2|6.1|6.2|6.8|5.2|
|iii.EBFs||n.a.|n.a.|n.a.|n.a.|n.a.|
|_2.lmplicit budget subsidies_||_0.7_|_3.2_|_7.6_|_7.4_|_10.4_|
|i. Federal||0.7|1.9|2.1|2.2|3.0|
||a.Offsets|0.7|0.6|0.8|0.8|0.4|
||b.Net increase of tax arrears|n.a.|1.3|1.2|1.4|2.7|
|ii.Subnational||n.a.|1.2|1.9|3.0|3.1|
||a.Offsets|n.a.|n.a.|1.3|1.9|1.8|
||b.Net increase of tax arrears|n.a.|1.2|0.6|1.1|1.3|
|iii'EBFs||n.a.|n.a.|3.6|2.2|4.2|
||a.Offsets|n.a.|n.a.|n.a.|n.a.|n.a.|
||b.Net increase of tax arrears|n.a.|n.a.|3.6|2.2|4.2|
|**_I Total budget subsidies_**||**10.9**|11.8|15.5|16.0|16.3|
|_I. Total implicit subsidies fro_|_m state monopolies_|**_3.9_**|**_4.0_**|**_4.2_**|**_3.2_**|_n.a._|
|i.Gazprom||1.3|1.8|1.8|1.6|n.a.|
||a.Change in net inter-industry receivables|0.6|0.9|0.9|0.8|n.a.|
||b.Subsidies embedded in non-equivalent barter|0.7|0.9|0.9|0.8|n.a.|
|ii.RAO UES||2.6|2.2|2.4|1.6|n.a.|
||a.Change in net inter-industry receivables|1.5|1.0|0.9|0.1|n.a.|
||b.Subsidies embedded in non-equivalent barter|1.1|1.2|1.5|1.5|n.a.|
|_For reference:_|||||||
|_GDP, Rb bn_||_610.7_|_1,585.0_|_2,200.0_|_2,585.0_|_2,684.0_|

Notes. 1.Only available information is summed up in totals 2. Budgetary subsidies are calculated on a gross basis, since consistent information about budgetary arrears is not available (as of end-97, the _stock_ of budgetary arrears was about Rb 50 bn, less than 2 percent of GDP).
**Table 12. FSAP’s Dispersion**

||**By In**<br>|**come**<br>|**Level**<br>||**By Date**||**By Type**|**of FSAP**|
|---|---|---|---|---|---|---|---|---|
||HIC|MIC|LIC|1999- 2001|2002 - 2004|2005- 2007|Initial|Update|
|StandardDeviation|29.6|22.1|17.2|22.9|22.7|23.7|21.1|23.7|
|Total Recommendations|20.3|36.1|37.7|31.7|35.3|29.0|33.5|32.6|

# **C.  Regional variations**

We now analyze to what extent FSAPs differ regionally (Appendix Table 4). Clear contrasting patterns appear. Starting with the Latin American and Caribbean region, it has received relatively more attention than the average as regards selected areas of infrastructure (insolvency and the judiciary, reporting and security registries, payments systems), oversight (pensions, global oversight, and crisis management), and public policy (monetary and foreign exchange management).<sup>12</sup>

- The heightened attention to crisis management, monetary management, and insolvency is consistent with the region’s higher volatility, poorer macroeconomic record, and the resulting higher incidence of financial crises.

- The emphasis on the judiciary (including the very strong emphasis on out-ofcourt resolutions) is consistent with a generally poor institutional environment, which may motivate a search for alternative conflict resolution mechanisms.

- The stress on pensions is most likely a reflection of the introduction and rapid development of private pension schemes in most countries of the region as a reaction to unsustainable and poorly run public pension schemes.

Countries in the European and Central Asian region set themselves apart through the fact that infrastructure received special attention as regards the judiciary, accounting and auditing, and corporate governance. As regards oversight, capital markets were overemphasized, microfinance under-emphasized. Except for fiscal and public sector management (state enterprises), public policy received generally less attention than in other regions.

- The emphasis on capital markets, accounting and auditing, and corporate governance, is consistent with the relatively rapid development of more advanced forms of intermediation in this region, which puts a premium on the accompanying (and relatively more sophisticated) infrastructure.

- The emphasis on (non financial) public sector issues probably reflects the residual importance of the state in transition economies.

As regards Africa, the judiciary (but not out-of-court resolutions) received particular attention; in contrast, other areas of infrastructure (particularly the informational framework) were de-emphasized. As regards oversight, there was a very clear emphasis

> 12 It is also interesting to notice that FSAPs in the LAC region have an unusually high number of recommendations (see Table 11). This could reflect a number of factors, including more issues, more openness from the authorities to explore a broader set of issues, mission composition effects, or presentational issues (with more recommendations being reported in the summary tables).

24