rafmacalaba/datause-encoder-data huggingface dataset:
**Table A1. Unit Root Tests: Augmented Dickey-Fuller and Phillips-Perron Tests**

|||ADF||||P|<br>P|
|---|---|---|---|---|---|---|---|
||Level||Fir|st difference||Level|First diff|
|Variables:|With constant No constant|With trend W|ith constant|No constant W|ith trend|With trend|With trend|
|Log of GDP|-1.55<br>0.965|-2.179|-1.412|-1.202|-1.372|-1.433|-4.14*|
|Log of AG_GDP|-1.527<br>-0.045|-3.335*|-1.937|-1.933*|-1.499|-1.919|-5.912*|
|Log of REVENUE|-2.124<br>1.003|-2.042|-4.477*|-4.405*|-4.397*|-2.492|-7.723*|
|Log of P_COFFEE|-2.062<br>-0.18|-1.89|-4.146*|-4.222*|-4.236*|-2.308*|-5.744*|
|Log of RAIN|-1.466<br>-0.91|-1.718|-2.866*|-2.971*|-2.761|-4.579*|-13.082*|

_Note_ : Optimal lag lengths for tests for each variable were chosen based on Schwarz Information Criterion for the ADF tests as follows: LN_GDP (3), LN_AG_GDP (4), LN_REVENUE (1), LN_P_COFFEE (1), LN_RAINFALL (2). An asterisk (*) indicates the rejection of the unit root null hypothesis at 90 percent critical level or better.

# **Table A2. Regression Results for Coffee Price Shocks**

``` . regress dlnrevenue lnp_coffee war ```

|`Source | SS df MS Number of obs = 32`|
|---|
|`-------------+------------------------------ F( 2, 29) = 1.23`|
|`Model | .056297984 2 .028148992 Prob > F = 0.3081`|
|`Residual | .665610861 29 .022952099 R-squared = 0.0780`|
|`-------------+------------------------------ Adj R-squared = 0.0144`|
|`Total | .721908845 31 .023287382 Root MSE = .1515`|
|`------------------------------------------------------------------------------`<br>`dlnrevenue | Coef. Std. Err. t P>|t| [95% Conf. Interval]`<br>`-------------+----------------------------------------------------------------`|
|<br>`lnp_coffee | .1053907 .0740651 1.42 0.165 -.0460895 .2568709`|
|`war | -.0292527 .0599567 -0.49 0.629 -.1518779 .0933726`|
|`_cons | -.5639143 .4200392 -1.34 0.190 -1.422991 .2951622`<br>`------------------------------------------------------------------------------`|

32
Finally, in-kind transfers in the form of education and health services have an even larger positive effect on inequality when moving from consumable income to final income: the Gini coefficient reduces to 0.420, equivalent to a 15 percent decline relative to market income plus pensions (Figure 2). The marginal contributions to inequality reach 0.032 Gini points for education, and 0.014 Gini points for health, and the equalizing effect holds for all levels of education, as indicated by their positive marginal contribution to inequality (Figure 6)<sup>26</sup> . The large effect of in-kind transfers on inequality is not surprising given that Chile spends significantly more on education and health care (roughly 8.1 percent of the GDP) than on direct transfers and pensions (1.6 percent of the GDP). Yet, such result must be interpreted with caution because in-kind transfers are monetized at average government cost, which does not necessarily reflect the actual value of the education and health services provided and there are no adjustments for differences in quality across the distribution. The method assumes that a poor person living in rural areas receives the same benefit as an urban middleclass person, for instance.

**Figure 6: Marginal contributions of in-kind transfers to income inequality** _(Gini points)_

<!-- Start of picture text --> All levels of education 0.032<br>Primary education 0.014<br>Health 0.014<br>Secondary education 0.008<br>Pre-school 0.005<br>Tertiary education 0.003<br>0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Martinez-Aguilar and Ortiz-Juarez (2016).

While most fiscal interventions in Chile are found to be equalizing, a second fundamental question then emerges: Are fiscal interventions also poverty-reducing? While as expected direct taxes and social contributions increase the incidence of poverty (Figure 7.A) —which is mostly driven by social contributions as the population in poverty likely does not pay direct taxes—, direct transfers more than compensate this effect. Specifically, poverty headcounts decline by nearly 3 percentage points (or around 40 percent) with respect to market income plus pensions for both the official extreme and $4/day poverty lines, and by 4 percentage points (or 24 percent) for the official moderate poverty line.

> 26 A summary of the marginal contributions for all fiscal interventions analyzed is shown in the appendix Table A1.

15
just completing the process of shifting from enrolling senior secondary graduates to taking junior secondary graduates, and adding a fourth year to the curriculum. In some cases, a few selected STS will continue to enroll senior secondary graduates for more "technical" skill areas.

In all, there are about 15,000 VTE schools run by local education bureaus, technical ministries, and enterprises (Table 2). Of these, 347 were designated national "key" SVS in 1980, on the basis of the quality of their management, teaching staff qualifications, teaching performance, and equipment. Since 1980, the number has shrunk a little, but these schools continue to receive extra funds from the State (mainly to purchase equipment). In addition, there are a further 600-700 schools designated as 'main stream" schools by provincial aut? rities. However, the State Education Commission considers that evua these schools are seriously below standard in many respects.

|Table 2:|Secondary<br>Vocation<br>Institutions<br>and|al<br>and Technic<br>Enrollments:|al<br>Training:<br>1985|
|---|---|---|---|
||Number of<br>Institutions|Enrollment<br>Capacity|Enrollment|
|SWS (1985)*<br>of which:|3,548|1,300,000|740,000<br>(of whom,|
|Run by national<br>Ministries and<br>and Commission|<br>s<br>945||190,000 are<br>in-service)|
|Runby<br>enterprises<br>Run by<br>provinces|1,621<br>412|||
|STS<br>of which:|3,557|?|1,571,100|
|Teacher<br>training|1,028||558,200|
|SVS|8,070|?|2,295,700|
|Total Voc. and T|ech. Training|||4,606,800|
|Total Upper Sec.|Goa. Education||7,411,300|

*Source: Ministry of Labor and Personnel.

Other data from Statistical Yearbook of China, 1985.

- 13 -
expenditures are found to have private benefits to firms in some circumstances (Cai et al. forthcoming). Alternatively, this measures the average regulatory burden, which should have negative effects on firm performance.

The second variable is the standard deviation of the share of senior managers’ time that is spent in dealing with government regulators in the same city (―deal uncertainty‖). A larger value means that the deal environment is more arbitrary and less certain. We expect this variable to have a negative effect on firm performance. Indeed, based on the World Bank Enterprise Surveys of around 100 countries, Hallward-Dreimeier et al. (2010) find evidence that the average time cost has positive effects while deal uncertainty has a negative effect on firm performance. However, as we emphasized, policy effects may differ in specific contexts. For instance, the effects of regulatory burdens and uncertainty may depend on the organizational details of bureaucracy. When firms have to go through decentralized corruption—that is, when firms have to bribe multiple, decentralized government regulators, and each regulator has discretion to stop the deal—the effect of regulatory burden and arbitrariness may be more pronounced (Shleifer and Vishney 1993, Berkowitz and Li 2000). Many observers of corruption have viewed India’s corruption and regulation as typical of a decentralized setup, while China as being typical of a centralized setup (Berkowitz and Li 2000, Sun and Johnson 2009). We therefore expect the effects of these two variables to be more pronounced in India than in China. Table 3 shows that China features both higher time costs and deal uncertainty. While Indian firms on average spent 14% of their senior managers’ time on dealing with regulators, the corresponding number of China is 20%. There are significant more variations in deal uncertainty in China as well: the standard deviation of this time cost is 13% in India, but 17% in China.

To summarize, relative to India, China has a better infrastructure (as measured by the share of losses of sales due to power outage), uses more skilled labor, and has greater flexibility in labor market. In contrast, Indian firms have better accesses to short-term bank finance and lower regulatory time costs and deal uncertainty than Chinese firms.

# **IV. Empirical Results**

We now conduct empirical analyses to examine the significance of these business environment factors in affecting firm-level productivity. We run cross-sectional regressions as follows, along with some more restricted versions: 16
Another important issue with IV estimation is to test for the instrument relevance. Since we are using more instruments than endogenous variables we do not know if the instruments collectively capture the independent variation in the right-hand-side variables. One way to assess this issue is to take a closer look at the magnitude of the R<sup>2</sup> in the first stage for each endogenous variable. The Shea first stage R<sup>2</sup> shows that the partial R<sup>2</sup> for changes in average FDI is between 30 and 40 percent in all eight model specifications, which is reasonable. For the interactive term, the figures are even better, as the Shea first stage R<sup>2</sup> is in the range of 0.45 to 0.50, indicating a relatively good fit. For the other two explanatory variables, all Shea’s partial R<sup>2</sup> are above 10 percent, which means that all instruments are relevant in Shea’s sense and thus, the instruments have sufficient relevance for the right-hand side variables in the growth regression.

In another set of regressions, we repeat the exercise for the top 30, 40 and 50 percent most regulated economies. These further tests are useful to ascertain whether the results are influenced by the particular threshold level chosen for the regulation dummy. In comparison to the top 20 percent most regulated countries, the significance levels of the coefficient for the interactive term are somewhat weaker but roughly similar if we set the cut-off point at the top 30 percent most regulated economies (Table 4). The interactive term is also statistically significant in six out of eight OLS regressions, but only in four out of eight IV regressions. Yet the number of significant coefficients declines considerably if we increase the threshold level to top 40 or 50 percent most regulated countries. These results mean that there is a particular threshold level, which is highly relevant for our results. In other words: Low quality regulations do not allow the top 20 or 30 percent most regulated economies to take advantage of FDI inflows.

18
**_<u>________________________________________________________</u>_**

private and non-government organizations. It published a second report in May 2001, which evaluated over 50 emerging market economies’ position in the digital economy. A third report is under development with a target release in mid 2002.

_-_ _<u>Bridges: E Assessment Reports</u>_ Bridges provides a compendium of the tools from different sources available and those that are being developed for assessing e-readiness, along with their own analysis. The Reports include:

- _<u>Comparison of E-Readiness Assessment Models</u>_ **- describes the various tools that are available** and what they measure, including the tools' underlying goals and assumptions which shape their outcomes. With this report, we aim to foster informed decisions about approaches to e-readiness assessment, as national governments consider their IT policies and undertake development initiatives.

- _<u>Survey of Who is Doing What and Where</u>_ **- looks at where assessments have already been conducted in the world.** _Updated March 24, 2002._

- _<u>Overview of E-Readiness</u>_ - **bridges.org's overview of the e-readiness processes that was featured in Infodev's 2001 Annual Report** .

- _<u>Consultation report on e-readiness</u>_ **for the South African Development Community - World Economic Forum -** policies and recommendations for improving e-readiness in SADC countries, in a methodology that can be replicated in other areas of the world.

- _-_ -

- _<u>List of other e readiness resources and sample assessments</u>_ **a repository of online tools and resources** for e-readiness assessment and understanding the digital divide.

# _<u>NUA.com How Many Online?</u>_

Nua was founded in 1996. Nua Internet Surveys was acquired in June 2001 by the Scope Communications Group, Ireland's leading IT Media Company.

This online source for information on Internet demographics and trends maintains a dataset estimating how many people are online, based on extensive examination of surveys and reports from around the world. The data can be viewed by region and country.

Governance and corruption though not related directly to trade are important measures of the environment of investment and business. (See Annex I for indicators of investment climate, nontariff barriers and standards)

# **2.6 Development Indicators**

_<u>Development Data Platform (DDP)</u>_ DDP is a web resource for retrieving and reporting development-related data. It can prepare web reports, charts and maps which can be published and shared over the web. It provides powerful features to customize these reports and manage them. DDP includes both time series (macro) and survey (micro) data on a wide range of topics of interest to development practitioners. It provides basic analytical tools and secure access to time series databases and to surveys, including their 37 ____________________________________________________________________________________________________________
# **References**

Akabayashi, H. and Psacharopoulos, G. 1999. The trade-off between child labor and human capital formation: A Tanzanian case study. _The Journal of Development Studies_ , 35(5): 120-140.

<mark>Angelucci, Manuela, and Giacomo De Giorgi. 2009. "Indirect Effects of an Aid Program: How Do</mark> <mark>Cash Transfers Affect Ineligibles' Consumption?"</mark> _<mark>American Economic Review</mark>_ <mark>, 99(1): 486508.</mark> <mark>Attanasio, Orazio, et al. 2010. "Children’s schooling and work in the presence of a conditional cash transfer program in rural Colombia."</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>58(2): 181-210.</mark> <mark>Augsburg, Britta, Ralph de Haas, Heike Harmgart, and Costas Meghir. 2015. “The Impacts of Microcredit: Evidence from Bosnia and Herzegovina.”</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>, 7(1): 183-203.</mark> Baird Sarah, Joan Hamory Hicks, Michael Kremer, and Edward Miguel. 2016. “Worms at Work: Long Run Impacts of a Child Health Investment.” _Quarterly Journal of Economics_ , 131(4): ‒ 1637 1680.

- Baird, Sarah, Francisco H. G. Ferreira, Berk Özler, and Michael Woolcock. 2014. “Conditional, Unconditional and Everything in Between: A Systematic Review of the Effects of Cash Transfer Programs on Schooling Outcomes”, _Journal of Development Effectiveness_ , 6(1): 1- 43.

<mark>Baird, Sarah and Berk Ö</mark> zler. 2012. “Examining the Reliability of Self-Reported Data on School Participation.” _Journal of Development Economics_ , 98(1): 89-93.

Baland, Jean-Marie and James A. Robinson. 2001. “Is Child Labor Inefficient?” _Journal of Political Economy_ , 108(4):663-679.

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# **Concluding Remarks**

This study provides evidence that the analytical and advisory products of the World Bank are effective at influencing the design, direction, and implementation of government policy. By contrast, we do no find robust evidence that the World Bank’s development policy lending or investment lending services consistently influence government policy in low-income and middle-income countries. These results have significant policy implications. For every dollar that that the World Bank spends on non-lending services (or analytical and advisory products), it spends approximately $150 dollars on lending services.<sup>34</sup> Therefore, if a central goal of the World Bank is to influence government policy in its client countries, our results imply that it can achieve better value-for-money through its provision of ESW products.

Our study also highlights the value of collecting granular data on (a) the nature, timing, and distribution of analysis and advice from aid agencies and development banks, and (b) the direction, design, and implementation of policies in low-income and middle-income countries. In the absence of treatment and outcome variables with significant variation across countries, policy areas, and time, it would not have been possible to benchmark the policy influence effects of the World Bank’s lending and non-lending services. Our study therefore calls attention to a broader lesson: relatively inexpensive investments in the collection of data can help international development organizations that aspire to influence government policy measure the effectiveness of the instruments that they create for this express purpose.<sup>35</sup>

> 34This is a lower-bound estimate based on the conservative assumption that the World Bank allocates $30 billion a year to lending services and $200 million a year to non-lending services. 35 It cost approximately $500,000 to design and implement the _2014 Reform Efforts Survey_ . The Development Research Group of the World Bank contributed $113,000 to this effort.

27
Povcalnet are included in Figure 15 for comparison. The projections in Povcalnet are extrapolated using the consumption distribution of NSS-2011 and applying the growth in private final consumption expenditure observed in national accounts. The method therefore assumes that inequality has remained unchanged since the NSS-2011<sup>24</sup> . We compare our approach to Newhouse and Vyas (2019) and Edochie, et al.(2022) in Section 5.2 and reflect on the potential reasons for why their estimates are lower. Gupta, Malani and Woda (2021b) use the raw CPHS data to construct headcounts for 2019 and the post-pandemic period; our reservations with this approach are documented in Section 3.

The rate of poverty reduction between 2004 and 2011 is estimated at approximately 2.5 percentage points per year. After 2011 poverty reduction has slowed down. By our estimates, poverty has declined by an average of 1.3 percentage points per year between 2011 and 2018. It should be noted that at lower levels of poverty, it would take increasingly larger rates of consumption growth and/or reductions in inequality to sustain the high rates of poverty reduction (e.g. Bourguignon, 2003).

Figure 12 in Appendix 4.1 dis-aggregates the trends in poverty by rural and urban. Three observations stand out. First, rural poverty in 2019 is 14.7 percentage points lower than in 2011 while urban poverty reduced by 7.3 points over the same period. This is consistent with a continuation of the rural-urban poverty convergence observed over the past six decades (see Datt, Ravallion and Murgai, 2019).<sup>25</sup> Second, urban India experienced a churn in poverty trends around 2016. Urban poverty rose by 2 percentage points in that year followed by a rapid rise in consumption that drove poverty down by 3.2 percentage points in the following year. Third, the fastest poverty reduction occurred in the years 2017 and 2018. Thereafter, the rate of poverty reduction stalled considerably.

Headcount poverty rates at the international $3.2 and $5.5 poverty lines are shown in Figure 13 of Appendix 4.2. A similar reduction in poverty is observed for both lines. The average rate of poverty reduction at $3.2 and $5.5 was 2.1 and 0.8 percentage points per year between 2004 and 2011. By comparison, all years since 2015 clock an average rate of 1.2 and 0.6 percentage points poverty reduction per year relative 2011. The $5.5 line also shows poverty rising between 2018 and 2019. This dynamic is detected in the consumption data but not by changes in demographic and asset levels. The rise is mainly on account of urban households where headcount rates rose by 2.5 percentage 24Povcalnet projections can allow for some changes to the distribution. For instance, the 2014.5 estimate employs a pass-through rate of 0.559 for urban and 0.733 for rural areas; see box 1.3 in World Bank (2018) and box 1.2 in World Bank (2020) for details. However, the distribution within rural and urban areas is assumed to be unchanged.

> 25Note that rural poverty reduction in the decade(s) prior to 2004 was more modest and heterogeneous, see e.g. Lanjouw and Murgai (2009) and Himanshu et al. (2013).

39
Nine of the ten grants that the Finance Commission has awarded are tied. Tied grants for maintenance (separate grants for roads, buildings, and forests) are conditional on states’ increasing their own spending in these areas, as are the health and education grants, which are only for the poorer states. The obvious challenge for the states is to ensure that these funds are productively spent, including through further strengthening of public expenditure management and financial accountability.

An important remaining issue on the borrowing side for states pertains to the rapidly growing “small savings” funds (largely savings mobilized through India’s post offices). States are compelled to borrow all the proceeds of small savings raised in their states, on which the interest rate is set artificially high. This amounts to a form of “forced borrowing,” which is expensive even for the least creditworthy states, and which teaches the states that they should borrow whatever is available, not what they can afford. Reforming small savings is the next important area of center-state fiscal reform.

Buoyant tax collections, following the introduction of the state-level VAT by most of the major states, along with higher central government transfers owing to the TFC reforms have helped the states to significantly reduce their combined deficits in 2005/06 and 2006/07. In aggregate, states are likely to meet most of the targets set out by the fiscal responsibility acts in 2006/07, two years ahead of schedule. Estimates for 2006/07 suggest that states have already achieved the target of eliminating the revenue deficit; and the consolidated fiscal deficit is also forecast to be less than 3% in 2006/07. However, performance among states remains divergent. Although progress so far is significant, risks remain: progress so far could for instance be seriously impacted by the Sixth Pay Commission pay awards.<sup>38</sup> 3.3 Creating Fiscal Space for Infrastructure Infrastructure remains a constraint on private investment, especially in the manufacturing sector, two others commonly identified being rigid labor laws and major restrictions on bankruptcy and exit of firms.<sup>39</sup> Mohan (2002) argues that the latter two constraints serve as a brake on the reallocation of resources towards labor intensive manufacturing, and eventually, on more rapid employment growth, which is essential in view of India’s demographics. We focus on infrastructure because of its fiscal implications, and start by giving an idea of the size of investments involved.

> 38 Other risk factors also remain, including the large debt overhang, losses of State Public Sector Undertakings, and a lack of proper user charges.

> 39 Besley and Burgess (2004) find a negative impact of pro-worker labor regulation on output, employment, investment and productivity in the formal sector based on an analysis of data from Indian states for the period 1958-92.

22