rafmacalaba/datause-encoder-data huggingface dataset:
country receives as a ratio of its GNI is likely to be measured with error. Not all donors report their aid to the DAC in all years. For example, aid from the former Soviet Union and from China in the Mao era to other communist countries was not reported to the DAC. Aid from China and some other emerging donors has increased in recent years and is also not included in the DAC data. Additionally, but better understood, the denominator, GNI, is also measured with sizeable error for many less developed countries (Jerven, 2013). With classic measurement error, the OLS estimate of the effect of aid is biased towards zero. Demeaning or first differencing the model would exacerbate the bias if aid levels are persistent over time.

The natural experiment we exploit in this paper provides a unique opportunity to test the hypothesis that measurement error in foreign aid biases the OLS estimate towards zero. We have shown that the amount of aid a country receives tends to decline substantially following its crossing of the IDA threshold from below. Assuming that the measurement error is _i.i.d._ , it would contribute much less to the total variation in ΔAid!" in periods closer to threshold crossings. Thus the OLS estimates of equation 4 using only periods in the neighborhood of the crossings are likely to provide more accurate estimates of aid’seffect on growth than the one exploiting all of the variability in aid from all periods. To test this, we re-estimate Equation 4 by OLS and 2SLS (using Crossing!,!!! to instrument aid), successively narrowing the window of periods used in the analysis around the crossing point of each country.<sup>27</sup> We expect the OLS estimate of the effect of aid on growth to increase as we narrow the window of estimation. Naturally, given our identification strategy, the 2SLS estimate of the same parameter should remain stable independent of the window used for estimation. We find exactly that in Table 7, Panel B.

> 27 We rely on first differenced models in this exercise because changing the number of periods also affects the estimation of the country fixed effects, and we want to hold everything constant except for the signal to noise ratio in aid.

23
15 was 32 percent, about 8 percent of which came from customers.<sup>11</sup> This is a quite different institutional arrangement than found in most Sub-Saharan African countries, where funding for electricity expansion is mainly provided through government-sponsored projects to national power companies.

Well-designed subsidies can sometimes create a critical mass of customers, with positive effects on the energy sector in general. A socioeconomic survey conducted in 2004 in the − Lao People’s Democratic Republic showed that approximately 20 40 percent of households had not been able to afford the connection charge of $100 because of limited access to financing. In response, a financing scheme, Power to the Poor (P2P; box 4), was devised by the utility Electricité du Laos (EDL) to help the poorest households as well as femaleheaded households obtain connections (World Bank 2007, 2011a). The household connection rate under the pilot varied slightly across 20 villages, but even the lowest rate (84 percent) showed success. In 2009, EDL decided to extend the P2P program to all electrified villages in the country by establishing a revolving fund to provide financial support to targeted beneficiaries. The program was part of a comprehensive electrification strategy that included solid planning (including off-grid solutions when cost-effective), lowcost electrification technologies, improvements in operating efficiency, and actions to improve financial sustainability.

> 11 These programs address system development costs as well as connection charges.
<!-- Start of picture text --> 2011-12<br>° H Number of Questions10 1°<br>HEE Accots ane acsees to sosewece Sonetestpayment irom torest-retated services<br>GEE rewo0sans cnercost EEE Govera n sanceinstnstons<br><!-- End of picture text --> <!-- Start of picture text --> 2010-11<br>2013-14<br>H .0 Number of QuestionsPy By<br>(EERE Access ane cosven to sesowrees Soneteatpaymenttrom toreet-retated services<br>Gr 005 ans crarcont I covernanceanc inatntions<br><!-- End of picture text -->
19

# **Figure 9. Trade barriers faced by RCEP members before and after trade liberalization within the block, trade weighted averages in %**

<!-- Start of picture text --> RCEP, Tariffs faced RCEP, NTBs faced<br>China<br>India<br>Indonesia<br>Philippines<br>Korea<br>Thailand<br>Vietnam<br>Singapore<br>New Zealand<br>Malaysia<br>Japan<br>Brunei Darussalam<br>Australia<br>Laos<br>2015 2030<br>Cambodia<br>Southeast Asia<br>0 1 2 3 4 5 0 5 10 15 20<br><!-- End of picture text --> Source: Authors’ estimates

**Figure 10. Trade barriers faced by FTAAP members before and after trade liberalization within the block, trade weighted averages in %**

<!-- Start of picture text --> FTAAP, Tariffs faced FTAAP, NTBs faced<br>Russion Federation<br>China<br>Indonesia<br>Philippines<br>Korea<br>Thailand<br>USA<br>Vietnam<br>Singapore<br>New Zealand<br>Malaysia<br>Japan<br>Brunei Darussalam<br>Australia<br>Peru<br>Mexico 2015 2030<br>Chile<br>Canada<br>0 1 2 3 4 5 0 5 10 15 20<br><!-- End of picture text --> Source: Authors’ estimates
industrial and trade policy reforms and the general shift towards market-oriented economic policies that occurred in the 1980s and 1990s in India. Theory does not offer clear-cut predictions regarding the effect of industrial de-licensing, trade liberalization, and foreign direct investment reforms on spatial concentration of manufacturing industries, and hence the need for empirical analysis.

In this paper we ask the following questions. What are the determinants of the high level of spatial concentration in Indian manufacturing? How much of the changes in the spatial distribution of Indian manufacturing industries can be attributed to policy reforms, after conditioning on the traditional determinants on 1 1 - endowments, Mlarshalliantype linkages and knowledge spillovers? Does the spatial distribution of manufacturing plants of different sizes differ in response to policy changes? To answer these questions, we focus on indices of geographic concentration of manufacturing industries in India constructed following Ellison and Glaeser **(1997) -** henceforth **EG** indices **-** based on plant-level data from the Annual Survey of Industries **(ASI)** for the period **1980-99.** Our results show that industrial de-licensing has a significant negative impact on geographic concentration levels (a one standard deviation increase in the proportion of output de-licensed leads to a 9.; V decline in the **EG** index) and the same is true for foreign direct investment (FDI) liberalization (with a one standard deviation increase in the proportion of industry output open to FDI reducing the average **EG** index **by** 10. ). However, our estimates also show that trade policy has no effect on the spatial concentration of Indian manufacturing. Further we find that there is significant size-based variation in the response of concentration to the policy as well as the traditional determinants **of** :w1iii i:f1ij. Delicensing and FDI liberalization cause small plants to disperse. Trade policy has no effect on the concentration of medium-size plants but a one standard deviation decline in the effective rate of protection leads to a decline in the **EG** index of large plants and a 2.1 **"** increase in the **EG** index of small plants. Our results are robust to heteroskedasticity-correcting **FGLS** techniques and controls for persistence in the h111 i..ij process as well as to a wide variety of fixed effects and specification tests.

**A** large literature provides evidence of a significant positive effect of :' economies on firm-level as well as iiregate-level productivity. Thus, our finding that policy reforms

**3**
Theory.‖ _Journal of International Economics_ , 46 (2), pp. 183-203.

Moulton, Brent R. 1990. ―An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units.‖ _Review of Economics and Statistics_ , 72 (2), pp. 334-38.

Nunn, N., and Trefler, D. 2006. Putting the Lid on lobbying: Tariff Structure and Long-Term Growth when Protection is for Sale, NBER Working Paper No. 12164.

32
**Table 6 IV‐Stochastic Frontier model**

||Model 1|Model 2|
|---|---|---|
||SFA|IV‐SFA|
|expeduc|0.353***|0.309***|
||(0.001)|(0.058)|
|expeduc squared|‐0.013***|‐0.018***|
||(0.006)|(0.004)|
|Intercept|0.484*|0.831***|
||(0.252)|(0.252)|
|Observations|144|99|
|||Wald chi2(2) = 28.99|
||Wald chi2(2) = 199.2|Prob > chi2 = 0.0000|
||Prob > chi2 = 0.0000|Average efficiency|
||Average efficiency<br>score: 0.793|score: 0.797|

*Significant at 10%; **significant at 5%; ***significant at 1% The input and output variables are logged, and the marginal effects are elasticities

Model 1 in Table 6 estimates Equation 1 applying a basic stochastic frontier model (similarly to model 1 in table 3) and model 2 provides an IV estimation of the same equation.<sup>12</sup> The estimated marginal effects decrease when endogeneity is controlled for. This means that the effects of public education expenditure on the access to quality education may be over‐estimated in Table 3 as a result of endogeneity but remains positive and significant when endogeneity is controlled for. Therefore, the average efficiency seems to slightly increase by less than 1 percentage point in the IV model, but the overall empirical distribution of the efficiency scores changes only slightly. This implies that even though endogeneity is ignored in this analysis, the effect of endogeneity on the estimated efficiency scores remains small. It is important to mention that given the missing data related to the instrumental variables, estimating an IV‐SFA model along with the control variable would have decreased the sample size to less than 80 countries. This is the reason why the basic SFA model is preferred because it offers better country coverage as well as a higher degree of freedom in the econometric estimations.

# _Distribution of the efficiency scores_

The model discussed in section 2 assumes that the inefficiency parameter (λ) follows a half‐normal distribution. There are two alternative distributions commonly used in the literature: The truncated‐ normal distribution and the exponential distribution. Table 7 shows that the half‐normal and the exponential distributions provide very similar average efficiency while the efficiency estimated applying the truncated normal distribution seems lower. This could imply that the efficiency of public education expenditure estimated in this paper using a half‐normal distribution provides average efficiency levels.

> 12 The two instruments are strongly correlated to LAYS in the first stage regression with an F statistic of 11. However, a test of over‐identification could not be performed because of the lack of proper methodology.

15
- 35 -

# REiPERECES

Eaton, J., M. Gersovitz and J. Stiglitz (1986), "The Pure Theory of Country Risk," European Economic Review, 481-513.

- Fischer, S. (1987), "Resolving the International Debt Crisis," NBER Working Paper No. 2373.

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- GeCmill, C. and K. Gordon (1988), "Commodity Indexed Debt," City University Business School, mimeo.

- Gersovitz, M. (1983), "Trade, Capital Mobility and Sovereign Immunity," Research Program in Development Studies Discussion Paper, 108, Princeton University.

- Grossman, H. and J.B. van Huyck (1985), "Sovereign Debt as a Contingent Claim, Excusable Default, Repudiation and Reputation," mimeo, Brown University.

- Hellwig, M. (1986), "Comment on the Pure Theory of Country Risk," European Economic Review, 521-527.

- Jensen, M. and Meckling (1976), "The Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure," Journmal of Financial Economics, 3, 305-360.

- gletzer, K.M. (1984), "Asymmetries of Information and LDC Borrowing with Sovereign Risk," Economic Journal, 94, 287-307.

- Lessard, D. (1986), "The Management of International Trade Risks," The Geneva Papers in Risks and Insurance, 11, 255-264.

- Merton, R. (1973), "The Theory of Rational Options Pricing," Bell Journal of Economics and Management Science, 4, Spring, 141-183.

- Ozler, S. (1984), "Rescheduling of Sovereign Government Bank Debt," mimeo, Stanford University.

- Powell, A.P. and C.L. Gilbert, "The Use of Commodity Contracts for the Management of Developing Country Commodity Risks," in Black J. ad "Macroeconomic Interactions between North and South," Cambridge University Press (forthcoming).

- Priovolos, T. (1987), Commodity Bonds: A Risk Management Instrument for Developing Countries, IECCM Working Paper No. 1987-12, The World Bank, Washington, DC.

- Priovolos, T. (1988), "Efficient Frontier of Commodity Returns: Is the King Naked?", Discussion Paper, XXVth International Conference on International Commodity Market Modeling, Applied Econometric Association, The World Bank, Washington, DC.
In the last decades Haiti has made little creditable headway in reducing income poverty. In 2001, Haiti’s extreme poverty for households, measured by P0, was still very high at 49 percent (Figure 5.1).<sup>13</sup> This means that more than 3.9 million people living in extreme poverty. This is more than twice the poverty rate of middle-income countries in the region such as Brazil (21.9 percent). Since the Haiti Living Conditions Survey (HLCS) dataset is the first household survey completed for the country, it is not possible to analyze the extent to which income poverty has changed in the past decade. In the past two decades, GDP per capita fell dramatically (see Section 2). In conjunction with the information on income inequality presented below, this may indicate that income poverty has increased in recent decades.

There are large differences in headcount poverty among localities and regions in Haiti. Data from 2001 indicate that rural households had the highest rate of extreme poverty: 58 percent were extremely poor in that year (Table 5.1). Households in the metropolitan area had the lowest extreme poverty rate: 20 percent were extremely poor. Households in other urban areas had a household poverty rate only slightly below that of the rural population: 56 percent were extremely poor. Hence the West region, unsurprisingly, has the lowest extreme poverty rate: 29 percent of households were extremely poor in 2001. The regions with the highest extreme poverty rates are the Northeast and Northwest, where 80 and 65 percent of households, respectively, have a per capita income that takes them below the extreme poverty line of US$1 per day.

|Table|5.1 : Indig|ent Pove|rtyRate|byLocali|tyand Re|gion,20|01||
|---|---|---|---|---|---|---|---|---|
|West|South-<br>east|North|North-<br>east|Arti-<br>bonite|Center|South|Grand-<br>Anse|North-<br>west|
|28.9|56.6|62.7|80.3|58.6|55.6|63.0|60.8|65.0|
|Total|Metro-||||||||
|Haiti|politan|Urban|Rural||||||
|48.9|19.7|56.3|58.1||||||

_Source:_ Own calculations based on HLCS 2001.

The level of poverty in Haiti can also be measured by indicators such as adult illiteracy, infant mortality, and malnutrition; all are very high. In the period 1970-2000, the adult illiteracy rate fell sharply from 78.0 percent to 39.5 percent (Table 5.2). The greatest improvement was in the 1970-1992 period. The female illiteracy rate, however, has fluctuated. In 2000 and in 1970, fewer males were illiterate (33.4 percent) than females (43.3 percent). Male illiteracy fell throughout the 1970-2000 period. Female illiteracy declined from 82.0 percent in 1970 to 37.9 percent in 1995, but since then the rate has increased, reaching 43.3 percent in 2000. Illiteracy is a major problem in Haiti. Efforts to lower illiteracy are hampered by the fact that many of the illiterates are adults, the result of years of educational neglect. It is more difficult to teach basic skills to adults

> 13 As the poverty rate is for households the income (including self-consumption) is for the household. Not a single household reports zero income (including self-consumption) – lowest reported value is of 100 gourdes.

22
<!-- Start of picture text --> ——=_— a!<br><!-- End of picture text --> <!-- Start of picture text --> =<br><!-- End of picture text --> <!-- Start of picture text --> ~<br><!-- End of picture text --> <!-- Start of picture text --> ee<br><!-- End of picture text --> <!-- Start of picture text --> ger nt he oS “ +<br><!-- End of picture text --> <!-- Start of picture text --> Legend 2 La " =" 1<br><!-- End of picture text --> <!-- Start of picture text --> _ io ;<br><!-- End of picture text --> <!-- Start of picture text --> = aes ied Me a<br><!-- End of picture text --> <!-- Start of picture text --> ‘s Poverty headcount<br><!-- End of picture text --> <!-- Start of picture text --> aha<br><!-- End of picture text --> <!-- Start of picture text --> la 5 Bee<br><!-- End of picture text --> <!-- Start of picture text --> fi<br>ao 3i Se<br><!-- End of picture text --> <!-- Start of picture text --> \se Fy A3 i. fm2Wl 10NoJan data year2015 floodevent<br><!-- End of picture text -->
10. Current Bank policy or. cost recovery is based<sup>on the above</sup> theoretical work on optimal priciing and taxation. Operational Manual Statement 2.25, "Cost Recovery Policies for Public Sector<sup>Projects,</sup> -- which is concerned with recovering the costs<sup>of public sector</sup> investments through pricing or taxation -- reflects the continued<sup>reliance</sup> on the theoretical pricing literature:16/ 10.

The revenues resulting from charging such<sup>"efficiency prices,</sup> (marginal cost prices) may or may not recover<sup>the total financial</sup> cost of the facilities provided. The second<sup>set of considerations</sup> relates to the desirability of adjusting the<sup>efficiency prices</sup> (optimal pricing above mar.anal cost), or charging alternative taxes (optimal general taxation!, because of fiscal and financial concerns . . .(emphases added) 11. In the transport sector internal Bank research has<sup>provided</sup> detailed guidance for pricing and cost recovery in<sup>roads (both urban and</sup> inter-urban) and ports. Summaries of these recommendations can be found in the EDI publication Pricing Policy for Development Management.17/ Recent work on roads emphasizes the use of road<sup>user charges to recover</sup> the costs of the road network.13/ The derivation<sup>of "optimal' road user</sup> charges to minimize the distortion in consumption is<sup>reminiscent of the</sup> Ramsey pricing formulas for the entire economy presented<sup>above.19/ In the</sup> railways sector, where the emphasis has been on improving costing systems, 16/ The World Bank, OMS 2.25, 1977, p.l.

17/ Meier, Gerald M. (ed.), Pricing Policy for<sup>Development Management,</sup> Economic Development Institute of the World Bank, the Johns Hopkins Press, Baltimore, 1983. See Chapter 4.2 for *idges, Chapter<sup>4.7 for roads,</sup> Chapter 4.8 for ports, and Chapter 4.11 for general<sup>prescriptions for</sup> transport pricing.

18/ See D.M. Newbery et al., Road Transport Taxation<sup>in Developing</sup> Countries: The Design of User Charges and Taxes for<sup>Tunisia, World Bank</sup> Discussion Paper No. 26, 1987 and review in Vincent<sup>Hogg, Pricing and User</sup> Charging in the Transport Sector: A Review of FY88<sup>Transport Project</sup> Operations, The World Bank, Report INU-ORl, April 1989,<sup>p. 11.</sup> 19/ Op Cit, Newbery et al., p. 55 and p.75.

7
**Table 3.  Share of Respondents Receiving Food Assistance**

<!-- Start of picture text --> 3a. National<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>3c. IPC 2 Regions 3d. IPC 3 Regions<br>0.60 0.60<br>0.50 0.50<br>0.40 0.40<br>0.30 0.30<br>0.20 0.20<br>0.10 0.10<br>0.00 0.00<br>3d. IPC 3+ Regions 3e. IPC 4 Regions<br>0.60 0.60<br>0.50 0.50<br>0.40<br>0.40<br>0.30<br>0.30<br>0.20<br>0.20<br>0.10<br>0.10<br>0.00<br>0.00<br>Sep-2015 Oct-2015 Nov-2015 Dec-2015 Jan-2016 Feb-2016 Mar-2016 Apr-2016 May-2016 Jun-2016 Jul-2016 Aug-2016 Sep-2016 Oct-2016 Nov-2016 Dec-2016 Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jun-2018 Jul-2018<br>Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018 Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018<br>Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018<br>Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018<br><!-- End of picture text --> Notes: Authors’ calculations using the WFP mobile Vulnerability Analysis and Mapping Survey. The survey publicly reported the national share of respondents receiving food assistance since August 2015; and the survey publicly shared the governorate-level share of respondents receiving food assistance since January 2017. IPC groupings are as reported in the 2017 IPC announcement, and IPC 3 + governorates refers to regions that were classified as IPC 3 but would have been IPC 4 in absence of humanitarian assistance.

27
# **<mark>4. Methodology</mark>**

<mark>Next, in an attempt to overcome the biases in the online survey, as observed in section 3.2, we construct weights based on the propensity scores to examine whether applying such weights would make online surveys nationally representative. For this, we estimated a logit model of the following type:</mark> <mark>Here, the dependent variable, y</mark> i <mark>, takes the value 1 for the online survey data (for a specific wave) and 0 for the Internet using population of the GMD. The exception is Sri Lanka where Internet usage is unavailable in the GMD, and where the dependent variable takes the value 0 for all GMD observations. X</mark> i <mark>is a vector that denotes the set of control variables that are associated with an individual’s participation in the online survey. We estimate this model for each crosssection of the online survey wave with the GMD. Equation (1) describes the possible factors that influence a person who has access to the internet and was exposed to the Internet survey, as to whether they take up the survey or not.</mark> <mark>Variables in X</mark> i <mark>include gender, age categories, education, rural versus urban residence, sector of work pre-COVID, household size categories, a variable set of assets specific to each country, and region fixed effects.</mark><sup>20</sup> <mark>We also include many interactions of these demographic variables between themselves, and with region.</mark><sup>21</sup> <mark>These demographic factors are highly correlated with an</mark>

> 20 <mark>The regional aggregations are different across countries, based on data availability. In some cases, the lowest administrative unit level where the data is available differs across Internet Surveys and GMD. In such cases, we harmonize at a higher level when running models or even in simply comparing indicators from the two datasets. Specifically, we define region at the state level in Brazil, province level in Indonesia and Sri Lanka, region level in Egypt, and county level in Kenya. For Türkiye, location data are missing in GMD, so region fixed effects are not used.</mark>

> 21 The specific interaction variables used in each country/wave model are available upon request.

15