rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9:** Regression results of errors in yields against correlates of measurement errors in yields

||Measurement|Errors in Yields|
|---|---|---|
||log SR- log CC|log ML - log CC|
|Household labor|0.210<sup>_∗∗∗_</sup>|0.028|
||(0.042)|(0.061)|
|Plot area|_−_0.283<sup>_∗∗∗_</sup>|_−_0.169|
||(0.106)|(0.104)|
|Seeds|0.087|_−_0.123|
||(0.105)|(0.131)|
|Unit SR harvest|_−_0.076<sup>_∗∗_</sup>|_−_0.003|
||(0.034)|(0.035)|
|*Respondent v1 : female|1.733<sup>_∗∗∗_</sup>|1.903<sup>_∗∗∗_</sup>|
||(0.455)|(0.381)|
|*Respondent v1 : manager|0.519|0.376|
||(0.359)|(0.430)|
|*Respondent v2 : age|0.015<sup>_∗∗∗_</sup>|0.010<sup>_∗∗_</sup>|
||(0.005)|(0.005)|
|*Respondent v2 : female|_−_0.017|_−_0.0005|
||(0.016)|(0.015)|
|*Respondent v2 : manager|_−_0.331|_−_1.266<sup>_∗∗∗_</sup>|
||(0.449)|(0.409)|
|Dammage crop-cut|0.160|_−_0.567<sup>_∗∗_</sup>|
||(0.175)|(0.242)|
|Respondent literate x # plots of Hh|_−_0.083<sup>_∗∗∗_</sup>|_−_0.031<sup>_∗_</sup>|
||(0.018)|(0.016)|
|Rain fall - July|0.003|0.004|
||(0.003)|(0.004)|
|Rain fall - December|0.046|_−_0.005|
||(0.033)|(0.037)|
|Number of cotton plots|_−_0.061|_−_0.058|
||(0.069)|(0.044)|
|Pre-harvest loss|_−_0.010<sup>_∗∗∗_</sup>|_−_0.0004|
||(0.003)|(0.002)|
|*Respondent v1 : age|_−_0.008|_−_0.007|
||(0.016)|(0.015)|
|Observations|386|386|
|R<sup>2</sup>|0.361|0.150|
|Adjusted R<sup>2</sup>|0.297|0.065|

_Notes:_ Estimation conducted in the test sample of the first sample split simulation. Labor, plot area and quantity of seeds are in log form. * indicates that the variable is interacted with enumerator fixed effect. v1 indicates post-planting visit and v2 indicates post-harvest visit. Enumerator fixed effect included and errors clustered by village. Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 27
Policy Research Working Paper 9425

# **Abstract**

This paper provides insights to inform government efforts to attract and retain foreign direct investment, by analyzing the results of a survey of more than 2,400 affiliates of multinational enterprises across 10 middle-income countries. The paper explores corporate perspectives and decision-making on countries’ legal and regulatory environments, political risk, and investment promotion activities. The survey finds that a business-friendly policy environment is critical to multinational enterprises’ investment decisions, confirming the importance of removing regulatory barriers to foreign direct investment (particularly approval processes), lowering political risks, and having investment promotion agencies. The survey results also show that investors are heterogeneous, with affiliates’ sectors, trading behaviors, sizes, ages, source countries, and foreign ownership levels affecting their perceptions of and sensitivity to various policy factors. Thus, policy makers should tailor their policy efforts to the needs of priority investor segments. Notably, the analysis consistently finds variation based on the extent to which affiliates import their inputs, suggesting that this relatively understudied topic deserves increased research and policy attention.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at asaurav@worldbank.org and rkuo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Figure 11. External vulnerabilities in EMDEs

# A. External debt

<!-- Start of picture text --> Percent of GDP<br>60 2018 2007<br>55<br>50<br>45<br>40<br>35<br>30<br>EMDEs Commodity- Commodity-<br>exporting EMDEs importing EMDEs<br><!-- End of picture text -->

# C. Current account balance

<!-- Start of picture text --> Percent of GDP 2018 2007<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>EMDEs Commodity- Commodity-<br>exporting importing<br>EMDEs EMDEs<br><!-- End of picture text -->

# E. Foreign reserves adequacy

# B. External debt

<!-- Start of picture text --> Number of economies<br>30 2018<br>25 2007<br>20<br>15<br>10<br>5<br>0<br>0-25 26-50 51-75 76-100 100+<br>Percent of GDP<br><!-- End of picture text -->

# D. Current account balance

Number of economies <!-- Start of picture text --> 80<br>70 2018 2007<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br> < -5 -2.5 to -5 0 to -2.5 > 0<br>Percent of GDP<br><!-- End of picture text -->

# F. Non-resident holdings of

# local-currency debt

<!-- Start of picture text --> ARA metric Percent of total debt<br>3.5 Median Mean 30 Interquartile range Mean Median<br>3.0<br>25<br>2.5<br>20<br>2.0<br>15<br>1.5<br>10<br>1.0<br>0.5 5<br>0.0 0<br>2007 2019 2007Q4 2018Q2<br><!-- End of picture text --> Source: Ha, Kose and Ohnsorge (2019); International Monetary Fund; World Bank.

A. Unweighted average of total external debt-to-GDP ratios for 31 EMDE commodity exporters and 30 EMDE commodity importers.

B. Based on data for 61 EMDEs.

- C. Unweighted average of current account balance-to-GDP ratios for 88 EMDE commodity exporters and 55 EMDE commodity importers.

- D. Based on data for 143 EMDEs.

E. Based on data for 48 EMDEs. Dark blue bars show minimum and maximum values. Assessing Reserve Adequacy (ARA) metric is based on IMF (2011) which determines the appropriate reserve cover on a risk-weighted basis covering short-term debt, medium and long-term debt, and equity liabilities. Broad model and export earnings. Risk weights are based on observed outflows during periods of exchange rate pressure. Values above 1 suggest that countries are fully able to meet balance of payments needs using reserves.

F. Based on data for 23 EMDEs.
aWle 4 Cross-section estimation using twenty-seven year averages ana levets of trace roticy

||(1)|(2)|(3)|(4)|(5)|(6)||
|---|---|---|---|---|---|---|---|
|Intercept|0.0<br>(.01)|0.0<br>(0.0)|-0.0<br>(0.0)|-0.02<br>(.01)|<br>-0.01<br>(.01)|<br>0.01<br>(.01)||
|TRI|-0.05<br>(.16)|||-||||
|TR|-|0.0<br>(0.0)|-|_|_|||
|SLACK|-||-.013**<br>(.005)|||||
|DOLLAR||||0.01<br>(.02)||||
|TRADE SHARES||-|||0.013<br>(.008)|||
|INDIRECT|-|-|-|-|-|1.14<br>(2.14)||
|LAND|-0.48<br>(0.28)|0.50<br>(.27)|-0.10<br>(.73)|-0.12<br>(.21)|-0.01<br>(.14)|0.02<br>(.28)||
|LABOR FORCE|0.65<br>(.27)|0.98<br>(.33)|0.44<br>(.17)|0.74<br>(.35)|0.34<br>(.18)|0.07<br>(.47)||
|EDUCATION<br>(0-3 Years)|0.21<br>(.14)|-0.13<br> <br>(.08)|-0.03<br> <br>(.06)|0.01<br>(.10)|0.02<br> <br>(.06)|0.01<br> <br>(.10)||
|EDUCATION<br>(3-9 Years)|0.09<br>(.07)|-0.01<br> <br>(.05)|0.06<br> <br>(.03)|*0.01<br>(.06)|0.07<br> <br>(.03)|-0.05<br> <br>(.07)||
|CAPITAL<br>STOCK|0.37<br>(.10)|0.45<br>(.09)|0.48<br>(.06)|0.60<br>(.10)|0.52<br>(.07)|0.61<br>(.10)||
|N|17|32|65|31|67|17||
|R-Square<br>TabLe S Cross-section estimation|86<br>using twenty-sev|.72<br>en year a|.66<br>verages and|.72<br>changes|.65<br>in trade p|.87<br>oticy||
||()|(2)|(3)|(4)|(5)|(6)|(7)|
|TR I|0.18<br>(.94)||||-|||
|TR11|-|-.02<br>(.02)|-|-||||
|BLACK|-|-|-0.09<br>(.07)|-||||
|DOLLAR|-|-|-|-.38<br>(.35)||||
|TRADE SHARES|||||0.78*<br>(.33)|-||
|INDIRECT|-|-||-|-|2.2<br>(26.8)||
|Movement towards<br>Internatonat<br>Prices|||||||0.04<br>(.07)|
|R-Square|.85|.73|.63|.73|.67|.87|.87|

Nctec Standard "rrors in i)."*" indicates significant at 5 percent Levtl; * indicates significant at 10 percent LeveL. Intercep **t** erm included in *ll regressions but not reported here.

28
- Bitran, R. 1995. "Efficiency and Quality in the Public and Private Sectors in Senegal." _Health Policy and Planning_ (Health Policy Plan.) 10(3): 271-83.

- Bitran-Dicowsky, R. and D. W. Dunlop. 1993. "The Determinants of Hospital Costs: An Analysis of Ethiopia." In Mills and Lee, eds., _Health Economics Research in Developing Countries._ Oxford: Oxford University Press.

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- De Geyndt, W. 1995. "Managing the quality of health care in developing countries." Technical Paper No. 258. World Bank, Washington, D.C.

- de Silva, A. 1999. "A Framework for Measuring Responsiveness." GPE Discussion Paper Series: No. 32. WHO.

- Deci, E. L. 1975. _Intrinsic Motivation._ New York: Plenum Press.

- Dehn, J., R. Reinikka, and J. Svensson. 2000. "The Supply Side of Service Delivery: A Quantitative Survey Approach." Development Research Group. World Bank, Washington, D.C. Processed.

- Dixit, A. 1997. "Power of incentives in private vs public organisations." _American economic Review Papers and Proceedings_ 87(2).

- Dixit, A. 2001. "Incentives and Organizations in the Public Sector: An Interpretative Review." Processed.

- Donabedian, A. 1980. _Explorations in Quality Assessment and Monitoring, Volume 1: The Definition of Quality and Approaches to its Assessment._ Washington D.C.: Health Administration Press.

- Dor, A. 1987. "Estimates of cost functions for the Peruvian hospital sector: Some policy implications." Unpublished report, The Urban Institute.

- Dor, A. 1994. "Non-minimum Cost Functions and the Stochastic Frontier: On Applications to IHealth Care Providers." _Journal of Health Economics_ 13(3): 329-34.

- Drummond, M. F., B. O'Brien, G. L. Stoddart, and G. W. Torrance. 1997. _Methods for the economic evaluation of health care programmes._ Oxford: Oxford University Press.

- Ensor, T. and S. Witter. 2001. "Health economics in low income countries: adapting to the reality of the unofficial economy." _Health Policy_ 57(1): 1-13.

- Farrel, M. J. 1957. "The Measurement of Productive Efficiency." _Journal of the Royal Statistical Society,_ A CXX(3): 253-90.

41
**Figure 6:** Effects on Public Goods Provision & Village Actions (Survey Data)

<!-- Start of picture text --> (a) Access to services (b) Service quality<br>-.2 -.1 0 .1 .2 -.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger Margin of victory of best-ranked challenger<br>0.09 0.2<br>0.03 0.1<br>-0.03 -0.1<br>Service quality (index)<br>Access to services (index)<br>-0.09 -0.2<br>-0.15 -0.4<br><!-- End of picture text -->

**(c)** Village actions to improve services

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger<br>0.20<br>0.08<br>-0.04<br>Village Govt Takes Action Index -0.15<br>-0.27<br><!-- End of picture text -->

_Notes_ : In panel (a), the dependent variable is an index of access to local services constructed using our survey data. In panel (b), the dependent variable is a standardized index of service quality. In panel (c), the dependent variable is an index indicating whether the village government took action or made investments on a public service. All indices have the following components: water access, garbage collection, paved roads, electricity, kindergartens, primary schools, and community healthcare. The dots are conditional means of each outcome across binned intervals of the margin of victory of the best-ranked challenger on each side of the RD threshold, with 95% confidence intervals in solid gray lines.

32
in the regulation of the electricity distribution businesses in Britain. If revenue is allowed on a yardstick basis (unrelated to the firm’s own costs) for one expenditure category but set on the basis of the firm’s own costs for another, Distribution Network Operators (DNOs) have an incentive to transfer costs from the “yardstick” category to the “firm specific” category.

This is illustrated in Figure 4, where we show two firms, 1 and 2, who both produce the same amount of output, q. Both firms have the flexibility to substitute labor and capital.<sup>3</sup> We further assume that – at the outset – the regulator allows both firms to charge out their actual cost to customers. Firm 1 is more technically efficient because it is producing the same output as 2 but is using less of both capital and labor to do so. Thus we can say that both firms are allocatively efficient (because both do not charge in excess of cost), but only firm 1 is technically efficient and firm 2 is technically inefficient – its efficiency is given by the ratio 0A/0B. Now consider a stylized version of Offer’s<sup>4</sup> regulatory practice in the 1993 price review:

- the regulator decides to allow the firm to recover labor (or operating) costs per unit only up to a yardstick, L*/q. This reference level is set by the firm which is using the lowest ratio of L/q among all firms observed;

- the regulator signals that he will recognize investment in determining the regulatory asset base – this has the effect of allowing firms to pass through any depreciation and an allowed rate of return on the capital actually employed; and

- 3 Isocost lines (ICi) illustrate combinations of labor and capital input that result in the same level of unit cost. Curves closer to the origin represent lower levels of cost. The slope of the lines is determined by the factor price of labor relative to capital. The Isoquants illustrate the firms` ability to produce a given level of output with alternative combinations of labor and capital. Isoquants closer to the origin represent lower levels of output. Cost minimizing firms therefore try to be on a low Isocost line and a high Isoquant. It is optimal for each firm to choose a production point where the Isocost line is tangential to a Isoquant. If – in this point - the firmed substituted some labor for capital or some capital for labor, the cost of production would increase for a given level of output.

- 4 Offer, the Office for Electricity Regulation, was the regulatory agency for the sector from 1989 until 1999, when it was merged with the gas regulator, Ofgas, to create the Office of Gas and Electricity Markets (Ofgem).

17
robustness checks. There are three rounds of data during which eligible households in control communities did not receive transfers (October 1998, May 1999 and November 1999), and a straightforward comparison of treatments and controls is possible. Once control communities were incorporated into the program at the end of 1999, an additional three rounds of data were collected during which eligible households in both types of communities were receiving benefits (May 2000, November 2000 and November 2003). In addition, baseline data from the 1997 pre-intervention ENCASEH census is used for a total of seven rounds of data between 1997 and 2003.

Our sample consists of 7,658 poor households that were eligible for the program in the 320 treatment communities and 4,644 poor households that were eligible for the program in the 186 control communities (Table 2). We refer to this sample as the “intent to treat” or ITT sample. Baseline data for this sample are described in detail in Table 3A. The sample is well balanced across control and treatment groups. We report the test of difference in means across control and treatment groups, finding no statistically significant differences for 33 out of 35 characteristics measured at baseline. This suggests that the randomization was effective in generating truly exogenous variation in the treatment.

We also conduct the analysis with a second sample designed to estimate the effect of treatment on the treated (TOT). In these analyses, we use the approximately 90 percent of eligible households that actually took up the program, 6,819 treatment households and 4,159 control households that took up the program when offered in late 1999. A potential source of bias arises in the TOT sample if observed and unobserved time varying characteristics are driving the household’s decision to participate in the program. We assume the group of controls that took up the program when offered in late 1999 would have taken up the program if offered earlier in 1998. The fact that take up rates amongst treatment and controls are almost identical (90%) supports this hypothesis (Table 2 and Graph 1). Furthermore, the TOT sample baseline observables are well balanced, with only 1 statistically significant difference out of 35 characteristics tested (Table 3B).

6
activities. They report a variety of elasticities in their paper.7 We adopt their estimate of 2.67 for use in this work as the overall farm/off-farm transformation elasticity for the total rural labor force.

To obtain separate estimates of the farm/off-farm transformation elasticity for three skill levels of labor, we utilized the rate at which increased schooling attainment enhances the transformation elasticity,<sup>ε</sup> _l_<sup>, based on the study by Zhang, Huang and Rozelle (2002). These</sup> authors explore the labor-supply behavior of a panel of 310 individuals in 109 families observed in four villages of Jiangsu province in the years 1988, 1992, and 1996. They find that for every additional year of education, farmers had a 14% greater chance of finding an off-farm job in 1996, _ceteris paribus_ . Using the base year ratio of the shadow value of labor in agriculture relative to the off-farm wage rate, as well as the total farm and off-farm labor supplies, this increasing opportunity to access off-farm jobs associated with higher educational attainment translates into an increment of 0.58 in transformation elasticity for each additional year of schooling. This is used to calculate the farm/off-farm transformation elasticities by skill level, according to the average years of schooling for each skill level of the rural labor force. The resulting values for this elasticity are 0.68 for unskilled labor, 4.01 for semi-skilled labor and 7.49 for skilled labor.

> 7 Due to the variety of labor supply elasticities in response to the three different wages in their model, the authors obtain a variety of labor transfer elasticities, depending on the “thought experiment” being conducted. These are asymmetric, with the response to a change in shadow wages differing from the response of labor supply to a change in the market wage. However, this response is treated as symmetric in our model. This makes it difficult to choose the correct parameter for our analysis. We focus on the transfer of labor from agriculture to market wage employment in response to a change in returns to agriculture, since this transfer accounts for the bulk of the labor flow in our analysis.

13
regression equation (using the differenced equation with lagged levels as instruments) or removing them from the instruments (using differences as the instrument in a levels regression). The INS dataset has a relatively short time dimension ( _T_ = 11, max, from 1997 to 2007, but most have less than 11 years of observations) and a larger firm dimension ( _N_ = 16,471, unbalanced), which is an advantage of using GMM.

In addition to innovation variables, I consider material as an endogenous variable, as in previous literature (Ackerberg et al. 2006). Firms can quickly adjust their material inputs based on their knowledge of productivity and final output, while it takes more time to adjust labor and capital inputs. Therefore, I use lagged values of the dependent variable, innovation variables, and material as its own instrument, by removing them from the regression equation (using the differenced equation with lagged levels as instruments) and from the instruments (using differences as instruments in the levels regression).

Due to gaps in innovation variables for previous years in the weakly balanced INS data, using a dynamic equation significantly reduces the number of innovative firms that can be tested. A dynamic model that has lag dependent variable and/or lag factor inputs as additional regressors requires further lags of those additional regressors when they are used. Therefore, I use a static equation, which is a long-run description, since it might take some time for inputs, such as innovation investment, to influence the output. To understand the short-run effects, my estimation required an assumption that the current year’s innovation investment of a firm reflects the previous years’ innovation investment. This might be a valid assumption given that the overall number of innovative firms and the amount of innovation investment has been stable over the data period.

In addition to using GMM, I also considered other econometric techniques widely used in previous literature to deal with simultaneity issues in firm-level analysis; these include Olley and Pakes (OP) (1996) and Levinsohn and Petrin’s (LP) (2003) semi-parametric estimator. However, critical assumptions of OP methods on monotonicity of investment in productivity might not hold when introducing an innovation status variable (Van Biesebroeck 2003; De Loecker 2007, 2013). Also, the INS data are weakly balanced; thus, there is no information about those firms that enter into and exit from the market, which is important in controlling 24