rafmacalaba/datause-encoder-data huggingface dataset:
- 7 -

very partial. One study concludes that: "There<sup>is no evidence of a</sup> statistically satisfactory kind to suggest that public enterprises in LDCs have a lower level of technical efiiciency than private<sup>firms operatirng</sup> at the same scale of operation. (But) on a less formal<sup>level the tendency... seems</sup> to be pointing in that direction.';6 Other more partial studies, of industrial and manufacturing public enterprises and private<sup>operators in jute and textile</sup> in Bangladesh also found no clear evidence of private sector<sup>superiority.</sup> 7 The studies made in developed countries about the<sup>relative performance of</sup> public and private electric power companies are inconclusive in this respect. Also, although no conclusive studies have been made,<sup>it seems that at least in</sup> developed countries, the operational performance of a number<sup>of competitive</sup> state-owned industrial enterprises--for example, in Western European countries--is not noticeably different from similar enterprises<sup>in the private</sup> sector.

One can still postulate that ownership matters and that private sector units will be more efficient. It is likely,<sup>but the differe,nce</sup> in some cases might not be tlhs. significant. More importantly, it might not be 6 For surveys of the literature comparing the performance of public and private enterprises in the same industries, see R.<sup>Milward, "Measured Sources</sup> of Inefficiency in the Performance of Public and<sup>Private Enterprises in LDCs",</sup> in Paul Cook and Colin Kirkpatrick (eds.) Privatization<sup>in Less Developed</sup> Countries. New York: St. Martins Press, and<sup>S. Domberger and J. Piggott,</sup> "Privatization Policies and Public Enterprises: A Survey",<sup>Economic Record,</sup> 62, 1986. Also Bela Balassa, "Public Enterprises<sup>in Developing Countries:</sup> Issues of Privatization' in Public Finance and Performance of Enterprises, Detroit, Michigan: Wayne University Press, 1989.

7 John Nellis and Sunita Kikeri: "Public Enterprise Reform: Privatization and the World Bank". World DeveloRment, Vol. 17, No. 5, 1989, Page 664.
Woolridge (1999) shows that weighting an outcome regression by the inverse of the sampling probabilities of program participation leads to appropriate estimators and efficiency gains that are better than what would have been possible using population weights.

The results suggest that regular microfinance is as good as PRIME in mitigating both seasonal and year-round poverty, as well as in affecting other welfare indicators (Table 8). For example, PRIME participation increases monthly employment hours by 21 percentage points, compared to a 23 percentage-point gain for regular microfinance. Similarly, PRIME reduces food deprivation (either starvation or meal rationing) during the non- _monga_ period by 3.9 percentage points, which is slightly higher than the reduction with regular microfinance (3.5 percentage points). The last column in Table 8 presents the Wald test statistics, which show that the differences between the impacts of alternate programs (i.e., PRIME versus regular microfinance) are not statistically significant for nearly all outcomes.

# **7. Do the Findings Differ with Panel Estimation Methods?**

The cross-sectional estimates of program effects are potentially subject to bias due to violation of assumptions employed in such techniques. For example, the PSM method may not yield consistent estimates of program effects if unobserved characteristics of both villages and households influence program participation, and consequently, outcomes. Thus, alternative methods must be explored to check the validity of the cross-sectional findings.

One way to verify the results is to use panel analysis. Panel data at the household level can resolve the bias due to unobserved characteristics. Panel analysis also helps control for common season-specific bias. For example, when we examine the incidence of starvation or meal rationing for a particular season, we may invariably introduce a common seasonal effect, such as seasonal preference that affects both the participation decision and consumption. It is also possible that a common seasonal shock could induce all households 33
# **Data appendix**

This appendix details the different sources of data used to construct the various variables used in the analysis of this paper.

# **Non-spatial variables**

# **_Real GDP per capita_ ,** **_Pop. Growth_ ,** **_Aver(I/Y)_ ,** **_Aver(G/Y)_ ,** **_Openness_**

The underlying data is from the Penn World Table v 6.2 (Heston _et al._ , 2006) and was downloaded from http://pwt.econ.upenn.edu/php_site/pwt_index.php. _Pop. growth_ is the (natural) logarithmic growth rate of population and was calculated as [ln( _Pi,t_ ) - ln( _Pi,t-T_ )]/ _T_ where _Pi,t_ denotes country _i_ ’s population level in year _t_ and _T_ is the number of years over which the population growth rate is calculated. _Aver(I/Y)_ , _Aver(G/Y)_ and _Openness_ are all measured as averages over the period of interest, with the level of _Openness_ in any one year being given by ( _Xi,t_ + _Mi,t_ )/ _Yi,t_ where _Xi,t_ denotes country _i_ 's level of exports in year _t_ , _Mi,t_ its level of imports and _Yi,t_ its level of real GDP. Real GDP is measured in 2000 constant international dollars.

# **_Civil war_**

Measured as the number of years in a given time period for which a country was characterized by civil war, where civil war is itself defined as an intra-state conflict involving 1000+ annual battle deaths. The underlying data is from the _International Peace Research Institute_ , Oslo, and was downloaded from <u>http://www.prio.no/CSCW/ Datasets/.</u>

# **_LL_**

A dummy variable indicating whether or not a country can be classified as landlocked (1 = landlocked, 0 = not landlocked). Both the Democratic Republic of Congo and Sudan are classified as landlocked, despite the fact that they have coasts. This is on the basis of their lack of access to their coast lines. Ethiopia is classified as being landlocked throughout the 1970-2000 sample-period. The data is from the data appendix of Collier and O'Connell (2007), supplemented with information from Wikipedia.

# **_RR92_** , **_RRnew_**

Dummy variables indicating respectively whether or not a country could be classified as resource rich in 1992 (1 = resource rich, 0 = not resource rich) and whether or not a country for which _RR92_ = 1 became resource rich during the sample-period 1992-2000 (1 = became resource rich, 0 = did not become resource rich). The variables are constructed from data contained within the data appendix of Collier and O'Connell (2007) which gives the first year in which they classify a country as being resource rich based on several criteria, supplemented, in some cases, with judgemental adjustments.

# **_Infra_**

The construction of this variable is explained in section 6 and follows Limão and Venables (2001), as well as Bosker and Garretson (2008). The underlying data is from the World Bank's _Development Data Platform_ .

41
different things. The specialization index identifies the extreme value for a district across all industries, while the diversity index considers the district’s composition as a whole. It is quite feasible for a district to have specialization and diversity values that are both above average. In the 2000 Indian data, 18% of observations have above-median values on both indices, while 18% of observations have below-median values on both indices.<sup>3</sup> The metrics have on average a correlation of about -0.2 across the surveys. This correlation is statistically significant at a 10% level, such that higher values of the specialization index are associated with lower district-level diversity values. The correlation, however, is sufficiently low that we can model the two indices together. We observe empirical evidence below that both of these traits can simultaneously promote manufacturing plant productivity.

Second, the metric designs (especially specialization) can yield extreme values. Likewise, the sampled nature of our Indian data may contribute to outliers among small districts. Thus, we winsorize our specialization and diversity metrics at their 2% and 98% values. This winsorization is done separately for each year. We use these winsorized values for descriptive statistics in the next section, except where noted otherwise, to focus on the more meaningful variations and trends in the data. When we move to productivity and growth estimations, we primarily report results that use a ten-point scale for the decile in which a district’s specialization or diversity falls compared to the whole set of Indian districts. For example, a district receives a value of 3 if its specialization level is between the 30th and 39th percentile for India. This approach makes the scales and variances of our indices more comparable and aids in interpretation, although we derive quite similar results with other approaches.

Finally, it is important to note that the indices do not directly relate to or build upon other properties of districts (e.g., size, income per capita, etc.). Both measures are calculated over industry distributions within districts and thus do not build upon these features specifically. This is not to say, however, that the indices are orthogonal to these properties either. For example, it is increasingly difficult for large districts with lots of employment to have a very undiversified industrial base, while it is easier for large districts to maintain specialization in one industry.

> 3 In the 2011 U.S. data, 11% of observations have above-median values on both indices, while 12% of observations have below-median values on both indices.

9
57 Kerwin, J. T., & Thornton, R. (2015). _Making the grade: Understanding what works for teaching literacy in rural Uganda_ . Unpublished manuscript, University of Michigan, Ann Arbor, MI.

- Lai, F., Luo, R., Zhang, L., Huang, X., & Rozelle, S. (2012). _Does computer-assisted learning improve learning outcomes? Evidence from a randomized experiment in migrant schools in Beijing_ (REAP Working Paper No. 228). Stanford, CA: Stanford University, Rural Education Action Project.

- Lai, F., Zhang, L., Qu, Q., Hu, X., Shi, Y., Boswell, M., et al. (2012). _Does computer-assisted learning improve learning outcomes? Evidence from a randomized experiment in public schools in rural minority areas in Qinghai_ (REAP Working Paper No. 237). Stanford, CA: Stanford University, Rural Education Action Project.

- Loyalka, P., Liu, C., Song, Y., Yi, H., Huang, X., Wei, J., et al. (2013). Can information and counseling help students from poor rural areas go to high school? Evidence from China. _Journal of Comparative Economics_ , _41_ , 1012-1025. doi:10.1016/j.jce.2013.06.004

- Lucas, A. M., & Mbiti, I. M. (2012). Access, sorting, and achievement: The short-run effects of free primary education in Kenya. _American Economic Journal: Applied Economics_ , _4_ (4), 226–225. doi:10.1257/app.4.4.226

- Mo, D., Zhang, L., Lui, R., Qu, Q., Huang, W., Wang, J., et al. (2013). _Integrating computerassisted learning into a regular curriculum: Evidence from a randomized experiment in rural schools in Shaanxi_ (REAP Working Paper No. 248). Stanford, CA: Stanford University, Rural Education Action Project.

Nitsaisook, M., & Anderson, L.W. (1989). An experimental investigation of the effectiveness of
Embarrassingly, neither the product price effects nor the economywide expansion in unskilled labor intensity are observed in the data, suggesting that the observed rise in skill premia in OECD countries is not mainly due to cheaper unskilled-labor-intensive imports (trade). Lawrence and Slaughter (1993), Sachs and Shatz (1994), Robbins (1996), Desjonqueres, Machin and van Reenan (1999), and many others, using different methodologies “inspired” by the Heckscher-Ohlin type model, all find that trade has little explanatory effect on changes in labor demand/relative wages across industries. The same is true of the early papers that estimate the demand for labor, a labor cost function or decompose the sources of employment change into domestic demand, trade and productivity elements. They, too, generally found that trade factors played only a minor role in job losses/wage inequality—with productivity growth being the main factor displacing labor in the short run. Thus, e.g., Freeman and Katz (1991), Katz and Murphy (1992), Revenga (1992), Bernard and Jensen (1995) and Berman, Bound, and Griliches (1994) all of them heavily cited papers, conclude that skill-biased technical change (SBTC) accounts for the lion’s share of the action (e.g., on the basis of a strong positive association between R&D expenditures/computerization and a rise in the relative return to skilled labor).<sup>9</sup> Thus, despite different methodologies, the labor and trade literatures have been in substantial agreement on the effect of trade on wages (employment): SBTC dominates.<sup>10</sup> This does not mean trade can be completely ignored, however, as a source of wage inequality within developed or developing countries. Researchers focusing on the labor content of trade (so-called factor content studies) obtained some of the largest estimates of the effects of imports on wages (e.g., Murphy and Welch 1991, Wood 1994). The analysis in these papers centers on the growth in the “effective” unskilled labor force that is implied by the greater imports of unskilled-labor-intensive products from developing countries. That is, estimates are made of the labor being displaced by a given amount of imports. The premise of these papers—

> 9 As discussed below, this literature suffers from endogeneity problems. Thus, growth in imports may stimulate faster productivity growth. Trade-induced productivity growth may result from the pro-competitive impact of trade on _x_ -efficiency; reduced rents and employment of unionized labor, or relocation abroad of (unskilled) laborintensive stages of the value chain. There is substantial evidence that firms improve productivity following greater competition from imports. Greenaway et al (1999), using an industry production function approach, find this to be important in the UK, as do Bernard and Jensen (1995) for the USA.

> 10 See Acemoglu (2002) for an in depth survey of the literature on (the determinants of) skill biased technical change over the last 60 years.

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# _Capital Flows to Central and Eastern Europe and Former Soviet Union_

_By_ _Stijn Claessens, Daniel Oks and Rossana Polastri World Bank_ Paper prepared for the NBER-Study: CAPITAL INFLOWS to EMERGING MARKETS, organized by Sebastian Edwards. We would like to thank the discussant, Michael Dooley, and other participants in the pre-conference and conference, Ricardo Martin, Frank Lysy, Marcelo Selowsky and participants in a World Bank workshop for useful comments.
Policy Research Working Paper 9436

# **Abstract**

Restricting cross-border trade through export bans in an attempt to stabilize domestic prices has been a particularly popular policy tool used by many sub-Saharan countries in recent years. However, little is known about how the variability in harvests and seasonality—two critical dimensions of smallholder agriculture in Africa—mediate the effects of export bans on household welfare. This study assesses the short-term impact of export bans on prices and welfare of households in Malawi, accounting for these heterogeneities. It uses monthly panel data on maize prices from 152 markets in Malawi and neighboring countries. To identify the impacts of the bans, the study compares the change in price dispersion between a domestic market in Malawi and another market in a neighboring country, relative to the price dispersion between the domestic market and other markets within Malawi that are at a similar distance as the domestic-foreign market pair. The findings show that export bans, in the short run, are associated with lower domestic prices, lower relative prices, and less seasonality in prices in Malawi. This is after accounting for harvest levels and the existence of trade restrictions in neighboring countries. The short-run effects of the export bans help explain why policymakers are likely to engage in the use of such policies. However, the welfare analysis shows that the welfare gains and poverty reduction effects are small in magnitude and likely to be offset by the long-run distortionary effects of restrictive trade policies.

This paper is a product of the Poverty and Equity 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 HFuje@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
estimator discussed in this paper has several practical advantages. First, our estimator for the quantiles is a two-step method: the first step coincides with the one of Donald and Hsu (2014), but unlike that method, our QR estimator for the object of interest is obtained without having to invert the CDF. This is possible because of the second advantage of our method: QR has a linear program representation, which makes practical computation simple and allows using weights directly into the objective function that is solved. Finally, if one is interested in quantiles, and its transformations, using the proposed estimator is attractive due to its computational efficiency and accuracy in finite samples.<sup>6</sup>

**Remark 2.** _One can also easily use the multi-step estimator defined above to obtain estimates_ � _for other functionals of interest. For example, the estimator of QTE will be_ ∆(<sup>�</sup> _τ_ ) = _q_ 1( _τ_ ) _−_ � � � _q_ 0( _τ_ ) = [1 _−_ 1] _Q_<sup>�</sup> ( _τ, τ_ ) _and for QTT will be_ ∆<sup>�</sup> _A_ ( _τ_ ) = _qA_ 1( _τ_ ) _− qA_ 0( _τ_ ) = [1 _−_ 1] _Q_<sup>�</sup> _A_ ( _τ, τ_ ) _. Other functionals, such as the Makarov bounds for the quantiles of the distribution of treatment effects, Y_ (1) _− Y_ (0) _, are estimated using the analytical expressions of these estimated bounds as functions of Q_<sup>�</sup> _A_ ( _τ, τ_<sup>_′_</sup> ) _and Q_<sup>�</sup> _B_ ( _τ, τ_<sup>_′_</sup> ) _._

# **3.3 Asymptotic properties**

In this section, we derive the asymptotic properties of the multi-step estimator for the quantile process. We first focus on the properties of the estimator of _qt_ ( _τ_ ) and establish the uniform consistency and the weak limit of _q_ � _t_ ( _τ_ ), in _l_<sup>_∞_</sup> ( _T_ ). The extension to _q_ � _At_ ( _τ_ ) and � _qBt_ ( _τ_ ) is direct. We also establish the consistency and the weak limit of _Q_<sup>�</sup> ( _τ, τ_<sup>_′_</sup> ), _Q_<sup>�</sup> _A_ ( _τ, τ_<sup>_′_</sup> ) and _Q_<sup>�</sup> _B_ ( _τ, τ_<sup>_′_</sup> ) in _l_<sup>_∞_</sup> ( _T_ ) _× l_<sup>_∞_</sup> ( _T_ ). The asymptotic properties of the _GIC_<sup>�</sup> ( _τ_ ) and _GIC_<sup>�</sup> _∗_ ( _τ_ ) follow from these results. In addition, we derive the uniform semiparametric efficiency of the estimator. Finally, we discuss how in practice we estimate weights used to compute _q_ � _t_ ( _τ_ ). The two last results are collected in the Appendix.<sup>7</sup>

## **3.3.1 Consistency**

Consistency is a desired property for most estimators. For the consistency of process _q_ � _t_ ( _τ_ ) over _τ ∈T_ , consider the following conditions.

> 6We refer the reader to Koenker, Leorato, and Peracchi (2013) for a discussion and comparison on the statistical properties of the distribution regression and the quantile regression approaches.

> 7In Appendix 6.2, we provide results for the uniform semiparametric efficiency of the estimator. In Appendix 6.3 we discuss the practical estimation of the corresponding nuisance parameters, _wt_ ( _·_ ), _wAt_ ( _·_ ), and _wBt_ ( _·_ ).

11
28 depreciation increases the budget deficit (relative to GDP) when the public sector has more expenses than revenues denominated in foreign currencies -- as in countries where the foreign debt service is a large part of public<sup>spending</sup> (e.g., Brazil, Philippines, and Turkey). In countries where the public sector obtains much revenue from import taxes or commodity exports -- oil in Mexico, phosphates in Morocco, and copper in Zambia -- a real depreciation<sup>tends to</sup> decrease the budget deficit.

In most countries the bulk of saving is accounted for by the private sector. While government policy can readily alter the disposable income<sup>of</sup> the private sector, in a market economy it has only limited influence on the share of disposable income that the private sector saves. Key policies that affect private saving are the rate of return on saving, the level and form of taxation, the rate of inflation, the real exchange rate, the flight of capital, the business cycle, the inflows of foreign capital, and the rate of growth (Table 6).

Credit rationing and controls on interest rates discourage saving in<sup>many</sup> countries. The low or negative real interest rates on deposits and targeted credits reduce the supply of loanable funds and hence<sup>effectively</sup> ration investment." Financial reforms that raise real interest<sup>rates to</sup> market clearing levels are justified because they improve the<sup>efficiency of</sup> resource use, which boosts long-run growth. I The effect of higher real interest rates on the level of private saving is ambiguous, however.<sup>The</sup> 17 For a recent review of financial systems and financial liberalization in developing countries, see World Bank (1989b).

**i6** This policy prescription is taken from R. McKinnon (1973); and E.S. Shaw (1973).