rafmacalaba/datause-encoder-data huggingface dataset:
In this modeling framework, all elements in y, are treated as endogenous variables, thus circumventing possible issues of simultaneity. However, it is worth noting that endogeneity of the elements of y, in the estimation of a VECM does not necessarily imply that a particular estimated model necessarily has a structural interpretation vis-avis all of the elements in Y,. What allows us to give a structural interpretation to a particular estimated equaition and its associated cointegrating vector is the set of identifying restrictions that are imposed on the system. Normalization to unity of the coefficient associated with money demand, as well as prior (theory inspired) information on the acceptable signs of the coefficients on other variables, together with the assumption that the supply of money adjusts to meet demand, lay the foundations for a structural interpretation of an estimated model as a money demand function. However, there is no implication that the system of estimated equations also contains a structural representation of variables other than money demand in Yt **_.16_** Estimation of a VECM is carried out using maximum likelihood methods and a conventional model selection strategy has been followed.1<sup>7</sup>

# A **combined approach to estimating money demand in a cointegration framework**

The first step in the proposed approach consists of estimating a VECM for money demand as described in the earlier section. In the second step of the combined modeling approach, we represent the change in the log of the demand for real money balances (Am,) as, where iTm represents an adjustment coefficient associated with the error correction term, assumed to be exogenous for estimation purposes; E, is the lagged value of the error-

**16** What cointegrating relations do imply is that there is a set of long-run economic relations among the elements in y, that behave as 'attractors" i.e. they draw economic variables together in the long run. For a discussion of structural cointegrating relations, including issues of identification and parallels with 1(0) system estimation, please refer to section 16.6 in J. Davidson _(ibid)._

**17** We used the EViews 4 software to estimate the VECMs discussed in this paper. Please refer to Quantitative Micro Software (2000) for information on EViews 4.

Money Demand in Venezuela, Mario A. Cuevas 12
In a related study, spatial misallocation is shown to have had similarly dramatic costs in terms of output in China and India. Hsieh and Klenow (2007) found that moving to “U.S. efficiency” in terms of the spatial allocation of economic activity would increase total factor productivity (TFP) by 30-50% in China, and 40-60% in India. The output gains would be roughly twice as large if capital accumulated in response to aggregate TFP gains. They further estimate that deteriorating allocative efficiency may have shaved 2% off Indian manufacturing TFP growth from 1987 to 1994, whereas China may have boosted its TFP 2% per year over 1998-2005 by addressing these distortions.

These studies reflect the consensus in the literature: migration and the spatial distribution of economic activity are crucial factors for economic development. For this reason, the very low rates of domestic migration in Uzbekistan – recorded both in official government statistics (Table 1) and the independent survey data used in this study – are cause for concern. The State Statistics Committee (SSC) of Uzbekistan estimates that the migration rate for the 2018 calendar year stood at about .5 percent of the population.

**Table 1: Official Permanent Migration Flows, 2018**

||Arrival|Departure|Net|Total|Arr. %|Dep. %|Net. %|
|---|---|---|---|---|---|---|---|
|National|160|176.2|-16.2|32,639|0.49%|0.54%|0.05%|
|Karakalpakstan|10.8|15|-4.2|1,874|0.58%|0.80%|0.22%|
|Andijan|6.6|8.5|-1.9|3,011|0.22%|0.28%|0.06%|
|Bukhara|6.1|9|-2.9|1,875|0.33%|0.48%|0.15%|
|Jizzakh|11.1|12.1|-1|1,330|0.83%|0.91%|0.08%|
|Kashkadarya|11|15.2|-4.2|3,130|0.35%|0.49%|0.13%|
|Navoi|13.1|14.3|-1.2|990|1.32%|1.45%|0.12%|
|Namangan|4.6|5.7|-1.1|2,651|0.17%|0.22%|0.04%|
|Samarkand|11.9|15.6|-3.7|3,748|0.32%|0.42%|0.10%|
|Surkhandarya|11.9|14.2|-2.3|2,517|0.47%|0.56%|0.09%|
|Syrdarya|8.6|9.3|-0.7|817|1.05%|1.14%|0.09%|
|Tashkent|19.2|24.8|-5.6|2,881|0.67%|0.86%|0.19%|
|Fergana|13.3|14.7|-1.4|3,522|0.38%|0.42%|0.04%|
|Khorezm|6.3|7.6|-1.3|1,818|0.35%|0.42%|0.07%|
|Tashkent city|25.5|10.2|15.3|2,476|1.03%|0.41%|0.62%|

_Source: Uzbekistan State Statistic Committee, figures reported in thousands. Note that the reported figures include both international and domestic migration flows._ Data from L2CU paint a similar picture: respondents reported that about 5 percent of the population was currently living in a province other than where they were born. Only about .3 percent of the population reported moving in the preceding year. The 95 percent confidence interval of this estimate 13
affect enterprises’ export opportunities. For example, enterprises located in land-locked countries might find it more difficult to export than enterprises located in other countries.<sup>16</sup>

# **III.3 Results**

_Customs and Trade Regulations._ Although most countries have reduced tariffs, export taxes and other formal barriers to trade, customs and trade regulations remain a serious concern in many countries in Africa. When the enterprise’s assessment of the extent of problems due to customs and trade regulation is included directly in the regression, the coefficient is positive but statistically significant (see Table 8). Results are similar whether country dummies are included (see column 2) or excluded (see column 1). Since higher values on the index indicate that the enterprise believes that trade and customs regulations are a greater barrier, this suggests that firms that perceive customs and trade regulations as a greater barrier export more than enterprises than believe them to be a lesser barrier. Although this might seem counter-intuitive at first glance, it is probably not surprising. As noted earlier (see Table 5), enterprises involved in exporting are more likely to be concerned about trade and customs regulations than enterprises that operate wholly in domestic markets.

To control for the possibility of reverse causation, the regressions are repeated substituting the average rating of trade and customs regulation for exporters in the same region and sector as the enterprise of interest. Since this does not depend upon whether the enterprise actually exports or not, reverse causation is unlikely. When this variable is substituted for the enterprise’s own perception, the coefficient is statistically significant and negative. This indicates that enterprises in sectors and regions where trade and customs regulations are particularly problematic export less than other enterprises. The effect is relatively modest, however. The point estimate of the coefficient suggests that a 1-point average increase in the

> 16 Wood and Jordan (2000) note that it is 870 miles from Kampala to the nearest port, Mombasa (in Kenya).  Overall, they argue that poor infrastructure partially explains why Uganda exports less manufacturing goods than Zimbabwe.  However, as noted above, it is important to note that most exports for the small and medium-sized enterprises in the investment climate surveys are to neighboring countries.

14
The randomization was stratified at the subdistrict level – within each subdistrict, one branch was randomly allocated to treatment and one to the control group. All of the selected communities in treatment branches were treated in 2007 while the control communities were not treated until after the endline survey in 2011.

Table S1.4 reports baseline balance tests which compare the characteristics of ultrapoor households in treatment and control groups at baseline. For every variable, the table reports the mean and standard deviation in treatment and control communities, as well as the p-value on a test of equality of means (column 7) and the normalized difference of means (column 8). Overall, the samples are well balanced: only 2 out of 26 tests for the equality of the means are rejected; and all of the normalized differences are below the .25 threshold recommended by Imbens and Wooldrige (2009). Thus we can conclude that the randomization was successful and the control sample provides a valid counterfactual for the treatment group.

# **5 Empirical Analysis**

## **5.1 Estimation**

In order to estimate the effects of the TUP program, I pool observations from the two followup surveys and estimate an ANCOVA model. In particular, I estimate: where _yidt_ is the outcome of interest for household _i_ from subdistrict _d_ at survey wave _t_ with time periods referring to 2007 baseline (t=0), 2009 midline (t=1) and 2011 endline (t=2); _Ti_ is an indicator variable equal to 1 if household _i_ lived in a treatment branch and 0 otherwise; _yid_ 0 is the baseline level of the outcome variable for household _i_ ; and _δd_ are subdistrict (randomization strata) fixed effects. The parameter of interest is _λ_ , the difference between treatment and control observations. The standard errors are clustered at the BRAC branch office level (the unit of randomization) in all the regressions. Under the identifying assumption that the control branches represent a valid counterfactual for the treated branches in the absence of the program, namely that trends in all outcomes of interest are the same in treatment and control branches, _λ_ identifies the causal effect of the TUP program on _yidt_ . I will estimate (1) on the full sample of ultra-poor households, thus estimating the intention to treat (ITT) effect.<sup>20</sup> In order to test for the differences in treatment effects at midline and endline surveys,

> 20Of the households identified as ultra-poor in treatment branches, 86% eventually received an asset. The other 14% either ceased to meet the eligibility criteria when transfers were implemented, or chose not to take-up the program. As such, the ITT estimates reported in the paper are close to the average treatment effect on the treated (ATT).

15
goods sector, where developing countries already have a large market share, making it difficult for new entrants to become large-scale exporters, services appear to be steadily expanding, with catch-up opportunities continuing to rise, and entry for all.

The globalization of service provides alternative opportunities for low income countries to find niches, beyond manufacturing, where they can specialize, scale up and achieve explosive growth, just like the East Asian Tigers did. A service led growth can be sustained because the current globalization of services is only the tip of the iceberg, and service is the largest sector in the world, accounting for more than 70 percent of global output. The Lions of Africa can sustain can sustain service-led growth because there is enormous space for catching up and convergence. Nevertheless, it is tricky to identify the best growth escalators for the latecomers to development, as there is still so much more that we still do not know and understand.

# **Reference**

AfDB, OECD, UNDP and ECA (2013) _African Economic Outlook 2013: Structural Transformation and Natural Resources_ , African Development Bank, Organisation for Economic Co-operation and Development, United Nations Development Programme and Economic Commission for Africa.

Acemoglu, Daron. 2002. “Technical Change, Inequality, and the Labor Market.” _Journal of Economic Literature_ 40 (1): 7–72.

Acemoglu, Daron, and David Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” In _Handbook of Labor Economics_ , Vol. 4, Part B, edited by Orley Ashenfelter and David Card, 1043–1171. Amsterdam: Elsevier.

Acemoglu, Daron, David H. Autor, and David Lyle. 2004. “Women, War, and Wages: The Effect of Female Labor Supply on the Wage Structure at Midcentury.” _Journal of Political Economy_ 112 (3): 497–551.

Autor, David, and David Dorn. 2009. “This Job Is ‘Getting Old’: Measuring Changes in Job Opportunities Using Occupational Age Structure.” _American Economic Review_ 99 (2): 45–51.\ Autor, David H., and David Dorn. 2013. “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market: Dataset.” _American Economic Review_ . ttp://dx.doi.org/10.1257/ Acemoglu, Daron, Simon Johnson, and James Robinson. 2003. “An African Success: Botswana.” In _In Search of Prosperity: Analytical Narratives on Economic Growth_ , ed. Dani Rodrik. Princeton, NJ: Princeton University Press.

Allen, Franklin, Elena Carletti, Jun Qian, and Patricio Valenzuela. 2013. “Growth, Finance, and Crises.” Paper prepared for the Global Citizen Foundation project on “Towards a Better Global Economy.” 21
# _Distribution of Nitrogen Use Efficiency (NUE)_

Generating the NUE at the most disaggregated level possible is of particular interest for the analysis of spatial fertilizer profitability in this study. The “plot” multilevel model allows for the estimation of NUE at the plot-level. Moreover, as table 3 indicates, the estimated mean NUE of the “plot” multilevel model is very similar in magnitude to that of the mean plot-level FE model, which is our most consistent estimate. Using the “plot” multilevel model, the NUE is estimated to range from 2.82kg to 25.98kg with a mean and standard deviation of 11.82kg and 2.42 respectively (figure 1). NUE ranges between 10kg and 15kg for majority of the plots (75%); between 5kg and 10kg for 16% of the plots; between 15kg and 20kg for about 8% of the plots; and at least 20kg for only about 1% of the plots.

Overall, the average NUE estimated in this study is quite low, and lower than those calculated by the MPTF task force using data from master farmers in the late 1990s in Benson (1999) and used by the official FISP evaluation (Dorward et al. 2008), and other recent studies by Arndt et. Al (2015) and Harou et al. (2017). However, these estimates are consistent with other studies using recent data on smallholders in Malawi. NUE estimates in these studies range from 7.1 kg to 11.0 kg by Snapp et al (2013), between 6.6kg and 11.5kg by Ricker-Gilbert and Jayne (2011, 2012); and 11.3kg by Holden and Lunduka (2011). The low NUE in Malawi is likely to be one of the main reasons why the use of commercial fertilizer for crop production is very low in the country. The fertilizer use literature suggests that the low NUE observed in this and other studies in Malawi is not an isolated case in SSA. The NUE has been estimated to be between 8kg and 13kg in Nigeria, and between 11kg and 20kg in Kenya (Liverpool-Tasie et al. 2015; Matsumoto and Yamano, 2011; Marenya and Barrett 2009; Sheahan et al, 2012).

21
- ii -

# _Table of Contents_

# _Tables_

|1<br>|Private Commercial Finance and Relative Indebtedness at the<br>Turning Point in CEE (in US$ million) ..................................................<br>2<br>|
|---|---|
|2<br>|Net Borrowing from Official Sources (in US$ million) ........................................ 4<br>|
|3<br>|Breakdown oi Total External Debt by Maturity (in US$ million) ............. ...........8<br>|
|4<br>|Net Short-Term Flows to the Region ..........<br>......................................<br>8<br>|
|_5_<br>|Country Risk Forecasts .................................................<br>9<br>|
|6<br>|Hungary: Breakdown of External Financing (in US$ million) .......................... 14|
|7<br>|Hungary: External Financing Flows (in US$ million) ........................................ 15<br>|
|8<br>|Czechoslovakia: CSFR External Debt (in US$ million) .................................... 16<br>|
|9<br>|Czechoslovakia: CSFR External Financing Flows (in US$ million) ................. 17<br>|
|10<br>|Poland: External Debt (in US$ million) .................................................<br>20<br>|
|11<br>|Poland: External Financing Flows (in US$ million) .......................................... 20<br>|
|12<br>|Bulgaria: External Debt (in US$ million) ................................................<br>22<br>|
|13<br>|Bulgaria: External Financing Flows (in US$ million) ........................................ 22<br>|
|14<br>|Romania: External Debt (in US$ million) ................................................<br>23<br>|
|15|Romania: External Financing Flows (in US$ million) ........................................ 24|
|_Figures_<br>||
|1<br>|Debt to GDP Ratio (Mexico, Brazil, Argentina, Hunoy )<br>.**.**...........................**.**...<br>ry<br>3<br>|
|2<br>|Average Interst Rate on Commercial Debt (Bulgaria, Hungary, CSFR,<br>Poland) ....................................<br>3<br>|
|3<br>|Hungary: Average Interest Rate on External Debt<br>...................................<br>3<br>|
|4<br>|Bulgaria: Average Interest Rate on External Debt<br>...................................<br>3<br>|
|_5_<br>|Ratio of Total Debt to Total Exports (Romania, Poland, Bulgaria<br>Hungary, CSFR .......<br>.4<br>|
|6<br>|Ratio of Debt Service to Total Exports (Bulgaria, CSFR, Hungary, Poland<br>Romania) ...................................................<br>4<br>|
|7<br>|Net Borrowing by Source - 1989 (Hungary, CSFR, Bulgaria, Romania) ............. 5<br>|
|8<br>|Net Borrowing by Source - 1990 (Hungary, CSFR, Bulgaria, Poland<br>Romania)<br>_.5_<br>|
|9<br>|Net Borrowing (estimate) by Source 1991: (Hungary, Poland, Bulgaria<br>CSFR, Romania)<br>_.5_<br>|
|10|Net Borrowing (projected) by Source 1992 (Hungary, CSFR, Poland,<br>Romania, Bulgaria)<br>.6|
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Table 3: Probit selection model results.

|**Determinants**|**Coefficient**|**Std. Error**|
|---|---|---|
|AIJ investment equation|||
|Investor attributes|||
|AIJ subsidy|0.230<sup>a</sup>|0.058|
|Investor commitment|0.267<sup>a</sup>|0.066|
|Host attributes|||
|Investment climate|0.017<sup>b</sup>|0.008|
|Host commitment|0.205<sup>b</sup>|0.104|
|Government effectiveness|0.037|0.093|
|Political stability|0.329<sup>a</sup>|0.116|
|Constant|‐1.805<sup>a</sup>|0.426|
|Bilateral aid equation (selection)|||
|Income|‐0.112<sup>a</sup>|0.010|
|Population|0.210<sup>a</sup>|0.014|
|Proximity|0.651<sup>a</sup>|0.141|
|Trade agreement|0.335<sup>a</sup>|0.110|
|Government effectiveness|0.126<sup>a</sup>|0.016|
|Political stability|‐0.149<sup>a</sup>|0.007|
|Voice and accountability|0.120<sup>a</sup>|0.038|
|Corruption|‐0.183<sup>a</sup>|0.042|
|Constant|‐1.312<sup>a</sup>|0.182|
|Independence test|||
|Correlation estimate,ρ|‐0.834|0.060|
|Wald test, ρequals zero|χ<sup>2</sup>(1)= 36.52<sup>a</sup>||

Note:<sup>a</sup> and<sup>b</sup> denote statistical significance at the 1, and 5 percent level, respectively. Potential heterogeneity in the investment error term is accounted for by including information on the project count for each investor-host dyad Table 4: Average marginal effects.

|**Investment determinant**|**Marginal effects**|
|---|---|
|Investor attributes||
|AIJ subsidy*|0.0261|
|Investor commitment*|0.0291|
|Host attributes||
|Investment climate|0.0017|
|Host commitment*|0.0218|
|Government effectiveness|0.0037|
|Political stability|0.0329|

Note:<sup>a</sup> and<sup>b</sup> denote statistical significance at the 1, and 5 percent level, respectively; * indicates that marginal values are based on a discrete change in the dichotomous determinant. The predicted probability of an investment relationship is 0.0482.

27
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
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|WPS1 019 How Effective are Directed Credit<br>Policies in the United States?<br>A Literature Survey|Anita M. Schwarz|November 1992|M. Raggambi<br>37664|
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|WPS1027 Lessons from Bank Privatization<br>in Mexico|Guillermo Bames|November 1992|W. Pitayatonakarn<br>37664|
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|WPS1 029 Controlling Tropical Deforestation:<br>An Analysis of Alternative Policies|Robert T. Deacon|November 1992|P. Pender<br>37851|
|WPS1030 Measuring the Effects of Urban<br>Transportation Policies on the<br>Environment: A Survey of Models|Alan J. Krupnick|November 1992|P. Pender<br>37851|
|WPS1031 Measuring the Possibilities of<br>Interfuel Substitution|Robert Bacon|November 1992|P. Pender<br>37851|
|WPS1032 East Asia and Pacific Region,<br>South Asia Region Population<br>Projections. 1992-93 Edition|Eduard Bos<br>My T. Vu<br>Ann Levin|November 1992|0. Nadora<br>31091|