rafmacalaba/datause-encoder-data huggingface dataset:
||ML_b_|0.089|(0.063)<br>0.012|(0.033)<br>_−_0.038|(0.056)|Yes|3,569|ession and||
|---|---|---|---|---|---|---|---|---|---|
||CC|0.003|(0.050)<br>0.055_∗_|(0.031)<br>_−_0.050|(0.056)|Yes|1,098|OLS regr|1|
||SR|_−_0.173_∗∗∗_|(0.053)<br>0.151_∗∗∗_|(0.035)<br>0.143_∗∗_|(0.058)|Yes|3,569|omputed via|.05; ***p_<_0.0|
||ML_b_|||_−_0.112_∗_|(0.059)|No|3,569|ents were c|0.1; **p_<_0|
||CC|||0.006|(0.045)|No|1,098|ta coefci|evels: *p_<_|
|of Yields|SR|||0.396_∗∗∗_|(0.096)|No|3,569|that the be|gnifcance l|
|s||||||||s|Si|
|Measure|ML_b_||0.218_∗∗∗_|(0.047)||No|3,569|_b_ indicate|lications.|
||||_−_|||||ML|rep|
||CC||_−_0.069_∗_|(0.039)||No|1,098|and CC.|with 100|
||SR||0.148_∗_|(0.082)||No|3,569|vel for SR|bootstrap|
||ML_b_|0.219_∗∗∗_|(0.063)|||No|3,569|on area le|ained via|
||CC|0.056<br>|(0.048)|||No|1,098|enumerati|ons are obt|
||SR|_−_0.194_∗_|(0.099)<br>eds per ha|abor per ha||fect?<br>No|s<br>3,569|ard errors clustered at the|errors for the ML predicti|
||||Se|h l||E|on|and|rd|
|||ea|ty|f H||ed|ati|St|nda|
|||ar|ti|o||ix|v|_s:_|ta|
|||Plot|Quan|Days||EA F|Obser|_Note_|the s|

34
and ranks Tonga 121<sup>st</sup> out of 190 countries.<sup>4</sup> Average incomes in New Zealand are about six times as high, with a per capita GNI of $24,400 in 2012. Tonga and New Zealand both rank 26 places higher in the Human Development Index than they do in terms of GNI, making New Zealand the largest positive outlier of very high human development countries and Tonga the second largest outlier of countries with medium levels of human development (UNDP, 2013, p.27). Hence, this migration flow allows a very large positive income shock, where the higher incomes are not compensating for lower levels of other aspects of human development.

Emigration out of Tonga is high, with 30,000 Tongan-born living abroad, mainly in New Zealand, Australia and the United States. Despite an earlier history of employment migration to New Zealand, family reunification (mostly marriage) was the main channel of access in the 1990s following New Zealand’s implementation of a points-based immigration system that favors highly skilled migrants. In 2002, New Zealand introduced a new migration program, the Pacific Access Category (PAC), that lets a quota of 250 Tongans permanently migrate each year. Any Tongan citizens aged 18 to 45 who meet certain English, health and character requirements may register. A random ballot selects among applicants, with odds of about 10% during the 2002-05 ballot years that our sample is drawn from. If their ballot is selected, applicants have six months to obtain a full-time job offer in New Zealand that meets an income threshold similar to the minimum wage. This ensures self-reliance since Tongans are not eligible for most forms of welfare until they reside in New Zealand for two years. After a job offer is filed along with a residence application, it typically takes from three to nine months to receive residence approval and immigration to New Zealand must then occur within 12 months. Spouses and any unmarried children up to age 24 are also eligible to immigrate with the ballot winner (and are part of the annual quota of 250 people).

> 4 All statistics in this section are from the 2013 _Human Development Report_ (UNDP, 2013).

6
(column 1), in the legislative (column 2) or an index of both (column 3). One of the main di↵erences is that policy credibility is more strongly associated with trust in the executive than in the legislative, possibly reflecting the stronger attribution of policy responsibility to the executive.

Table 5: Correlates of trust (ordered logit model): Alternative dependent variables

||(1)<br>Trust in the executive|(2)<br>Trust in the legislative<br>Trust in re|(3)<br>presentative institutions (index)|
|---|---|---|---|
|Satisfaction with|0.247***|0.250***|0.255***|
|security services|(0.0526)|(0.0513)|(0.0529)|
|Satisfaction with|0.249***|0.304***|0.295***|
|social services|(0.0854)|(0.0625)|(0.0726)|
|Satisfaction with|0.269***|0.284***|0.266***|
|water and sanitation|(0.0542)|(0.0540)|(0.0478)|
|Satisfaction with|0.167**|0.171***|0.184***|
|economic performance (Index)|(0.0709)|(0.0619)|(0.0629)|
|Integrity perceptions|0.638***|0.580***|0.600***|
|(Index)|(0.108)|(0.102)|(0.105)|
|Equitable treatment|0.126***|0.137***|0.132***|
|by the administration|(0.0313)|(0.0414)|(0.0333)|
|Policy credibility|0.348***|0.270***|0.314***|
||(0.0584)|(0.0744)|(0.0671)|
|Region|X|X|X|
|Urban/rural|X|X|X|
|Gender|X|X|X|
|Income|X|X|X|
|Marital status|X|X|X|
|No. of HH members|X|X|X|
|Income<br>X|X|X|X|
|Education<br>X|X|X|X|
|Employment status|X|X|X|
|Observations|5,916|5,916|5,916|
||Standard errors cl<br>*** p_<_0.|ustered by region in parentheses<br>01, ** p_<_0.05, * p_<_0.1||
||Data source: Wor|ld Bank survey on trust (2021)||

Table 6 shows the results of our main model with an additional control for individual positive self-assessment in column 2. The variable “positive perceptions of individual situation” corresponds to a self-assessment of individual’s living conditions (on a scale of 1 to 10, where 1 are the worst possible conditions and 10 the best). The dependent variable “institutional trust” refers to trust in the executive. This control variable helps 19
- Jakiela, P. and Ozier, O. (2016). Does Africa need a rotten kin theorem? Experimental evidence from village economies. _The Review of Economic Studies_ , 83(1):231–268.

- Jayachandran, S. (2021). Social norms as a barrier to women’s employment in developing countries. _IMF Economic Review_ , pages 1–20.

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- Klinger, B. and Sch ̈undeln, M. (2011). Can entrepreneurial activity be taught? quasiexperimental evidence from Central America. _World Development_ , 39(9):1592–1610.

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26
not been limited to improvements in monetary indicators but has concerned multiple aspects of living conditions including health, education and labor market conditions. In fact, the decline in multidimensional poverty appeared to have been more pronounced than the decline for monetary poverty. The multidimensional approach also confirms that the decline in poverty has been stronger in rural as opposed to urban areas, although the starting level was much higher in rural areas.

Robustness tests including stochastic dominance tests validate both the monetary and the multidimensional findings. Interestingly, multidimensional inequality shows invariably less inequality than the monetary counterpart, in both urban and rural areas, which may be explained by the fact that larger families tend to be more monetary poor in per capita terms but also tend to own a larger number of assets on a household basis. The paper has also confirmed that the urban-rural divide remains very deep in Morocco and should continue to rank high in terms of policy priorities.

# **References**

Alkire, Sabina and Foster, James E., (2011), Counting and multidimensional poverty measurement, Journal of Public Economics, 95, issue 7-8, p. 476-487.

Asselin, L. M. (2009) Analysis of Multidimensional Poverty: Theory and Case-Studies. Economic Studies in Inequality, Social Exclusion and Well-being, Volume No. 7, Springer Atkinson, A. and Bourguignon, F.(1982) The comparison of multidimensioned distributions of economic status, Review of Economic Studies 49, 183–201.

Benzécri, J-P. (1973) Analyse des Donnees, Vols 1 and 2. Dunod Decancq, Koen and Lugo, Maria Ana, (2010), Weights in multidimensional indices of well-being: an overview, Open Access publications from Katholieke Universiteit Leuven, Katholieke Universiteit Leuven.

Douidich M., Ezzrari A., Soudi K. (2009), Dynamique de la pauvreté au Maroc : 1985-2007, Cahiers du plan N° 26, Haut Commissariat au Plan – Rabat.

Duclos, J.-Y., Sahn, D. and Younger, S. (2001) Robust multi-dimensional poverty comparisons, Economic Journal, 116(514), 943-968.

Greenacre, M.J. (1984) Theory and Applications of Correspondence Analysis. Academic Press.

Kolm, S.C.(1977) Multidimensional egalitarianisms, Quarterly Journal of Economics _,_ 91 (1977), 1–13.

Maasoumi, E.(1986) The measurement and decomposition of multidimensional inequality, Econometrica 54, 771–779.

19
3 <!-- Start of picture text --> Real 7<br>hntweat .o: ng 1 Sa<br>Rate a<br>6 - .... .. ... .. .. . ... --'<br>5.......<br>5 . ....... . '...' .. ........ . ' 4 . ..... ' 1 / .::'.'.'..'. .' '.......<br>32 . ....... 1 . ..\ . .... .. ........ . . ................ .<br>.2 . .. . ..... .... .. ..... . ...... ........ ...... ....... ..... ....<br>-~~~~~~~<br>2 . h.st<br>5 10 15 20 25 30<br>Saving, hvestment (per cent of GNP)<br><!-- End of picture text --> Figure 1: World Saving and Investment, 1970-1991.

important reason for the worldwide decline in saving is rising government deficits: up from 2.9 per cent in the period 1972-1980 to 4.5 per cent in the period 1981-1988 _(International Financial Statistics: 1988 and 1991 Yearbooks,_ p. 156).

The decline in world saving implies that not every country can maintain its level of domestic investment by increasing foreign capital inflows. Overall, the decline in saving has to be matched by an equal decline; investment. In fact, saving and investment ratios have fallen in all geographical regionis of the world since 1982, but least in developing countries of Asia and the Pacific. As world saving has shrunk, so the world real interest rate has risen from 1.5 per cent during the period 1970-1980 to 4.8 per cent in the period 1981-1991, as iUustrated in Figure 1. With no signs of a reversal in the declining trend in global saving and the immediate saving-ieducing impacts of the war in the Gulf, reunification of Germany, reconstruction of Eastern Europe, and deliberate current account-reduction policies being implemented by Japan, Korea, and Taiwan, the costs of foreign borrowing can be expected to rise stiU higher in the 1990s as the saving curve in Figure 1 moves even further to the left.

The decline in foreign capital inflows to developing countries has necessitated structural adjustmnent in the form of an increase in export earnings or a reduction in import expenditure. The national accounting identities imply that the adjustment has to raise national
The GMM estimator based on the conditions in 3.4 and 3.5 is known as the difference estimator. Notwithstanding its advantages with respect to simpler panel data estimators, the difference estimator has important statistical shortcomings. Blundell and Bond (1998) and Alonso-Borrego and Arellano (1999) show that when the explanatory variables are persistent over time, lagged levels of these variables are weak instruments for the regression equation in differences. Instrument weakness influences the asymptotic and small-sample performance of the difference estimator toward inefficient and biased coefficient estimates, respectively.<sup>5</sup> To reduce the potential biases and imprecision associated with the difference estimator, the estimator developed in Arellano and Bover (1995) and Blundell and Bond (1998) is used. It combines the regression equation in differences and the regression equation in levels into one system. For the equation in differences, the instruments are those presented above (i.e. lagged levels of the explanatory variables). For the equation in levels (equation 3.2), the instruments are given by the lagged differences of the explanatory variables.<sup>6</sup> These are appropriate instruments under the assumption that the correlation between the explanatory variables and the country-specific effect is the same for all time periods. That is,

> 5 An additional problem with the simple difference estimator involves measurement error: differencing may exacerbate the bias stemming from errors in variables by decreasing the signal-to-noise ratio (see Griliches and Hausman, 1986).

> 6 The timing of the instruments is analogous to that used for the difference regression: for the variables measured as period averages, the instruments correspond to the difference between _t-1_ and _t-2_ ; and for the variables measured at the start of the period, the instruments correspond to the difference between _t_ and _t-1_ .

11
by firms (Mehrtens et al, 2001). Public procurement, through e-procurement, can directly lead firms to adopt internet technologies as it is a requirement to fully harness the procurement process. Finally, e- procurement can generate competitive pressures as firms compete with each other by adopting internet technology in order to outdo each other to win the contract. The latter is more likely to be true for small and medium-sized (SME) enterprises in developing economies.

# **3. Data**

The analysis is based on two data sets - the World Bank Group’s Enterprise Surveys (ES) and the World Bank Group’s Benchmarking for Public Procurement databases. The ES consist of firmlevel data that capture a firm’s business environment. The respondents are typically managers or owners of the business. In addition, a firm’s characteristics and performance are measured. The ES conducted between 2006 and 2016 used a common questionnaire and sampling methodology (stratified random sample) across economies, thereby allowing for cross-country comparisons, which is a rarity in most data sets. The surveys are representative of the formal (registered) private sector of the economies excluding extractive sectors such as mining as well as agriculture.<sup>2</sup> Measures of participation in public procurement, corruption, infrastructure, innovation and online engagement available in the ES are utilized for the analysis in this study. The sample of ES firms in this study includes over 59,000 firms across 109 mostly developing economies. The list of countries is provided in table A1.

The public procurement data are based on structured expert surveys. This database has also been used by Djankov et al. (2017). Respondents were chosen based on their expertise in public

> 2 Details of the ES methodology and coverage can be found in the Enterprise Surveys website <u>http://www.enterprisesurveys.org.</u>

7
# **A.3 Figures**

<!-- Start of picture text -->
1.200<br>1.100<br>1.000<br>0.900<br>0.800<br>0.700<br>0.600<br>0.500<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Labor Energy Materials<br><!-- End of picture text -->

Figure A.1: Capital Stock Efficiency Indexes, Chemical, Rubber, Plastics and Fuel Products, United States, 1991-2005 <!-- Start of picture text -->
1.200<br>1.100<br>1.000<br>0.900<br>0.800<br>0.700<br>0.600<br>0.500<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Labor Energy Materials<br><!-- End of picture text -->

Figure A.2: Capital Stock Efficiency Indexes, Electrical and Optical Equipment, United States, 1991-2005 53
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Table 14: Finances of Public Sector Enterprises, 1981-87 (Z of GDP)

||1981|<br>1982|1983|<br>1984|1985|1986|1987|
|---|---|---|---|---|---|---|---|
|Current Savings|2.1|<br>1.9|2.3|<br>2.3|<br>2.9|2.1|2.0|
|Overall Balance (-=deficit)|-4.8|<br>-5.4|-6.5|-6.5|<br>-2.7|-1.8|-2.3|
|Net External Financing|2.9|<br>2.0|2.9|<br>5.0|<br>3.1|<br>0.7|0.3|

# Source: Central Bank of Honduras.

73. The financial assessment of the performance of public sector enterprises is hampered by extensive non-market interventions such as price controls on outputs, as well as conversion of their debt into equity, implicit and explicit transfers received from the Central Government, assumption of external debt obligations of some enterprises by the Central Government and other implicit subsidies they receive from the rest of the public sector (i.e. non-repayment of telephone bills etc.). Interest subsidies and tax privileges also make it difficult to assess the actual financial performance of these enterprises.

# Suggested Action Program

74. To address issues regarding public sector enterprises, a mediumterm action program needs to be prepared and implemented. Such a program should include the following actions:

- (i) increasing tariffs and user charges;

- (ii) reducing expenditures;

- (iii) introducing efficiency measures;

- (iv) improving services;

- (v) restructuring ENEE's external debt; and

- (vi) privatizing and closing some enterprises.

75. Specific examples of an action program are illustrated below:

# A) Reduction in expenditures:

- a) Decrease in employment levels (HONDUTEL);

- b) Introduction of efficiency measures with pre-established targets to increase efficiency (SANAA, ENEE, HONDUTEL); and

- c) Deferring low priority investments (HONDUTEL).
producer support levels are determined in response to some omitted variable that also influences water quality directly, we should expect to see a detectable effect of future PSE on water quality today.

# 3. Results

## Agricultural Productivity

Table 1 presents the impacts on yields across the fertilizer distribution and Figure 1 provides a visualization of the estimates in Table 1 and plots the global response curve. The results show that nitrogen fertilizer can have significant but heterogeneous effects on yields across the fertilizer distribution. The response of yields to increasing levels of nitrogen application gradually rises till it reaches a peak after which it falls, indicating diminishing productivity. This means that yields are increasing but require increasing amounts of nitrogen to achieve this. Going from quantile 1 to quantile 2 (or 7) of nitrogen usage increases yields by about 10 (or 20) percent, but the increase slows down thereafter such that going from quantile 1 to quantile 8 (or 9) increases yields by only 17 (or 13) percent (respectively). Put simply, at low and moderate levels of use, nitrogen fertilizer has the intended beneficial impact on yields, but at high applications, there is a law of diminishing returns whereby extra nitrogen has a diminishing effect on yields.

_Table 1 Global response of agricultural yields to quantiles of nitrogen fertilizer (grid cell-level analysis)_

|||LogNPP on cropl<br>|and<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|N fertilizer (Q2)|0.0480***|0.0423***|0.0462***|
||(0.013)|(0.013)|(0.013)|
|N fertilizer (Q3)|0.1205***|0.1132***|0.1075***|
||(0.013)|(0.013)|(0.013)|
|N fertilizer (Q4)|0.1195***|0.1151***|0.1128***|
||(0.014)|(0.014)|(0.014)|
|N fertilizer (Q5)|0.1417***|0.1457***|0.1469***|
||(0.017)|(0.016)|(0.017)|
|N fertilizer (Q6)|0.1874***|0.1910***|0.1956***|
||(0.020)|(0.019)|(0.019)|
|N fertilizer (Q7)|0.2000***|0.2046***|0.2091***|
||(0.019)|(0.019)|(0.019)|
|N fertilizer (Q8)|0.1494***|0.1577***|0.1705***|
||(0.019)|(0.019)|(0.019)|
|N fertilizer (Q9)|0.1096***|0.1108***|0.1314***|
||(0.023)|(0.023)|(0.023)|
|N|94724|94724|94724|
|Year FE|Y|Y|Y|
|Grid cell FE|Y|Y|Y|
|Country Trends|Y|Y|Y|
|R-sq|0.927|0.930|0.932|
|AdjR-sq|0.921|0.924|0.927|

Note: Dependent variable is log of NPP in cropland pixels where cropland makes up more than half of the share of the total land area (55%). All regression models include grid cell fixed effects; year fixed effects; country specific trends. Controls for contemporaneous precipitation shocks (i.e. Whether annual precipitation in the grid cell was at least 1 SD higher or lower than the long-run mean of the grid cell), temperature, and population are added in turn in columns 1, 2 and 3 respective with the last column including all controls. Statistical significance is given by +P < 0.10, *P < 0.05, **P < 0.01, and ***P < 0.001.

8
Formally, for a single endogenous regressor, the IVProbit model can be stated as: (3) _Yi* = Ei β + Xi θ + ui_ (4) _Ei = Xi Π1 + Zi Π2 + vi_ where i = 1,..., N, Ei is a vector of endogenous variables, Xi is a vector of exogenous variables, Zi is a vector of instruments that satisfy the requirements of instrumental exogeneity and relevance, β and θ are vectors of structural parameters, equation (4) is written in its reduced form, and Π1 and Π2 are matrices of reduced form parameters. The model is jointly estimated using maximum likelihood estimator and is derived under the assumption that (ui,vi) is independently and identically distributed multivariate normal for all observations. In this set up, we do not observe Yi*; instead observe Yi = 1 for Yi* ≥ 0 and Yi = 0 for Yi* < 0. For the model to be identified, as it would be the case with the two-stage least squares regression, the number of additional instruments should be greater than or equal to the number of endogenous variables.

# **_4.2. Instrumental Variables_**

For IV regressions to work, each instrument must satisfy two conditions, namely instrumental relevance and instrumental exogeneity. If an instrument is relevant, then the variation in the instrument is related to the variation in the instrumented variable _._ In addition, it must satisfy the exclusion restriction, i.e. the instrument must be uncorrelated with the outcome variable of choice.

The ALSMS05 provided us with a number of potential instruments to identify the migration decision. Specifically, in the first and the third model, the instrumental variables employed are: (i) _Language90_ , a binary variable equal to 1 if a current or previous household member spoke any Greek or Italian in 1990; (ii) _Network90_ , a binary variable equal to 1 if the head of household or his/her spouse had any family relative or friend living abroad in 1990; (iii) _MinimumDistance_ , the minimum distance in kilometers between the place of residence of the household and one of the four main exit points for Albanian emigrants, namely the ports of Vlore and Durres, and the border crossings of Kakavije and Kapshice; and (iv) _AverageShocks_ , which

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