rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: _Regression results - new estimator using all households from 2010 census_

|Dependent variable: Income|Coefcient estimate|95% confdence interval|
|---|---|---|
|Phone|0.448|[0.318; 0.579]|
|Employment status|-0.518|[-0.668; -0.367]|
|Urban|0.233|[0.126; 0.340]|
|Education|0.335|[0.248; 0.422]|
|Household members|-0.159|[-0.188; -0.130]|
|Constant|2.655|[2.449; 2.861]|
|Number of census households|21,543||
|Number of municipalities|282||
|_R_<sup>2</sup>|0.0950||

households (year 2000) and 21,543 (year 2010) from the respective censuses and treat the resulting data sets as new censuses. The reason for that is not only computational convenience but also the fact that the state of Minas Gerais is the small area of interest and should therefore exhibit a population size similar to common empirical applications in small area estimation. The true headcount ratios in these artificial censuses change substantially over time, from 0.27 percent in 2000 to 0.11 percent in 2010.

As variables with sufficient variation between municipalities and power to explain variation in income we use location (urban or rural), number of household members, availability of a phone as well as employment status and level of schooling completed of the person with the highest educational attainment in the household. When all households from the 2010 census are used, a linear regression with these explanatory variables yields an _R_<sup>2</sup> of 0.095. The estimates of the regression coefficients can be found in Table 3. We also added squares of the variables, interactions and many other variables to this simple model without obtaining a substantially higher predictive ability measured by the Akaike Information Criterion. The estimated cluster effects variance in a linear mixed effects model based on the 2010 census is 0.02 and small compared to the estimated household residual variance of 0.88.

We draw artificial surveys from the 2010 census by first sampling randomly without replacement 100 municipalities and then sampling without replacement 10 households randomly from each of those municipalities, resulting in an overall survey sample size of 1,000 households. As the number of households differs between municipalities, the estimation at the first stage has to account for these differences by using appropriate weights. Note that this requires knowledge of the number of households in the municipalities at the time of the survey. In practice, when no recent census is available, the number of households at the cluster level can be obtained from a listing exercise which is usually also needed for the sampling scheme for the household survey.

We use a weighted linear regression in the first stage. Means of the explanatory variables over municipalities for the year 2000 are used to explain household per capita income in 2010. To remove apparent right-skewness in the dependent variable, a log-transformation is applied after adding one to the household 13
<!-- Start of picture text --> foo} — Real Jordan |<br>— OIC-All |<br>o | OIC-Minus PAK Ie Refugee Surge<br>é OIC-SAL<br>|<br>l<br>>|<br>I<br>I<br>|<br>52<br>ge |<br>= I<br>= |<br>we?<br>to<br>wo Y<br>wo =<br>—<br>w<br>st<br>1975 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->
Goossens, L.H.J., Harper, F.T., Kraan, B.C.P. and Metivier, H. (2000), “Expert Judgment for a Probabilistic Accident Consequence Uncertainty Analysis,” Radiation Protection and Dosimetry, Vol. 90, No. 3, pp.295-303.

Goossens, L.H.J., Cooke, R.M. and Kraan, B.C.P. (1998), “Evaluation of Weighting Schemes for Expert Judgment Studies,” _Probabilistic Safety Assessment and Management_ (Proceedings of PSAMA 4), pp.2389-2396.

Gustafson, D., Shulka, R., Delbecq, A., and Walster, A. (1973), “A Comparative Study of Differences in Subjective Likelihood Estimates Made by Individuals, Interacting Groups, Delphi Groups, and Nominal Groups,” _Organizational Behaviour and Human Performance,_ Volume 9, pp. 280-291.* Hatfield, A.J. and Hipel, K.W. (2002), “Risk and Systems Theory,” _Risk Analysis_ , Volume 22, No. 6, pp.1043-1057.* Honano, E.J., Hora, S.C., Keeney, R.L., and von Winterfeldt, D. (1990), “Elicitation and Use of Expert Judgment in Performance Assessment for High-level Radioactive Waste Repositories,” NUREG/CR-5411.* Humphreys, R. (1988), “Human Reliability Assessors Guide,” Safety and Reliability Directorate, United Kingdom Atomic Energy Authority* Jenni, K. and Fischhoff, B. (1997), “Attributes for Risk Evaluation,” 1997 Annual Meeting of Society for Risk Analysis.* Jouini M. N., and Clemen, R. T. (1996), “Copula Models for Aggregating Expert Opinions,” _Operations Research_ , Volume 44, Issue 3, pp. 444-457.* Kirwan, B. (1994), “A Guide to Practical Human Reliability Assessment,” Taylor and Francis, London.* Kline, A. and Renn, O. (2002), “A New Approach to Risk Evaluation and Management: RiskBased, Precaution-Based, and Discourse-Based Strategies,” _Risk Analysis_ , Volume 22, No. 6, pp.1071-1094.* Kraan, B.C.P. and Cooke, R.M. ( 2000), “Processing Expert Judgment in Accident Consequence Modeling,” Appearing in Radiation Protection Dosimetry (Expert Judgment and Accident Consequence Uncertainty Analysis; special issue), Vol. 90, No. 3, pp.311-315.

Lindley, D.V., and Singpurwalla, N.D. (1986), “Reliability (and Fault Tree) Analysis Using Expert Opinions,” _Journal of the American Statistical Association_ , Volume 81, Issue 393, pp. 87-90.* Lindley, D.V. (1987), “Using Expert Advice on a Skew Judgmental Distribution,” _Operations Research_ , Volume 35, Issue 5, pp 716-721.

14
# **1. Introduction**

A large body of literature argues that foreign direct investment (FDI) promotes economic growth (Adams 2009, Alfaro 2000, Borensztein, De Gregorio and Lee 1998 and Basu and Guariglia 2007). Alfaro (2003:1) contends it can “be a source of valuable technology ... which can help jump start an economy” while Wacziarg (2001) suggests FDI perpetuates trade benefits which then promote economic growth.

Some even argue FDI is important for alleviating poverty. Tambula (2004) states increased tax revenues from FDI results in poverty reduction, Gohou and Soumare (2011) find FDI has more impacts on welfare in poorer countries than wealthier countries while Mahmoud (2010) claims FDI has been a key source of employment for women in developing countries. Masron and Abdullah (2010:115) notes “Foreign direct investment (FDI) is strongly believed to have a major role to play in the economic development of emerging markets”.

How then can countries improve their FDI inflows? Since 2004, the World Bank Group has published data on the Ease of Doing Business in selected countries while in 2006 it has started ranking countries according to their Ease of Doing Business. Although the World Bank does not argue that improvements in the rankings attract FDI, the rankings have a signalling effect with governments, institutions and media. For example, a newspaper article quotes the International Monetary Fund Representative for Sri Lanka and the Maldives claiming Sri Lanka requires a higher ranking than India to attract FDI Inflows (Jayasinghe 2011) while another states “Kenya has dropped three positions in the World Bank’s ranking for ease of doing business handing other east African peers an upper hand in the battle for attracting foreign direct investments” (Omondi 2011). Despite such claims, there appears to be no rigorous panel study investigating whether improvements in the official rankings improve FDI inflows.

This paper uses a panel data set of approximately 84 countries from 2006 to 2009 to show on average, improvements in the World Bank’s Ease of Doing Business Rankings (forthwith referred to as the Doing Business Rankings) do significantly improve FDI inflows. However, countries undertaking relatively more reforms as proxied by large improvements in the Doing Business rankings do not significantly attract greater FDI inflows than other countries. Further, when restricting the sample to 56 developing countries, the relationship between improvements in rankings and FDI inflows is insignificant.

This paper is structured as follows. Section 2 outlines the determinants of FDI, section 3 explains the data and methodology used in the analysis, section 4 presents the results and section 5 concludes.

# **2. Determinants of FDI**

Different researchers propose different models and variables when investigating the determinants of FDI (see for example Bergstrand and Egger 2007, Stein and Daude 2007 and Walsh and Yu 2010). Clearly the inclusion or exclusion of certain variables could materially influence the results of the analysis. As such, this paper considers various combinations of institutional variables and the key macroeconomic 2
more complex, with increasing south-south trade in primary products (Figure 9c). Some key developing economies (e.g., China, Figure 9d) are now specializing in the export of manufactures. All these phenomena are related: the same Chinese manufactures embody a combination of primary imports from developing countries, as well as intermediate inputs from emerging Asia, in addition to Japan; Taiwan, China; and Korea. This is a natural consequence of trade integration both within the developing world and between developing and high-income countries.

**Figures 9(a)-(d):  Trade and the multi-polar growth world**

<!-- Start of picture text --> (a) Share of World Merchandise Exports (b) Developing Country Imports from<br>25% Other Developing Countries (% of Total<br>Imports), Average for Decade<br>30<br>20%<br>25<br>15%<br>20<br>Developing Countries' (excl<br>10% BRICs) Share 15<br>BRICs' Share<br>Russia 10<br>5%<br>included<br>5<br>in BRICs<br>0%<br>0<br>1960s 1970s 1980s 1990s 2000s<br>(d) Structure of Chinese Merchandise Exports, 2009<br>(c) Developing country exports of primary<br>products to other developing countries, $ billions<br>Primary products:<br>500.0 5.4%<br>Animal and vegetable<br>450.0<br>oils and fats<br>400.0 Mineral fuels,<br>lubricants and relat<br>350.0<br>Crude materials,<br>300.0<br>inedible, except f<br>250.0<br>Beverages and tobacco<br>200.0<br>150.0 Food and live animals Manufactured Goods,<br>(including chemicals,<br>100.0 machinery and transport<br>equipment):<br>50.0<br>94.6%<br>0.0<br>Source: WITS<br>Source: WITS<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Source: World Development Indicators (except (c) and (d)).

16
Figure 3 (Herakovic et al., 2011; Herakovic, 2010).<sup>8</sup> Robots designed to carry out quality control inspections (see Figure 3) can also help reduce the numbers of defective products that reach the customer while at the same time reducing the need for quality inspection staff (Argote and Goodman 1985).

For more basic robot applications, such as pallet handling robots and manual cutting robots (see Figures 4 and 5), the precision tolerances are likely to be higher than sophisticated laser guided or laser cutting robots. However, the quality gains from adoption will also depend on the prior technology used, for instance, a transition from unskilled labor to basic robots. Even rudimentary robots are commonly able to repeatably perform some tasks more accurately than human labor – for example, manual cutting robots in the 1980s were able to operate to within 1mm accuracy (Gillespie, 1980). In addition, more basic robots are likely to have fewer adjustment costs and so be more readily implemented in production of less advanced firms and countries.

**Figure 2: Laser guided welding robot**

Source: Servo-Robot (2021).

**Figure 3: Vision guided inspection robot**

Source: Metrology.news (2018)

> 8 The automotive industry relies on robots to monitor quality through a host of production processes such as defaults in body panels, uneven paint finishes, breaks in adhesive sealants, irregular welding beads, all of which are found to increase the quality of the end product (Bogue 2013). Within the food industry, automated machines with optical sensors and spectroscopic techniques are incorporated in processing and inspection of food products by providing instantaneous safety and quality control assessment (Singh and Jayas, 2012).

9
**Table G3: Descriptive Statistics of Key Variables (Pakistan)**

|**Variable**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|
|_Z-scores_|||||
|HAZ|-2.43|2.29|-6|6|
|WAZ|-1.52|2.03|-5.98|5.78|
|_Food Security Measures_|||||
|Per capita kcal|2731.44|1563.09|360.36|20766.51|
|1-SSR|0.50|0.17|0.04|0.99|
|1-SSEXR|0.77|0.16|0.133|1|
|DDS|9.14|1.44|3|12|
|PC expenditure|2431.16|1647.19|295.8|35901.3|
|Food share|0.62|0.15|0|1|
|Child is female|0.47|0.50|0|1|
|Child age (in months)|27.28|17.27|0|59|
|Age squared|1042.14|977.20|0|3481|
|Household size|10.01|5.61|2|43|
|Mother is literate|0.20|0.40|0|1|
|Father is literate|0.43|0.50|0|1|
|Distance to health facility|14.15|22.16|0|180|
|Source of drinking water (tap = 1)|0.40|0.49|0|1|
|No Toilet Facility|0.27|0.44|0|1|
|Latrine|0.19|0.39|0|1|
|Flush Toilet|0.54|0.50|0|1|
|Structure of the House|||||
|Kacha|0.33|0.47|0|1|
|Pacca|0.43|0.50|0|1|
|Mixed|0.24|0.43|0|1|
|Asset index 1 (poorest)|0.23|0.42|0|1|
|Asset index 2|0.17|0.38|0|1|
|Asset index 3|0.21|0.41|0|1|
|Asset index 4|0.20|0.40|0|1|
|Asset index 5|0.19|0.39|0|1|
|No illness|0.75|0.43|0|1|
|Child has suffered from Diarrhea|0.10|0.31|0|1|
|Other illness|0.15|0.35|0|1|

74
**Fiscal Policy, Poverty and Inequality in Jordan** The Role of Taxes and Public Spending<sup>1</sup>

Laura Rodriguez and Matthew Wai-Poi

**JEL:** D31, H22, I38

1 Laura Rodriguez (lrodrigueztak@worldbank.org, Economist, Poverty and Equity Global Practice) and Matthew Wai-Poi (mwaipoi@worldbank.org, Lead Economist, Poverty and Equity Global Practice). The authors are grateful for peer review comments from Vibhuti Mendiratta, Sailesh Tiwari and Marijn Verhoeven, as well as from Jaime de Piniés on behalf of MNADE Quality Team.

This paper was prepared as a technical background paper for the Jordan Fiscal Public Expenditure Review and accompanies the - policy summary published as Rodriguez and Wai-Poi (2021): https://documents.worldbank.org/en/publication/documents <u>reports/documentdetail/113451615905549990/fiscal-policy-poverty-and-inequality-in-jordan-the-role-of-taxes-and-public-</u> - - <u>spending policy summary</u>
Table 4: Impact of MNREGA on area irrigated by source

|||Area irri|gated by:||
|---|---|---|---|---|
||Wells|Canals|Tanks|Other|
|Post-MNREGA|0.17|0.04|-0.02|-0.04|
||(0.05)|(0.06)|(0.05)|(0.05)|
|95% CI|[0.07,0.27]|[-0.09,0.16]|[-0.12,0.09]|[-0.14,0.06]|
|N|6021|6021|6021|6021|
|Pre-MNREGA mean (000ha)|76.45|35.63|7.24|5.38|
|**Fixed Efects:**|||||
|District|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|

Notes: Our outcome is the IHS transformation of the area irrigated by each irrigation source. 100 _×_ the coefficient is the approximate percent change in the area irrigated after the implementation of MNREGA. All regressions include controls for monthly temperature, a quadratic control for monthly precipitation and a quadratic state _×_ year trend. Errors are clustered at the district level. Our estimates are based on the approach described in Callaway and Sant’Anna (2021) and implemented using their Stata package `csdid` .

24
where i corresponds to the measures of public employment over labor force, i log of population, i stands for GDP per capita, i for openness (imports plus exports as share of GDP), i for ethnic GG SS GG fractionalization and i for other controls. Table 8 reports a set of descriptive statistics of the main GG EE variables used in the analysis, where each observation corresponds to a 5-year average. XX _Table 8: Descriptive statistics_

||N|mean|sd|min|max|
|---|---|---|---|---|---|
|Central gov. employment (% LF)||6.97|4.55|0.22|21.82|
|General gov. employment (% LF)||14.85|6.61|1.58|31.25|
|Public sector employment (% LF)||19.72|8.03|2.33|54.09|
|Population (Millions of persons)||33.32|56.38|0.03|321.02|
|GDP per capita, PPP (constant 2011||25993.43|17031.91|601.82|91798.26|
|international $)||||||
|Openness(% GDP)||99.50|73.30|15.16|422.39|
|Ethnic fractionalization||0.32|0.22|0.00|0.88|
|High income||0.66|0.47|0.00|1.00|
|Age dependency ratio, young||34.32|16.03|15.09|94.00|
|Age dependency ratio, old||18.02|7.75|0.90|42.47|
|Gini||36.27|9.48|17.50|69.70|
|Observations|286|||||

The extended model shows a negative and statistically significant association of public employment (as a % of labor force) with country size by population and a positive association with income (Table 9). The size of the income coefficient is similar to Rodrik’s’ and the impact of a change in income is similar to recent IDB estimates according to which a 25% increase in GDP per capita in LAC is associated with a 1 percentage point increase in public employment.<sup>8</sup> However, openness is negatively associated with public employment (see column 1 in _Table 9_ ), contradicting Rodrik’s model prediction.<sup>9</sup> Table 9: Determinants of general government employment; extended model.

Dependent variable: Log of general government employment as share of labor force.

|_Variables_<br>_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|

> 8 The impact of a 25 percent increase in GDP per capita evaluated at the average general government employment level (14.85%) is 0.25x0.223x0.1485=0.008, or 0.8%.

> 9 This result also holds when external risk is defined as in Rodrick’s paper (see Table 17 in the appendix).

17
_-_ 41 <!-- Start of picture text --> (4.3) NPRI = D<br>P8 /PNVA<br><!-- End of picture text --> Note that the total nominal protection rate is given as:

# _(4.4)_ **_NPRT_** _=_ **_NPRD +_** _NPRI =_ **_(P1 IPNA)_** _I_ **_(PB /P,A)_** _1._

In our case we use an equilibrium real exchange rate,<sup>c</sup><sup>**=**</sup><sup>**_(E/Pv,4,)'_**,instead of the nominal</sup> equilitrium rate **E'.** This requires adjusting the formula for<sup>**_PB /PvA_**in equations (4.2) and (4.3) as</sup> below:

**_(4.5)_** _P/(PNA =_ **_CCPAT2e_** _+ (1-a)_

where e is the equilibrium RER.

Using annual Sudanese data (see appendix table (A4.2) and<sup>our ERER and overvaluation</sup> derived estimates in equations (4.2) - (4.5) above, I calculated<sup>the direct, indirect, and total indexes</sup> of taxation imposed on Sudanese agriculture.

The results are shown in table (4.2) below. The numbers on<sup>direct intervention provide</sup> estimates of the percentage by which doliiestic producers' prices<sup>diverge from those that would have</sup> prevailed in an environment with no taxes on agricultural<sup>tradables (given the actual exchange rate</sup> and the degree of industrial protection). This measure<sup>is equivalent to the rate of nominal</sup> protection, KSV (1988). The results reveal that direct interventions<sup>have been rather substantial over</sup> the first half of 1970s with an annual average in excess of<sup>20%. For the following period however,</sup> direct taxation of agriculture declined considerably to average<sup>less than 10% per year and in 1987 and</sup> 1988 it even dropped to less than 4%. This evidence is<sup>partially explained by the fact that the</sup>
Table 6: Regressions for principal components of gross financial inflows, balanced quarterly panel, 2000Q1–2013Q2<sup>_†_</sup>

||**C1**<br>_1PC_|**C2**<br>_3PC_|**C3**<br>_3PC_|**C4**<br>_3PC_|**C5**<br>_3PC_|**C6**<br>_3PC_|
|---|---|---|---|---|---|---|
|All QE<br>episodes|0.902<br>(0.34)<sup>_∗∗_</sup>|1.534<br>(0.71)<sup>_∗∗_</sup>|0.594<br>(0.24)<sup>_∗∗_</sup><br>_Liquidity _|1.012<br>(0.40)<sup>_∗∗_</sup><br> _channel_|0.588<br>(0.29)<sup>_∗_</sup>|0.862<br>(0.39)<sup>_∗_</sup>|
|3M T-bill|-1.727|-2.329|-1.289|-1.838|-1.283|-0.702|
|rate|(0.52)<sup>_∗∗∗_</sup>|(1.16)<sup>_∗_</sup>|(0.56)<sup>_∗∗_</sup>|(0.52)<sup>_∗∗∗_</sup>|(0.61)<sup>_∗_</sup>|(0.61)|
|Money supply|-16.740|-29.119|-5.165|-19.694|-5.014|15.830|
||(6.29)<sup>_∗∗_</sup>|(13.29)<sup>_∗∗_</sup>|(8.47)|(7.65)<sup>_∗∗_</sup>|(10.57)|(14.47)|
||||_Portfolio bala_|_nce channel_|||
|Yield curve|-1.869|-2.680|-1.174|-2.017|-1.166|-0.605|
||(0.46)<sup>_∗∗∗_</sup>|(1.17)<sup>_∗∗_</sup>|(0.50)<sup>_∗∗_</sup>|(0.48)<sup>_∗∗∗_</sup>|(0.56)<sup>_∗_</sup>|(0.76)|
|Interest rate|||0.125||0.126|0.197|
|diferential|||(0.04)<sup>_∗∗_</sup>||(0.05)<sup>_∗∗_</sup>|(0.09)<sup>_∗_</sup>|
|Global PMI|0.257|0.395|0.188|0.280|0.187|0.146|
||(0.07)<sup>_∗∗∗_</sup>|(0.16)<sup>_∗∗_</sup>|(0.06)<sup>_∗∗∗_</sup>|(0.08)<sup>_∗∗∗_</sup>|(0.08)<sup>_∗∗_</sup>|(0.10)|
|Growth||||0.086|-0.003|0.086|
|diferential||||(0.13)|(0.11)|(0.26)|
||||_Confdence_|_channel_|||
|VIX|-0.085<br>(0.02)<sup>_∗∗∗_</sup>|-0.161<br>(0.04)<sup>_∗∗∗_</sup>|-0.082<br>(0.02)<sup>_∗∗∗_</sup>|-0.086<br>(0.02)<sup>_∗∗∗_</sup>|-0.082<br>(0.02)<sup>_∗∗∗_</sup>|-0.062<br>(0.03)<sup>_∗∗_</sup>|
|Crisis dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Basic controls|No|No|No|No|No|Yes|
|Adj. R<sup>2</sup>|0.924|0.906|0.945|0.921|0.940|0.936|
|Gain in ft (%)|||4.3|1.7|3.8|3.3|
|N|24|24|24|24|24|24|

> _†_ All level variables are in logarithmic form, but rates, indices, and indicator variables are untransformed. The dependent variable is either the varimax orthogonal rotation of the first principal component (1PC) or proportion-weighted sum of the first three principal components (3PC). Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. A time trend, constant term, and crisis-related dummies (crisis period and post-crisis) were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

# **7 The effects of tapering and monetary policy renormalization on developing economies**

## **7.1 Normalization scenarios for unconventional monetary policy**

We estimate (2) by populating **TC** _it_ = [ **L** _it_ **PB** _it_ **C** _it_ **X** _it_ **GFI** _it_ ] with the (weighted average) G4 short-term interest rate ( **L** _it_ ), the G4 yield curve ( **PB** _it_ ), the VIX ( **C** _it_ ), real GDP growth in the G4 and developing world ( **X** _it_ ), and aggregate gross capital inflows in developing economies ( **GFI** _it_ ) 28