rafmacalaba/datause-encoder-data huggingface dataset:
Using the FE estimators for the participants sample of Table 4, we calculated the combined effects, reported in Table 5.

Column (2) reports the combined effects of 9 types of PCs."<sup>7</sup> The first row shows that bare bones PCs-- PCs with wage elasticity equal to zero, without bidding or posting managerial bond, and with primary PC targets other than profits and the average contract duration, for firms with the mean markup ratio-are negatively correlated with productivity levels (significant at the 5 percent level) and growth rates (not statistically significant). Bare bones PCs such as this accounted for roughly 11% of all PCs in our sample (column one). Second, bare bones PCs with bidding only, in other words PCs that appointed managers by bidding, without any other "good" provisions, have ambiguous effects. They are correlated with a drop of productivity of roughly 28 percentage points (significant at the 1% level), but with an improvement in productivity growth rates of roughly 9 percentage points (significant at the 5% level). In the third simulation, bare bones PCs with profit orientated targets are found to be somewhat better than bare bones PCs with other targets, since the negative effects on productivity levels becomes insignificant and smaller. However, firms with this type of PC still would not perform any better than firms without PCs. In the fourth simulation firms with a bare bones PC where the manager posts a bond are found to be indistinguishable from firms without PCs in terms of growth rates (the correlation is negative but insignificant), yet they are associated with a significant and negative productivity level (of 12 percentage points). Roughly 9 percent of our sample had PCs similar to this one. The fifth line 17 Since contract duration is found to have little effects, we do not calculate PC effects by this dimension.

24
# **A Global and Local Factors in Foreign Direct Investment: Case Studies**

There is considerable evidence that multinationals make investment, financing, and cash management decisions at the parent company level. Tests have found that, controlling for local variables, domestic investment responds to changes in such variables as the parent company's aggregate supply ofl iquidity, aggregate debt to equity ratios, and worldwide cost of capital and hurdle rates.2 For example, Mills and Newberry (2002) show that worldwide tax incentives have led subsidiaries to shift income into the **US** during **19871996 by** changing their subsidiaries' leverage.

In this paper,h owever, we inspect the role of global factors which, being outside the control of the multinational, are the drivers of the parent company's aggregate variables. This section discusses three cases of direct investments of multinationals highlighting the role of global and local factors in the decision making process. We view this presentation as particularly interesting since the foreign direct investment literature has not identified any role for global factors up to now, which is reflected in the way empirical analysis is conducted.

## _Enron International's Dhabol Power Pmject,_<sup>_India2 3_</sup>

The Dhabol Power Project consisted of a2 ,000m egawatt power station located at Dhabol, near Bombay. It amounted to a **$2.8** billion investment from a joint venture formed **by** the **US** energy giant Enron Corporation (with **79.93** percent of equity stake), Bechtel Enterprises Inc. and General Electric's Financial Services (each with equal shares). Construction was expected to start in mid-year **1995** and was expected to be completed **by** December of **1998.** _The global factors:_ **A** country's energy sector is very dependent on the domestic business cycle. Thus, Enron viewed its strategy of foreign expansion as a way to diversify

> 22The interested reader is referred to Caves **(1996, pp** 137-140).

> 23This discussion is based on Eun and Resnick (2001) and in several newspaper articles obtained from the database Lexis-Nexis.

**28**
of bribe equation is estimated including the residual from heteroskedastic probit as the selection correction term. The available Monte Carlo evidence shows that Klein and Vella (2009, 2010) approach works well when there is substantial heteroskedasticity in the data, as is the case in our application (see Millimet and Tchernis (forthcoming), Ebbes et al. (2009)).

A comparison of the OLS estimates in Table 2 with the IV estimates in Table 5 shows interesting di¤erences. While the OLS results suggest a signi...cant positive e¤ect of income on the amount of bribe paid, we ...nd no statistically signi...cant e¤ect of income on bribe amount in the IV regressions. The results thus indicate that the positive correlation between income and bribe paid in the OLS regressions is most likely driven by unobserved preference and ability heterogeneity. The direction of omitted variables bias in the amounts of bribes paid is thus same as that in the propensity to pay bribes discussed earlier. This consistency across the results enhance our con...dence in the credibility of the results. The results underscore the importance of credible identi...cation in resolving the debate about possible unequal burden of corruption on rich and poor households.

# (8) Robustness of the IV Results

We report additional robustness checks in this section for the IV results. We use the heavy rainfall dummy as the identifying instrument. The results from using interaction of heavy rainfall with household head’s age and religion as instruments are similar and thus are omitted for the sake of brevity alone.

## Alternative Sets of Controls

The IV results reported in Tables (3)-(5) are based on two sets of control variables. Here we report IV results for three more speci...cations with alternative sets of control variables. The ...rst speci...cation reports estimates from a bare bone speci...cation that includes only the regional ...xed e¤ect and no household or individual level controls. As can be seen from columns (1) and (2) of Table 6, the estimated e¤ects of income on propensity to pay bribe and on the amount of bribes remain essentially unchanged when compared to the main results reported in Table (3) and (5). The next two columns of Table 6 report results from a speci...cation that adds age, sex and religion of the household head as additional covariates; the results remain robust.

33
16 deviation is of the initially assumed size _x_ 0. This way we have the same initial shock size, zero residue after 10 years, and essentially the assumed constant decay factor.

# **4. Quantitative Impact of Pandemic Shocks**

## **4.1. Impact on Output**

We explore output dynamics when a pandemic has the maximal impact considered, on both schooling and unemployment. This assumes a disruption to pre-college schooling for two school years, and an initial increase in unemployment of 10 percentage points.

Figure 4(a) shows the results. The initial impact on GDP is similar in all countries, as employment has declined in all countries by 10 percent. The losses are slightly less than 10 percent, however, because the shock mostly affects workers whose earnings were relatively low in the first place (the uneducated and the young).

Seventy-six percent of the global population lives in an MIC (World Bank, 2020b). Thus, the MIC trajectory is a good approximation for the average trajectory for the world.<sup>33</sup> There are two phases to the output dynamics in Fig. 4(a). First, there is the direct impact, which ends in year 10. After year 10, output starts to decline again. Also note that the disruption to human capital accumulation has a greater impact on HICs.

Figure 4(b) displays what happens when there is no employment shock at all. The impact of the schooling shock is delayed, as it takes time for the students whose human capital was impacted to enter the job market. The impact is also highly persistent.

The impact of the schooling shock on the LIC is smaller, with maximum loss around 20 years out at 2 _._ 4 percent of steady state GDP. The maximum loss in the MIC is after 36 years (3 _._ 6 percent), while in the HIC the maximum loss is after fully 40 years (4 _._ 1 percent).<sup>34</sup> The impact of the schooling shock is most severe in the HIC, as the quantity of schooling was higher to begin with, so disruptions to schooling affect more workers. In addition, because more workers are college educated, their lost returns are higher.

> 33Since our study is forward-looking, we count as MICs countries that recently entered that category, such as Bangladesh, Ghana and the Lao People’s Democratic Republic. HICs include recent climbers such as Chile, Hungary and Poland.

> 34Ichino and Winter-Ebmer (2004) study the impact of disruptions to schooling in Germany and Austria due to World War II. Forty years later workers who were students at the time experience measurable earnings losses. GDP per capita in the 1980s was depressed by 1 percent in both Germany and Austria as a result.
Table 8: Difference in accuracy within areas: in-sample vs. out-of-sample

||E|BP|Cu|bist|X|GB|B|RF|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Panel A: Assets**|||||||||
|Burkina Faso|-0.088***<br>(0.013)|-0.106***<br>(0.009)|-0.074***<br>(0.011)|-0.051***<br>(0.003)|-0.055***<br>(0.009)|-0.016***<br>(0.002)|-0.089***<br>(0.014)|-0.018***<br>(0.002)|
|Madagascar|-0.017***|-0.010***|-0.021***|-0.003**|-0.020***|-0.004***|-0.024***|-0.005***|
||(0.005)|(0.001)|(0.005)|(0.001)|(0.005)|(0.001)|(0.004)|(0.001)|
|Malawi|-0.164***|-0.058**|-0.139***|-0.057**|-0.118***|-0.040*|-0.239***|-0.016**|
||(0.027)|(0.028)|(0.028)|(0.023)|(0.026)|(0.021)|(0.044)|(0.006)|
|Mozambique|0.016***<br>(0.005)|-0.010***<br>(0.001)|0.020***<br>(0.005)|-0.010***<br>(0.001)|0.031***<br>(0.005)|-0.008***<br>(0.001)|0.039***<br>(0.006)|-0.008***<br>(0.0009)|
|Sri Lanka|-0.083***|-0.041***|-0.053***|-0.026***|-0.060***|-0.024***|-0.067***|-0.012***|
||(0.010)|(0.005)|(0.007)|(0.004)|(0.008)|(0.003)|(0.013)|(0.002)|
|Tanzania|-0.064***|-0.011|-0.052***|-0.004|-0.040*|0.002|-0.035|0.002|
||(0.018)|(0.008)|(0.017)|(0.006)|(0.022)|(0.004)|(0.024)|(0.004)|
|Vietnam|-0.298***<br>(0.112)|-0.129***<br>(0.021)|-0.145**<br>(0.059)|-0.056***<br>(0.012)|-0.150***<br>(0.030)|-0.071***<br>(0.011)|-0.189***<br>(0.029)|-0.055***<br>(0.006)|
|All|-0.040***|-0.024***|-0.032***|-0.013***|-0.024***|-0.010***|-0.042***|-0.008***|
||(0.005)|(0.002)|(0.004)|(0.001)|(0.004)|(0.001)|(0.006)|(0.0006)|
|**Panel B: Poverty**|||||||||
|Malawi|-0.066***|-0.013*|-0.046***|-0.019**|-0.042***|-0.006*|-0.084***|-0.011***|
||(0.008)|(0.008)|(0.008)|(0.009)|(0.006)|(0.003)|(0.009)|(0.003)|
|Tanzania<br>All|-0.050***<br>(0.012)<br>-0.062***|-0.014***<br>(0.003)<br>-0.014***|-0.040***<br>(0.010)<br>-0.044***|-0.015***<br>(0.003)<br>-0.017***|-0.042***<br>(0.010)<br>-0.042***|-0.019***<br>(0.003)<br>-0.012***|-0.019**<br>(0.007)<br>-0.068***|-0.009***<br>(0.002)<br>-0.010***|
||(0.007)|(0.004)|(0.006)|(0.005)|(0.005)|(0.002)|(0.007)|(0.002)|
|**Fixed efects:**|||||||||
|Simulation|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Area|No|Yes|No|Yes|No|Yes|No|Yes|

Note: Standard errors are clustered at the simulation and area level. Each cell is a separate regression, where the dependent variable is absolute deviation from truth for a given area/estimator and the independent variable is an indicator for whether the area appears in the sample in a given simulation. The all rows estimate regressions with all countries simultaneously (separately for assets and poverty in Panel A and Panel B, respectively). * p<0.1 ** p<0.05 *** p<0.01
French terminology and classification used in Rwanda‘s budget lines are mapped to the GFSM 2001 economic classification (based on the 2004 budget).

**Table 3.6  Rwanda economic code mapping**

|**Expenditures breakdown**<br>**(economic classification)**|**Rwanda budget line item**|**_Which includes:_**|
|---|---|---|
|Wages and salaries|Traitements et Salaires|Personnels statutaires<br>Personnels contractuels<br>Indemnités|
|Social contributions|Contributions sociales|Cotisations de l’état aux pensions de retraite et de<br>prévoyance<br>Contributions de l’état aux soins de sante|
|Use of goods and services|Autres dépenses de biens et de services|Loyers et charges locatives<br>Documentation générale et officielle<br>Frais de poste et télécommunications<br>Rémunération de tiers<br>Frais divers<br>Dépenses d’interventions diverses reparties<br>Autres dépenses d’intervention non reparties|
|Other current expenditure|Frais de deplacement et de mission|Frais de transport<br>Frais de mission officielle|
|Capital expenditure: buildings,<br>structures, equipment, and<br>machinery|Achat de biens consommables|Petits matériel et fourniture de bureau<br>Consommations d’eau et d’énergie|
|_... rehabilitation_|Entretien et reparations|Immeubles<br>Véhicules et autre matériel de transport<br>Matériel et outillage techniques|

_Source:_ World Bank (2007).

During the functional and economic reclassification of expenditures, the principle of item indivisibility applies—that is, a spending item cannot be split across multiple categories. In all cases the item should be reclassified in the category that best captures the nature of the item, and the criteria used when performing this code mapping properly documented.

# **_Extraction of budget lines from budget documents_**

The second step is to identify, in the budget book, the functional codes pertaining infrastructure, as per the definition of infrastructure and the code mapping prepared in previous steps, and list them in a spreadsheet that includes in an adjacent the economic nature of each expenditure as in the budget books. This spreadsheet should not include functions from the budget that are not relevant to infrastructure e.g. Affaires étrangères, Services concernant le sport, Enseignement Sécondaire etc. In the working spreadsheet the user should have only the infrastructure related functions and the corresponding data and figures under each.

44
(2) the annual GDP growth rate is negative (i.e., GDP recession); or (3) the annual per capita GDP is negative (i.e., Welfare reduction), and zero otherwise.

The independent variables are the CVI and a set of control variables. We also include year dummies and weigh each observation by the ratio of the total sum of common shares of the firms in our sample to stock market capitalization in each country, which we use as a proxy for the representativeness of the CVI in a country. The CVI and control variables enter the regression lagged by one period to ameliorate endogeneity concerns.

The macro-economic control variables are the current account balance, general government balance, government debt, real GDP per capita, and inflation rate. We further control for the commonly used DaR based on the ICR to test whether the CVI contains additional information.

**Table 2A: Descriptive Statistics**

|**Variable**|**Obs**|**Mean**|**Std.**|**p25**|**p75**|**Min**|**Max**|
|---|---|---|---|---|---|---|---|
|Prob (Unemployment rise) (%)|300|21.7|18.0|0.0|36.4|0.0|54.5|
|Prob (Recession) (%)|367|7.5|9.6|0.0|9.1|0.0|36.4|
|Prob (Welfare reduction) (%)|367|14.4|13.6|0.0|18.2|0.0|54.5|
|Corp. Vulnerability Index (0 to 1 index)|367|0.11|0.07|0.06|0.16|0.00|0.36|
|DaR ICR (% Reported Total Corporate Debt)|367|19.0|16.9|6.4|26.0|0.0|89.3|
|Unemployment Rate (%)|300|9.0|6.1|5.5|10.1|0.7|34.9|
|GDP growth (yoy, %)|367|4.0|3.6|2.2|6.1|-7.8|18.0|
|GDP per capita growth (yoy, %)|367|2.5|3.7|0.5|4.9|-15.1|13.6|
|GDP per capita level (US$ constant 2010)|367|10051|11000|2981|12223|454|7468|
|Current Account Balance (% GDP)|353|-1.5|7.1|-5.4|2.5|-25.5|30.4|
|General Government Balance (% GDP)|362|-2.4|4.8|-4.8|-0.6|-16.5|22.2|
|General Government Debt (% GDP)|367|42.0|22.8|26.5|56.0|1.6|142.0|
|Inflation Rate(yoy,%)|350|5.7|4.3|2.7|7.9|-1.0|26.2|

Note: Prob (X) is the unconditional probability that event X happens in a particular country between years 2006 and 2016. Unemployment rise: Dummy variable 1 if unemployment rate increases, 0 otherwise Recession: Dummy variable 1 if GDP growth is negative, 0 otherwise Welfare reduction: Dummy variable 1 if GDP per capita growth is negative, 0 otherwise Sources: WB World Development Indicators, IMF International Financial Statistics, Macro-Financial Initiative 20
**Table 1:** Unit root tests

|Variable|Augmented|Dickey-Fuller test|Philli|ps-Perron test|
|---|---|---|---|---|
||Level|First difference|Level|First Difference|
|_ffr_|-1.8433|-3.5214***|-1.5620|-6.0814***|
|_z_|-2.8946*|-7.8303***|-5.6547***|-14.8536***|
|_bmgdp_|3.3403|-7.3074***|2.5853|-7.3152***|
|_gygap_|-2.9672**|-4.3173***|-3.9751***|-7.4338***|
|_infl_|-5.5818***|-8.1268***|-5.3785***|-23.3783***|
|_ttbgdp_|-2.1970|-9.8562***|-2.1667|-9.8559***|
|_tbeugdp_|-1.0499|-8.9135***|-1.0499|-8.9131***|
|_tbusagdp_|-1.7918|-9.1621***|-1.8007|-9.1621***|
|_lendint_|0.2436|-8.1607***|0.1383|-8.1201***|
|_intresvgdp_|-0.0837|-3.1247**|0.9367|-5.3481***|

1. Null hypothesis: non-stationary

2. The asterisks ***, **, and * indicate 1%, 5%, and 10% of significance levels, respectively.

# **4.3 VAR estimation results**

Variables are included in the VAR estimation as their levels or first differences depending on whether they are I(0) or I(1) variables. In the baseline case, _ffr_ , _ttbgdp_ , _bmgdp,_ and _lendint_ are I(1). Hence, their first differences are included. For _z_ , _gygap,_ and _infl_ , their levels are used as they are I(0) variables. The selected lag length for this VAR estimation is one, based on AIC. For the case in which the total trade balance is decomposed into trade balance with the EU ( _tbeugdp_ ) and trade balance with the US ( _tbusagdp_ ), the lag length of one is also used for the estimation from AIC selection. Both _tbeugdp_ and _tbusagdp_ are included in the estimation by using their first differences since they are I(1) variables from the unit root tests.

# **4.3.1 Impulse response results for the baseline case**

In examining the impact of a US policy interest rate shock on Cambodia’s highly dollarized economy, VAR-based impulse responses for the variables under consideration are analyzed. This is done by applying the Cholesky method, which requires that variables in the VAR system are ordered from the exogenous one to the most endogenous one. Following Duma (2011), the order in which variables are introduced in the baseline model is from D( _ffr_ ), to _z_ , to D( _ttbgdp_ ), to D( _bmgdp_ ), to D( _lendint_ ), to _gygap_ , and to _infl_ , where “D” indicates the first difference. As indicated above, two exogenous variables are also included: _goilp_ capturing the fluctuation of crude oil price and the _dummy_ capturing political unrest and socio-economic instability.

Figure 2 illustrates the results of impulse responses for the variables under 11
not without more intensive support. When we calculate an effective wage on the time invested in these businesses, it is close to the opportunity cost of time, and indeed lower than the food-forwork wage of $1.35 paid by the other major TASAF social protection program at the time. The trainings alter the organization of the groups, empowering rank and file members and leading to some pull-back of time and contributions by group elites, but do not improve business outcomes overall. Most remarkably, the completely private and individual cash transfers are largely invested into the group enterprises, and indeed induce substantial additional individual investment by group members who did not receive any cash. In this case, the fundamental premise of small-scale business investment providing a steady profit stream for beneficiaries seems to have failed, and the group members find themselves in the same boat as the overall program; piling investment into an entrepreneurial activity that yields disappointing returns.

We can delve into the magnitudes of these effects using detailed data that we collected from the group-based enterprises on reported inputs, assets, and earnings six, 12, and 36 months after baseline. Beginning with average disbursements of $5,867, groups retained about $5,000 in assets at the 6-month follow-up and by 12 months had built the value back up almost to the original value before beginning a long-run decapitalization. In groups with no unconditional cash transfers (UCTs), the total business assets declined to $2,660 or just about 40% of the mean value of the grants from TASAF at 36 months, while groups in which at least one member received UCTs retained, on average, $5,000 in assets.<sup>3</sup> The average net earnings of beneficiaries from the group business was $3.35 per month ($3.80 in _no UCT_ and $2.80 in _any UCT_ ). Even assuming no depreciation of group assets and all decrease in stock value is consumed, the increase in consumption in the _no UCT_ arm cannot be larger than $1.50 per person per month when the flow

> 3 All figures in this paper are in real 2008 US$, the time of the baseline, with subsequent survey amounts adjusted using a district-specific CPI.

4
Policy Research Working Paper 6377

# **Abstract**

The World Bank's president has been an American by tradition. Yet little work has explored the consequences for this connection in influencing visions of development in the organization across time. This paper uses evidence from archives, congressional hearing records, and memoirs and histories of World Bank presidents to investigate United States-World Bank relations and development policy during four presidencies—Eugene Meyer, Eugene Black, Robert McNamara, and James Wolfensohn. The author argues that at times the political arrangements had the effect of pushing the Bank toward greater institutional independence from the United States, particularly when partisanship in American politics rose and new United States presidential administrations came into office with the World Bank president’s term holding over from before. At other times, United States-World Bank connections pulled the Bank into foreign policy issues in the United States that the Bank might not otherwise have addressed when advocates pressed their case on Capitol Hill.

This paper is a product of the World Bank Group Archives, Information Management & Technology Department. 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://econ.worldbank. org. The author may be contacted at kathryn.lavelle@case.edu.

_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