rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Two Stage Least Squares Estimates of the Partial Correlation between Unemployment and the Digital Economy across Countries**

||**(1)**||**(2)**||**(3)**||
|---|---|---|---|---|---|---|
|**Explanatory Variables**||**Panel A - I**|**V Estimates: Dependent**|**Variable - Un**|**employment**||
|Payments<br>Users|-0.464<br>(0.06)<br>0.308<br>(0.111)||-0.553<br>(0.073)<br>0.399<br>(0.082)||-0.268<br>(0.068)<br>0.248<br>(0.127)||
|||**P**<br>|**anel B - First Stage: Dep**<br>|**endent Varia**<br>|**bles**<br>||
||Payments|Users|Payments|Users|Payments|Users|
|Telephones 75-99 (log)|1.423|5.134|2.258|5.906|2.163|5.652|
||(0.415)|(0.000)|(0.224)|(0.000)|(0.236)|(0.000)|
|Bank credit 2000-16 (% of GDP)|0.0875|0.0557|0.077|0.046|0.064|0.043|
||(0.012)|(0.022)|(0.027)|(0.058)|(0.053)|(0.060)|
|Legal Origin DummyVariables|No||No||Yes||
||||**Panel C - Specifica**|**tion Tests**|||
|H0: Exogeneity. p-value of Durbin<br>Test|0.030||0.010||0.009||
|Weak IVs: Shea's partial R-sq|0.071|0.200|0.050|0.170|0.159|0.227|
|IV Validity: Sargan Overid Test p-<br>value|n.a.||n.a.||0.666||
|Other controls*****|Informality, Outp|ut Gap|Informality, Output G|ap, Severance|Informality, Output G|ap, Severance|
|Observations|100||99||97||

**Source** : Authors' calculations. **Notes** : See text and appendix for data sources and variable definitions. "Users" = internet users (% of adults 15+). "Payments" = Used internet to make payments (% of adults 15+). "Telephones 75-99 (log)" = log of average number of fixed telephone lines per capita, 1975-1999. "Bank credit" = Bank credit to the private sector (% of GDP). "Informality" = share of informal employment. "Output Gap" = log difference between observed GDP and trend GDP. "Severance" = severance costs % of annual wage. P-values are reported within parentheses in Panels A and B. "n.a." = Not applicable. ***** Informality is not statistically significant in any of these specifications as a regressor of in the unemployment equation but it has a negative sign. The output gap is negative and significant only in specification 3. Severance is not significant in any specification.

28
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- 11 -

planned ID interventions across sectors in investment projects which typically include measures for (a) strengthening the existing functions of project related organizations or project implementation units (PIUs); (b) adding new functions to existing organizations to make them mere effective; (c) creating new organizations or units for project implementation; and (d) divesting or privatizing existing organizations or their pdrts in some of the more recent projects.18 All of them invariably call for the development or upgrading of skills.

In contrast to investmenc: projects, the design of institutional components in sector loans and SECALs usually consists of much broader elements: sector-wide organizational restructuring and strengthening, regulatory and procedural reform, and bu!lding policy and planning capability for sector management. The relative frequency of the three categories tend to vary from one operation to another. TablJe 4 provides a comparative picture of the types of institutional interventions included under the trade, industry and agriculture SECALs and the frequency of each type within each sector, as indicated by the percentages.

**TabLe**

||||**FREQUENCY**OF I|NSTITUTIONAL|REFORMS|||
|---|---|---|---|---|---|---|---|
|-------------------------------------------|**Policy**<br>**PIn**<br>Capacity<br>**B**<br>---------------------|**ins**<br>**uilding**|**Orgtlantimal**<br>**and Streng**|**letruturing**<br>**then**|Regulatory<br>Procedural<br>__-----------__----------------------|**and**<br>Reform|TOTAL|
||Number|X|Number|X|Number|X|Number|
|Industry|13|25|21|40|19|36|53|
|Agriculture|38|32|52|**44**|29|24|119|
|**Trade**|13|24|27|50|14|26|54|
|**TOrAL**|64|28|**100**|44|62|27|226|

(2) A review of the ID design content in Bank projects based on sample surveys of investment projects and discussions with Bank staff shows that the first ID objective of supporting project implementation (investment) is the dominant preoccupation of ID components.19 The redesign of agency organizational structures, training of project personnel or other ministry staff and reform of management systems are examples of ID work focused on implementing agencies (see Diagram I in Chapter VI for a hierarchy of 18 Based on a review of 80 Bank projects approved durin& FY83-FY87 by D. W. Brinkerhoff in Institutional Analysis and Institutional Development: A Survey of World Bank Proiect Experience, CECPS (mimeo), 1989.

19 Ibid. However, only about a fourth of the projects approved in FY89 had PIUs.
TABLE **I (Continued).** OLS ESTIMATE'S. Dependent variable: **_LIBERALIZATION_**

|**_CONSTANT_**|**(8)**<br>0.18***<br>(7.82)|**(9)**<br>0.20***<br>(6.92)|**(10)**<br>0.20***<br>(6.83)|**(1 1)**<br>0.08**<br>(2.41)|**(12)**<br>0.09***<br>(3.50)|
|---|---|---|---|---|---|
|**_LIBERALIZATION(-1)_**|0.78***|0.78***|0.77***|0.72***|0.74***|
||(25.00)|(23.4)|(22.16)|(22.3)|(17.47)|
|**_FREEDOM_**|**-**|**_-_**|**-**|0.01*******<br>(3.27)|0.01**<br>(2.47)|
|**_FREE_**|0.02|-|0.02|||
||(1.31)||(1.02)|||
|**_NONFREE_**|**_-_**|-0.04<br>(1.45)|-0.03<br>(1.33)|-||
|**_COHESIDEOL_**|0.02**|0.01|0.02*|0.02**|0.02**|
||(2.12)|(1.26)|(1.38)|(2.03)|(2.28)|
|**_AID_**|0.003***|0.003**|0.003***|0.003***|0.004***|
||(2.79)|(2.18)|(2.38)|(3.32)|(2.86)|
|**_WAR_**|-0.04**|-0.04**|-0.03*|-0.04**|-0.04**|
||(2.44)|(2.19)|(1.94)|(2.27)|(2.29)|
|**_INITFREEDOM_**|**_-_**|**_-_**|**_-_**|0.01**<br>(2.30)|**0.01*****<br>(2.78)|
|**_ELECTION_**<br>**_(-1)_**|**_-_**|**_-_**|**_-_**|0.03**<br>(2.05)|-|
|**_ELECTION_**<br>**_(+1)_**|**-**|**-**|**-**|**-**|-0.03*<br>(1.89)|
|**Adjusted R-squared**|0.90|0.90|0.90|0.91|0.91|
|**F- statistic**|229***|234***|194***|188***|189***|
|**White test for**<br>**heteroscedasticity**|31.7**|32.3**|33.6**|33.4|56.14***|
|**Number of observations**|<br>125|125|125|125|125|

Absolute value of t-statistic in parenthesis. *, **, and *** mean that the coefficient is significant respectively at 10%, 5% and 1 % level of confidence.

The t-statistics in equations (8)-(1 0) and (12) are obtained using the White heteroscedasticity-consistent standard errors.

14
provide non-social subsidies, expanding farm size beyond optimal levels is a rational response by farmers signaling their disposition to offer bribes in exchange for subsidies (Bulte, Damania, and López, 2004). Land expansion is thus a vehicle to promote subsidies, and knowing that governments are prone to subsidize induces farmers to expand land area. That is, land expansion and subsidies are the cause and effect of each other.

In summary, this paper conceptually justifies and empirically tests three hypotheses: (i) that countries that, ceteris paribus, spend a greater share of rural public expenditures in private goods have lower agricultural per capita income than countries that spend more on rural public goods; (ii) that agriculture in these countries tends to be more dependent on the expansion of the land area rather than on intensification; and (iii) that rural poverty in these countries is worse.

To test these hypotheses we use a newly assembled data set for the period 1985–2000 for the rural sector for several countries in Latin America. These data consist of annual rural public expenditures collected using a consistent methodology across countries and over time. The high degree of detail of these data is sufficient to allow us to aggregate rural expenditures in public goods and in private goods.

The remainder of this paper is organized as follows. First we discuss general conceptual issues, in Section II. In Section III we present the empirical model. Section IV provides an overview of the data used. Section V presents the econometric results. Section VI concludes.

# **II. Conceptual Issues**

The classification of public expenditures into _public_ goods and _private_ goods arises from the conceptual view that governments’ primary responsibility is to, using use taxpayers’ money, supply public goods, including goods and services that are undersupplied as a consequence of missing markets or of market imperfections.<sup>8</sup> The importance of the public versus private good dichotomy is that while the public good tends to be complementary with private investment, the private good, as we shall see, tends to be a _substitute_ for private investment.

_Political motivation and the structure of public expenditures_ . The political motivation of the government to invest in public goods is fundamentally different than its motivation to do so in private goods, and such different motivations cause important socio-economic effects of the public/private good choice. By definition the benefits of public or semi-public goods tend to

> 8  In practice, as we shall see, the distinction between public and private goods is not sharp. But by and large, more often than not, a reasonable separation between these goods is possible.

4
_Africa’s Macroeconomic Story_ exchange rate pressures. Precautionary saving is especially important for countries unable to access capital markets on a regular basis. Moreover, foreign assets can provide a hedge against currency risk for countries with foreign currency denominated debt and gives the monetary authority more scope to implement serialization operations.

One can argue that holding foreign assets is suboptimal, given the high potential social and economic returns from social spending priorities and growth-enhancing expenditures. Inadequate public infrastructure is a serious bottleneck throughout much of the region, constraining productivity of private investment in non-resource sectors and undermining long-term growth prospects. Improving public infrastructure, especially in electricity and transport sectors, can also alleviate capacity constraints and support the development of the non-resource sector, by moderating the potential for Dutch disease.<sup>28</sup> The historical record indicates that public infrastructure projects often do not generate the benefits projected at the planning stage, especially in countries with weak institutions and limited managing capacity. Capacity constraints limit how quickly public investment can be scaled up. Projects need to be implemented at a measured pace taking into the prospective impact on inflation and the exchange rate as well as the administrative capacity for managing and implementing projects efficiently.<sup>29</sup> Foreign assets also provide a contingent source of funding for existing and planned projects.<sup>30</sup> Although the region as a whole has tremendous potential for developing resources, this is not assured for individual countries. Many countries currently face declining resource revenues due to depleting oil resources. Net oil export revenues are expected to decline significantly over the next five years in the top six oil-producing countries (Figure 25).<sup>31</sup> Intergenerational equity plays a central role 28 An empirical study by Calderón (2009) finds that expanding expenditures on public infrastructure in Africa has had sizeable growth benefits.

29 Berg el al (2012) have developed a model designed to help developing countries examine these trade-offs. Az provide a comprehensive analysis of the various fiscal risks entailed public infrastructure investment projects undertake in Africa.

30 A recent empirical study by Dessus and Varoudakis (2013) indicates that countries in West African Economic and Monetary Union (WAEMU) tend to cut capital expenditures in response to adverse fiscal shocks but do not raise capital expenditures in response to favorable shocks. As a consequence, capital expenditures tend to remain well below planned levels over the course of the business cycle.

31 These projections do not take into account the possibility of discovering new reserves or technological innovations that could make proven reserves commercially viable 39
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Cameroon > Electricity investments > Macro results**

|Variable|Definition|Reference|Reduction in<br>other public<br>expenditure|Ele<br>VAT|ctricity<br>Import<br>duties|Foreign<br>aid|Income<br>tax|
|---|---|---|---|---|---|---|---|
|Ym|Aggregate household income|5,409,614.00|0.51|0.90|0.99|1.22|0.85|
|EV|Equivalent variation||0.51|0.83|0.90|1.27|–0.78|
|s|Wage|1.00|0.54|1.40|1.59|2.11|1.29|
|yg|Government income|1,500,596.00|0.77|6.04|6.04|6.04|6.04|
|g|Total government expenditure|656,249.00|–5.52|6.54|6.54|6.54|6.54|
|ye|Firm income|2,118,815.00|1.10|0.10|–0.11|–0.70|0.23|
|It|Total private investment|1,213,585.00|1.99|0.65|0.40|–0.33|–0.07|
|GDP|GDP|6,395,129.00|0.80|0.79|0.80|0.80|0.80|
|e|Nominal exchange rate|1.00|0.29|0.28|–1.68|–5.87|–0.07|

# **Cameroon > Electricity investments > Sectoral results**

||||Reduction in|El|ectricity|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign<br>aid|Income<br>tax|
||Crop agriculture|1,088,074.00|0.72|–0.27|–0.40|–0.65|–0.93|
||Export agriculture|419,908.00|1.46|0.43|0.33|0.17|0.47|
|Va|Mining and gas|623,479.00|0.38|0.34|0.27|0.11|0.34|
|<br>Value added or|Industries|1,266,964.00|1.94|0.68|0.90|1.10|1.21|
|output|Construction|108,698.00|8.94|7.03|6.71|8.37|8.15|
||Private services|2,265,916.00|1.17|0.49|0.53|0.44|0.42|
||Public services|622,090.00|–4.12|3.61|3.48|3.79|3.75|
||Crop agriculture|1.00|0.48|1.22|1.36|1.44|0.70|
||Export agriculture|1.02|0.51|1.41|1.92|1.41|0.41|
||Mining and gas|1.04|0.51|0.46|0.71|–5.68|0.06|
|pq<br>Market rices|Industries|1.11|–0.36|1.44|1.73|–1.33|–0.59|
|p|Construction|1.00|–0.07|1.37|1.56|–0.53|–0.14|
||Private services|1.03|–0.29|0.78|0.45|0.23|–0.34|
||Public services|1.00|0.28|1.34|1.49|1.24|0.74|
||Crop agriculture|1.00|1.43|1.03|1.05|1.24|0.08|
||Export agriculture|1.00|2.93|1.72|1.70|1.89|1.68|
|r<br>|Mining and gas|1.00|0.94|0.45|–1.98|–7.03|0.44|
|Rental rate of<br>capital|Industries|1.00|0.78|–1.47|–0.75|0.26|–0.29|
||Construction|1.00|9.57|8.21|8.01|10.59|9.44|
||Private services|1.00|0.86|0.35|0.61|0.96|0.10|

50
## **3.6. Competitive Equilibrium**

An equilibrium in this economy is as follows:

- a collection of sequences of aggregate prices, wages, capital rental rates and prices

- for intermediate goods;

- a collection of sequences of intermediate firms’ value functions, profits, exit rules,

- and innovation and rent-seeking decisions; and

- distributions of operating firms and measures of entering firms such that

- households maximize utility subject to the budget constraint,

- intermediate firms and final-good producers maximize profits, and

- feasibility constraints (such as market clearing for all markets) are satisfied

and the distribution of operating firms evolves endogenously.

# **4. Calibration**

In this section, I calibrate the model to the data. First, I need to provide a functional form for the costs of innovation, _cz_ ( _φ_ ). I assume that the costs are given by which is strictly convex in _φ_ , with _hz_ zz _z_ > 0 and zz _bzz_ > 0. For the cost of bribing, zz _cττ_ ( _φ_ ), I assume the mm (φφ) = h exp(bb φφ), which is strictly convex in _φ_ , with _hz bzz cττ_ ( _φ_ ), I assume the same functional form, given by which is strictly convex in _μ_ , with _h_ zz _τ_ > 0 and zz _bτ_ > 0. zz mm (μμ) = h exp(bb μμ), For the sake of clarity, I will classify the model’s parameters into four groups: (a) { _γ_ , _ρ_ , _α_ , Ω} are the standard parameters, (b) { _δ_ , _fe_ , _f_ } are technological parameters, (c) {Δ _z_ , _hz_ , _bz_ } are parameters related to innovation, and (d) {Δ _τ_ , _ψ_ , _τmin_ , _τmax_ , _hτ_ , _bτ_ } are parameters related to rentseeking in the form of bribing.

The strategy I used to calibrate the parameters in the steady state has three parts. First, I set some of the parameters according to existing microeconomic and macroeconomic evidence in the literature. Second, I calibrated the technological parameters to an undistorted economy where _τ_ = 0 for all firms. In order to map this economy to the data, I followed the literature and used the U.S.

20
# Analysis Using Pre-Registered Covariates

We deviated from the pre-registered covariates including age and controlling for pre-GPA as a 4th degree polynomial term. Following on the pre-analysis plan, the registered covariates included: prior GPA, gender, ethnicity, initial cognitive ability, quality of school and geographical location. Initial cognitive ability was intended to be derived from the national standardized test, which was not possible as the country has no reliable national standardized test (confirmed by the Ministry of Education), similarly for quality of schools. For the initial ability, at baseline, the team conducted two measures (Baseline Math and Baseline Reading Comprehension), however due to a printing error of the baseline survey, only 20% of the original sample took the Math and Reading baseline skills. For the replication of baseline covariates, prior GPA was used as surrogate for Cognitive Ability. Quality of schools was originally planned to be based on the national standardized test. Given the absence of such test, adjusting for school as a source of clustering was used as surrogate for quality. For geographical location we use the municipality indicator (79-level categorical measures) as the surrogate effect of the location.

_Table A4.3: Impacts on Deliberate Practice Beliefs and Grit, Pre-Registered Covariates (Z-Scores)_

||Deliberate Practice Beliefs<br>(1)|Grit<br>(2)|Grit: Effort<br>(3)|Grit: Interest<br>(4)|
|---|---|---|---|---|
|Treatment 1|0.157***|-0.048*|0.060**|-0.115***|
|“Student Self-|_(0.019)_|_(0.020)_|_(0.020)_|_(0.019)_|
|Learning”|||||
|Treatment 2|0.230***|-0.029|0.060**|-0.097***|
|“Teacher|_(0.018)_|_(0.021)_|_(0.019)_|_(0.021)_|
|Delivery”|||||
|N|24,151|21,815|22,929|23,153|
|N Control|9,429|8,507|8,930|9,056|
|N Treatment 1|7,041|6,346|6,692|6,723|
|N Treatment 2|7,681|6962|7,307|7,374|
|_R2_|_0.335_|_0.350_|_0.348_|_0.232_|

p<0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 42
Table 4. Regression Results Considering Large Dams Only, Dependent Variable: CP

||(1)|(2)|(3)<br>|(4)<br>|
|---|---|---|---|---|
|**D(LARGE)**|||66.58<sup>**</sup>|321.1<sup>**</sup>|
||||(24.74)|(70.34)<br>|
|**UD(LARGE)**|||36.59|258.6<sup>**</sup>|
||||(27.66)|(91.77)|
|**SPI**|57.82<sup>*</sup>|57.88<sup>*</sup>|57.12<sup>*</sup>|55.27<sup>*</sup>|
||(28.43)|(28.02)|(28.92)<br>|(26.67)<br>|
|**EVAP**|−75.80|−71.42|−72.27<sup>*</sup>|−74.67<sup>*</sup>|
||(42.60)|(41.15)|(36.47)|(34.66)|
|**RF**|0.00856||||
||(0.0130)||||
|**RFL**||−54.27<sup>**</sup>|−53.53<sup>**</sup>|−50.39<sup>**</sup>|
|||(19.45)|(19.23)|(17.98)|
|**RFH**||8.636|7.659|4.781|
|||(16.94)|(16.26)|(15.60)|
|**DAMS**|LARGE|LARGE|LARGE|LARGE|
|**IV**|No|No|No|Yes|
|**Observations**|4,460|4,460|4,460|4,460|
|**Basins**|223|223|223|223|

_Note:_ Driscoll and Kraay (1998) standard errors corrected for spatial and second-order autocorrelation are given in parentheses;<sup>**</sup> and<sup>*</sup> are 1 percent and 5 percent significance levels. CP (dependent variable) denotes cropland productivity. Regressions include river basin fixed effects, former homeland year interactions, interactions of homeland basin predicted dams with river basin gradient, perennial and ephemeral river lengths, river basin area, river basin elevation, river gradients, and river gradient-year interaction terms.

In the third column, we include the number of large dams within a basin as well as the number of large dams located upstream, both in their noninstrumented form (that is, we estimate the ordinary least squares version of equation (3)). Accordingly, we find that large irrigation dams positively increase cropland productivity within their own basin, but they have no effect on cropland located immediately downstream. Importantly, however, instrumenting our dam variables changes these findings. In particular, as shown in the fourth column, the coefficient for upstream dams is now significant, suggesting that these dams do increase productivity. Moreover, the size of the own-vicinity dam 31
We dene two groups of rms, according to whether their idiosyncratic productivity _A_ is below or above the 80th percentile, and denote them with _ZL_ and _ZK_ . In each period, rms in the group _ZL_ (low productivity rms) face a probability (1 _− ζ_ ) of falling into an absorbing state of _A_ = 0 , which means exiting the market as present and future prots become zero. The probability of exit is zero for rms in _ZH_ (high productivity rms). These simplifying assumptions are based on the empirical observations of Dunne et al (1988, 1989), who nd that exiting rms are smaller than continuing rms, and in the simulations of Clementi and Palazzo (2016), who estimate close-to-one probabilities of survival for rms with high productivity. The probability of exit introduces a simple mechanism of selection that correlates with productivity. Notice that because the distribution of entrants is truncated at the 80th percentile, all new rms fall into the _ZL_ group and face a positive probability of falling into the absorbing state (1 _− ζ_ ) . This matches the empirical fact that entrants are more likely to exit than incumbents (Dunne et al, 1988).

In any given period, the probability of being in group _ZL_ , denoted with _ρLt_ , is given by The probability of exit, denoted with _ρxt_ , is _ρxjt_ = (1 _− ζ_ ) _ρLjt_ . The number of rms that exit the market is given by _nxjt_ = _ρxjtnjt−_ 1 .

Under these assumptions, the expected rm value prior to entry is given by where the expectation _Et_ is taken over next period's values of sector shocks and wages. The last term corresponds to the required stochastic initial investment drawn from _μ_ 0 . The expected value _V_ 0 _jt_ varies across periods according to state variables _bjt_ and _wjt_ . Wages are determined in equilibrium (equation 11). They depend on the current labor allocations between sectors, and on aggregate shocks and rm distributions through labor demand. With the introduction of entry and exit, wages further depend on the mass of active rms, _njt_ . When there are sector shocks, incentives to enter the market are aected, and equilibrium is restored through changes in number of rms, labor demand, equilibrium wages, and rm prots.

To simulate aggregate responses to tari shocks we use our previous estimates for the baseline model parameters, and calibrate the new parameters to match statistics from Dunne et al (1988).

53
CONTEFTS

|||Page|
|---|---|---|
|PART 1:|THE MACRO-ECONOMIC BACKGROUND||
|1.1|Introduction|5|
|1.2|Decline in growth, 1981-86|6|
|1.3|The financial system|7|
|PART 2:|IMPACT OF RECESSION ON THE FINANCIAL SYSTEM||
|2.1|The banking system|8|
|2.2|Non-bank financial intermediaries|13|
|PART 3:|ISSUES AND RESOLUTION||
|3.1|Approach towards ailing financial institutions|15|
|3.2|Monetary and Prudential measures, 1985/86|17|
|3.3|Modus Operandi of Rescue Packages|19|
|3.4|Options for Rescue|20|
|3.5|The Rescue of the Problem Banks|21|
|3.6|The Rescue of the 24 Deposit-taking Co-operatives|22|
|PART 4:|EVALUATION||
|4.1|Results and Lessons|25|
|4.2|Losses and costs|26|

# REFERENCES

# TABLES