rafmacalaba/datause-encoder-data huggingface dataset:
6 long-term income-generating investment projects commissioned by the government, but financed by the private sector, which is to be repaid from the proceeds from the operation of the project. It is not clear to what extent these projects represent a fiscal liability, even though they do not involve immediate fiscal outlays. Moreover, the "BLT" arrangements also raise questions about the extent of private participation involved, other than in the construction itself, and consequent implications for competition in the sector. The amounts involved are significant: for 1997, the Mexican government had programmed 24 billion Pesos (equivalent to 24 % of the 1997 public investment budget) in "proyectos financiados" in the hydrocarbons, electricity and highway sectors. These projects are expected to represent an increasing share of public investments devoted to incomegenerating projects.

# **C. Public Investment and Economic Growth: Time Series Analysis**

_Crowding out or crowding in?_ The discussion in Section A indicated that the impact of public investment on economic growth can be positive or negative, depending on the circumstances under which it is carried out. The preceding overview indicates that these underlying circumstances have varied significantly in Mexico over the last four decades, so that its impact is likely to have varied accordingly over time. Even so, Mexico's experience in this regard is shown below to broadly mirror the international experience described in Easterly et al (1994), which found that public investment has tended, on balance, to exert a crowding out effect on private investment.

The equations in Table 3 describe the results from regressing private investment **(JP)** on the consolidated public sector expenditures (EG), which are decomposed into public investment (IG) and government consumption expenditures (CG), on consolidated public sector revenues (RG) and GDP (Y), using simple least squares.<sup>7</sup> The regressions are based on annual data from 1970 to 1996 from the national income accounts. These series are non-stationary, so the regressions were carried out on the differenced variables to avoid spurious correlation. The equations in Table 3 were also reestimated with Two-Stage-Least Squares, under the assumption that GDP was also an dependent variable (not shown here). This different procedure did not appreciably change the results.

Three issues are of interest in light of the earlier discussion: (a) whether public investment crowds out or crowds in private investment, (b) whether that impact occurs solely through its impact on the overall deficit (which leads to financial crowding out), or whether public investment exerts an additional influence, and (c) whether there has been any break in these relations after the Government embarked on its new economic strategy in the mid-1980. The main findings from the regression with regard to these issues are described next: 7These equations are similar to the ones estimated by Rodriguez (1994) for Argentina.
47

# **Table 1b**

## **Summary Statistics: Control and Human Capital Indpendent Variables**

||**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Growth of Capital**|**84**|**5.3917**|**3.3044**|**-0.0968**|**17.2024**|
|**Growth of Labor**|**84**|**2.2056**|**0.8845**|**0.2877**|**4.5267**|
|**Initial GDP (1960)**|**84**|**0.3105**|**0.2694**|**0.0341**|**1.2204**|
|**HC15**|**84**|**1.7530**|**0.4492**|**1.0581**|**2.8472**|
|**Square of HC15**|**84**|**3.3147**|**1.7509**|**1.1205**|**8.2040**|
|**HC15 extended**|**84**|**1.7315**|**0.4965**|**1.0693**|**4.7060**|
|**Square of HC15 extended**|**84**|**3.3172**|**2.6991**|**1.1445**|**24.2639**|
|**HC25**|**84**|**1.6892**|**0.4574**|**1.0433**|**2.8800**|
|**Square of HC25**|**84**|**3.1051**|**1.7545**|**1.0891**|**8.4101**|
|**HC25 extended**|**84**|**1.6579**|**0.4683**|**1.0515**|**4.2994**|
|**Square of HC25 extended**|**84**|**3.0363**|**2.3272**|**1.1066**|**20.2922**|
|**Log (Educational Quality)**|**84**|**3.7544**|**0.3076**|**2.9047**|**4.2785**|
|**Log (Patents)**|**84**|**2.4327**|**2.7420**|**0.0000**|**10.9275**|
|**Phones**|**84**|**167.4538**|**198.0019**|**0.8461**|**717.4805**|
Our meta-regression, like all other quantitative analyses in this review, are conducted with Stata version 16.0, primarily relying on the metan and metareg commands.

# _Risk of bias assessment_

We grade studies as having low, medium and high risk of bias with a tool developed for reviews in social science<sup>22</sup> based on suggestions by the Cochrane Effective Practice Organisation of Care,<sup>23</sup> the Coalition for Evidence-Based Policy,<sup>24</sup> and the Cochrane Handbook for Systematic Reviews of Interventions<sup>16</sup> which classifies studies into high, medium or low bias risk groups. The tool is presented in Appendix 5, and the bias assessment results for each included study is shown in Table 1, alongside information on the studies’ evaluation design.

# **Results**

# **_Search and data extraction results_**

The PRISMA chart shown in Figure 2 depicts the results of our search and eligibility screening process. In the initial 2016 search round, we extracted a total of 6,289 references. After deduplication, title-abstract screening identified 470 potentially eligible references, of which 30 proved compliant with our inclusion criteria after full-text screening. Reasons for exclusion varied across interventions but methodological grounds such as the use of non-compliant identification strategies or the lack of statistical uncertainty estimates were common across all. In repeated searches in 2017, 2018, and 2019, and 2021, we found 28 additional references meeting our inclusion criteria, bringing the total number of included references to 58, with 24 reporting on PBF programs, one on vouchers and PBF, eight on vouchers alone, and 25 on CCT programs alone. From the 58 references, we extracted a total of 212 impact estimates across our six outcomes of interest. When aggregating to the program level, there are 130 program-specific effect sizes, with 75 effect sizes from 22 PBF programs, 31 effect sizes from ten voucher programs, and 34 from 20 CCT programs (for studies with multiple treatment arms we consider as separate programs treatment arms which differ in terms of having status quo as opposed to income equalized control groups, or in terms of introducing complementary demand- or supplyside financial incentives). Appendix 6 provides a breakdown of references, impact estimates, and program-specific effect sizes per outcome.

# **_Study characteristics_**

8
military aid in foreign currency;<sup>_P_</sup> _n_<sup>is the average price of non-trade goods and services which</sup> includes the public sector, _P_ * is the foreign price level; and _P_ is the aggregate price level, which is _Pn_ δ ( _EP_ * )1 −δ where δ is the share of non-tradable goods and services in aggregate expenditure.

~ * ′ ′ ~ * Assume that _F_ = _t n_ = _t m_ = _P_ = 0 (where (~) denotes growth rate) and note ~ ~ ~ that _P_ = δ _Pn_ + 1( − δ ) _E_ . In addition, the assumption of complete-pass through from the exchange rate to domestic prices of devaluation is relaxed and it is assumed that prices of nontradable goods and services are indexed to the nominal exchange rate, which is a way of τ β indirectly reflecting public sector wage indexation<sup>16</sup> , i.e. _Pn_ = ( _E_ ) . Real government revenue can be rewritten as, and the revenue adjustment to devaluation takes the following form<sup>17</sup> , where η _m_ ,<sup>η</sup> _f_ and η _n_ are the revenue shares of imported goods, grants and non-tradable goods and services respectively, τ ∈ []1,0 is the pass-through elasticity and β∈ []1,0 is the degree of wage indexation. Using the latest data for 2004, the corresponding revenue shares are η _m_ = 303.%, η _n_ = 322.% , η _f_ = 375.%. Hence revenue from foreign currency denominated grants and military aid constitutes the largest share.

In static terms, a nominal exchange rate devaluation in Djibouti could increase revenues through higher trade taxes by raising import value in terms of local currency. It could also have a positive impact on revenues deriving from foreign currency denominated grants. If public sector wages were indexed, nominal exchange rate devaluation would also contribute to higher income taxes. However, tax revenues from the import sector may fall if import demand declines with a ~ * devaluation ( _M_ < 0 ) implying that demand is price elastic<sup>18</sup> . At the same time, the overall price level could rise<sup>19</sup> given Djibouti’s high import content of overall consumption and may cause real revenues to decline if the increase in aggregate prices is higher than the gain in nominal revenue<sup>20</sup> .

> 16 This is a reasonable assumption for Djibouti considering the relatively large size of the public services in the formal non-tradable sector.

> 17 See appendix for a fuller derivation of this expression.

> ~

> 18 In addition, if a currency devaluation is contractionary in the non-tradable sector ( _Yn_ < 0) , tax revenues would also fall.

> 19 The magnitude of the overall increase in domestic prices will be dependent on the degree of exchange rate passthrough

> 20 For the purposes of the paper, the growth rate of imports is assumed to remain unaffected by the nominal exchange rate devaluation.

12
_PoUsh_ **_FLvcaJ Crbis_** _5_ since if the firm is to continue as a going concern, the inputs that are used up must be replaced at the current, higher, price level.

Not subject to this inflation bias are profits expressed on a cash flow (CF) basis, in which ar. input is counted as a cost when thes expendititre on the input' **kes** place. Approximate cash flow for the enterprise sector can be derived from the published historical cost accounts, inventory data, and an accounting identity; see the Appendix for details ar.d some worked-ouit examples.<sup>5</sup> The intuition behind the calculation is that the while inflation biases historica material costs downwards, it simlLtaneously biases nominal inventory accumulation upwards, and these biases are identical!:y equal (except for the sign).

The result of applying this calcilation is a series which I sLall call, with some abuse of terminology, "cash flow".' It difters from the standard HCA profit series ("wynik finansowy", the profit tax base) in three ways:

1. The 'cash flow" series does not suff4r from an inflation bias. Everything is in current terms.

2. Real inventory accumulation (decumulation, will decrease (increase) cash flow but will have no effect on HCA profits.

3. As is standard with cash flow figures, amortization deductions have been added back in.

Everything else counted as a cost or a revenue (e.g. interest charges, wages, financial incomes of all sorts) in HCA profits is also counted in the cash flow series.

Figure 1 shows the results of applying this adjustnent to profits in the Polish enterprise sector. The difference between the HCA and cash flow series is dramatic. HCA profitability (profits as a percent of sales at producer prices) was very high in 1989 and 1990, and collapsed in 1991. Cash flow profitability, by contrast, shows a much smaller drop in 1991.

Another useful and inflation-bias-free measure of profit is profit calculated on a current-costaccounting (CCA) basis. This is similar to HCA profit except that it uses current prices of inputs instead of the (historical) prices actually paid. The differences bctween CCA profit and cash flow a&e:

1. Real accumulation (decumulation) of inventories decreases (increases) cash flow but has no effect on CCA profit.

2. Again, cash flow is gross of depreciation and CCA profit is net. CCA profit measures the profitability of production, whereas cash flow measures the cash earned by the enterprise.

- **_S._ I** should note that **the** method of calculating cash flow is correct **in** principle, but may suffer from problems in **practice.** It is **therefore beat** treAted as an approximation.

**6.** This **"cash flow'** series is basically 'net cash itow from current operations", **but** with **the** difference that extraordinary profits and losses **are** included. Extrordinary profits nd losses cannot be straightforwardly **separated** out beawusethey **include** paper capital gains from explicit inventory revaluations, and **these must** be **left in** for the derivation of **cah flow** to **work.** Sx the Appendix.
# **Appendix B: Summary statistics and additional data issues**

# 1 **. Summary statistics on estimating sample**

# **_Appendix Table B1. Summary statistics on estimating sample Panel A. Number of HS 6-digit products and US imports for AGOA and control countries_**

||Num<br>|ber of HS 6-digit<br>|Products and<br>|US Imports<br>|
|---|---|---|---|---|
||AGOA C|<br>ountries (44)|Control C|<br>ountries (164)|
||Mean|Standard<br>Deviation|Mean|Standard<br>Deviation|
|Number of HS 6-digit products per<br>country (with imports>0)|97|240|734|1,068|
|Number of AGOA eligible HS 6-digit|||||
|products per country (with|28|57|188|226|
|imports>0)|||||
|Log (imports) per country-HS 6-digit<br>product|9.84|1.05|10.71|1.25|
|<br>Log (imports +1) per country-HS 6-<br>digit product|<br>0.24|<br>0.57|<br>1.91|<br>2.59|

**_Panel B. Number of GSP LDC-eligible or AGOA-eligible HS 6-digit products for AGOA countries_**

||||AGOA/GSP|Countries||
|---|---|---|---|---|---|
|||GSP|<br>AGOA|AGOA|<br>AGOA|
|||LDC|<br>Non-LDC|Only|Apparel|
|||(810)|(769)|(91)|(239)|
|||Fo|r products wit|h imports>|100|
|Number of HS 6-digit|Mean|4|26|5|23|
|total number of products<br>products per country|in the HS<br>Max|classificatio<br>11|n at 6-digits is<br>195|5,070 and e<br>34|ach cell shows the aver<br>120|

Note: In Panel B the total number of products in the HS classification at 6-digits is 5,070 and each cell shows the average across AGOA countries over the sample period.

# **2. Relative preference margin**

The formula for the RPM is based on Nicita (2011) and is given by: where j is the country exporting to the US, X is export value, V are other exporting countries competing with country j, t is the tariff paid in the US, hs is an HS 8-digit product. Relative to Nicita (2011) we simplify the formula in that we do not consider trade elasticities thus assuming that all countries’ export flows react similarly to a reduction in tariffs. Ideally, to calculate the true preference margin, we would also include the tariff-equivalent of the quotas of the MFA and the impact of its end on the preference margins, but this exercise is beyond of scope of this study.

4
conditions on average.<sup>11</sup> For consistency, we use the gross returns to capital information (Table 4) in the last column of Table 5 to report the gross returns to saving, by deducting from gross of tax returns, taxes per unit of capital, as well as the value added of financial services divided by the capital stocks plus inventories and land. We can observe that, although there is some small difference in the trends, the weighted average real returns to different instruments and the topdown net returns (gross returns less taxes and capital intermediation) give very similar averages. The top-down calculation is used for the rates of return on savings and the average estimated return on different instruments as the basis to estimate the marginal cost of foreign savings.

**Table 5 Rates of return to pension funds, private sector, and public sector bonds, 2003–2011**

|Year|Siefores<br>pension funds<br>(real rates of<br>return)|Traded<br>equity<br>(real rates of<br>return)|IFS<br>government<br>securities<br>(real interest<br>rates)|Banxico<br>corporate<br>bonds (real<br>interest<br>rates)|World Bank<br>external debt<br>(real interest<br>rates)|Weighted<br>average*<br>(real rates of<br>return)|<br>Top-down gross<br>returns to<br>capital less taxes<br>and financial<br>intermediation|
|---|---|---|---|---|---|---|---|
|2003|13.5%|15.8%|5.0%|3.4%|5.9%|9.2%|7.9%|
|2004|11.6%|14.3%|5.1%|4.9%|6.1%|8.9%|8.5%|
|2005|10.8%|13.5%|6.4%|8.0%|6.5%|9.6%|8.1%|
|2006|11.4%|13.0%|4.6%|6.9%|7.0%|8.9%|8.7%|
|2007|10.4%|11.7%|4.2%|6.1%|6.9%|8.1%|8.9%|
|2008|7.6%|11.2%|2.3%|4.0%|5.3%|6.8%|8.3%|
|2009|6.6%|12.6%|5.1%|3.4%|4.8%|7.7%|7.2%|
|2010|8.8%|12.3%|3.5%|4.6%|4.4%|7.4%|7.9%|
|2011|8.7%|12.2%|4.3%|4.6%|4.7%|7.6%|9.4%|
|**Average **|<br>**9.9%**|**13.0%**|**4.5%**|**5.1%**|**5.7%**|**8.2%**|**8.3%**|

_Source:_ INEGI; Banco de México; IFS (IMF); Global Finance Statistics; World Bank External Debt Data.

* Weighted average return based on 40 percent equity and 60 percent debt at average government, corporate, and external interest rate.

# **3.3 Results of the EOCK estimation for Mexico for the 2003–2011 period**

**This section shows estimations obtained for Mexico’s EOCK.** The shares of investment for the private sector, as well as domestic and foreign savings data, have been incorporated in percentage terms from the national accounts. Based on several reference studies, we also adopt mid-range values for the price elasticities of demand for investments (η = −1.0), for supply of domestic savings (Eds = 0.30), and for supply of foreign savings<sup>12</sup> (Efs = 3.0).<sup>13</sup> In the

> 11 Note that a large share of private savings is in residential housing that yields a real financial rate of return similar to debt interest rates.

> 12 Foreign savings are defined as the amount of net inflows of foreign capital funds (debt, portfolio investments, direct investment, etc.) into Mexico reduced by the amount of net outflow of investment by Mexicans outside of Mexico and by the net additions to foreign reserves.

19
11

# **IdT** =.AI* dTB * d **dY dTB dB** dY

where dT = percentage change in motor fuel tax revenue; dY = percentage change in GDP; dTB = percentage change in the taxable base; dB = percentage change in the true market value base; Buoyancy from the first component [ dT | is derived from the rate structure of motor fuel dTBJ

tax. Under a flat rate ad valorem tax, rate elasticity would be unity. One reason for inelasticity of motor fuel tax observed in many LDCs, is that a specific (per gallon) tax rate is levied; hence, periodic discretionary adjustments are required for revenue to keep pace with the rising price of imported petroleum.y Such adjustments are politicaUy difficult, and failure to make them frequently staUs the revenue growth of motor vehicle tax. For example, in Yugoslavia, failing to adequately index the tax rate reduced road user tax revenue by about one-half between January 1987 and March 1988. This problem has led Yugoslavia, and also Argentina, to adopt ad valorem tax rates in recent years.

33. The second component in this simple formula is taxable base elasticity - that is, the extent to which the taxable value of motor fu consumption proportionately increases with the market value of motor fuel consumed. This is, potentially, another source of revenue inelasticity. In most LDCs, the central government fixes gasoline prices, detennining the size of taxable base elasticity. Taxable base elasticity is less than unity in most LDCs if the government does not let the taxable price increase with the market price. In Indonesia, for example, one nationally owned oil company (Pertamina), is responsible for all production, but the price of motor fuel is set by Presidental decree. In practice, the gas tax (subsidy) is set by V _If_ **a** specific tx rate (t ents pr allon) is levied, then revenue from **a** tax at the pump will inres by tdG wher dG - chage in gallons of consumption no matter what the value ineas of the potential taxable base (ftG) where **P** is the pump price.

Since tdG < dfG) in most cas, the rate claslicity will be **less** fn unity for any given

|T = tG averapetaxmrtre -g-
participates in the paid labor force; for example, women‘s participation rate in MGNREGS is about twice their share of other (non-PW) casual wage work.<sup>28</sup> The variation across states is striking; between the two extremes, only 7% of the work goes to women in Jammu & Kashmir as compared to 88% in Kerala (Table 1). The female share on MGNREGS work is greater than their share of the work in the casual wage labor market in all states.

Women are less likely to participate (relative to men) in MGNREGS in poorer states. Figure 7 plots the share of person days of employment going to women against the poverty rate. We see a negative correlation (r = –0.47). By contrast, women‘s share of casual (non-PW) wage work tends to be slightly higher on average in poorer states, though the difference is not statistically significant (r=0.09). While the scheme is clearly bringing women into the paid workforce, it is less effective in doing so in states with higher poverty rates.

Why do we see less of the available work going to women in poorer states? The NSS does not allow us to identify rationing at the individual level (it is a household variable). However, we can gain some clues to how rationing has affected women from the inter-state comparisons. It would seem unlikely that the effect of household poverty on demand for work amongst women is any different than amongst men. Assuming that the effect of being a poorer state on demand for work is the same for men and women, the pattern in Figure 7 suggests that the rationing process is less favorable to women in poorer states.

Do women have equal access to the scheme, when they need it? Again, we cannot give a direct answer from the survey data. However, the patterns in the inter-state data are suggestive. We observe a negative correlation between the female share of work and the overall rationing rate (Figure 8). It might be conjectured that this correlation actually reflects differences in the extent of poverty. Women may well be less aware of their rights and less empowered to demand work in poorer states. For example, when other work is scarce, they may get crowded out by men. However, the negative correlation between the female share of work and the rationing rate persists when we control for the poverty rate, as can be seen in Table 7, which gives regressions for the female share of employment on MGNREGS against both the rationing rate and the

> obvious reason to question their veracity for measuring the gender composition of the work provided. 28  We estimate that the share of women in the total person days of casual labor in 2009/10 was 23.3%, based on the 2009/10 NSS.

16