rafmacalaba/datause-encoder-data huggingface dataset:
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-49 -

# GRAPH 3b: ACTUAL COMMODITY PRICE CHANGES ALICPMM FORECASTS AND ERROR BARS

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# **5.5 Robustness**

In this section we conduct a series of robustness tests of the baseline results. We first explore a continuous treatment measure that takes into account the size of the investment incentive available to each firm. In Table 7 we replace the dummy treatment variable with a continuous version that measures the number of pounds a firm’s average 2005/06 investment is below the AIA threshold in that year. By so doing we attempt to capture differences in how strongly a firm was treated by the AIA - firms with a larger unused investment allowance are likely to have a stronger incentive to invest more.<sup>40</sup> As the estimation method of Callaway and Sant’Anna (2021) is applicable only for binary treatment variables, in the investment regression we take advantage of the recent development by Gardner (2022) that allows for continuous treatment in two-way fixed effects settings with a staggered treatment design.<sup>41</sup> For the cloud regression we continue to use a long-difference specification estimated using OLS. These results provide further evidence that firms with the greatest scope for increasing investment responded to the AIA by increasing their investment and reducing the likelihood of adopting cloud services. Each additional £1,000 of AIA allowance reduced cloud diffusion by 0.3 percentage points.<sup>42</sup> Given the focus of the paper, we concentrate the remaining tests of robustness on the adoption of cloud. Investment data in the UK is, as in most other countries, highly skewed. A small number of firms make very large investments, whereas most firms invest a more modest amount each year. The concern is therefore whether our results are driven by the presence of these largest firms. In column 1 of Table 8 we exclude firms with the largest 5 percent of investment, using investment defined consistent with the treatment status of the firm i.e. the average across 2005/06, while in column 2 we exclude the largest 10 percent 40We also examined AIA allowance quartiles and found evidence that the impact of the AIA allowance is largely increasing linearly by quartile. Thus, the continuous measure employed in Table 7 appears appropriate. Results are available upon request.

> 41This uses the estimation command that implements the approach of Gardner (2022) created by Butts and Gardner (2021).

> 42Each additional £1,000 of AIA allowance also increases investment by 0.4%.

28
premia, see section 4.2). This suggests that adjustments applied to both the REER and CPI-PPI ratio are justified.

Upon estimating the cointegrating relation coefficients using the whole sample of data, estimates of misalignment converge (see Table 3, columns 2-5). According to the unadjusted REER, the peso’s overvaluation is estimated at 27% (31% without the HP filter). If instead we use the adjusted-REER measure, the peso becomes overvalued at an estimated 46% (62% without the HP filter).<sup>31</sup> This is an upper bound if we consider that the CPI-to-PPI ratio is underestimated.<sup>32</sup> In fact, when we use our upward-adjusted CPI/PPI ratio, misalignment estimates converge further: overvaluation becomes 39% (49% without the HP filter) for the adjusted-REER. Finally, in a scenario of large CPI/PPI adjustment, estimates of currency misalignment converge to 21% (26% without the HP filter), considered to be a lower bound.

This analysis reveals that, considering our adjusted measures for REER and CPI/PPI, the peso is overvalued by approximately 39%, with a range of 21–46% when we correct the REER and CPI/PPI ratio for underestimated CPI.

<u>Table 3: VEC Cointegration Relation</u>

||<br>Pre-2008<br>RE|<br>Full sample<br>ER||Adj-REER<br>Full sample||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Terms of trade|0.642***|0.586***|0.402**|0.537***|0.567***|
|Productivity differentials (CPI/PPI)|1.456***|1.673***|1.828***|||
|Reserves|0.312***|0.128*|-0.700***|-0.139|0.359***|
|Trade openess|-0.634***|-0.489***|0.840***|-0.055|-0.880***|
|Constant|-2.717|-2.910|-5.791|-3.963|-1.377***|
|Prod. diff. (Adj. CPI/PPI)||||1.730***||
|Prod. diff.(Large Adj. CPI/PPI)|||||1.584***|
|Error correction|-0.031|-0.032|-0.010|-0.026|-0.031|
|Half life (years)|1.8|1.8|5.6|2.2|1.8|
|Cointegrating rank|1|1|1|1|1|
|Lags in VAR|1|2|2|2|2|
|Overvaluation as of October 2015||||||
|No HP filter|-|31%|62%|49%|26%|
|HP filter|-|27%|46%|39%|21%|
|Overval. using coefficients in column (1)||||||
|No HP filter||25%|114%|70%|12%|
|HP filter||21%|110%|64%|4%|

> 31 In Table 3 column 3, coefficients for reserves and trade openness are significant with opposite signs to theoretical predictions. We believe this is because after 2008: (i) the REER appreciation arose due to inflationary pressures rather than higher demand for pesos (explaining the negative sign on reserves), and (ii) there was a rise in prices of traded goods (in part due to the commodity boom raising export prices) rather than a rise in trade volumes. For the next version of the paper, we could consider using data on trade volumes rather than values to test this hypothesis, although such data is only available from INDEC since 1986.

> 32 In the robustness section, we provide evidence supporting our claim that the CPI-to-PPI ratio is underreported, making 62% an upper bound using our adjusted-REER measure.

18
14. Escribano, A., N. Peltier-Thiberge, L. Garrido, and H. Singh, 2005: The Impact of Infrastructure on Competitiveness in Latin America: A Firm Level Analysis Based on Investment Climate Assessment. The World Bank. Washington D.C.

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16. Estache, A. 2004: “Emerging Infrastructure Policy Issues in Developing Countries: A Survey of Recent Economic Literature.” Background Paper for the October 2004 Berlin meeting of the POVNET Infrastructure Working Group. The World Bank, Washington, D.C. Processed.

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22. Gannon, C. and Z. Liu, 1997: “Poverty and Transport.” _TWU Discussion Paper 30_ . The World Bank, Washington, D.C.

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24. Guasch, J.L., 2004: _Presentación: Elementos de Una Estrategia de Desarrollo de la Competitividad en un Entorno Descentralizado_ . Mimeo.

25. Guasch, J.L., M. Fay. 2003, Economic Activity, Agglomerations and Logistics in the Mexican Southern States. In _Mexico: Southern States Development Strateg_ y. The World Bank. Washington, D.C.

26. Guasch, J.L. and J. Kogan, 2001: Inventories in Developing Countries: Levels and Determinants – A Red Flag for Competitiveness and Growth. In _Policy Research Working Paper 2552._ The World Bank. Development Economics. Office of the Senior Vice President and Chief Economist. Latin America and the Caribbean Region. Finance, Private Sector and Infrastructure Unit. Washington, D.C.

27. Guasch, J.L. and J. Kogan, 2003: Just-in-Case Inventories: A Cross Country Analysis. In _Policy Research Working Paper 3012._ The World Bank. Latin America and the Caribbean Region. Finance, Private Sector and Infrastructure Unit. Washington, D.C.

44
**Table 15 Employment Growth with Three Size Groups**

||(1)|(2)|
|---|---|---|
|Medium (20‐99)|‐0.097|‐0.103|
||(0.032)***|(0.031)***|
|Large (≥100)|‐0.085|‐0.088|
||(0.043)*|(0.044)**|
|Age|‐0.003|‐0.003|
||(0.001)**|(0.001)**|
|External Fin|0.004||
||(0.001)***||
|Line of credit (y/n)||0.311<br>(0.073)***|
|Constant|0.197|0.09|
||(0.084)**|‐0.081|
|Observations|668|665|
|R‐squared|0.188|0.185|

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Additionally control for foreign and government ownership, export status, 2‐digit industry, and region fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

23
Overall this paints a picture of the low pro...ts group as much smaller in size, with subsistence level income. For this group we see no impact of the grants on business pro...ts. This is consistent with the ...nding in Sri Lanka, where the grants had no impact on female-owned businesses. The Sri Lankan businesses are similar in scale to the low initial pro...ts female ...rms in Ghana –the 95th percentile of pro...ts is only 70 per month in the Sri Lankan sample, which is the 10th percentile of pro...ts for the high initial pro...t group in Ghana. So for the types of female-owned businesses in Ghana that are similar in scale to those in Sri Lanka, we obtain similar results. Such businesses ...t the hypothesis of Emran et al. (2007) that many of the women drawn into subsistence self-employment have very low e¢ cient scale and are there only because of labor market imperfections. As a result, according to our models, neither cash nor in-kind grants should have any long-term impacts on business pro...tability for these types of low productivity ...rms. The di¤erence is that the Ghanaian sample also includes a group of more successful female-owned businesses with larger scale, who do show increased pro...t growth from at least the in-kind treatment.

# 7 Why does the impact of cash and in-kind treatments vary?

We now examine more in detail the possible reasons for the di¤erence between the impact of cash and in-kind grants, particularly for women. We ...rst discuss whether the RTI models can account for this di¤erence.

The only way that the RTI models we presented in the conceptual section can account for a di¤erence between the two treatments is if the in-kind treatment Et cannot be liquidated immediately and the ...rm has already reached its steady-state. If this is the case, pro...ts and capital are predicted to rise above their steady state after the in-kind treatment, but only until illiquid capital can be divested.

This e¤ect is unlikely to be prominent in our study. There is a relatively long lag between treatment and ex post surveys. The time lag between receiving a grant and the immediately subsequent survey is nearly 3 months. For those who received the grant after round 2, there is an interval of nearly 12 months between receiving the grant and the last survey round. For those receiving the grant after round 3, the time interval is 9 months. Given that recipients of in-kind grants invest in raw materials, inventories, or simple equipment, it would be easy to decapitalize in-kind grants between treatment and the next survey round. Furthermore, as we 31
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48
Table 14: Teacher attendance

||(1)<br>Teacher|(2)<br>Teacher|(3)<br>Teacher|(4)<br>Teacher|(5)<br>At least|
|---|---|---|---|---|---|
||present|present|present|present|one teacher|
|Year|2000-2002|2000|2001|2002|2000-2002|
|Treatment|0.01|0.02|0.01|-0.08|0.02|
||(0.05)|(0.05)|(0.07)|(0.09)|(0.03)|
|Constant|0.72|0.80|0.65|0.80|0.81|
||(0.03)***|(0.03)***|(0.05)***|(0.06)***|(0.02)***|
|Obs|61|69|63|57|50|
|R-squared|0|0|0|0.01|0.01|
|Max. nr. of visits|13|4|7|2|13|

Notes: The dependent variable regressions (1)-(4) is average teacher attendance over the relevant period. Absences for reasons of maternity leave, sickness leave and training are counted as absences. Absences due to de...nitive quits are counted as non-observations. The sample is restricted to teachers who were in post at the time of the baseline survey (Feb-Mar 2000). The standard errors are clustered at the school level. Robust standard errors are reported in parentheses. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent selection into the schools, because it is based on participating children rather than on baseline children.

# 5.5 Teacher attendance

The school meals program did not increase school attendance of teachers, despite the fact that it provided relatively strong incentives to the teacher. First, the program substantially increased fee payments by the parents (cf. section 6.2). Second, the provision of porridge to the teacher was a non-negligible income transfer to the teacher: one portion of porridge cost a teacher’s average daily salary. Third, by its very setup, the income transfer was totally dependent on the teacher attending school. Restricting the sample to teachers who were in post before the program started, we ...nd that both in the treatment and in the comparison schools, teachers were absent approximately 30 percent of the time (Table 14, columns 1 to 4). In addition, there is no evidence of a change in the probability that at least one teacher was present in school, despite the fact that treatment schools hired new teachers to cope with the increasing number of pupils in the classes (Table 14, column 5).

This apparent inelasticity to the provision of incentives parallels ...ndings from other stud37
**Table 2 Key Determinants of the Introduction of PPP in Electricity Renewable Generation**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|**GOVER**<br>**SECTORALG**|**NANCE VARIAB**<br>**OVERNANCE V**<br>***|**LES**<br>**ARIABLES**<br>***|**|
|**Introduction of feed-in tariffs**_FIT_|0.802<br>(0.146)|0.556<br>(0.158)|0.407<br>(0.175)|
|**ECONOMYWID**|**EGOVERNANCE **<br>|**VARIABLES**<br>||
|**Control of Corruption**_wgi_cc_|0.445***<br>(0.092)|0.452***<br>(0.104)|0.456***<br>(0.106)|
|**Degree of democracy****_Polity2_**|0.025***<br>(0.009)|0.021*<br>(0.011)|0.020*<br>(0.011)|
|**GDP**<br>_ln_GDP_||0.039<br>(0.068)|0.020<br>(0.076)|
|**Population**<br>_ln_Pop_||0.247***<br>(0.077)|0.271***<br>(0.035)|
|**LONG RUN ENVIRONM**|**ENTALSUSTAIN**|**ABILITY FACT**<br>|**ORS**<br>|
|**Price of oil**||<br>|-0.011<br> (0.007)|
|**Price of oil*Kyoto Protocol**||<br>|0.011*<br> (0.008)|
|**Transmission investment**|||0.002***<br>0001|
|**REGI**|**ONAL CONTRO**<br>|**LS**<br>|(.)<br>|
|**Regional Dummies**|Yes|Yes|Yes|
|**Constant**|-1.097***<br>(0.087)|-6.128***<br>(0.800)|-5. 570***<br>(0.838)|
|**N**|1185|1143|1143|
|**Log likelihood**|-444.67|-202.34|-391.75|
|**χ**<sup>**2**</sup>|130.52***|202.34***|218.66***|
|**Pseudo R**<sup>**2**</sup>|12.80|20.19|21.82|

_Note:_ *, **, *** indicate significance of the coefficient respectively at 10%, 5% and 1% confidence level.

In what follows the key results are highlighted, focusing on the specific hypotheses tested in each of the regressions.

**Regression 1 confirms Hypothesis 1.A, namely the introduction of a feed-in tariff is positively and highly significantly associated to the introduction of PPPs in renewable electricity generation.**

**Hypothesis 2A is also confirmed, that a higher standard of economy wide governance is more likely to attract PPPs in renewable electricity generation.** The result extends more broadly to all form of (price and quantity based) support to renewable-based energy. The results are presented only for feed-in tariff, as they represent the predominant support mechanism used in the developing countries sampled in our study.

19
|**Table 8. Dependent Va**<br>_Interaction is:_|**riable: Real**<br>mar<br>Whether|**Profits**<br>ried<br>they are|Share o<br>brought to|f assets<br>marriage|Difference in<br>between|education<br>spouses|Differen<br>between|ce in age<br>spouses|Spend i<br>without|ncome<br>consent|Baselin<br>as share|e profits<br>of income|Basel<br>as share o|ine profits<br>f consumption|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|
||Female|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|Male|
|Cash Treatment|‐9.640|16.78|20.54|1.823|18.69|2.177|‐0.0262|‐3.651|0.710|‐47.59|‐8.692|‐25.43|0.0859|7.697|
||(12.84)|(24.73)|(17.50)|(60.36)|(17.00)|(30.95)|(25.64)|(33.45)|(19.53)|(47.88)|(20.83)|(27.64)|(13.84)|(21.89)|
|In‐kind Treatment|40.89|34.99|49.01*|23.79|40.52**|20.61|43.21|26.00|‐4.982|29.59|32.56|147.1|64.50*|24.56|
||(33.71)|(23.63)|(27.46)|(63.60)|(17.86)|(60.32)|(27.16)|(28.63)|(12.88)|(25.65)|(23.58)|(104.1)|(33.17)|(23.21)|
|Cash*Interaction|11.56|‐17.11|‐6.289|‐14.47|0.752|1.820|‐0.162|0.0687|2.671|55.74|8.387|34.63|‐2.005|16.82|
||(17.18)|(33.14)|(27.52)|(77.95)|(2.474)|(5.634)|(2.339)|(6.121)|(24.14)|(54.47)|(24.37)|(39.97)|(17.24)|(26.29)|
|In‐kind*Interaction|‐11.91|‐12.19|‐16.09|‐6.332|0.101|6.441|0.0207|‐2.986|42.57**|‐13.18|‐0.605|‐135.2|‐54.76|12.98|
||(35.28)|(34.08)|(39.86)|(84.32)|(3.249)|(9.761)|(2.550)|(4.113)|(17.84)|(43.76)|(32.46)|(108.0)|(34.21)|(37.70)|
|Observations|2,599|1,593|1,273|864|956|799|1,520|932|1,730|972|2,408|1,501|2,604|1,599|
|Number of firms<br>Robust standard errors<br>*** p<0.01, ** p<0.05,|473<br>in parenthes<br>* p<0.1|289<br>es|222|151|172|147|264|163|312|178|431|267|474|290|
with poverty by examining the extent to which young people with disabilities live in households with lower economic status. Section 4 investigates the long run association with poverty by examining the association between disability and school participation among school-aged youth.

# **2. Data**

The data used for this analysis are from 11 nationally representative household surveys from 9 countries. Three of the surveys are associated with the Living Standards Measurement Study (LSMS) surveys: Jamaica 1998, Jamaica 2000, and Romania 1995. Three of the surveys are national socio-economic surveys (SES): Cambodia 1999, Indonesia 2000, and Mozambique 1996. One survey is a Demographic and Health Survey (DHS): Cambodia 2000. Four of the surveys are End of Millennium Multiple Indicator Cluster Surveys (MICS2) carried out under the guidance of UNICEF in 2000: Burundi, Myanmar, Mongolia, and Sierra Leone.<sup>3</sup> These surveys are typically used to calculate poverty statistics, or derive basic health indicators such as child mortality or the use of health services, and underlie much empirical poverty and social analysis in developing countries. Most of the surveys have a sample size of between about 4,000 and 25,000 households (with Jamaica and Myanmar being outliers with 1,800 and over 65,000 households surveyed respectively).

In order to select these datasets all LSMS, DHS, and MICS surveys were reviewed for any questions on disability and all those with a clear question on disability for a relevant age-range were included. In addition, the SES from Cambodia, Indonesia and Mozambique are accessible from national statistics offices and are some of the most recent in the world with information on disability. There is relatively little data of this kind in developing countries: the datasets, and therefore the countries, for this analysis were selected on the basis of data availability. The countries were not selected to be representative of developing countries in general.

This is clearly a heterogeneous group of countries. Population living on less than a dollar a day ranges from 55 percent in Burundi to two percent in Jamaica and Romania; under-5 mortality—an indicator of basic health status—ranges from 206 per thousand live births in Mozambique to 15 in Romania (Table 1). There are three countries from Africa, four countries from Asia, one country from the Caribbean, and one country from Eastern Europe. While country variety is good since the results will reflect on a range of underlying conditions, little draws these countries together besides having the data available for this analysis.

> 3 LSMS data are available online at <u>http://www.worldbank.org/lsms; national socio-economic surveys are</u> available from the countries’ national statistics offices; DHS data are available online at <u>http://www.measuredhs.com; MICS2 data are available online at http://www.childinfo.org.</u>

2