rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2:** Steady-state Values in percentage of total output

|**Steady-state rati**|**os**<br>**Description**|**Values**|
|---|---|---|
|_Y _<sup>_O_</sup>_/Y_<br>|Oil revenues to GDP ratio in steady state|0.072|
|_I_<sup>_/_</sup>_Y_<br>|Total investment to GDP ratio in steady state|0.1468|
|_Y _<sup>_T_</sup>_/Y_<br>|Tradable to GDP ratio in steady state|0.329|
|_Y _<sup>_N_</sup>_/Y_<br>|Tradable to GDP ratio in steady state|0.665|
|_L_<sup>_N_</sup>_/L_<br>|Share of employment in non-tradable sector|0.583|
|_L_<sup>_T_</sup>_/L_|Share of employment in tradable sector|0.417|
|_C_<sup>_N_</sup>_/C_<br>|Non-tradable goods consumption to C|0.316|
|_G_<sup>_C_</sup>_/Y_<br>|Government consumption to GDP ratio in steady state|0.53|
|_G_<sup>_I_</sup>_/Y_|Public investment to GDP ratio in steady state|0.32|
|_F _<sup>_∗_</sup>_/GDP_|Sovereign wealth fund to GDP ratio in steady state|0.08|

# **4.1 All-investing and saving in a sovereign wealth fund**

In response to an increase in the oil production, oil output and total output rise gradually, and this drives up the oil revenue. Under the saving in a sovereign wealth fund policy, foreign reserves increase permanently, reaching around 10 percent of GDP after 10 years (Figure 1, panel A and C). As higher output means more income to households, under the all-investing approach, this exogenous shock leads to an increase in the private consumption to reach about 0.25 percent after 5 years (Figure 1, panel A). On the other hand, since the government budget constraint includes interest earnings from foreign reserves, private consumption also rises under the saving in a sovereign wealth fund policy, reaching around 0.17 percent after 6 years. Higher private consumption, in turn, leads households to reduce labor supply by about 0.25 percent and lower the marginal product of private investment. Consequently, wages increase sharply in both the tradable and the non-tradable goods sectors. Lower labor supply and private investment lead to a decline of non-oil GDP (Figure 1, panel A and B). Public capital also rises sharply by around 0.35 percent after 3 years and half under the all-investing approach, and remains at the pre-windfall level under the all-saving approach because none of the resource income is allocated for public investment (Figure 1, panel B).

25
comparisons of corruption across countries using this indicator result in differences that are significant at the 90 percent confidence level, and nearly three-quarters of comparisons are significant at a less-demanding 75 percent confidence level. Clearly, far fewer pair-wise comparisons would be significant if they were based on any single individual indicator whose margins of error have not been reduced by averaging across alternative data sources. For example, if we take an individual data source with a typical standard error from the WGI Control of Corruption indicator, such as Global Insight-DRI, we find that only 16 percent of cross-country comparisons based on this one data source would be significant at the 90 percent confidence level.

As we have noted, however, the WGI are unusual among existing governance indicators in their transparent recognition of such margins of error. The vast majority of investment climate and governance indicators simply report country scores or ranks without any effort to quantify the measurement error that these rankings inevitably contain. This has tended to contribute to a sense of spurious precision among users of these indicators, and to an overemphasis of small differences between countries.

Of course aggregate indicators have their own shortcomings as well. Foremost among these is an inevitable loss of specificity that comes with aggregate indicators of governance. If we average one indicator of judicial corruption and another indicator of bureaucratic corruption, we arguably have a more informative indicator of overall corruption, but we do not have a more informative indicator of either of the two particular types of corruption. Similarly if we average an indicator of press freedoms with an indicator of electoral integrity we have a more informative indicator of overall democratic accountability but we do not have any more precise an indicator of either of these two specific concepts. For some purposes the broad aggregate indicators will be useful, while for other purposes the disaggregated underlying indicators will be more useful. However, we do not view this as a major shortcoming, since after all, virtually all aggregate governance indicators can readily be disaggregated into their constituent components, giving the user the freedom to choose the appropriate level of aggregation for the task at hand.<sup>19</sup>

> 19 In the case of the WGI, the full dataset of individual indicators underlying the aggregate indicators is available through an interactive website at www.govindicators.org.

28
entrepreneurs in Ghana.<sup>8</sup> Specifically, before we take the respondents through any questions about their business performance, we ask them to give their best estimate of business profits for the most recent month after accounting for all business revenues and costs. Moreover, the question on recalled profits is shifted to the very beginning of the survey rather than after the _Money In_ section.

Table 4 presents the results of this comparative analysis. Consistent with the previous results, we find that the AAA measure leads to profit values that are significantly larger (with average AAA profits of 349 USD compared to average self-reported profits of 300 USD), with a p-value of 0.01 on a difference in means test. We also find a reduction in CV, from 0.68 in self-reported profits to 0.64 in AAA profits. Furthermore, we find 5% of the sample was unable to provide any response on self-reported profits. While this nonresponse rate is lower than what other studies have found (e.g. Bruhn and Zia, 2013), our analysis nevertheless confirms that the nonresponse falls to zero for the AAA measure.

Table 4 also compares AAA profits to calculated profits as in previous tables and finds similar pattern of results. Specifically, we find an adjustment factor of 63% between the two measures and a coefficient of variation that falls from 2.06 for calculated profits to 0.64 for AAA profits.

Finally, the Spearman and Pearson correlation coefficients in Table 4 show that self-reported profits and AAA profits are highly correlated with correlation coefficients ranging from 0.70-0.78. By contrast, the Pearson correlation coefficient between calculated profits and AAA profits is as low as 0.14.

# **3.4. Comparison with Administrative Data**

Thus far our results have established that the AAA performance measures are significantly larger and more precise (i.e. lower CV) than traditional estimates. However, are these estimates more accurate, i.e. more representative of the true values?

> 8 These data were collected by our Research Assistant from a sample of entrepreneurs in greater Accra in 2018; the data were not part of any evaluation. Table 1 does not include summary statistics for this sample.

22
(1) l 0 + 1l l 2<sup>l</sup> 0<sup>l</sup> 0<sup>l</sup> tt tt<sup>+ ββ</sup> tt<sup>+ ββ</sup> tt<sup>+ ββ</sup> tt<sup>+ εε</sup> tt where (measured at date/time t): llPP = ββ ββ<sup>lll</sup> P = Pollutant concentration measured by CAMS-3 [PM 2.5 (ug/m<sup>3</sup> ); NO2 (ppb)] M = Average satellite-based measure for the .01-degree cell surrounding CAMS-3 H = Average humidity (%)

- T  =  Average temperature (C)

- W =  Average wind speed (km/hr)

ε = Random error term Table 3 reports our estimation results. Our primary estimates use variable levels, but we also include estimates for time-differences to test the possible effect of serial correlation.<sup>6</sup> Columns 1- 2 test the relationship between CAMS-3 NO2 measures and Sentinel-5P’s measure of tropospheric column NO2 density. The S5P measure has very high significance. On the other hand, the cloud cover and weather variables have no consistent patterns of significance.

Columns 3-8 test the relationship between CAMS-3 PM2.5 and three satellite measures, controlling for weather variables. As the results show, the weather variables introduce deviations between ground- and satellite-based measures that vary in size and significance across estimates. We focus on the comparative significance of the satellite-based estimates as indicators of ground-level PM2.5.

Columns 3-4 report level and difference results for S5P’s absorbing aerosol index. We find no significance for this variable.

Columns 5-6 re-introduce the S5P NO2 measure, since nitrates from NO2 emissions are a significant source of PM2.5 (Hodan and Barnard 2004). Here we find very high significance for both level and difference estimates.

Columns 7-8 test the relationship between CAMS-3 PM2.5 and MODIS Aerosol Optical Depth (AOD). We find very high significance for MODIS AOD, although we should note that relatively sparse observations yield many fewer degrees of freedom for the AOD case.

In summary, we find that CAMS-3 PM2.5 is strongly related to Sentinel-5P NO2 and MODIS AOD; CAMS-3 NO2 is strongly related to Sentinel-5P NO2.

> 6 We use one-day differences for this exercise.

10
be more associated with less remunerative self-employment activities, such as the sale of phone cards or prepared food, although evidence suggests only a slight increase in the likelihood of being self-employed for women among the urban population with some tertiary education.<sup>5</sup> A final test measured a more subtle distinction but one that is possibly important for applicants already interested in training for the ICT sector: associations between gender and the concepts of _professionalism_ and _unprofessionalism_ .<sup>_6_</sup> Prior to the sorting tasks, respondents were asked explicit questions about gender and professionalism. Appendix Table 1 presents the basic format of the IATs used during the baseline survey, and Appendix Table 2 lists the wording of the explicit question and the words used to represent _home_ , _office_ , _petty trade_ , _professionalism_ , and _unprofessionalism_ .

The endline survey took place by phone between February and April 2014. Enumerators in call centers contacted applicants using the contact information provided during the baseline survey and administered a relatively short end line questionnaire over the phone. They reached 2,733 applicants for a response rate of 91 percent. Training providers also collected attendance data for all trainees and post-assessment scores for applicants who accepted treatment (1,007 individuals), administered at the end of training.

# _Outcomes_

Our main outcome of interest is employment in the ICT sector, the objective of the program, specifically defined as working in the BPO sector or information and communications technology more generally. During the phone survey, we asked the original baseline participants if they were working and which sector they worked in. We counted anyone who chose response options including information and communication technology, software, computers, call centers, or business process outsourcing as their sector as working in ICT.

The training provided skills that could easily transfer to other office jobs outside of ICT-enabled services. To determine whether any observed increase in ICT employment results from an increase in labor market participation or sector switching, we also measure impacts of the training program

> 5 In Nigeria’s 2011 General Household Survey, 28 percent of urban women with some tertiary education worked in nonagricultural self-employment activities, compared with 21 percent of urban men with similar education.

> 6 Working with Project Implicit (https://implicit.harvard.edu/implicit/) and local focus groups, we collected a set of words that the target group associated with the relevant concepts (home, office, petty trade, professionalism).

10
Figure 2 shows the distribution of services across the seven agroecological regions of Vietnam. These regions are Northern Uplands (NU), Red River Delta (RR), and North Central Coast (NCC) in the North, and South Central Coast (SCC), Central Highlands (CH), South East (SE), and Mekong Delta (MD) in the South. These figures illustrate that the coverage across the regions for most basic services is sufficiently close and are very high. The electricity variable denotes the share of households that have electricity at home. Road access is defined as whether there is a road that goes through the commune<sup>12</sup> . Market access is defined as the share of communes that have a permanent market in the commune<sup>13</sup> . On the other hand, access to credit variable is illustrated by the share of households that have received loans from a formal government bank. The private banking system is almost non-existent.

**Figure 2a-2f. Regional Access**

<!-- Start of picture text --> a. Household's Electricity Access by b. Average Road Access by Region<br>Region<br>120<br>100 100<br>80 80<br>1993<br>60 1993 60<br>1998<br>40 1998 40<br>20 20<br>c<br>0 0<br>NU RRNCCSCCCH SE MDTOTAL NU RRNCCSCC CH SE MDTOTAL<br>Percent<br>Percent<br><!-- End of picture text -->

> 12 The road quality variable only exists for 1998. See section 2.4 for details of the transportation availability.

> 13 There are also periodic markets which, when added together does cover more than half of the communes.

8
Table 5: The decision to start importing capital goods

|Dependent va|riable: dummy equ|al to one if the fr|m_i_imports capital|goods in_t_.||
|---|---|---|---|---|---|
||(1)||(2)|(3)|(4)|
|||Non importer in||Non im|porter in|
|||the last year||the last t|wo years|
|Input tariff(s)(t-1)|-0.210**|||-0.249*||
||(0.099)|||(0.133)||
|Output tariff(s)(t-1)|0.069||0.088|0.083|0.094|
||(0.046)||(0.065)|(0.060)|(0.077)|
|Capital goods tariff(s)(t-1)|-0.248**||-0.234*|-0.376**|-0.443**|
||(0.118)||(0.124)|(0.172)|(0.201)|
|Input tariff(s)(t-1)_×_Low initial TFP|||-0.262||-0.289|
||||(0.153)||(0.182)|
|Input tariff(s)(t-1)_×_Medium initial TFP|||-0.304**||-0.393**|
||||(0.127)||(0.186)|
|Input tariff(s)(t-1)_×_High initial TFP|||-0.082||-0.269|
||||(0.155)||(0.187)|
|Herfndahl index(s)(t-1)|Yes||Yes|Yes|Yes|
|Firm level controls|Yes||Yes|Yes|Yes|
|Firm fxed effects|Yes||Yes|Yes|Yess|
|Year fxed effects|Yes||Yes|Yes|Yes|
|Observations|9,228||4,245|5,439|2,758|
|R-squared|0.029||0.031|0.056|0.066|

_Notes_ : The dependent variable is a dummy for firm _i_ having positive imports of capital goods in year _t_ . All control variables are defined in table 3. Heteroskedasticity-robust standards errors are reported in parentheses. Errors are corrected for clustering at the 3-digit industry level.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5 and 10 percent levels respectively.

In these cases, the coefficients on input tariff are higher compared to the baseline specification. We should keep in mind that this could be due to the reduction of the sample size to half from 14,425 to around 9,200 or 5,500 observations.

_The role of firm ownership_ 41
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coefficients of the successive size-groups give the relative increase as one moves to a higher size category.

It is seen that the structure of earnings related to size groups in Ghana is very similar to that in the Cameroon. In the Eastern African countries of Kenya and Zambia, the jump in earnings as one moves from micro to small firms are smaller. But then the latter two have a sharper increase in earnings than Ghana between the medium and large categories, leaving the overall differential between large and micro enterprises almost the same in all four countries. Zimbabwe stands alone in having a sector with very large firms, and the earnings differentials increase much more for the larger enterprises.

The era of decline of wages could have been expected to narrow the differential in wage levels between the large and small firms since the wage structure is compressed against a threshold defining either the subsistence or the efficiency level of wages. The finding that average earnings, after controlling for the measurable human capital factors, increased continuously with enterprise size in Ghana, as in other African countries, is an important one. Although the size-related wage differential is much more for Zimbabwe, the extent of the premium enjoyed by workers in the large firms compared to the microenterprises is quite large in the other countries, including Ghana-of the order of nearly 90 per cent.

# **4.3 Causes of the Increase in Earnings with Firm Size**

The first hypothesis which comes to mind is that the increase in earnings with firm size is due to institutional factors affecting the wage level . Ghana prescribes minimum wage levels, and it might be argued that these levels are enforced in larger firms and not in micro or small firms. The average monthly earnings of workers in the manufacturing sector at the date of the 1992 RPED survey have been given in Table 3.4 above together with, the minimum wage level prevailing at this date.

It is seen that the minimum wage was more or less at par with the average earnings in micro enterprises, and as such could be considered to have provided a floor to the wage level. It might be argued that wages of the least paid or unskilled worker in an enterprise would be less than the average earnings. If this were the case the minimum wage could conceivably fall between the levels of the bottom wage in micro and small firms. On the further assumption that minimum wages are at least enforceable in small firms, but difficult to enforce in micro enterprises, we could at a stretch ascribe the differential between micro and small firms to the minimum wage laws. But this explanation leaves untouched the reasons for the continuous increase in earnings, after controlling for measurable worker quality, with further increases in firm size.

Other possible routes of institutional influence are public sector wage policy and unionization. The levels of wages established for public sector enterprises, particularly in the state owned enterprises could have a disproportionately strong effect on the larger firms. But this argument has the same difficulty as the minimum wage hypothesis-it 23
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