rafmacalaba/datause-encoder-data huggingface dataset:
25

- Net farm income (agricultural value added) in developing countries is estimated to be 5 percent higher than they would have been without the reforms of since the early 1980s, which is more than ten times the proportional gain to non-agricultural households, and if policies remaining in 2004 were removed those net farm incomes would rise a further 6 percent.

Together, these findings suggest both inequality and poverty could be further alleviated by such reform, given that three-quarters of the world’s poor are in farm households in developing countries (World Bank 2008).<sup>19</sup> Furthermore, those latter results are from a comparative static economy-wide model and so underestimate the gains by ignoring the dynamic gains that typically accompany market liberalization.

Ideally the reform processes of the past quarter century would continue, boosting global economic growth, reducing inequality within and between countries, and alleviating poverty. If the convergence of national RRAs towards zero continues (from below by most developing countries and from above by higher-income countries), there would continue to be a re-location of global farm production (in global share terms) from high-income to developing countries, reversing the policy distortion-driven opposite trend in the quarter century prior to the mid-1980s. Whether international food prices would rise or fall would depend on the relative size of the two groups of countries and which had the larger RRA change (bearing in mind that some export restrictions still remain, including in Argentina). According to the global modeling exercise reported in table 9, the net change in international prices would be very small if all goods market distortions as of 2004 were removed globally. Those results also suggest international markets would be ‘thicker’, so their volatility from year to year would be less, further boosting global food security.

That rosy scenario would imply that the early 1960s to the mid-1980s was an aberrant period of welfare-reducing policy divergence (negative and very low RRAs in newly independent developing countries, positive and rising RRAs in most high-income countries) that has given way to growth-enhancing, welfare-improving and inequality- and povertyreducing reforms during which the two country groups’ RRAs, like their NRAs, are converging towards zero. In this view the reforms could be seen as the result of learning from the differing growth experiences of more- and less-open developing economies.

> 19 A new set of economy-wide national and global modeling studies that uses the NRA agricultural distortion estimates in Anderson finds that removal of those and distortions to other tradable goods markets does indeed lower estimated inequality and poverty. See Anderson, Cockburn and Martin.
submission period does not adversely impact companies’ capacity to prepare bids adequately (for more details see section 6.2 below). Instead, e-procurement makes very short advertisement periods (less than 15 days) virtually disappear which should improve advertisement quality leaving more time for bidders to prepare bids. Moreover, it also significantly reduces very long periods (more than 30 days) which is the primary reason behind the declining average advertisement length.

74. In Models 2a and 2b, we observe that, contrary to our expectations, e-procurement increases decision period length compared to manual tender evaluation. While overall small, our models predict that e-procurement increases the decision period by 2.0-2.2 days (from 20.5 to 22.7 days in Model 2a and from 22.8 to 24.9 days in Model 2b). The surprising increase in decision period length can be attributed to two potential reasons. First, as shown, the number of bidders per tender increased and more bidders naturally mean more work for bid evaluators. When normalizing the decision period length by the number of bidders, we find a decrease in decision making time as expected (see normalized regressions in Appendix 9.5). Second, the increasing participation of non-local bidders implies more validating work for procuring entities because they have to obtain documents to assess the validity of the experience quoted by bidders. This takes comparatively longer compared to a procuring entity contacting a bidder it already contracted with.

75. Regarding the signing period, that is the number of days needed for signing the contract once the contract is awarded, we find a substantial decrease in processing time. E-procurement reduces the average signing period by 4.1-6.9 days compared to manual tenders (21.9 versus 14.9 days in Model 3a; and 18.2 versus 14.1 days in Model 3b).

76. The observations from the administrative data are further corroborated by the perceptions of the times as reported by surveyed procuring entity officials. PE officials reported similar decreases in processing times (a detailed analysis has been given in Appendix 9.10). Moreover, 22% of PE officials mentioned the lower turn-around time required for processing tenders as one of the two main advantages of the e-procurement system. Similarly, 59% of bidders reported preparation times of tenders as one of the main drawbacks of the paper-based system that is alleviated by the transition to the electronic system.

27
of the service good from the oblasts within the federal okrug. Domestic service providers do not import the specialized primary factors available to the multinationals. Figure 4 for Russian business service providers is analogous to figure 6 for multinational service providers except for the nest for imported primary inputs. Foreign service providers also must use a specific factor to produce the output and this implies that additional output can only be obtained at increasing marginal costs. Since the structure of costs for all multinational firms that provide a service in a given region m is identical and demand is symmetric, there is a unique price for all multinationals providers of service s in federal okrug m.

For multinational firms, the barriers to foreign direct investment affect their profitability and entry. Reduction in the constraints on foreign direct investment in a region will induce foreign entry that will typically lead to productivity gains because when more varieties of service providers are available, buyers can obtain varieties that more closely fit their demands and needs (the Dixit-Stiglitz variety effect).

# **Factors of Production**

Primary factors include skilled and unskilled labor and three types of capital; (i) mobile capital (within regions); (ii) sector-specific capital in the energy sectors reflecting the exhaustible resource; and (iii) sector specific capital in imperfectly competitive sectors. We also have primary inputs imported by multinational service providers, reflecting specialized management expertise or technology of the firm. The existence of sector specific capital in several sectors implies that there are decreasing returns to scale in the use of the mobile factors and supply curves in these sectors slope up.

The above list of primary factors exists in all regions. In the case of skilled and unskilled labor it is natural to assume that the representative agent in the region obtains the returns from these factors of production. Consistent with standard trade models, in our central model we assume that capital and labor are immobile between regions. However, this model is a regional disaggregation of a national model of Russia; consequently, it does not seem reasonable to assume that all capital in a region is _owned_ by the agents in that region. Thus, in our central scenario, we allow the capital in any region to be held by all Russians. It is convenient to think of a national mutual fund that holds the capital in each region. For all three capital types, this mutual fund invests in all regions and obtains an overall return. Individual households obtain a share of the returns of this mutual fund in proportion to their own capital earnings as a share of total capital earnings based on the NOBUS data set. We do sensitivity analysis, where we allow a fraction of the capital in any region to be held by agents inside the region.

.

12
**Figure 1: Real Short-term Interest Rates, January 1995 – August 1998**<sup>a/</sup>

**(% per year)**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Jan-95 Feb-95 Mar-95 Apr-95 May-95 Jun-95 Jul-95 Aug-95 Sep-95 Oct-95 Nov-95 Dec-95 Jan-96 Feb-96 Mar-96 Apr-96 May-96 Jun-96 Jul-96 Aug-96 Sep-96 Oct-96 Nov-96 Dec-96 Jan-97 Feb-97 Mar-97 Apr-97 May-97 Jun-97 Jul-97 Aug-97 Sep-97 Oct-97 Nov-97 Dec-97 Jan-98 Feb-98 Mar-98 Apr-98 May-98 Jun-98 Jul-98 Aug-98<br><!-- End of picture text --> Source: Figure 1, Kharas, Pinto and Ulatov (2001).

a/ Average nominal secondary market GKO yield deflated by 12-month inflation target under IMF program.

Why did real interest rates jump to such high levels, effectively killing growth? For three reasons: first, as Table 1 shows, the original fiscal program targets agreed to between Russia and the IMF were met in 1995 but greatly exceeded thereafter; in fact, the fiscal deficit went up sharply after 1995 instead of shrinking.<sup>9</sup> This meant more borrowing than anticipated, hurting credibility and pushing up real interest rates, especially as a domestic credit squeeze was simultaneously on to lower inflation.

**Figure 2:  Real Effective Exchange Rate, January 1995 – September 1998**<sup>a/</sup> **Index, 1995=100**

<!-- Start of picture text --> 140.0<br>130.0<br>120.0<br>110.0<br>100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>1995M1 1995M3 1995M5 1995M7 1995M9 1995M11 1996M1 1996M3 1996M5 1996M7 1996M9 1996M11 1997M1 1997M3 1997M5 1997M7 1997M9 1997M11 1998M1 1998M3 1998M5 1998M7 1998M9<br><!-- End of picture text --> Source: Figure 2 in Kharas, Pinto and Ulatov (2001).

a/ Ratio of Russian CPI to trade-weighted geometric mean of trading partners CPI times nominal exchange rate. Upward (downward) movement signifies appreciation (depreciation).

> 9 NB: the program targets were continually revised.  What is relevant for a post-mortem is how actual developments compare with the _original_ path envisaged for inflation and fiscal deficits.

6
Kim, M. and Sachish, A. (1986) “The Structure of Production, Technical Change and Productivity in a Port”, _The Journal of Industrial Economics_ , XXXV (2): 209-223.

Kumbhakar, S.C., Ghosh, S. and McGuckin, J.T. (1991) “A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms”. _Journal of Business & Economic Statistics_ , 9: 279-286.

Liu, Z. (1995) “The Comparative Performance of Public and Private Enterprise. The Case of British Ports”, _Journal of Transport Economics and Policy_ , 29 (3): 263-274.

Lovell, C.A.K., Richardson, S., Travers, P. y Wood, L. (1994) “Resources and Functionings: a New View of Inequality in Australia” in Eichhorn, W. (ed.), _Models and Measurement of Welfare and Inequality_ , Springer-Verlag, Berlin.

Martín, M. (2002) “El sistema portuario español: regulación, entorno competitivo y resultados. Una aplicación del análisis envolvente de datos”, tesis doctoral, Universitat Rovira I Virgili.

Martínez Budría, E. (1993) “Un análisis económico de los puertos. El sistema portuario español”, tesis doctoral, Universidad de La Laguna.

Martínez Budría, E., Díaz Armas, R., Navarro Ibañez, M. and Ravelo Mesa, T. (1999) “A Study of the Efficiency of Spanish Port Authorities Using Data Envelopment Analysis”, _International Journal of Transport Economics_ , XXVI, (2): 237-253.

Morrison, C.J., Johnston, W.E. and Frengley, G.A.G. (2000) “Efficiency in New Zealand Sheep and Beef Farming: the Impacts of Regulation Reform”, _Review of Economics and Statistics_ , 82 (2): 325-337.

Nombela, G. and Trujillo, L. (1999) “El sector portuario español: organización actual y perspectivas”, _Papeles de Economía Española_ , 82: 71-85.

Notteboom, T.E., Coeck, C. and Van den Broeck, J. (2000) “Measuring and Explaining Relative Efficiency of Container Terminals by Means of Bayesian Stochastic Frontier Models”, _International Journal of Maritime Economics_ , 2 (2): 83-106.

Orea, L. (2002) “Parametric Decomposition of a Generalized Malmquist Productivity Index”, _Journal of Productivity Analysis_ , 18: 5-22.

Pestana, C. (2003) “Incentive Regulation and Efficiency of Portuguese Port Authorities”, _Maritime Economics & Logistics_ , 5: 55-69.

Peston, M.H. and Rees, R. (1971) _Port Costs and the Demand for Port Facilities_ , National Ports Council, London.

Puertos del Estado (some years) _Anuario estadístico del sistema portuario de titularidad estatal_ .

20
services reported not being able to access such services at some point since the start of the pandemic, with the most cited reasons being mobility restriction and increased cost of public transportation during the lockdowns (Balogun et al., 2021). In a study of 18 lowand middle-income countries, Ahmed et al. (2021) find a correlation between monthly reductions in health service utilization and stringency of mobility restrictions, even controlling for the monthly COVID-19 burden. If lockdowns reduce coverage of life-saving health services, then we would be under-estimating the ratio between excess child deaths and COVID-19 mortality averted by a given lockdown. As for reduced demand for services out of fear of contracting the disease in health facilities, it may happen independent of whether lockdowns are imposed. In sum, it is not clear whether non-COVID-19 mortality due to reduced health service utilization (or reduced service quality) would be higher with lockdowns or with health system congestion.

**State capacity** In our framework, the lockdown is successfully implemented by the government. One may be worried that if a particular country does not have the state capacity to enforce a lockdown, then our quantification is of limited policy relevance to that country. This is a general critique of policy analysis in low-state-capacity environments. Nonetheless, our conclusions are still relevant for countries with sufficient state capacity to enforce lockdowns. Judging from the widespread use of lockdowns in countries across all levels of development, governments at least believed that their policies would have some effect. There are also journalistic accounts that low-income country governments deployed police to enforce lockdowns.<sup>8</sup>

The substantial GDP contractions in 2020 are suggestive evidence that lockdowns had consequences in even the poorest countries. While real GDP data are annual and thus will have imperfect overlap with lockdowns, more high-frequency data sources also point to large contractions in economic activity during lockdowns. Aminjonov et al. (2021) analyze mobility data from Android smartphones to document widespread mobility declines in every

> 8See, e.g., India: https://www.reuters.com/world/india/police-enforce-lockdown-indias-nagpurinfections-surge-2021-03-12/, https://www.aljazeera.com/news/2021/4/21/for-indias-poor-lockdownpolicing-adds-to-pandemic-hardships; and Africa: https://www.dw.com/en/covid-19-security-forces-inafrica-brutalizing-civilians-under-lockdown/a-53192163, https://www.aljazeera.com/news/2020/4/28/unraises-alarm-about-police-brutality-in-covid-19-lockdowns.

18
economic information helps sort good and poor projects. As controls, I also use the principal components of the supervisory and regulatory indices (as in BCL) – simple indices which sum over the different components were also tried and gave similar results. However, in these specifications, the coefficient on T loses significance when pmindex pc, the complete BCL monitoring index, is included and instead the BCL index is significant. Interestingly, when apmindex is used instead, it is not significant. In addition, overall corruption in the economy is not significant. When law and order is used as the governance measure as a control, it is significant, but has the wrong sign for the npl variable and but now pmindex pc is no longer significant. T continues to be significant in most specifications. Table 7 shows the IV specification using law and order – transparency raises private credit and lowers non-performing loans. Note that corruption overall has no impact on either dependent variable while law and order is usually significant for higher levels of private credit. These results suggest that economic transparency is quite robustly related to better financial sector performance. The results in the various specifications also suggest that greater economic transparency may be a substitute for greater transparency within the financial system in the sense that both types of transparency allow market participants to judge the performance of banking portfolios and of banks.

23
- Gimpel, James (2014) “Immigration’s Impact on Republican Political Prospects, 1980 to 2012,” _Center for Immigration Studies_ .

- Goldstein, Judith L. and Margaret E. Peters (2014) “Nativism or Economic Threat: Attitudes Toward Immigrants During the Great Recession,” _International Interactions_ , 40 (3), 376–401.

- Grigorieff, Alexis, Christopher Roth, and Diego Ubfal (2020) “Does information change attitudes toward immigrants?” _Demography_ , 57 (3), 1117–1143.

- Haaland, Ingar and Christopher Roth (2020) “Labor market concerns and support for immigration,” _Journal of Public Economics_ , 191, 104256.

- Hainmueller, Jens, Dominik Hangartner, and Dalston Ward (2019) “The effect of citizenship on the long-term earnings of marginalized immigrants: Quasi-experimental evidence from Switzerland,” _Science Advances_ , 5 (12), eaay1610.

- Hainmueller, Jens and Michael J. Hiscox (2007a) “Educated Preferences: Explaining Attitudes toward Immigration in Europe,” _International Organization_ , 61 (2), 399–442.

- (2007b) “Educated Preferences: Explaining Attitudes toward Immigration in Eu-

- rope,” _International Organization_ , 61 (2), 399–442.

- Hainmueller, Jens, Michael J. Hiscox, and Yotam Margalit (2015) “Do concerns about labor market competition shape attitudes toward immigration? New evidence,” _Journal of International Economics_ , 97 (1), 193–207.

- Hainmueller, Jens and Daniel J. Hopkins (2014a) “Public Attitudes Toward Immigration,” _Annual Review of Political Science_ , 17 (1), 225–249.

- (2014b) “Public Attitudes Toward Immigration,” _Annual Review of Po-_

- _litical Science_ , 17 (Volume 17, 2014), 225–249, https://doi.org/10.1146/ .

- annurev-polisci-102512-194818

51
# **Figure 5 (continued). Infrastructure Investment in Egypt (2003-2007) (Percentage of GDP)**

## **(d) Water**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>2003 2004 2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> (e) Suez Canal<br>0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>2003 2004 2005 2006 2007<br>Public Private<br><!-- End of picture text --> 35
respondent was enrolled in school. These questions are asked for each of the seven school terms between Term 1, 2008 and Term 1, 2010. As self-reported data may overstate enrollment, we crossvalidated these data by visiting the school the study participants reported attending and asked the same question in the school surveys to the teachers of the core respondents. The enrollment indicators from the school survey are coded ‘zero’ if the core respondent reported not being enrolled in school for that term or if the teacher reported her as not enrolled and ‘one’ if her teacher(s) confirmed that she was attending school during the relevant term. Finally, as enrollment may be a poor proxy for actual school attendance, we utilize the attendance ledgers for the 2009 school year and the first term of 2010 collected during the school surveys in Round 3 to construct an indicator for the percentage of days the core respondent enrolled in school was recorded ‘present’ during days the school was in session.

We did not independently monitor the school attendance of study participants through random spot checks. While studies such as Miguel and Kremer (2004), Kremer, Miguel, and Thornton (2009), and Barrera-Osorio et al (forthcoming) have measured attendance directly, we deliberately chose to forego this method of data collection to protect the validity of the UCT experiment. Despite having data on enrollment from the teachers and attendance from school ledgers, direct observation would clearly have produced superior evidence to the alternative measures of school participation used in this paper. However, as reported above in Section 2.3.3, girls in the UCT arm were fully aware that the attendance of CCT recipients were being regularly monitored, which led them to believe that program managers ‘cared about the education’ of the girls in the CCT arm. We were concerned that performing random spot checks of attendance for girls in the UCT arm could have given them the impression that they were also supposed to attend school 15
<!-- Start of picture text --> 120<br>100 —~<br>80 eee ~<br>° 60<br>o<br>a 40<br>20<br>0<br>1970 1980 1990 2000 2010 2020<br>—Ghana -—SSA, average —LMICs, average<br><!-- End of picture text --> <!-- Start of picture text --> 120<br>100<br>80<br>© 60 a<br>oO _——<br>o<br>__<br>404 _Z<br>20) oO<br>0<br>1970 1980 1990 2000 2010 2020<br>—Ghana SSA, average LMICs, average<br><!-- End of picture text --> <!-- Start of picture text --> 120<br>100<br>80<br>5<br>© 60<br>o<br>ao<br>40<br>20 Ts<br>ee _<br>0 - —<br>1970 1980 1990 2000 2010 2020<br>—Ghana SSA, average LMICs, average<br><!-- End of picture text -->
to rely so much on external finance. These results again point to limitations in external finance access in Egypt, even for this very selective sample of largest listed firms.

We find little or no effect of cash flows or net income on financial savings, while there is a clear positive relationship between net income and cash stocks (firms with higher cash flows are able to hold more cash).

# **8.2 Determinants of Investment**

In Table 7 we report similar regressions for investment to capital ratios. We find that larger firms make more investment into physical assets. This result parallels what we found earlier in the ICA data. Firm size is a dominant determinant of investment in our data as the regression with firm size produces much larger R squared.

We also find that sales growth is positively related to investment as sales growth is one proxy for good growth opportunities. It is expected that firms with good growth opportunities will make more investment. However, if we include firm size in this regression, sales growth becomes not significant (the p-value becomes close to 15%, which could be considered marginally significant).

The interest rate and net income both are positive and marginally significant (at about 15%). We do not find any other significant determinants of investment in our data.

Finally, we investigate how physical savings are related to financial savings over time. As discussed above, total savings can be allocated either toward physical savings or financial savings. Therefore, the contemporaneous relationship between financial savings and physical savings is likely to be negative – i.e. if firm spends money on new machinery, there is less money to save as financial assets. There is likely to be a decrease in cash stocks as well, since investment into PPE uses up liquid assets that firm has accumulated.

However, one of the key reasons firms accumulate financial savings is to invest in physical assets in the future. Therefore, overtime we should find that an increase in financial savings in one year is likely to lead to increase in physical savings (i.e. investment) in the future.

29