rafmacalaba/datause-encoder-data huggingface dataset:
Government of T&T take a number of measures that can be grouped in four categories: i) enhancement of the flexibility of the labor market; ii) reduction of trade restrictions; iii) development of infrastructure to maximize firms’ performance and; iv) strengthening of market institutions.

The development of the private sector seems to be hindered by a list of additional constraints. Balgobin and Omar (2006) claim the overlapping and conflicting responsibilities of line ministries weaken the expansion of the private sector. To develop a project, private investors must see their business proposals being evaluated by redundant public agencies resulting in administrative delays and unnecessary costs. Balgobin and Omar (2006) also indicated that institutional strengthening is a necessary but insufficient condition to boost national competitiveness. According to the World Economic Forum Global Competitiveness Report 2011/12, T&T has a clear technology handicap in both national infrastructure and individual use. The country’s performance in capacity for innovation and research-and-development (R&D) readiness could also be improved.

# _<u>2.1 Measurement of Diversification</u>_

To understand the diversification process in T&T, it is necessary first to measure the level of concentration of the economy. Second, the potential drivers of diversification must be identified. Third, to determine the factors affecting diversification, a control group of comparable countries is developed and the historical influence of the potential factors for the selected countries analyzed. Finally, the relevance of the lessons drawn at the international level must be assessed with domestic data for T&T.

Given that for most countries no data are available for the economy-wide level of diversification, the level of export concentration is often used as a reliable proxy. It is assumed that the composition of exports reflects the overall structure of the economy. The Herfindahl-Hirschman index (HH index hereafter) is used to measure export diversification and is the most commonly used statistic for this purpose. It is calculated as follows: where Si is the share of industry _i_ in total exports and _n_ the number of sectors in the economy.

The Herfindahl-Hirschman index is the sum of squared shares of each product in total export. A country with a perfectly diversified export portfolio will have an index close to zero, whereas a country which exports only one product will have a value of 1 (least diversified). Observations for the HH index are extracted from the World Integrated Trade Solution Database (WITS) and are calculated with annual country export data recorded in the SITC Revision 2 COMTRADE database. We use two-digit level data.

5
performing loans. This upward movement in NPLs also began in February 2016 and continued past October 2016.

Table 1 presents the same data and trends in the form of aggregate summary statistics for loan value across the three time periods.

The simple comparison of means shows an average decline in loan value after February 2016 and October 2016; and a corresponding increase in abnormal loans. This simple comparison, however, does not account for heterogeneity across sectors.

Table 2 improves on the analysis by studying these changes in a regression framework with strict sector-level controls. Disaggregated data at the sector-level are used for the analysis and the specification controls for sector fixed effects, thus accounting for all time-invariant differences across loan sectors.

The findings in Table 2 corroborate the trends seen in Figures 1(a) and 1(b); and Table 1. The coefficient on “Post Feb 2016” represents the average change in lending in the interim period between February 2016 and October 2016, and the coefficient on “Post Oct 2016” represents the additional average change in lending in the period after October 2016. Both coefficients represent comparisons with average lending in the months prior to February 2016.

Compared to loan values prior to February 2016, the interim period up to October 2016 saw a decline of 3.1% in lending from an aggregate base of Ksh. 167.65 billion, with a further 2.7% decline after the caps. In contrast, there was a sizable increase in abnormal loans, with a 52.6% increase in the interim period from an aggregate base of Ksh. 25.67 billion, and a further 7.8% increase after the interest rate caps.

These findings confirm the qualitative evidence in the previous section of this paper. There is certainly a statistically significant downturn in lending and upturn in NPLs after the interest rate caps. However, these respective upward and downward trends were present in the economy even before the caps, likely due to the prevailing economic and market downturn. Indeed, the CBK Commercial Banks’ Credit Surveys for the second and third quarters of 2017 confirm that commercial banks expect a continuing rise in the level of NPLs due to the industry’s perception of increased political risk and the general economic downturn. Furthermore, the analysis presented later in this paper documents that commercial banks started issuing debt of shorter maturity after the interest rate caps, likely further contributing to the rise in NPLs.

Overall, it is very difficult to disentangle and isolate the impact on loans due to these myriad causes. What we can say with confidence is that the interest rate caps certainly did not attenuate the negative trends in lending precipitated by the prevailing economic and market downturn.

On the loan demand side, we do not have clean data on loan applications but instead refer to the CBK’s Credit Surveys which find that even though the interest rate caps led to an increase in loan demand (attributable to cheaper credit), this increased demand had little impact on the 11
mechanical operations within the team and hire laborers as well. These teams typically also provide mechanical operation services to their neighboring farmers. They can use large-size machines on the consolidated land and upgrade their machines with subsidies provided from the government. However, they also face some constraints on keeping or expanding the consolidated land. First, the land rent-in contracts are mainly short term, often subject to renewal every year. The farmers who rent out their land expect that the rent will increase and thus hesitate to sign long-term contracts. The insecurity involved with using consolidated land (and, more generally, any short-term rental arrangements) make the operators less likely to invest in the land. Secondly, even though they would like to upgrade their machines, they may not be able to obtain the quota to buy the subsidized large-size machines.<sup>11</sup> Because of a move to off-farm employment, especially through migration, Chinese smallholders began to adopt mechanical farm operations to substitute for labor in production since the 1990s (Figure 1). The rapid expansion of mechanical operations occurred in plowing, sowing and harvesting. Mechanically plowed areas doubled with an annual growth rate of more than 3 percent between 1983 and 2006 (Figure 1, panel a).<sup>12</sup> The growth of mechanically-plowed areas accelerated to over 5 percent per year from 2008 to 2011. More than 72 percent of cultivated areas are now mechanically ploughed. Mechanical sowing areas also doubled during the 1990s, but mechanically harvested areas increased only around 1.5 times (Figure 1, Panels b and c). Furthermore, mechanical sowing and reaping have started to accelerate since 2003 with annual growth rates of 4.5 and 7 percent, respectively. By 2011, more than 40 percent of sown areas are mechanically sown or reaped.

# **3. Data**

> 11 In some regions, a quota of subsidized machine is distributed like a lottery. Only the one who is lucky enough to get the quota is qualified to buy the certain machines with the subsidy.

> 12 It should be noted that the sudden decline of the proportion of cultivated area under mechanical plowing is because cultivated area has been recorded to increase dramatically from 94.971 million ha in 1999 to 130.039 million ha in 2000.
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|Power|20|14<br>(5-40)|38|26|7<br>6<br>(2-47)|10|
|---|---|---|---|---|---|---|
|Coal|5|24<br>(0-52)|39|6|4<br>26<br>(0-62)|0|
|Oil/Gas|10|32<br>(0-69y|30|23|4<br>7<br>(0-34)|4|
|Telecoms|6|39<br>(15-69)|28|30|4<br>0|0|

120. Only in the irrigation and telecommunication **s** ectors was an attempt made to calculate the net impact of the project on the government budget. Four of the 14 irrigation projects reviewed made such calculations, two of which relied on the additional income and sales taxes to be paid out of **the** incremental income of beneficiaries. The latter approach is questionable unless the funds for the project were not fungible. Net fiscal impacts were calculated in five of the six telecons projects reviewed. Calculations of the net project impact on fiscal aggregates would also be useful in other sectors--such as ports and oil/gas--where investments may be partly government financed but means exist to transfer a portion of profits and rents back to government. Inter-project comparisons (within a sector) could help to develop norms of tax contributions as well as highlight tax inconsistencies or unintended consequences within a project.

# Earmarkin

121. The earmarking of project revenues for reinvestment in the sector is a controversial subject, and one on which Bank practice varies across sectors. Earmarking has been defined as "the practice of designating or dedicating specific revenues to the financing of specific public services... Normally, earmarking (means) the dedication of a single tax source to a single public service within a multitax, multiservice fiscal unit, but the identical effects are produced by the creation of specialpurpose fiscal units, such as school districts, fire districts, and sanitation districts, each of which is granted independent, but restricted, taxing powers."21/ Public finance experts have called earmarking "probably the worst danger to good budget practice."Z/ Yet in many industrialized countries it is a widespread practice with strong-taxpayer support.

122. Using the broad definition quoted above, the revenues of public enterprises--power, water, telecommunications, railways, fertilizer, coal and sometimes ports--are examples of a type of earmarking. All the tests of financial viability for such enterprises hinge on an explicit link between revenues from beneficiaries and the costs incurred in providing the service. In the housing sector, earmarking is routinely required in order to institutionalize replicability. In the case of roads, covenants to earmark funds for road maintenance were included in 45% of the projects reviewed. Many of the other projects included assurances that the government would allocate specific sums to maintenance in future years.

123. The disadvantages of earmarking are well-known. It places constraints on the budget authority to transfer resources in response to changing priorities. It distorts the picture of a government's finances and makes it harder to exert macroeconomic controls. The administrative
Table 1: Cobb-Douglas and the Taylor approximation

||Convergent case: _dp_1 = 0_._5:|Divergent case: _dp_1 = 2_._0|
|---|---|---|
|Order(_o_)|terms(_x_1[_−_1<sup>_o_ 1</sup><br>_o_<sup>∆</sup><sup>_po_</sup><br>1<sup>] )</sup><br>Cumulative terms|terms(_x_1[_−_1<sup>_o_ 1</sup><br>_o_<sup>∆</sup><sup>_po_</sup><br>1<sup>])</sup><br>Cumulative terms|
|1|-15.00<br>-15.00|-60.00<br>-60.00|
|2|3.75<br>-11.25|60.00<br>0.00|
|3|-1.25<br>-12.50|-80.00<br>-80.00|
|4|0.47<br>-12.03|120.00<br>40.00|
|**True value**|**-12.16**|**-32.96**|

to estimate the _CV_ and the _EV_ measures. One work that addressed this question is that of Hausman (1980), which, for some popular marshallian demand functions, contributed to define the exact functional form of the _CV_ and _EV_ measures. However, it is the the work of Vartia (1983) that provides a more general approach to estimate _CV_ and _EV_ measures.

Instead of assuming _ex-ante_ a utility function or trying to derive welfare changes from the Taylor’s expansion of an unknown utility function, Vartia proposes to work backward, starting from a known demand function and deriving from this function the utility change. This approach is based on the theory of revealed preferences and is the opposite of the approach where we first assume a utility function and derive from this function the demand schedules.

Vartia derives first the conditions that relate a demand function _h_ ( _._ ) to the indirect utility function _V_ ( _p, C_ ) and based on these conditions derives the first order differential equation in the cost (expenditure or money income) function _C_ ( _._ ): where _t_ refers to the number of the iteration. With knowledge of the demand function, it is possible to derive _x_ iteratively and estimate ∆ _C_ .<sup>13</sup> Hence, the _CV_ can be expressed in terms of sum of marginal changes over a known demand function.

For the _CV_ measurement we have that _p_<sup>_t_=0</sup> = _p_<sup>_a_</sup> is the initial price and _p_<sup>_t_=</sup><sup>_n_</sup> = _p_<sup>_b_</sup> is the final price when the number of iterates is _n_ and that _p_<sup>_t_</sup> _− p_<sup>_t−_1</sup> = _dp/n_ . The Vartia algorithm is reversible and this enables to estimate the _EV_ at price _pt_ as ˆ and _m_<sup>_t_</sup> = _m_<sup>_t−_1</sup> . Note that _p_<sup>_n_</sup> _k_<sup>_−t_</sup> for _t_ = 0 is simply the price of good _k_ in the final period. Below, we use an illustration from Chen (2009) to introduce the Vartia algorithm and show how to estimate the compensated income _m_<sup>_c_</sup> (Figure 4). We assume that the price decreases from _p_<sup>0</sup> to _p_<sup>3</sup> . The compensated income at _p_<sup>1</sup> is the initial income plus the area _a_ 0. However, this latter is simply approximated to _a_ 0 + _a_ 1 using intermediately the marshallian demand function for the estimation. After this first iteration, we move to the second iteration to assess the compensated

> 13See Bacon (1995) for a simple spreadsheet approach to the use of the Vartia’s method.

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Corporate investors’ inclination to invest in any such opportunities depends on their expectations about how well the government is likely to behave over the operational lifetime of the investment. In particular, foreign investors’ property rights are particularly vulnerable to partial, or even complete, expropriation by host country governments. Savvy foreign investors are therefore at least as worried about future government behavior as about current policies. Fiscal stress or political reorientation can easily transform a country’s business environment from welcoming to overtly hostile. Ideology can drive such transformations, but so can short-term political opportunism.

The purpose of constraints on executive power is to prevent abrupt changes in policy without due political debate and at least a degree of consensus. In the absence of such constraints, currently attractive institutions are in danger of being radically and unpredictably transformed should the country get a new head of government with new policies and priorities, different ethical standards, or a different level of affection for foreign investors.

Given these considerations, we evaluate each country’s quality of government with the variables gauging the strength of constraints on executive power, the general quality of government, and the government’s track record as regards its economy.

We use two variables to capture the strength of constraints on executive power: _Executive constraints_ **,** from the Polity IV database, ranges from 1 to 7. It is composed of indexes that gauge barriers to political entry (monarchy through dictatorship to open entry), the nature of political transitions (orderly or military), and the selection of successors (genetics through appointment to open election). This variable therefore captures the strength of institutionalized constraints on the decision making power of a country’s chief executives. The larger the number is, the stronger the constraints.

_Responsible government_ is constructed from the Polity IV database and rates each country on a democracy-autocracy scale. First, in the data base an autocracy variable is available, ranging from 0 to 10, with a larger number indicating a more autocratic government. Also 14
A final point to note is that the unincentivized willingness-to-pay question in our baseline survey would yield very different results on demand from those we obtain using BDM. The simple survey question results in a much lower estimate of WTP. The median unincentivized measure, albeit measured before coming to the demonstration session, is only J$5,000, compared to $15,000 in the BDM. Moreover, the correlation between the two measures is very low (0.07) and not statistically significant.

# **TIOLI Sample**

Demand is simpler to measure using take-it-or-leave-it offers – firm owners are offered the training at a given price, and decide either to buy it or not.<sup>18</sup> To estimate demand for the TIOLI sample, we regress a dummy for take-up of the course (paying the offer price) on the offer price. Column (7) in Table 4 shows that, as expected, demand is downward sloping: a J$1,000 increase in price reduces demand by 5.5 percentage points. In this case, the reduction in demand is more or less proportional with price, but we can still reject linearity (Column 8, p-value for linearity test=0.04).<sup>19</sup> Demand is again price-inelastic, with an elasticity averaging -0.37. As with the BDM sample, we do not see any effect of the credit treatment on take-up (Column 9).

Column (10) in Table 4 pools together the BDM and TIOLI samples to examine how the price sensitivity of demand varies between the two studies. The TIOLI sample has a higher base interest in training, and a more elastic demand: the effect of prices on demand is 17% larger for the TIOLI sample than for the BDM sample (the interaction coefficient is -0.008, while the effect for BDM is -0.047), a difference that is statistically significant. Figure 4 compares the demand curves for TIOLI and BDM samples. The shares paying the offer price at 75% or 50% of the cost of the course are larger for the TIOLI sample than for the BDM sample: they are 19 and 33 percent for the TIOLI sample, vs. 12 and 24 percent for the BDM sample, respectively (see Table 7 for details). However, the main difference between the two samples is observed at a price equal to 25% of the cost of the course. At this price, 71% of the TIOLI sample pays, while only 50% of the BDM sample does.

Finally, Panel B of Figure 3 shows that also for the TIOLI sample the credit option slightly increases demand at higher prices, and hampers it at lower prices, but the effects are

> This implies that respondents might have been over-optimistic about their ability to pay.

> 18This tells us aggregated demand at the randomized prices, but much less information about individual willingness to pay.

> 19If we express the coefficients in terms of reductions in demand per J$1,000 increase in price, we get: 0.058, 0.067, 0.054, respectively.

17
(2012) formula underestimates the gains from trade, because we must adjust for the foreign _wU lU_ <u>(</u> _ω_ <u>)</u> labor content in the production of final goods by _D_ : _PD_ ( _ω_ )<sup>.</sup> Conversely, we find that the Arkolakis et al (2012) formula based on final goods overstates the gains from trade for the upstream country _U_ . Specifically, we find that the difference in real wage compared to autarky equals: While these results are shown here only for a two-country case, our counterfactual simulations in Section 5 suggest that this insight holds more generally. Conditional on final goods trade, our results suggest that gains from trade tend to be underestimated for downstream (and generally richer) countries and overstated for upstream (and often poorer) countries. We calibrate our model and compute gains from trade by using input-output tables and information on domestic and foreign labor content which, as shown above, are crucial to obtain a more adequate measure of the gains from trade when production is fragmented across borders.

**Fragmentation and firm scope along the chain:** Finally, we examine the effect of trade on fragmentation and firm scope along the chain within each country.

A key determinant of firm scope along the chain is the Lagrange multiplier associated with the total range of tasks, i.e. the shadow cost of fragmentiation _λG_ ( _ω_ ) (which varies across varieties). At optimum, this shadow cost depends negatively on the relative wage in _U_ adjusted for trade costs _τ_ : (for final goods produced in _D_ ). Using Lemma 2, we obtain that the shadow cost of fragmentation decreases with trade cost, which lead to more fragmentation.

We can also express firm scope at all stages along the chain as a function of relative wages. In country _D_ , firm scope at stage _f_ is given by: A decrease in trade costs leads to a decrease in trade-cost adjusted wage _τwU_ and therefore a decrease in firm scope in _D_ at each stage. Moreover, the overall range of tasks _SD_ ( _ω_ ) performed in country _D_ decreases when trade costs decrease: 31
Table 4: Determinants of Migration Flows Across Libyan Provinces

||**Migration**<br>(Binary <br>|**flows in 2018**<br> Variable)<br>|
|---|---|---|
||**STERGM**<br>**Formation**<br>**(1)**|**STERGM**<br>**Dissolution**<br>**(2)**|
|Night lights|1.7785|-1.0517*|
|(1 = moving to a province with equal or more night lights)|(1.2099)|(0.6437)|
|Log distance|-0.3475<br>(0.7568)|-|
|Conflicts|-1.5842*|-0.2631|
|(1 = moving to a province with equal or more conflicts)|(0.9157)|(0.6216)|
|Number of migrants|0.4434|0.1486|
|(1 = moving to a province with equal or more migrants)|(0.9509)|(0.6029)|
|Intransitivity<br>(province_i_and_j_send migrants to province_z_, but they do not<br>share any migrant flow with each other)|0.2910<br>(0.2245)|-0.6251*<br>(0.2504)|
|Edges|-2.6633<br>(5.0537)|1.5719<br>(0.4939)|
|Number of Observations|342|342|
|R2|-|-|
|Akaike Information Criterion|63.26|97.39|

Source: International Organization for Migration Displacement Tracking Matrix.

Note: Separable Temporal Exponential Random Graph Model (STERGM) estimated coefficients and standard errors (in parentheses) are reported. *, **, *** indicate statistical significance at the 10, 5 and 1 percent level. Column (1) and (2) report respectively values for the network formation and dissolution model fitted by STERGM.

23