rafmacalaba/data-use-mentions-extended huggingface dataset:
ness and market shares relative to high-technology firms due to input-trade liberalization. Indeed, input tariff reductions imply an increase in the relative costs of domestic inputs vis-a-vis foreign intermediate goods. This is shown formally in the Appendix. Unfortunately, the Indian dataset that we exploit in the empirical analysis is not suitable to test this prediction since we cannot identify entry and exit of firms since firms are under no legal obligation to report to the data collecting agency, the Prowess data do not allow us to identify entry and exit of firms.

# _Testable implications_

In the empirical analysis, we focus on firms’ technological decision to import capital equipment goods in India. The simple model presented in the previous section yields two testable implications on the relationship between changes in input tariffs and firms’ decision to upgrade technology embodied in foreign capital goods.

Input tariff cuts imply a reduction of the relative costs of foreign inputs vis-a-vis domestic ones. Taking into account that the high-technology embodied in imported capital goods is biased towards imported inputs and the substitutability between intermediate goods, input-trade liberalization in this framework enhances the cost-advantage of high-technology firms. Thereby, input tariff cuts reduce the relative unit costs of using high-technology, increasing profits of high-technology firms relative to low-technology firms creating incentives to upgrade technology embodied in imported capital goods. Proposition 1 shows that input tariff reductions increase the likelihood of firms to adopt the high-foreign technology by importing capital goods.

**_Testable implication 1:_** _Input-trade liberalization has a positive effect on firms’ decision to import capital goods._

Which are the firms that decide to upgrade foreign-technology after input-trade liberalization?

23
Second, this analysis largely misses the beginning of the displacement crisis because the mobile phone survey began only five months after the escalation of the conflict. It is possible that the onset of conflict has a different impact on displacement than surges in violence in later periods. Although the survey captures many households that were part of the large initial displacement surge, the displacement module only captures the origin governorate. Without more detailed information on the origin locations, it is difficult to make strong inferences about the role violence played in displacement. It would be useful if future work addresses the onset of displacement crises.

And lastly, it is difficult to understand how applicable these results are to other displacement crises in different contexts. For example, it is unclear whether some of these results generalize to settings in which only part of a country is in conflict, or to settings in which households are forced to become displaced in foreign countries. Given the sheer size of the global displaced population and the challenges they face in meeting basic needs, more analysis is needed in a variety of contexts to better our understanding of this population and the complex set of factors that forced them to flee their homes.

21
# **(b) Random Forest Weekly Ensemble**

Importance Plot: Top 15 Features <!-- Start of picture text --> 7 day autocorrelation in daily max distancebetween towers used (pearson) ●<br>2 week autocorrelation in weekly max distancebetween towers used ●<br>Correlation between weekly outgoing duration andthe week before previous's outgoing calls ●<br>6 day autocorrelation in daily max distancebetween towers used ●<br>2 week autocorrelation in weekly outgoingduration, during weeks with activity ●<br>Fraction of outgoing duration during late night ●<br>Correlation between this week's outgoing SMS withlast week's outgoing duration ●<br>7 day autocorrelation in daily max distancebetween towers used (spearman) ●<br>2 week autocorrelation in weekly outgoing calls ●<br>3 day autocorrelation in daily outgoing dur ation ●<br>Ratio of magnitude of strongest repeating tempor al<br>magnitudes of all other patter ns, between firstpattern in weekly outgoing calls to sum of ●<br>and last activity (Fourier transform)<br>Correlation of weekly outgoing duration with 3week lagged outgoing calls ●<br>30 day autocorrelation of daily outgoing calls ,between first and last day with activity ●<br>2 week autocorrelation of weekly outgoing<br>duration, after first week with activity ●<br>(spearman)<br>Correlation between daily outgoing SMS andduration ●<br>0.24 0.26 0.28<br>Mean Decrease in Gini<br>Indicator<br><!-- End of picture text --> Ensemble of 4 random forests, each of 500 trees. Source: authors’ analysis from telecom data.

2
by shifting upward the demand for basic commodities through substitution between commodities across income categories (N. Gemmell and O. Morrissey, 2003). The Government of the Democratic Republic of Congo took a number of steps to grapple with distributional concerns associated with the adoption of the VAT.

A subsequent policy change, following the adoption of the VAT, led both to broaden the tax base so as to include milk and cement, and to exonerate oil companies. This change makes inference about the distributional impact of the entire VAT reform more complex. On the one hand, milk is likely to be a basic commodity for poor households; therefore, its inclusion in the VAT base seems regressive. On the other hand, exonerating oil companies may be a pro-poor policy, as it reduces the price of kerosene, which is an important consumer good for the poor. Furthermore, this exoneration of oil companies is likely to have positive spillovers by reducing the price of most consumption goods through the reduction of transportation costs. Thus, analysis concerned with the distributional impacts of the VAT adoption needs to account for behavioral responses to changes in relative prices.

This paper uses a conceptual framework, which is built on economic theory, to measure the welfare loss associated with the introduction of the VAT in the DRC among the poor and the non-poor in rural and urban areas. To do so, the paper relies on the compensating variation with data from a nationally representative household expenditure survey fielded in 2012. It examines how welfare loss is spread across expenditure categories in both rural and urban areas. Central to the analysis is the question whether the rate differentiation is well-targeted.

The paper is organized as follows; the second section presents the related literature; the third section presents the context and data; the fourth section describes the empirical model; while 2
especially those based on gender basis. Second, the reduction of gender-wage gap. The results of the study show that labor provisions decrease gender wage gap and gender discrimination in workplace only in the textile sector. However, the gap is still increasing in the other manufacturing sectors that are not concerned by these provisions. Furthermore, the impact of these provisions is proven to be significant and important only during the period of the agreement and while the International Labor Organization (ILO) is monitoring the proper implementation of these provisions. However, the impact of the provisions starts to decrease during the post-agreement period. In our paper, the impact of gender related provisions in RTAs is studied using the World Trade Organization (WTO) database that compiles provisions related to women’s empowerment and gender equality. In this dataset, 11
cross-market spillovers, the impact of program duration, and spillovers on other quality measures that were not part of the inspection process. The estimating equations and accompanying tables are detailed in Sections 7 and 9 of the Supplemental Material.

**Cross-Market Externalities** : Cross-market externalities, whereby control health facilities in markets located near treatment facilities are affected by the treatment, may bias our estimates of the impact of the regulation. We identify cross-market externalities using exogenous variation in the local density of facilities induced by the stratified market-level randomization, following a method similar to Miguel & Kremer (2004). We find no significant cross-market externalities in the JHIC score, patient load, OOP payments, exit and entry of new facilities (Table S12 in the Supplemental Material).

**Program Duration** : Next, we exploit variation in the timing of the inspections and the endline to examine the impact of program duration, which captures both the fact that facilities that were in the program longer will have been inspected more often (2.4 times versus 1.6 to 2.0 times for other groups) and that program impacts can fade out over time. Our main identifying assumption, which we verify in Section 7.2 in the Supplemental Material, is that conditional on the controls, the variation in the date of first inspection and the date of the endline are not correlated with the JHIC score.

In markets where the time elapsed from first (last) inspection to endline was 15 (10) months, the JHIC score increased by 7 percentage points (0.65 SD, p-value _<_ 0.01), compared to 4 percentage points for treated markets where the time from first (last) inspection to endline was 11 (7) months. This suggests little “fade-out” and potentially larger effects as the model scales up (Figure S4 and Table S14 in the Supplemental Material).

**Impacts on non-incentivized outcomes** : One concern with regulations on specific inputs is that they can reduce
# Synopsis

Upgrading infrastructure plays a critical role in the Republic of Congo’s quest to diversify its economy and reduce poverty. It is also an important source of growth on its own. A cross-country statistical analysis conducted for this report shows that infrastructure contributed one-half of one percentage point to the Republic of Congo’s per capita GDP growth annually from 2001 to 2006. However, if the country’s infrastructure could be improved to the level seen in Mauritius, the leading country in Sub-Saharan Africa, it could contribute more than 3 percentage points to annual per capita growth.

The Republic of Congo’s power infrastructure is inadequate and inefficiently operated. The country lags well behind peer countries in generation capacity and electrification. The parts of the population not served by the grid face exorbitant costs. The government has responded to these issues with an ambitious investment plan. However, if new assets are to operate effectively, major inefficiencies in the power utility will also need to be addressed. The utility’s transmission and distribution losses are 47 percent, more than double best-practice benchmarks, while the cost of overstaffing is 30 percent of utility revenue. Tariffs recover barely half the cost of service provision, even though full cost recovery would be affordable to the population.

In the ICT sector, the Republic of Congo has made good progress in developing its mobile telephony market in recent years, with high levels of signal coverage. The cost of international connectivity is currently high, but it should fall once the country connects to the international submarine cable and completes its domestic fiber optic network. On the other hand, the physically dilapidated and financially depleted condition of the fixed-line telephone operator is becoming a constraint to raising Internet penetration.

Restoring the national transport network will require major investment and careful policy decisions in the port, road, rail, and river transport sectors. Following the approval of the National Transport Plan, major road sector investments are underway to restore connections between Pointe-Noire, Brazzaville, and the north of the country. A road fund has
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Malawi has not made much progress in rural water sector reform thus far. Malawi has adopted a rural water policy and created a dedicated budget line, but does not yet have a rural water agency, a costrecovery policy, or a map of rural water points. This lack of attention to rural water can be seen in the growing reliance on surface water that has been observed in rural areas in recent years.

**Table 4. Benchmarking water and sanitation indicators**

||Unit|Low-income<br>countries|**Malawi**|Middle-income<br>countries|
|---|---|---|---|---|
|Access to piped water|% pop|10.1|**6.5**|56.4|
|Access to standposts|% pop|16.1|**12.7**|20.4|
|Access to wells/boreholes|% pop|38.3|**69.0**|6.3|
|Access to surface water|% pop|33.8|**11.7**|13.9|
|Access to septic tanks|% pop|5.3|**3.6**|44.0|
|Access to improved latrines|% pop|9.3|**1.2**|0.9|
|Access to traditional latrines|% pop|47.9|**80.7**|33.0|
|Open defecation|% pop|37.1|**14.5**|15.8|
|Domestic water consumption|liter/capita/day|72.4|**109.4**|Na|
|Urban water assets in need of rehabilitation|%|35.5|**42.0
Honig (2018), in which he shows – using a mix of quantitative data and case study analysis – that the more complex the policy intervention, the more necessary it becomes to grant discretionary space to front-line managers, and the more necessary such discretion is to achieving project success. Having ready access to relevant, high-quality quantitative data can aid in this ‘navigation’ but true navigation requires access to a broader suite of empirical inputs.

In a similar vein, Ladner (2015: 3) points out that “standard performance monitoring tools are not suitable for highly flexible, entrepreneurial programs as they assume that how a program will be implemented follows its original design”. In order to avoid ‘locking in’ a theory of change that prevents exploration or responsive adaptation, some practitioners have provided helpful suggestions for how to use various planning frameworks in ways that support program learning.<sup>3839</sup> The Building State Capability team highlights lighter touch methods, such as their PDIA “check ins”, which include a series of probing questions to assist teams in capturing

> 37 <u>https://mintzberg.org/blog/measure-it-manage-it. Says Mintzberg: “Someone I know once asked a most senior</u> British civil servant why his department had to do so much measuring. His reply: ‘What else can we do when we don’t know what’s going on?’ Did he ever try getting on the ground to find out what’s going on? And then using judgment to assess that?”

> 38 Teskey (2017) and Wild et al (2017) give examples of an adaptive logframe, drawn from DfID experiences, that sets out a set of clear objectives at the outcome level, and focuses monitoring of outputs on the quality of the agreed rapid-cycle learning process.

> 39 Strategy Testing (ST) is a monitoring system that The Asia Foundation developed specifically to track programs that are addressing complex development problems through a highly iterative, adaptive
growth of the economy._ Our starting point is the sectoral projection of growth ( carried out by the WB projections according to several scenarios of the evolution of the pandemic. ss gg ss= 1, ... SS) This is followed by _estimating the loss of employment at the level of each sector:_ The loss of employment at the level of each sector is given by . _Identification of individuals who will lose their jobs:_ ss ss ss in this step a logit model is estimated to predict the LL = EE × gg _Identification of individuals who will lose their jobs:_ in this step a logit model is estimated to predict the probability that each worker in the household loses his/ her job depending on four variables (education, age, gender, region of residence). Individuals with the lowest predicted probabilities<sup>8</sup> are assumed to leave the labor market. At this level, several hypotheses are adopted: each household has a certain amount of savings or employment insurance (even informal) that allows it to smooth its consumption. Some households can liquidate certain assets to finance current consumption. Thus, in each sector of activity we find two types of individuals: those who have managed to preserve their jobs and those who have lost them. For the first group, the impact of the pandemic on the household of an individual _i_ who continues to work in sector _s_ is given by , where and stand for pre-crisis and post-crisis ss<sup>ii</sup> ss<sup>ii</sup> ss ss<sup>ii</sup> ss<sup>ii</sup> II = CC × gg CC II

> 5 According to INS, the national rate translates to 15.2% using the 2015 data. Given this, we first update the 2015 data to create a new distribution of consumption, and observe a pre-covid 19 (2019) poverty rate. We then use growth projections to identify the distribution of
. We first estimate equation 1 on the full 2010-2016 data to estimate the month-of-year fixed effects with the highest possible degree of precision. We then recover the de-seasonalized ˆ outcomes _log_ ( _Z_<sup> ̃</sup> _t_ ) = _log_ ( _Zt,m_ ) _−gm_ and estimate the regression discontinuity with a shorter data set (bandwidth) around the reform. In this second step, we estimate equation 1 without the month-of-year fixed effects _gm_ and use the de-seasonalized outcomes as dependent variable. The standard errors from this procedure would need to be adjusted for the fact that we use a predicted outcome in the second-stage estimation. For both methods (columns), the table displays the estimates for our preferred specification using an 80-week bandwidth and for the optimal bandwidth as in Calonico et al. (2014) and shown in Figure B.5. This table is discussed in Section 4.3.

61
measure financial development with a five year<sup>lag between the last year we use in the</sup> financial development measure and the first<sup>year we use in the variability measure (i.e.,</sup> financial development is measured over the periods<sup>1951-55, 1961-1964, 1971-1973, and</sup> 1979-1982). These results confirm our initial<sup>findings for consumption and output vari-</sup> ability, however, in this case _BANK_ becomes<sup>insignificant in the investment regression</sup> while _PRIVATE_ becomes significant.

# [See Table 4.]

Although our data set is a relatively large macro<sup>data set, our analysis still may be</sup> sensitive to outliers. We therefore provide<sup>another estimate of the relationship between</sup> financial development and fluctuations this<sup>time using a robust regression technique that</sup> 11
power. For a consistent accounting of total national consumption, the net energy consumed by Electric _U_ Utilities is only the generation loss plus the Utilities own electricity consumption ( _elect,elect_ ).The generation loss is given by the energy embodied in the fuels combusted in the power plants _less_ the energy embodied in the delivered thermal electricity (total electricity minus renewables and nuclear ( _QPtelec_  _QPtPRIelec_ ). The net energy consumed by Electric Utilities, _EINDj=elect_ , is thus: The national total energy consumption is then the sum over all industries and final demand: _CONS_ This should be equal to<sup>_E_</sup> _t_ , the total computed from the production data in equation A107.

# _Emissions_

The national emissions of carbon dioxide may be computed from the production accounts by adding over the emissions from all fossil fuels _f_ . This is given by the quantity of fuel consumed ( _CFt f_ ), multiplied by the energy content coefficient ( _ef)_ , and multiplied by the CO2 intensity, (<sup>_c_</sup> _f_<sup>, tons of CO2 per sce of fuel</sup> _f_ ):

> The quantity of fuel _f_ consumed, _CFt f_ , is given in equation A107 above. For non-combustion sources of CO2 we only consider those from cement production processes; this is expressed as an emission factor ( _ccement_ ) multiplied by the cement component of the output of the Building Materials industry:

_noncmb cement_ (A120) _EMCO_ 2, _t_  _ccement_  _Build QIBuild_ , _t_ where _a_<sup>_cement_</sup> is cement’s share of the Building Materials industry’s output, and _QIBuild_ is the value of the output of the Building Materials industry (in billion yuan) which also includes glass and clay products.

Total carbon emissions are then the sum of the fossil emissions and non-combustion ones: (A121) _EMCO_ 2, _t_  _EMCOfos_ 2, _t_  _EMCOnoncmb_ 2, _t_ _Local pollutants_ ‐72‐
Policy Research Working Paper 6222

# **Abstract**

This paper shows that the top 1 percent of exporters critically shape trade patterns, using firm-level data from 32 countries. In particular, variation in average firm size (the intensive margin) explains over two thirds of the variation in the sector distribution of exports across countries, the remaining share is explained by variation in the number of firms (the extensive margin). Variation in average firm size across sectors is largely driven by variation in the sectoral distribution of exports from the top 1 percent of firms in a country—export superstars. In contrast, the sectoral distribution of exports from the remaining 99 percent of firms is more similar across countries, and the distribution of the total number of firms across sectors is very similar across countries. This paper also finds that current export superstars typically entered the export market relatively large, reached the top 1 percent after less than three years of exporting, and account for more than half of a country’s total exports, export growth and diversification. The results underscore the role of individual firms in determining both trade volumes and trade patterns.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cfreund@worldbank.org and mpierola@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the