rafmacalaba/datause-encoder-data huggingface dataset:
- 58 -

_i1( -_ u) and p, .37 This would, therefore, justify the procedure used in the METR analysis of treating the cost of finance as a weighted average of the average costs of debt and equity finance.

The relevance of this discussion of financing for investment incentives is rather limited since most investment incentives do not apply to the financing costs. At the same time, investment incentives may well play an important role in providing finance to the firm as we have already pointed out. Probably the cash flow effects of investment incentives are the most important ones.

# _6. The interaction of inflation and investment incentives_

Most corporate taxed have very little provision for inflation built into them. Interest deductions tend to be based on nominal interest rates and depreciation is calculated in historic rather than replacement terms. This implies that inflation affects the real value of the tax base. The effect of inflation on the METR is, however, ambiguous a _priori._ The lack of replacement cost depreciation implies that increases in inflation will reduce the value of the depreciation write-off, thereby tending to increase the METR.<sup>38</sup> At the same time, the ability to deduct nominal interest means that in times of inflation firms are effectively able to write off part of the principle of their debt. This reduces the cost of finance and tends to reduce the METR. Depending on the relative magnitude of these two effects, inflation could increase or decrease the METR. It is more likely to increase it the more important is debt as a source of finance and the slower is the write-off for depreciation. This ambiguity has been borne out in various studies of METRs. The absence of full loss offsetting will also be important in determining the effect of inflation on the METR.

Naturally investment incentives will have differing effects depending on the rate of inflation. Tax rate reductions tend to reduce the absolute size of the METR so preserve whatever inflation bias already exists. Accelerated depreciation reduces the disadvantage of historical cost depreciation so should be especially beneficial in times of inflation. Investment tax credits provide an additional advantage which is independent of the rate of inflation (since it occurs up front) without affecting the existing interest and depreciation deductions. Thus, its effect will be relatively independent of the rate of inflation, at least as long as the benefit of the tax credit is not postponed because of imperfect loss offsetting. This may be viewed as another advantage of investment tax credits over other forms of investment incentives.

37 This is demonstrated formaUy in Boadway (1987) and discussed in more **detail** in Boadway, Bruce and Mintz **(1987).**

> 38Simil&r arguments apply to the deduction for inventory usagc.
evidence that natural resource dependence is associated with monopolization of imports, and that trade policy is a mechanism behind this association. The term ‘monopolization’ is used to describe a shift in market structure toward one that is more concentrated.

The analysis exploits a novel database of all firm-level import transactions in 53 developing economies. These data reveal that natural resource-dependent economies have more concentrated markets for imported products. This main result is illustrated in Figure 1a, which shows a positive association between countries’ commodity exports as a share of total merchandise exports and the weighted average Herfindahl–Hirschman index (HHI) across their imported product markets. The HHI for an imported product market is the sum of the squared market shares of every firm importing that product. Econometric estimates show this relationship is robust to a variety of controls including country-product and productyear fixed effects. An alternative specification shows exogenous increases in world commodity prices also increase import market concentration, suggesting the relationship is causal.

Traditional models of Dutch disease emphasize the increase in the price of non-tradable goods relative to the price of tradable goods hence the appreciation of the real effective exchange rate. Yet, these models ignore elements of market structure and the presence of markups. These elements can affect prices over and above the effect on relative prices stemming from the traditional Dutch disease. Our results suggest that prices are elevated due to higher markups ensuing from monopolistic or oligopolistic pricing by importers. Data on import unit values and domestic price levels show that import market concentration can account for higher prices. This result is not obvious ex-ante: if higher market concentration is associated with a higher fixed cost but lower marginal cost of importing, import market concentration could be associated with lower prices, even if it contributes to higher markups. Along these lines, contributions by Edmond, Midrigan and Xu (2023) and Aghion, Bloom, Blundell, Griffith and Howitt (2005) show that increased markups stemming from high market concentration may (though need not) harm welfare. Higher prices associated with concentration could shape firms’ international input sourcing decisions, as recently ana3
costs which impede trade. Frankel (2010) offers a comprehensive survey of the relationship between resources and economic growth for countries around the world. We provide the definitions of these classifications and the detailed list of the countries in Table 2.4 in the Appendix.

We show in Figure 3 the transitions across the three categories for countries, conditional on the welfare status in the 1<sup>st</sup> period, in each classification. In particular, we plot for each country upward mobility (i.e., the percentage of the population that moves up one or two welfare categories in the 2<sup>nd</sup> period) against downward mobility (i.e., the percentage of the population that moves down one or two welfare categories in the 2<sup>nd</sup> period). To help with interpretation, we plot a 45degree line that separates countries into two groups: one group with more upward mobility, and the other group with more downward mobility (or less upward mobility); those in the former group are thus graphed above this line and the latter group below this line. We use the plus (+) symbol to mark the countries that are in fragile situation (Panel A), landlocked (Panel B), resource-poor (Panel C), and low-income (Panel D).

Several observations are in order for Figure 3. First, there appears to be no clear relationship between being in a fragile situation or being landlocked and economic mobility (Panels A and B), since the countries are scattered rather evenly above and below the 45-degree line. Second, resource-rich and middle-income countries mostly have more upward mobility than downward mobility. Indeed, Figure 3 indicates that out of these two groups, only Zambia—a resource-rich and middle-income country—has more downward mobility (Panels C and D).<sup>14</sup> Finally, notable

> 14 We plot a similar graph for economic mobility versus pro-poor growth and show results in Figure 2.2. Interestingly, several countries remarkably have _both_ more (or most) positive pro-poor growth scenarios and more upward mobility than downward mobility. These countries include Botswana, Chad, Mauritania, Ghana, Uganda, Sierra Leone, Congo DRC, Ethiopia, and Rwanda. On the other hand, two countries, Zambia and Madagascar, also stand out as having both negative pro-poor growth scenarios and more downward mobility.

19
shifted to bank accounts in havens when political institutions are weak (Andersen et al., 2017). Last, our results contribute to the broader literature on political corruption (Olken and Pande, 2012).

The remainder of the paper is organized as follows. Section 2 describes our key variables. Section 3 explains our empirical strategy. Section 4 presents the results. A final section concludes.

# **2 Data**

## **2.1 Cross-border bank deposits**

We use data on foreign bank deposits from the Locational Banking Statistics of the Bank for International Settlements (BIS). This quarterly dataset has information on the value of bank deposits in 43 financial centers owned by residents of around 200 countries. The deposit information is at the bilateral level, e.g. the value of deposits in Swiss banks owned by residents of Tanzania, and builds on confidential reports from individual banks on their foreign positions. Importantly, deposits are assigned to countries based on immediate ownership rather than beneficial ownership; hence, if a Tanzanian firm has a subsidiary in Bermuda, which holds a Swiss bank account, the account is assigned to Bermuda in the BIS statistics.

The dataset covers the vast majority of the world’s cross-border bank deposits: all significant banking centers contribute to the dataset and within each banking center coverage is rarely below 90% (BIS, 2011). This is one of the most reliable sources for information about foreign assets and is therefore frequently used by central banks to construct capital accounts; by macroeconomists to gauge net wealth positions (Lane and Milesi-Ferretti, 2007; Zucman, 2013); and by public finance economists to study offshore tax evasion (Johannesen, 2014; Johannesen and Zucman, 2014).

While the BIS generally makes deposit information publicly available at the country level (e.g. deposits held by Tanzanians in all foreign banking centers combined and deposits held in Cayman banks by all foreigners combined), it has traditionally restricted access to deposit information at the bilateral level (e.g. deposits held by Tanzanians in Cayman banks) to central bank staff and external researchers working under a confidentiality agreement with the BIS. In the main analysis, we use a dataset with restricted information at the bilateral level up until 2010, which allows us to break down each country’s total foreign deposits into deposits in havens and deposits in non-havens. In an auxiliary analysis, we exploit recently released information at 5
However, the RHS may not be representative for all provinces; more research is needed in order to check the robustness of this finding.

If one considers that the provision of drinking water, sewerage networks, and electricity to a dispersed rural population would be particularly costly, efforts should first be aimed at the agglomerated population in localities and in regions and provinces with the most acute level and highest density of poverty. At the same time, special programs should be defined, using appropriate technologies that improve the dispersed rural population’s access to water.

<!-- Start of picture text --> Figure 6.2: Access to Publicly and Privately Provided Water in Dispersed Rural<br>Areas in Argentina by Income Quintile, 2003<br>60<br>50<br>40<br>Mendoza<br>Santiago<br>30<br>20<br>10<br>0<br>1st (poorest) 2nd 3rd 4th 5th (richest)<br>Source : Own calculation based on RHS 2003.<br>Percent of households with access to water<br><!-- End of picture text -->

# **Social and Productive Assistance**

Recognizing that economic growth and social investment in education (and health) will still leave many in extreme poverty, the provincial and federal governments have a variety of programs aimed at reducing economic insecurity and targeted poverty. Programs fall into three categories: (i) those dealing with life cycle considerations such as social security and pensions; (ii) those dealing with income volatility such as unemployment insurance; and (iii) those dealing with social protection, aimed at improving the well-being of specific vulnerable groups.

36
Peruvian firms are of intermediate inputs and capital goods and we consider only those imports in the rest of the analysis.<sup>3</sup> We designate them in what follows as ‘imports of intermediates’, or ‘imports of intermediate inputs’.

We will consider for each exporting firm several measures of export performance in each year: total exports (in logarithms), growth in total exports (defined as the difference in logarithms between total exports in year _t_ and total exports in year _t-1_ ), number of destination countries, and relative average unit values (defined as an export share weighted average of the ratio between the unit value for the firm-HS6 digit product in a year and the average unit value for that same HS6 digit product across all firms that export it in the same year). Appendix Table 2 shows summary statistics for all the dependent and independent variables from the customs data used in the analysis in Sections 3, 4, and 6.

# **2.2 Summary Statistics and Characterization of Exporter-Importers**

Figure 2 shows the numbers of exporter-importers in Peru for all sectors as a whole and for agribusiness and apparel from 2000 to 2012 in Panel A; the participation of exporter-importers in the total number of exporters (relative to exporters only) in Panel B; and their share of total exports in selected years in Panel C. The key patterns identified are that the number of exporter-importers rose steadily in Peru from 2000 to 2012 and exporter-importers account for the bulk of total exports in all sectors. However, the share of importer-exporters in the total number of exporters is declining in importance, especially in apparel but also in agribusiness after 2006.

> 3 We use the BEC classification as provided in United Nations (2011) and concord the BEC categories to the HS 6-digit products imported by Peruvian firms using a concordance provided by the United Nations at http://unstats.un.org/unsd/trade/BEC%20Classification.htm. The BEC categories available are capital goods, intermediate goods, final goods and others. The category others includes all HS 6-digit products for which there is no correspondence to either capital, intermediate, or final goods.

7
per capita on the number of exporters and on average exporter size is smaller but remains positive and significant overall.

Finally, a potential concern about the results in Table 4 is that our measures are defined only for cells with a positive export flow but trade zeros can be important. By expanding our original data to have zeros in cells with no export flows, we find that the prevalence of trade zeros is negligible in the country-sector-year data (5.9% on average across countries) but is very large in the country-destination-year data (47% on average across countries) and the country-sectordestination-year data (91% on average across countries). The trade zeros in our data are not random but rather are more prevalent both in smaller countries and in less developed countries. Hence our findings could be influenced by the relationship between zeros, country size and stage of development. To investigate this possibility we use the non-linear Poisson pseudo-maximumlikelihood (PPML) estimator introduced by Silva and Tenreyro (2006) to estimate our specifications in Table 4 using the expanded data.<sup>27</sup> The corresponding estimates, shown in Table 6, reveal that our main results on stage of development are robust to the inclusion of zeros.<sup>28</sup> With the exception of average exporter size, the results on country size are robust in the country-sector and country-destination regressions, and all results remain strong in the country-sector-destination regressions.

_Stylized Fact on export-sector characteristics: Larger economies and more developed economies_ _have more exporters, a larger average exporter size, and more concentrated export sectors among_

> 27 Egger and Staub (2015) and Fally (2015) provide extensive discussions of the theoretical and econometric properties of the PPML estimator.

> 28 We do not report results on the share of the top 5% using the expanded data in Table 6 because concentration is undefined, but not necessarily small, when trade is missing.  Interpreting missing values as low levels biases the results towards finding higher concentration in larger and more developed countries, which have fewer cells with missing exports. Indeed, when we estimate using PPML, the results are qualitatively similar to previous results but with larger coefficients.

20
23 _Displacement of output effects_ seemed to be quite strong as local governments often wanted certain work done by their subsidised PSE employees which previously had been given to outside private entrepreneurs. From the result of the survey one can infer that this effect has an upper bound of 24% of work done by private entrepreneurs in the absence of the PSE programme.

Even though the mentioned distortive effects are not very strong the fact that virtually all possible such effects seem to occur points to a poor design of the PSE programme which in addition, as previously discussed, does not produce any efficiency gains.

# _3.2. Poland_

# 3.2.1. The identification of some "problem groups"

As a first task one might try to identify "problem groups" in the Polish labour market in general and among the unemployed in particular. The data used for this exercise are the data from the PLFS. There are two major advantages using LFS data; the picture one gets of the Polish labour market is richer than if one only used register data and unemployment is consistently defined along ILO-lines. The estimation of transition probabilities is done separately for men and women as the labour market experiences are quite different for the two sexes (cf. e.g. Boeri, 1994, OECD, 1993, and G6ra and Lehmann, 1992 and 1995). After this partitioning of the sample the data are further disaggregated by education, age, duration of unemployment (for the stock of male and female unemployed) and transition probabilities are then estimated for the various sub-groups.23 Yearly transition probabilities are estimated since, with the relative fine disaggregation of the data, quarterly flows (they can in principle be estimated) are often too infrequent for some of the categories to arrive at statistically meaningful estimates. The estimation of yearly transition probabilities does not cause major problems as far as flows out of employment and from the not-in-the-labour-force state are concerned. However, the estimated yearly probabilities out of unemployment should be taken with caution as they are subject to downward bias due to substantial round-tripping over the year. The PLFS data allow the construction of two yearly panels, May 1992 to May 1993 and May 1993 to May 23The important issue of regional diversification in the Polish labour market using Markovian flow analysis has been investigated by G6ra and Lehmann (1995). It will not be addressed here.
constraint affects the exposure or coverage of a reform effort and requires a phased-out approach in terms of coverage, RCT can be introduced by randomly selecting the locations or beneficiaries which would receive the exposure to the reform during the pilot stage. However, under circumstances where it is impossible to implement a RCT, quasi-experimental techniques, such as a regression discontinuity (RD) approach or matching in combination with difference-indifference (DID) or triple difference (DDD) techniques could be used utilizing unique features of the reform. Some of these features may include any threshold criteria for eligibility to participate in the reform program based on firm size, sectoral, or geographical characteristics, or staggered fashion of implementation of reforms in different location of a country.<sup>5</sup> A number of RCT and quasi-experimental studies have been undertaken recently to understand the effect of different types of IC reforms on firm performance from which this review draws.

# **3.1 Business Entry Reforms, Firm Formalization, New Firms, and Job Creation**

While the majority of private sector jobs and firms in many developing countries are informal, informality itself can have dire consequences in terms of firm growth, productivity, and job creation. A series of high-profile sector studies by the McKinsey Global Institute which compared the operation of formal and informal firms in a number of countries concluded that informality has a negative impact on productivity, accounting for nearly 50 percent of the overall productivity gap between countries, such as Portugal, Turkey, and the United States (Farrell 2004). Using WBES, La Porta and Shleifer (2008) find that formal firms have substantially higher productivity levels than informal firms, while as mentioned before, Ayyagari et. al. (2013) have demonstrated zero growth of employment of informal businesses over its life-cycle vis-àvis a two- to four-fold increase of employment in the formal sector in India.

## **Given these apparent substantial benefits to formalization, why does the majority of the private sector in many developing countries remain in the informal sector?** The popular

> 5 Good quality instruments still need to be used, however, if there is any potential endogeneity concern of the policy intervention in the difference-in-difference type of setting, and any such endogeneity concerns associated with policy intervention and selection criteria could make the RD design invalid.

11
– Table 3. SUMMARY TABLE FIXED EFFECTS ESTIMATES

||**Rate of**<br>**Disbursement -**<br>**Up to Mid-Term**|**Gap - Up to Mid-**<br>**Term**|**Rate of**<br>**Disbursement -**<br>**Adjusted by**<br>**Sector Average**<br>**– Up to Mid-**<br>**Term**|**Rate of**<br>**Disbursement**|**Gap**|**Rate of**<br>**Disbursement -**<br>**Adjusted by**<br>**Sector Average**|
|---|---|---|---|---|---|---|
|**T (quarters with IE==1)**|0.060***|-0.078***|0.40***|0.038|-0.067**|0.45***|
||(3.00)|(-3.08)|(3.39)|(1.54)|(-2.47)|(3.15)|
|**_TTL switch dummies_**|Y|Y|Y|Y|Y|Y|
|**_Polynomial of sector averages_**|Y|Y|Y|Y|Y|Y|
|**_Observations_**|7918|7921|7889|17784|17784|17728|
|**_Outcome mean_**|0.147|0.144|1.00|0.244|0.148|0.917|
|**_Effect in %_**|40.8%|54.2%|40.0%|15.6%|45.3%|49.1%|

_Note:_ Standard errors are clustered (sector and quarter level). T-stats are in parenthesis. All specifications include fixed effects for quarters of project life cycle.

Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent 30
~

- --

i >

-

~~~~~~~~~~~~~15

# References

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