rafmacalaba/datause-encoder-data huggingface dataset:
|(7)|0.594***<br>(0.095)<br>1.026***<br>(0.064)<br>-0.010<br>(0.007)<br>-0.601***<br>(0.159)<br>-1.824***<br>(0.331)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.593<br> The destinations are China<br>erm between the frm level<br>s is proxied by the number<br>h frms exporting the same<br>les. RER_jt_ is the bilateral<br>in parentheses._∗∗∗, ∗∗,_|
|---|---|
|(6)|-0.350*<br>(0.195)<br>-2.678***<br>(0.416)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.576<br>ncluded in all specifcations. <br> _×_China_j_ is an interaction t<br>number of foreign competitor<br>by the number of other Frenc<br>ry_j_ from the Penn World Tab<br>rm-country pairs are reported|
|(5)|1.431***<br>(0.117)<br>1.647***<br>(0.080)<br>-0.049***<br>(0.008)<br>-0.687***<br>(0.178)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.601<br>y and year and a constant are i<br>country_j_ in year_t_. Tariff_fjt_<br>l Herfndahl index, where the<br>foreign competitors is proxied<br>tural log of the GDP of count<br>standard errors clustered by f|
|Log export sales<br>(3)<br>(4)|1.441***<br>(0.117)<br>1.647***<br>(0.080)<br>-0.049***<br>(0.008)<br>-0.868***<br>(0.182)<br>No<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>34,327<br>0.600<br>0.574<br>ntry_j_ by frm_f_ in year_t_. Fixed effects by frm, countr<br> is the tariff measure faced by frm_f_ when exporting to<br>n competition_fjt_(N_◦_countries) variable is the frm-leve<br>is the frm-level Herfndahl index, where the number of<br>by each frm in the destination markets. GDP_jt_ is the na<br>onal Financial Statistics (IFS). Heteroskedasticity-robust|
|(2)|1.324***<br>(0.114)<br>1.579***<br>(0.079)<br>No<br>Yes<br>Yes<br>Yes<br>34,327<br>0.599<br>m of the export sales in cou<br>e and Thailand. Tariff_fjt_ <br>ina_j_. The extensive margi<br>ion_fjt_(N_◦_frms) variable<br>at the product level faced<br>countries from the Internati|
|(1)|2.039***<br>(0.121)<br>No<br>Yes<br>Yes<br>Yes<br>34,327<br>0.585<br>endent variable is the logarith<br>akistan, Philippines, Singapor<br>China and zero otherwise, Ch<br>he extensive margin competit<br>is the average import volumes<br>prices of France and importer|
|Dependent variable|Extensive margin competition_fjt_(N_◦_countries)<br>Extensive margin competition_fjt_(N_◦_frms)<br>Intensive margin competition_fjt_<br>Tariff_fj,t−_1<br>Tariff_fj,t−_1_×_China_j_<br>Country level controls<br>Firm fxed effects<br>Country fxed effects<br>Year fxed effects<br>Observations<br>_R_2<br>_Notes_: The regressions are OLS estimations of equation (3) for the period 1999-2005. The dep<br>and other Asian countries that already integrate WTO such as India, Indonesia, Korea, Japan, P<br>tariff measure and a dummy variable equal to one when the country of destination of exports is<br>of countries exporting the same product line that the French frms towards the Asian markets. T<br>product line towards the Asian markets. Finally, the intensive margin competition_fjt_ variable<br>real exchange rate between France and China and countries in the control group using producer<br>and_∗_indicate signifcance at the 1, 5 and 10 percent levels respectively.|

22
**Figure 8. Non-Performing Loans―Actual and Projected Changes**

(based on new vulnerable households after economic shocks) <!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Δ in NPLs (2008-11) Arbitrary threshold (ΔEH) Financial margin<br>threshold (ΔEH)<br><!-- End of picture text --> Source: Croatian National Bank, Croatia HBS and authors’ calculations.

How do these financial stability risks compare with banking sector developments in Croatia? Figure 8 shows the change in NPLs among households since 2008 and until mid-2011, as well as the change in the total debt obligations among vulnerable households― _EH_ using both risk thresholds and economic shocks that are likely in Croatia (i.e., those represented in Figure 5, Panel D). It can be concluded that reported NPLs are lagging what should be expected as a result of the crisis. While NPLs increased by 41⁄2 percentage points, the new risk loans have increased by 7 to 10 percent of all household loans depending on the risk threshold used to tag households as vulnerable. The fact that NPL increases are lower should be expected as banks are less prone to recognize problem loans during hard times and regulatory authorities are more inclined towards regulatory forbearance. Still, the increase in problem loans is limited and not much of a risk for Croatia’s well-capitalized banking sector.

# **Conclusions**

Although a source of concern, the risks posed by household debts remain manageable. Only about 2 percent of all households (or about 6 percent of households with debt) are facing financial distress as a result of a combination of economic shocks (using an arbitrary threshold to tag households as vulnerable; Table 1). The concerns are even less pressing when the financial margin is used to tag households as vulnerable. These exercises do not assume a sharp change in the kuna-euro exchange rate. But calculations assuming a devaluation of 35 percent in the kunaforeign currency exchange rate (in combination with interest rate and unemployment shocks in line with developments in 2007-10) suggest that even in such a case the additional households facing financial distress are not many; in the worst scenario only 4 percent of all households become vulnerable. In sum, household debt is unlikely to become a drag on aggregate economic activity given the low debt incidence levels among Croatian households. It should be noted that the analysis focuses on the first-round effects and, therefore, changes in income and growth, as well as subsequent losses in the banking sector, are not pursued. Still, we deem these to be limited on account of household debt. Other concerns exist, however, and these might negatively impact the banking sector (e.g., this paper does not examine risks originating in corporate debt nor the declines in growth that might follow from spillover effects originating in the eurozone).

In addition, banks are well-capitalized to address the challenges that arise from problem loans at the level of households. The risk loans rise by about 2.9 percent of all banking system assets 15
systematically cvt in the 1870s. lowsver, tbi decline continues beyond the period of less generous relief policy, up to the eve of World Var 1. This is probably due to the accelerate **d** ecline in fertility after the 1880s (Naclinuon, 1986, pp.332-3; Boyer and Williamson, fortbcoming). As the number of dependents declined, aore single parents were able to get by without being driven into pauperism. As in Table 16, column D shows lower fertility was not reflected in fewer children per widow on relief, but it ma reflected in there being fewer widows forced onto relief. These dependency rate effects should bave played a symmetric role when poverty rates were rising in the late 18th gad early 19th century, on the upswing of the English demographic transition which yields peak rates of population growth between 1820 and 1840. That is, a sood share of rising poverty up to 1820 or 1840 is likely to bave been driven by rising fertility and increasing dependency rates. The opposite seems to have been true in the late 19th century.

Sickness is often a cause of and caused by poverty. About half of able bodied paupers in Rngland from 1891 to 1908 were relieved due to sickness either of themselves or a family member. The diseases that figured most prominently among those aged 16 to 50 were forms of tuberculosis, bronchitis and pneumonia. We shall have more to say about sickness among the poor, and its correlation with urbanization, in Section IV below.

Ve have now looked at the composition of English pavperism. Bow about the composition of English poverty? We can get an impression of urban poverty at the end of the century from the social investigations of Booth, Rowntree and Bowley. Table 17 gives a breakdown of the proximate causes of poverty, and it is clear that most of the poverty found in these towns was associated with low wages or large families. Ev n at the end of the period, the lower end 50
As expected, the randomization procedure was successful in ensuring statistical balance between treatment and control groups. Table 1 below presents means, normalized differences,<sup>13</sup> and _t_ -statistics for several important variables using baseline survey data. Among the variables listed, mean differences are always smaller than 13 percent of the standard deviation.

# **IV. Hypotheses**

The main goal of this paper is to use the aforementioned identification strategy afforded by randomized evaluation of NSP to test the “hearts and minds” theory of counterinsurgency, which posits that development projects will increase economic welfare, improve attitudes to government, and reduce insurgent violence. The three hypotheses below formalize these predictions:

**Hypothesis 1:** Levels of economic well-being will be higher among people living in villages that have received a development project.

The “hearts and minds” theory focuses on attitude-driven behavior. Accordingly, subjective perceptions of one’s economic situation are as important as objective outcomes. To test this hypothesis, we look both at objective measures of economic well-being and at subjective perceptions, such as whether people report that their economic situation has improved in the past year and whether they expect it to improve in the future. The first hypothesis is consistent not only with the “hearts and minds” theory, but also with the “opportunity cost” theory, since an increase in income and in employment raises the cost of participation in the insurgency.

**Hypothesis 2:** Attitudes towards the government and allied entities will be more positive among people in villages that have received a development project.

This hypothesis asserts that economic benefits arising from development projects will improve attitudes toward the government. Since NSP is managed by the government of Afghanistan, but is funded by international donors and implemented by NGOs, we are also interested in the potential effect on levels of support for these other entities. The second hypothesis is important for distinguishing between different theories, since the “hearts and minds” approach is the only one that predicts that projects will result in an improvement in attitudes towards the government.

> 13 Per Imbens and Wooldridge (2009), normalized differences are differences divided by pooled standard errors.

11
|•|Unexpected (small) cost overruns or<br>shortfalls in capital expenditures of SOEs<br>not budgeted for|
|---|---|
|•|Environmental disaster costs, such as<br>emergency infrastructure repair costs|
|•|Unexpected (small) losses of SOEs<br>caused by nonenvironmental exogenous<br>shocks.|
||**Bailout risks**|
|•|Unexpected SOE recapitalization and<br>liability cleanup|
|•|Avoidance of default of large,<br>nonexplicitly guaranteed SOE debts|

_Source_ : Adapted from table 1.1 in Polackova Brixi and Mody (2002).

We zoom in on two of the main manifestations of implicit contingent liabilities—cash flow risk and bailout risk—because, according to Polackova Brixi and Mody, “public finance institutions, including systems for government budget management, debt management, accounting, financial control, and public scrutiny, often remain blind to government contingent liabilities.” Therefore, even when governments report “a balanced budget and low public debt” one cannot be ensured that “it will enjoy fiscal stability in the near future,” as many of the cash flow and bailout risks hide “outside the budgetary system” (Polackova Brixi and Mody 2002). In section 4, we propose ways to document and account for such risks.

The fact that some of these risks are hidden in the financial accounts of infrastructure SOEs is not an accident, as “contingent liabilities often grow from fiscal opportunism, when policymakers seek to hide the real fiscal cost of their decisions” (Polackova Brixi and Schick 2002). We are particularly concerned with the cases in which governments charge SOEs with QFOs that are not compensated for ex ante—QFOs that show up unexpectedly as contingent liabilities. Acknowledging them and doing something to minimize them is “first of all a question of political will” (Polackova Brixi and Schick 2002).

# **_2.1 Cash Flow Risk_**

Cash flow risk is the risk stemming from the volatility of SOE net income, which requires fiscal transfers to cover occasional and modest losses associated with demand and operational risk, QFOs, or shortfalls in cash flows to cover capital expenditures. Cash flow needs can be volatile. They are often covered by operational subsidies. They can also be covered by other means, such as deferring tax liabilities and providing loans from other SOEs or state-owned banks.

Cash flow risk is often a consequence of weak fiscal governance regulating the capacity governments have to use SOEs for QFOs and the capacity SOEs have to make ad hoc requests of funds from the government. According to Ter-Minassian (2019), QFOs can turn into fiscal risk when “SOEs are directed by their governments to pursue public policy objectives and are not 6
1998). However, regulation by contract or a combination of regulation by contract and regulatory independence may provide a better regulatory framework for developing countries aiming to privatize their distribution systems (Bakovic, Tenenbaum, and Woolf, 2003). The _size of the electricity sector_ can influence the reform capabilities and reform options of individual reforming countries. It is not clear if the smaller energy systems in developing countries require or benefit from vertical separation and third-party access. For example, the scope for competition may be limited implying that, in small energy systems; the benefits of liberalization and reforms may be small in relation to the costs.

Despite these notable differences, electricity sector reforms have been globally pursued under varying initial conditions. Some have had relative success while many have not lived up to the ambitions and expectations after more than two decades of reforms. For example, existing market driven reforms among OECD countries like Chile, Norway and Sweden appears to be performing well as compared to the UK, once considered as a successful model of electricity reforms. In contrast, the inability to attract private investments in the power sector in SubSaharan African countries like Uganda and Zambia remains a disappointment. According to the World Bank’s Private Participation in Infrastructure database, there was a boom in IPPs during the 1990s, which subsequently abated with the arrival of the financial crisis in the late 1990s.

Reforms seem to have failed to correct the chronic underinvestment in power supply in most developing and transition countries, which also accounts for the poor performance of the sector in these countries. For example, there was very little investment in the power sector from 1991 to at least the mid-2000s except for the Russian Federation and Turkey in the European and Central Asian countries (Barbara, 2010). Some countries (such as in Latin America) have made relatively advanced transition to the market in the energy sector while some (such as China, Russia, South Africa) are caught between the state and the market where the state still plays a dominant role in electricity sector operation and management (Nepal, 2013). Table 2 summarizes the power sector reform experience among selected cases in a power sector reform matrix.

7
the Brazilian economy proves to be able to sell a large number of products in many markets. This suggests a considerable potential to be explored in terms of export growth, since the initial fixed costs to start new markets have been largely overcome.<sup>3</sup> Figure 3. Product and Markets (2008–10) <!-- Start of picture text --> CHN<br>IND<br>KOR<br>THA<br>MEX BRA<br>IDN<br>ARG<br>COL VNM<br>CHL<br>PER PHL<br>URY<br>0 50 100 150<br>Number of countries<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br><!-- End of picture text --> _Source:_ World Integrated Trade Solution.

Figure 4. Markets and Products—the Herfindahl Index (2008–10 and 1998–2000) <!-- Start of picture text --> Herfindahl Index - Markets Herfindahl Index - Products (HS6)<br>ARG 0.063 0.108 ARG 0.023 0.036<br>BRA 0.060 0.082 BRA 0.013 0.035<br>CHL 0.0800.087 CHL 0.067 0.122<br>CHN 0.076 0.158 CHN 0.0050.008<br>COL 0.151 0.249 COL 0.0930.101<br>IND 0.047 0.079 IND 0.0250.029<br>PER 0.088 0.148 PER 0.043 0.061<br>RUS 0.0370.039 RUS 0.072 0.188<br>ZAF 0.0480.054 ZAF 0.0230.023<br>0 .05 .1 .15 .2 .25 0 .05 .1 .15 .2<br>0810 9800 0810 9800<br><!-- End of picture text --> _Source:_ World Integrated Trade Solution.

As a way to evaluate the recent evolution of Brazil’s export diversification, we calculated the Herfindahl Index for a group of countries (figure 4)—the lower the index, the less concentrated the export basket, the greater the level of diversification. In terms of markets, the index showed a reduction in the degree of

> 3 F or a discussion on this topic, see Melitz (2003).

7
extensive/spatial margin of trade where an increase in transport cost can sever the link between OD pairs who were engaged in trading during the pre-pandemic period. We use the trade flow and freight rate data during the pre-pandemic period to estimate elasticity and spatial margin of trade. The shift in freight rate during the post-lockdown period is then used to simulate the effects of this shock on these two margins of trade. The pandemic has affected every facet of the economy, including the way goods are transported, how people migrate across areas and how preferences shift in response to it. The exercise is not intended to predict the overall spatial effects of the pandemic or even trade flows and trucking costs in the long run.

Taking the observed changes in freight rates as given, we limit our analysis in favor of providing a suggestive view of how trade flows and spatial margin of trade may be affected in the short run.

# 4 **Estimating trade gravity regression during the pre-pandemic period**

The first step in analyzing the impacts of the increase in trucking costs on inter-regional trade flows is the estimation of elasticity of trade flow with respect to the freight rate, which to the best of our knowledge is not available from existing literature for developing countries, particularly for India. We start by describing how values of trade flows across districts are constructed.

The volume of goods transported is reported for each transaction in our trucking data set. Using these data, we construct district to district flow of volumes of goods by aggregating over all trips for each OD pair. However, the data set does not provide information on the value of traded goods which is needed for the estimation of trade elasticity. To estimate the value of trade, we web-scrapped daily prices of essential commodities reported by the Reserve Bank of India for 114 cities. As noted in Atkin and Donaldson (2014), it is important to establish the origin of comparable goods to trace out the trading and transport cost over distance. We select two commodities loose tea and palm oil whose qualities are comparable across markets. For each of these commodities, the origin can be ascertained easily from ports for palm oil which is mostly imported, and auction houses for tea. For each commodity, we select two cities nearest to the port or auction house. Origin is then selected as the city which has the lower wholesale price. We 13
- 119 -

becomes a matter of high priority. The optimum cannot be defined independently of the degree of price instability faced externally and tolerated internally. The adjustment mechanisms used to cope with this price instability often spill over into world markets, affecting commercial and political interests outside the country's borders. This spillover is as true for the crops involved in diversification as it is for the basic cereals themselves.

4.58 A more diversified, flexible agriculture has smaller adjustment costs as ptices change. For the same assumed level of stability in farmer incomes, both stabilization costs and adjustment costs are smaller if the agricultural system is more diversified. The difficult empirical question is determining the functional relationship between stabilization costs and adjustment costs, and then determining the size of investments needed to shift the cost frontier inward. The relationship between stabilization costs and costs of adjustment has received elegant conceptual treatment.33 Relatively little is known in principle or in fact about the amount of investment required to lower the costs of achieving price stability.34 4.59 Governments vary in the weights they place on stabilization costs incurred by the treasury relative to adjustment costs incurred by producers and consumers. There are certainly some environments in which governments seem to behave as if no adjustment costs must be borne by farmers. The rice economy of Japan is the clearest example, but much of the Common Agricultural Policy (CAP) of the European Community has a similar intent.

4.60 Lower adjustment costs are desirable even if no expensive stabilization program exists, but large budgetary costs incurred to prevent farmers from having to adjust do focus the issue in an acute fashion. As the Economist put it in a recent lead editorial: 33/ See Newbery and Stiglitz (1981).

- 34/ See World Bank, "Thailand Irrigation Subsector Review." Report No. 5847-TH. (Washington, D.C., Projects Department, East Asia and Pacific Regional Office, April 25, 1986), for a discussion of the costs of making irrigation systems more flexible in the dry season.
## Comparison of different types of disaster management investments

# (100% coverage, policy financed 50% by investment, 50% by consumption)

<!-- Start of picture text --> GDP compared to baseline Potential GDP compared to baseline<br>—— Adaptation —— Adaptation<br>2 —— Insurance 0 —— Insurance<br>0 —— Contingency fund —— Contingency fund<br>2 —— Fiscal spacej -2 —_——— No policypace(only shocks)<br>-4<br>-4<br>-6<br>-6<br>-8<br>-8<br>-10 -10<br>-12 -12<br>-14 -14<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Consumption compared to baseline Debt/GDP<br>—— Adaptation —— Adaptation<br>1 —— Insurance 160 —— Insurance<br>0 —— Contingency fund —— Contingency fund<br>—— Fiscal space 140 — Fiscal space<br>-1 —— No policy (only shi —— No policy (only shocks)<br>120<br>-2<br>100<br>-3<br>80<br>-4<br>5 60<br>-6 40<br>-7 20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->