rafmacalaba/datause-encoder-data huggingface dataset:
Chadefaux, Thomas. 2013. "Early Warning Signals for War in the News." Journal of Peace Research.

Collier, P, and A. Hoeffler. 2002. "On the Incidence of Civil War in Africa." Journal of Conflict Resolution 46,13–28.

Collier, P., and A. Hoeffler. 2004. "Greed and grievance in Civil War." Oxford Economic Papers 56, 563– 595.

Collier, Paul, and Anke Hoeffler. 1998. "On economic causes of Civil War." Oxford Economic Papers 50(4), 563-573.

Elliott, Graham and Allan Timmermann. 2016. Economic Forecasting. Princeton: Princeton University Press.

Elliott, Graham and Robert Leili. 2013. "Predicting Binary Outcomes." Journal of Econometrics. 174: 1526.

Esteban, Joan, Laura Mayoral, and Debraj Ray. 2012. "Ethnicity and Conflict: An Empirical Study." American Economic Review 102(4): 1310-1342.

Fearon, J. D. 2005. "Primary Commodities Exports and Civil War." Journal of Conflict Resolution.

Fearon, J. D., and D. D. Laitin. 2003. "Ethinicity, Insurgency, and Civil War." Am. Polit. Sci. Rev. 97,75–90.

Hegre, Havard and Nicolas Sambanis. 2006. "Sensitivity Analysis of Empirical Results on Civil War Onset." The Journal of Conflict Resolution 50 (4).

Hegre, Havard, Halvard Buhaug, Katherine Calvin, Jonas Nordkvelle, Stephanie Waldhoff and Elisabeth Gilmore. 2016. "Forecasting Civil Conflict Along the Shared Socioeconomic Pathways." Environmental Research Newsletter 11: 1-8.

Hegre, Håvard, J Karlsen, H Nygard, H Strand, and H Urdal. 2013. "Predicting Armed Conflict, 2010– 2050." International Studies Quarterly.

Hsiang, Solomon M., Marshall Burke, and Edward Miguel. 2013. "Quantifying the Influence of Climate on Human Conflict." Science.

Kalyvas, S. N. 2008. "Ethnic Defection in Civil War." Comparative Political Studies 41, 1043–1068.

Kraay, Aart, and Vikram Nehru. 2006. "When is External Debt Sustainable?" The World Bank Economic Review 20 (3), 341-365.

Muchlinski, David, David Siroky, Jingrui He and Matthew Kocher. 2016. "Comparing Random Forest with Logistic Regression for Class-Imbalanced Civil War Onset Data." Political Analysis 24: 87-103.

Mueller, Hannes and Christopher Rauh (2016). “Reading Between the Lines: Prediction of Political Violence Using Newspaper Text”. CEPR Discussion Paper No. 11516.

Mullainathan, Sendhil, and Jann Spiess. (2017). “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives 31 (2): 87-106.

31
Abbreviations and Acronyms

|BNPP<br>|Bank Netherlands Partnership Program<br>|
|---|---|
|CAPAM<br>|Commonwealth Association for Public Administration and Management<br>|
|DECRA<br>|Research Advisory, Development Economics and Chief Economist<br>|
|ECSPE<br>|Europe and Central Asia, Poverty Reduction and Economic Management<br>|
|HRMIS<br>|Human Resource Management Information System<br>|
|INE<br>|Instituto Nacional de Estadistica<br>|
|IRR<br>|Institutional Rate of Return<br>|
|LCSPS<br>|Latin America and Caribbean Region, Public Sector<br>|
|OED<br>|Operations Evaluation Departnent<br>|
|OLS<br>|Ordinary Least Squares<br>|
|OECD<br>|Organization for Economic Cooperation in Development<br>|
|NGO<br>|Non Governmental Organization<br>|
|PREM.<br>|Poverty Reduction and Economic Management<br>|
|SAFCO<br>|Sistema de Administraci6n Financiera y Control Gubernamental<br>Bolivian Law 1178 on Financial Management and Control<br>|
|SURE|Seemingly Unrelated Regression Equations|

ii
arose from supply side effects; the voucher had credential value to workers – it acted like a “letter of introduction” that few people had (and how it was allocated was a secret locally). This could not be revealed by the (randomized) evaluation, but required supplementary data. The extra insight obtained about how _Proempleo_ actually worked in the context of its trial setting also carried implications for scaling up, which put emphasis on providing better information for poor workers about how to get a job rather than providing wage subsidies.

Spillover effects also point to the importance of a deeper understanding of how a program operates. Indirect (or “second-round”) impacts on non-participants are common. A workfare program may lead to higher earnings for non-participants. Or a road improvement project in one area might improve accessibility elsewhere. Depending on how important these indirect effects are thought to be in the specific application, the “program” may need to be redefined to embrace the spillover effects. Or one might need to combine the type of evaluation discussed here with other tools, such as a model of the labor market to pick up other benefits.

The extreme form of a spillover effect is an economy-wide program. The evaluation tools discussed in this chapter are for assigned programs, but have little obvious role for economy-wide programs in which no explicit assignment process is evident, or if it is, the spillover effects are likely to be pervasive. When some countries get the economy-wide program but some do not, cross-country comparative work (such as growth regressions) can reveal impacts. That identification task is often difficult, because there are typically latent factors at country level that simultaneously influence outcomes and whether a country adopts the policy in question. And even when the identification strategy is accepted, carrying the generalized lessons from cross-country regressions to inform policy-making in any one country can be highly problematic. There are also a number of promising examples of how simulation tools for economy wide policies such as Computable General Equilibrium models can be combined with household-level survey data to assess impacts on poverty and inequality.<sup>62</sup> These simulation methods make it far easier to attribute impacts to the policy change, although this advantage comes at the cost of the need to make many more assumptions about how the economy works.

**_Is the evaluation answering the relevant policy questions?_** Arguably the most important things we want to learn from any evaluation relate to its lessons for future policies. Here standard evaluation practices can start to look disappointingly uninformative on closer inspection.

> 62 See, for example, Bourguignon et al. (2003) and Chen and Ravallion (2004).

55
14

## **Box 2 Developing a Standardized Short-Term Gas Contract**

Numerous calls from the industry prompted efforts by the Gas Industry Standard Board to develop a standardized contract for gas sale and purchase. After a year of drafting and consultation with the industry, the board presented a model short-term gas purchase and sale contract for comments in 1996. The model contract has three parts:

- The base contract, containing the names of contract parties and the contract provisions (selected from the general terms and conditions section).

- General terms and conditions, containing the list of all available contract provisions.

- Transaction confimnation, specifying the price, quantity, delivery points, delivery period, and type of transaction (firm or interruptible).

The model contract should facilitate transactions in spot markets, particularly on the electronic data interchange.

_Source:_ Gas Industry Standard Board.

# _Organization of natural gas trading_

The organization of natural gas trading has changed dramatically as a result of deregulation. Traditionally bilateral, transactions now often involve intermediation by natural gas marketers (figure 7). Marketers aggregate the demand of many end users and small local distribution companies and trade natural gas on their behalf, reducing the cost of transactions in the natural gas market. The concentration of trading in market centers and hubs has led to the development of natural gas spot markets. And the introduction of electronic information systems has promoted electronic trading in these spot markets.

Figure **4 Organization of Trading** in **the Wholesale Gas Market** <!-- Start of picture text --> I: - - -- - - - - distribution<br>__cmpanies<br>- - -d -- - - - - - ~E<br>Intermediation<br>|IProducwr | ~~ _ _ - |~~~companies|dtstnbutionl<br>Spot market<br>Ei3- |~~~~~~~~~~~~~~~~~~diutbutionl|<br>Miarketa , Mark<br>Ei- ' hb ~~~ 1 | MlukeXr t ~~~compais_ isdtributionI<br>~~~~~~~~~~~~~~ EndM G<br><!-- End of picture text -->
on a standard production function would likely show the two to be complements, but is in line with other work on the Indian economy which shows that human capital may enable less finance-intensive activities (e.g.Amin and Mattoo (2008)).

For this study, I use a new data set of district level Net Domestic Product (NDP) measurements from the internal records of the Reserve Bank of India (RBI).<sup>5</sup> The data document growth over the 1990s at the level of the district (in India, districts are the geographical government division that is just below the level of the state). In addition to NDP measurements, the dataset includes measures of adult literacy, credit and deposits in local commercial banks, geographic area, population, and road infrastructure by district in both 1990-91 and 2000-01.

The use of disaggregated district level data limits the omitted variable problems mentioned above. Districts in my sample exist within a unified political, legal, and monetary framework, there are no de jure barriers to trade or capital flows between them, and they are - for the most part - at similar stages of development. This homogeneity means that there are fewer omitted control variables relative to cross country growth studies. Additionally, district borders often correspond to the economic catchments around a single city making them a natural unit of study.<sup>6</sup> While disaggregate data help to address some of the methodological issues associated with cross-country data, they also allow me to focus on important substantive issues which have been largely ignored by the finance-growth literature. Studies of the financial sector’s influence on growth tend to focus on factors which apply at the national level. They do this explicitly by focusing on markets, such as stock and bond markets, for which access does not vary much across regions.<sup>7</sup> They also do it implicitly by choice of

> 5 NDP is GDP adjusted for estimated capital depreciation.

> 6 Many Indian districts derive from princely city-states which existed in pre-colonial India and whose borders were formed by older historical/economic forces.

> 7 See, e.g. Levine and Zervos (1996).

3
Education is also positively related to movement out of poverty. Workers with higher education have higher probabilities of transition out of poverty.

_The State-Owned Enterprises Sector_ The privatization of large-scale enterprises is a key challenge and priority for the Government of Bosnia and Herzegovina as laid out in the 2004 Medium-Term Development Strategy (MTDS). Since the 1995 Dayton Agreement, the privatization of small and medium-sized enterprises has made significant progress. However, the privatization of large state-owned enterprises (those employing over 50 employees with KM 5 million in <!-- Start of picture text --> Figure 9. EBRD Index of Large-Scale Privatization<br>More than 75 percent of enterprise<br>Little private ownership assets in private ownership<br>Slovak Republic<br>Hungary<br>Estonia<br>Czech Republic<br>Romania<br>Lithuania<br>Latvia<br>Kyrgyz Republic<br>Russia<br>Poland<br>Georgia<br>Macedonia<br>Croatia<br>Armenia<br>Ukraine<br>Slovenia<br>Moldova<br>Kazakhstan<br>Uzbekistan<br>Tajikistan<br>Serbia and Montenegro<br>Bosnia and Herzegovina<br>Albania<br>Azerbaijan<br>Turkmenistan<br>Belarus<br>0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5<br><!-- End of picture text --> capital, around $2.5 million using exchange rates in 2004) has proceeded very slowly due to lukewarm political commitment. To date, several hundred large enterprises are yet to be privatized, in contrast to the rest of the ECA region where privatization has largely been completed (Figure 9).

Nonetheless, in an economic environment characterized by a lagging privatization and restructuring program, labor market adjustment—or the reallocation of labor away from loss-making enterprises into enterprises in other sectors—has apparently taken place anyway. The next section provides evidence that while large privatization has largely stalled, many workers surveyed in the LSMS have apparently left the State Owned Enterprise (SOE) sector over the last four years or are reporting that their main employment activity currently lies outside the SOE sector.

_SOE Employment Dynamics_ SOE employment is dominated by men, older workers, and relatively more educated workers. Employment in relatively larger enterprises (over 100 employees) account for the majority of SOE employment. Workers in manufacturing, utility, and transport, storage and communication sectors represent the majority of SOE workers in both entities. There is a large share of SOE workers employed in mining, too, but this is largely underpinned by state-owned mining enterprises in FBH. Compared to non-SOE workers, SOE workers are relatively older and more educated. Men also account for a greater share of employment in the SOE sector than in the non-SOE sector.

SOE employment has fallen over time, coinciding with rapid increases in wages. As a share of total employment, employment in state-owned enterprises has fallen from 35.6 percent in 2001 to 20.5 percent in 2004.<sup>12</sup> As a share of formal sector employment,

> 12 Falling public sector employment, observed between 2001 and 2002, is documented in the CEM.

18
countries because of the possible endogeneity of their terms of trade,<sup>10</sup> and five developing countries where the terms of trade data exhibited long flat periods.<sup>11</sup> The different columns of Table 2 show some summary statistics for each of these variables for all countries in the sample. The cross-sectional correlations among these variables are reported in Table 3. There we observe the well documented positive correlations between the different structural characteristics and output growth, and between the measures of volatility and growth. We also observe that all the structural characteristics are positively correlated, although the magnitudes of the correlations are not particularly large with the exception of the correlation between financial development and firm flexibility, which reaches 66 percent. These relatively low correlations give us ground to expect to be able to sort out the role of the different characteristics for the transmission of shocks.

# **4. Results**

Our basic results are derived from estimating the cumulative output effect of a one-standard-deviation shock to the terms of trade at different levels of a particular structural characteristic. As explained in the methodological section, we conduct this estimation in the context of a panel (cross-country, time-series) vector autoregression with (detrended log) GDP changes as the dependent variable and (detrended log) termsof-trade changes as the exogenous variable. We allow the output effect of terms-of-trade shocks to vary with five country structural characteristics: trade openness, financial depth, financial (or capital-account) openness, labor market flexibility, and ease of firm entry. To analyze the effect of these structural factors, we compare the shock’s cumulative output impact measured at the 25<sup>th</sup> and the 75<sup>th</sup> percentiles of the world distribution of each structural characteristic. Comparing the impact statistics at these (relatively) low and high levels for a given structural factor provides a sense for how much it contributes to amplifying or dampening the external shock.

> 10 The excluded countries are United States, United Kingdom, France, Italy, Germany, and Japan. 11 These countries are Cape Verde, Grenada, St. Lucia, St. Kitts and Nevis, and Nepal.

10
migrants of $734. This overall effect is composed of a negative (statistically insignificant) effect at the extensive margin, and a highly significant positive effect on the intensive margin. The results are consistent when the second measure of education, years of schooling, is considered.

When we consider the individual country results, we see mixed results at the extensive margin, with education significantly positively associated with the likelihood of remitting in two surveys (the USA New Immigrant Survey and Survey of Brazilians and Peruvians in Japan), significantly negatively associated with this likelihood in three surveys (the USA Pew survey and both Spanish surveys), and no significant relationship in the other six surveys, with three positive and three negative point estimates. One general observation is that a more negative relationship appears in surveys which focus on sampling migrants through community-sampling methods, such as the NiDi surveys which go to agglomeration points where migrants cluster, and the Pew Hispanic surveys which randomly dial phone numbers in high Hispanic areas. One might expect the educated migrants who live in such areas (and who take the time to respond to phone or on the street surveys) to perhaps be less successful than educated migrants who live in more integrated neighborhoods and thus who wouldn’t be picked up in these surveys.

In contrast, at the intensive margin the individual survey results show a positive relationship in 10 out of 12 surveys, five of which are statistically significant, and negative and insignificant relationships in the remaining two surveys. Thus it is not surprising that when we pool the data we find a strong positive association at the intensive level, and that this outweighs the small negative and insignificant relationship when it comes to the total effect.

This point is made graphically by Figure 1, which plots the non-parametric relationship between total remittances and years of schooling, after linearly controlling for dataset fixed effects using a partial linear model (Robinson 1988), together with a 95% confidence interval, on a log scale. The vertical lines demarcate the quartiles of the distribution of years of schooling. Average remittances steadily increase from around $500 in the lowest education quartile to close to $1000 for those with university degrees. Moreover, the positive association is most strongly increasing for those with post-secondary education, which shows that not only do those with some university remit more than those without, but that postgraduates are remitting more than those with only a couple of years of university.

12
A plausible explanation for the temporary effects of fiscal rules is that establishing credibility in the initial years is critical for long-term success. However, when fiscal rules are adopted under adverse economic conditions, they are often driven by immediate macroeconomic pressures rather than broad consensus and careful preparation. As a result, their credibility may be weaker from the outset. In Argentina, for example, the Fiscal Solvency Law (1999) was passed during a period of macroeconomic distress and shrinking political support. Lacking institutional backing, the fiscal rules never gained traction (Artana et al. 2021).

If this hypothesis is correct, it might be that countries with strong institutions can overcome the difficulties in establishing credibility when adopting fiscal rules during weak states of the economy. However, panels (c) and (d) of Figure 6 show that similar patterns persist regardless of institutional strength. In other words, strong institutions alone do not offset the negative influence of adopting a rule in a weak economic environment, suggesting that the timing of adoption is an important factor for rule effectiveness. Taken together, our findings strongly suggest that adopting rules is more effective when economic conditions are favorable—'making hay while the sun shines’— rather than as a reactive measure, in line with the idea of 'never waste a good crisis’.

# **5.2. Fiscal position**

When fiscal sustainability pressures are high, adopting fiscal rules can provide crucial policy guidance. The literature on fiscal rule determinants has shown that fiscal rules are more likely to be adopted in times of high debt (Hallerberg and Scartascini 2015; Altunbaş and Thorton 2017). However, IMF (2009) argues that fiscal rules may be more credible if rule introduction is preceded by significant fiscal consolidation. Indeed, if fiscal pressures are already acute, the rules may struggle to mitigate the fiscal strain effectively; for example, not all rules have been effective in high-debt environments (Combes et al., 2017).

To assess the fiscal position at the time of rule adoption, we use the lagged government debt-to-revenues ratio, sourced from the World Economic Outlook (WEO), October 2024.<sup>23</sup> <u>Figure 7</u> shows the distribution of fiscal rule adoptions across different debt regimes. The sample median corresponds to a government debt-to-revenue ratio of 178%, with adoptions evenly split: 47% of adoptions occurring in high-debt regimes and 53% in low-debt regimes. Two cases of fiscal rule adoption under exceptionally high debt stand

> 23 We obtain similar results when we use the debt-to-GDP ratio (available on request).

22
- 20 -

location, and agglomeration economies were still found to be significant. Autocorrelation was also significant.

**Table 8 Instrumental Variable Spatial Autoregressive Model with Different** **<u>Agglomeration Variables</u>**

||<br>(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|ln_TCMK_|–0.322 **<br>(0.137)||–0.307 **<br>(0.124)<br>||
|ln_MAI_||0.512 ***||0.468 ***|
|||(0.195)||(0.181)|
|ln_N14_|0.694 ***|0.683 ***|||
||(0.092)|(0.093)|||
|ln_N13_|||0.757 ***|0.746 ***|
||||(0.089)|(0.091)|
|_POPDEN_|0.2910 **|0.2390 **|0.1700|0.1280|
||(0.1370)|(0.1110)|(0.1320)|(0.1070)|
|_INFO_|0.002|0.004|0.005|0.007|
||(0.026)|(0.026)|(0.024)|(0.024)|
|_UNEMP_|–0.011|–0.007|–0.009|–0.006|
||(0.014)|(0.014)|(0.014)|(0.013)|
|_EDU_|0.018|0.016|0.044|0.042|
||(0.095)|(0.095)|(0.089)|(0.089)|
|Constant|–14.100 ***|–13.400 ***|–15.210 ***|–14.550 ***|
||(4.913)|(4.967)|(4.628)|(4.672)|
|Observations|134|134|134|134|
|Number of|||||
|region dummy|||||
|variables|5|5|5|5|
|Autoregressive co|efficients||||
|λ|–0.303 *|–0.327 **|–0.294 ***|–0.315 ***|
||(0.156)|(0.152)|(0.104)|(0.105)|
|ρ|0.432|0.372|–1.091|–1.020|
||(1.330)|(1.318)|(1.249)|(1.250)|

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. 1 The instrumental variable estimation is performed with three instruments in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .

# **V. CONCLUSION**

Agglomeration economies are an important factor in increasing firm productivity and promoting economic growth. The evidence shows that firms prefer to be located close to one