rafmacalaba/datause-encoder-data huggingface dataset:
the event of rising manufacturing trade. On the other hand, RER volatility is reduced by 18% if equity-related financial openness elevates, and it increases by 34% in the event of higher loan-related financial openness. Note that if both (the log of the ratio of) equityrelated and loan-related financial openness were to increase at the same rate (say each ratio increases by 20%), RER volatility would decline by 4% due to equity-related financial openness and would increase by 21% due to loan-related financial openness. In sum, RER volatility would increase if equity and loan positions experience a similar rate of growth.

# **5.2 Openness and RER Volatility in “Turbulent” Times**

Fluctuations in RER can be determined by fluctuations of fundamentals at regular business cycle frequencies. However, drastic reversals in terms of trade, drops in output and productivity and sudden stops in capital inflows can produce sharp swings in the real exchange rate. In the present sub-section, we test whether the relationship between openness and RER volatility in “ _periods of tranquility_ ” (associated with regular business cycle fluctuations) remains invariant in “ _turbulent times_ ” (associated with episodes of currency crisis).

We capture RER volatility in turbulent times by either of the two binary variables: (a) _RER drops_ that takes the value of one whenever the real exchange rate depreciates more than 25 percent,<sup>12</sup> and (b) _currency crisis_ that takes the value of one for episodes of currency crisis as defined by Frankel and Rose (1996).<sup>13</sup> Hence, we run probit regressions for _RER drops_ and for _currency crisis_ using annual data for the period 1975-2005. The determinants on the incidence of either RER drops or currency crisis follows Milesi-Ferretti and Razin (1998): lagged values of economic growth, the ratio of reserves to imports, the extent of RER overvaluation, fixed exchange rate regimes, and external shocks such as shifts in terms of trade, external demand and international real interest rates. Again, our variables of interest are trade and financial openness.

_Openness and RER Volatility in “Turbulent Times.”_ Our probit regression results are reported in Table 9. We find that the likelihood of RER drops or currency crisis is smaller in countries with increasing growth, higher ratio of reserves to imports, lower inflation,

> 11 We also used the ratio of the stocks of portfolio equity and FDI assets and liabilities to GDP. Note that this variable indicates the level of equity (portfolio and FDI cross-holdings).

> 12 We have also defined a real exchange rate drop for declines in the RER of 15, 20, 30 and 40 percent, and the results have remained invariant. Although the results are not reported, they are available from the authors upon request.

> 13 Frankel and Rose (1996) define a currency crisis episode as the event where the local currency depreciates at least 25% and where the rate of depreciation has risen at least 10%.

28
**Table 5: Descriptive Statistics for Occupation**

**Panel 5A: Probability of Non‐farm Occupation for Adult Children and their Parents**

|**Year**|**Daug**<br>**Daughter**|**hters samp**<br>**Mother**|**le**<br>**Father**|**Son**|**Sons samp**<br>**Mother**|**le**<br>**Father**|**children**|**All sample**<br>**Mother**|**Father**|
|---|---|---|---|---|---|---|---|---|---|
|**1988**|0.15|0.04|0.21|0.22|0.04|0.20|0.18|0.04|0.21|
|**2002**|0.47|0.08|0.34|0.52|0.08|0.29|0.50|0.08|0.30|
|Notes||||||||||

1. off‐farming occupation dummy=1 if involved in non‐farming for 3 months or more in the survey year;

2. The samples are: 3231 (1988, daughters), 3363 (1988, sons), 2091 (2002 daughters), 3573 (2002, sons).

**Panel 5B:  Percentage of Children in Non‐farm conditional on Parent’s Occupation**

|**Adult**<br>**children**|**Neither parent is in**<br>**Non‐farm Occupation**|**At Least One Parent is in**<br>**Non‐farm Occupation**|**Both Parents are in Non‐**<br>**farm Occupation**|
|---|---|---|---|
|_Daughters_||||
|1988|9.4|33.2|71.1|
|2002|43.0|52.3|72.8|
|_Sons_||||
|1988|16.4|41.7|73.9|
|2002|47.4|60.9|75.7|

Note:

1. Non‐farm occupation dummy=1 if involved in non‐farm for 3 months or more in the survey year; 2. The samples are: 3231 (1988, daughters), 3363 (1988, sons), 2091 (2002 daughters), 3573 (2002, sons).

55
variable. Most average changes in outcome variables are not statistically di¤erent between early and late adopter municipalities. They are also not signi...cantly correlated with the quarter of implementation, suggesting that the identi...cation strategy is valid. The one exception is the average change in employment. Municipalities that adopted earlier appear to have smaller increases in overall employment. This would bias the analysis against ...nding an e¤ect of the reform on overall employment.

I also use a number of individual background variables from the ENE as control variables in my regression. These variables include age, gender, marital status, and education dummies. Summary statistics for these background variables and their pre-period changes are listed in the lower panels of Table 4a and 4b. The background variables are very similar across early and late adopter municipalities. The only statistically signi...cant di¤erence in Table 4a is in the fraction of females, where early adopter municipalities have about one percent fewer females.

# 6 Results

In this section, I put to test the predictions of the model developed above and analyze the e¤ects of the reform on average labor market outcomes. I also break down these e¤ects by pre-reform occupation and examine the impact of the reform on prices.

According to the identi...cation strategy described in Section 4, I obtain the main results by running the following regression with OLS where the subscript i denotes individuals, c denotes municipalities, and t denotes quarters. This regression includes municipality ...xed e¤ects, �c, and quarter ...xed e¤ects, �t. The variable SAREct is the reform dummy and, for each municipality, it is equal to one for the quarter in which the reform was implemented and for all following quarters. I run the regression without and with the individual background variables, Zict. When including individual background variables, I also include variables from the 1999 Economic Census (EC1999) interacted with a linear time trend, t. These variables are log GDP per capita, log number of economic establishments per 1000 capita, log ...xed assets per capita, and log investment per capita<sup>14</sup> . Note that I do not include individual dummies or time trends interacted with municipality dummies in my regressions since they take out a lot of the variation in the reform dummy.

The standard errors of the regressions are clustered at the municipality level. As explained in Bertrand, Du‡o, and Mullainathan (2004), clustering at the region level helps to prevent the problem that di¤erence-in-di¤erence estimates which use a large number of time periods

> 14 The economic variables are converted to per capita levels using 2000 Demographic Census population data.

17
# **5.2 Philippines**

We next analyze our survey in Central Mindanao, Philippines.<sup>17</sup> The Philippines data enables us to evaluate two aspects of our theory of IDP perceptions of engagement: first, we compare IDPs and those who are not displaced in Central Mindanao in Section 5.2.1, second, we analyze variation within IDPs in Section 5.2.2.

## **5.2.1 Displacement and Empowerment**

To analyze whether experiences with displacement change perceptions of empowerment, we compare IDPs in our sample to respondents do not have a history of displacement. Because these categories are unlikely randomly distributed, we used propensity score matching to estimate the Average Treatment Effect on the Treated (ATT).<sup>18</sup> Matching enables us to approximate the causal effect of displacement on perceptions within the case by maximizing the suitability of the comparisons we use in our data.

We create two matched samples based on displacement status: one using current displacement as our treatment and the other using previous history of displacement as the treatment. The propensity score was estimated using a probit regression of the treatment on the covariates. We used full matching on the propensity score, which yielded adequate balance, as indicated in the Appendix, Section E.

Using these matched samples, we run a series of regressions. We fit a linear regression model with the given empowerment variable as the outcome and treatment status and the covariates as additive predictors and included the full matching weights in the estimation. The coefficient on the treatment (displacement status) was taken to be the estimate of the treatment effect of being an IDP. We create matches with the following controls: age, gender, education, violence witnessed, and Province. Models based on the matched sample control for the same variables. We use clustered standard errors with matching stratum membership as the clustering variable. We plot the results from these models in Figure 4, with the estimates using Currently Displaced as the treatment in red and the estimates of the ATT for Ever Displaced in green.

Figure 4 show respondents with displacement experience – both current and previous – perceive themselves as being able to contribute to events in their communities at higher levels than non-displaced persons. We estimate the ATT of being currently displaced as 5%, 25%, 20% for contributing to events in the municipality, in Midanao, and in the Barangay, respectively. Previous history of displacement has similar estimates of the ATT: 11%, 7.6%, and 13%, respectively.

17Further details on the survey are provided in the Appendix, Section A.1 and in Vinck & Bell (2011). 18The ATT is the average effect of treatment on participants.

19
-8-

<!-- Start of picture text --> (Ohart IV<br><!-- End of picture text -->

# PUSUC SECTOR DEFICIT FINANCING

I<!-- Start of picture text --> ,0<br>60<br>50<br>4.0<br>30 -<br>20 --<br>10 03<br>0<br>aI a2 83 84 55 a6 87 E5 89<br>Year<br>0 Foreiqn + Non-CBE Credit Q caE<br><!-- End of picture text -->
# **Annex 6: Dispatch, sales, and T&D loss data**

**Table A6: Dispatch, sales, and T&D loss data for the reference utility in the reference year**

|**Country**|**Total dispatched or**<br>**purchased(GWh)**|**Sales (GWh)**|**Sales (kWh per**<br>**person)**|**T&D losses**<br>**(GWh)**|**T&D losses (% of GWh**<br>**dispatched orpurchased)**|
|---|---|---|---|---|---|
|Angola_2011|—|2.394,14|n/a|—|—|
|Benin_2013|1,102|875|85|227|21%|
|Botswana_2013|3,706|3,449|1,706|257|7%|
|Burkina Faso_2014|1,359|1,125|65|234|17%|
|Burundi_2014|258|209|20|49|19%|
|Cameroon_2014|6,080|4,360|191|1,720|28%|
|Cape Verde_2012|300|205|415|95|32%|
|Central African||||||
|Republic_2014|137|71|15|66|48%|
|Chad_2012|—|n/a|n/a|—|—|
|Comoros_2012|82|49|69|33|40%|
|Congo,Dem. Rep._2013|8,349|7,479|111|870|10%|
|Congo,Rep._2012|1,725|940|217|786|46%|
|Côte d'Ivoire_2014|8,152|6,466|311|1,686|21%|
|Equatorial Guinea_2011|—|n/a|n/a|—|—|
|Eritrea_2011|337|273|46|64|19%|
|Ethiopia_2012|6,290|4,702|51|1,588|25%|
|Gabon_2014|2,172|1,650|964|522|24%|
|Gambia,The_2014|266|194|101|73|27%|
|Ghana_2013|8,479|6,496|n/a|1,983|23%|
|Guinea_2013|654|498|42|156|24%|
|Guinea-Bissau_2014|—|12|7|—|—|
|Kenya_2015|9,280|7,655|168|1,625|18%|
|Lesotho_2010|676|615|306|61|9%|
|Liberia_2014|56|42|10|14|25%|
|Madagascar_2014|1,488|1,000|42|487|33%|
|Malawi_2014|1,906|1,456|87|450|24%|
|Mali_2014|1,578|1,214|77|364|23%|
|Mauritania_2013|642|495|127|147|23%|
|Mauritius_2013|2,613|2,384|1,894|229|9%|
|Mozambique_2014|4,961|3,855|146|1,106|22%|
|Namibia_2013|4,219|3,861|1,676|358|8%|
|Niger_2014|936|758|41|178|19%|

93
Asturias, J., García-Santana, M., and Ramos, R. (2019). Competition and the welfare gains from transportation infrastructure: Evidence from the Golden Quadrilateral of India. Journal of the European Economic Association, 17(6), 1881-1940.

Atkin, D., and Donaldson, D. (2015). Who’s getting globalized? The size and implications of intra-national trade costs (No. w21439). National Bureau of Economic Research.

Baldomero-Quintana, L. (2020). How Infrastructure Shapes Comparative Advantage. Mimeo.

Banerjee, A., Duflo, E., and Qian, N. (2020). On the road: Access to transportation infrastructure and economic growth in China. Journal of Development Economics, 145, 102442.

Behrens, K., Brown, W. M., and Bougna, T. (2018). The world is not yet flat: Transport costs matter!. Review of Economics and Statistics, 100(4), 712-724.

Berg, C. N., Deichmann, U., Liu, Y., and Selod, H. (2017). Transport policies and development. Journal of Development Studies, 53(4), 465-480 Bergquist, L. F., and Dinerstein, M. (2020). Competition and entry in agricultural markets: Experimental evidence from Kenya. American Economic Review, 110(12), 3705-47.

Bergquist, L., Faber, B., Fally, T., Hoelzlein, M., Miguel, E., and Rodriguez-Clare, A. (2019). Scaling agricultural policy interventions: Theory and evidence from Uganda. Unpublished manuscript, University of California at Berkeley.

Bonfatti, R. and Poelhekke, S. (2017). From mine to coast: Transport infrastructure and the direction of trade in developing countries. Journal of Development Economics, 127, 91-108.

Borraz, F., Cavallo, A., Rigobon, R., and Zipitria, L. (2016). Distance and political boundaries: Estimating border effects under inequality constraints. International Journal of Finance & Economics, 21(1), 3-35.

Brancaccio, G., Kalouptsidi, M., and Papageorgiou, T. (2020). Geography, transportation, and endogenous trade costs. Econometrica, 88(2), 657-691. Carballo, J., Graziano, A., Schaur, G., and Volpe Martincus, C. (2021). The Effects of Transit Systems on International Trade. Forthcoming at the Review of Economics and Statistics.

Cavallo, A. and Rigobon, R. (2016). The billion prices project: Using online prices for measurement and research. Journal of Economic Perspectives, 30(2), 151-78.

22
**Figure 7  Unemployment fluctuates with changes in business conditions, but remains high in CEE countries**

# **Unemployment Rate, 1992-2002 CEE**

<!-- Start of picture text --> 25.0<br>20.0<br>Czech R.<br>Bulgaria<br>Estonia<br>15.0<br>Hungary<br>Latvia<br>Lithuania<br>Poland<br>10.0 Romania<br>Slovakia<br>Slovenia<br>5.0<br>0.0<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>percent<br><!-- End of picture text --> Source: ILO LABORSTA database (LFS data) In Russia and Ukraine the evolution of unemployment has been somewhat different from that in CEE. Unemployment reached its peak later, only in 1998 (i.e. four years later than in CEE). This is likely to reflect delayed restructuring and initially heavier reliance on wage adjustment (as opposed to employment adjustment) in response to the negative output shock. Since 1999 unemployment in both Russia and Ukraine has begun to decline. Again, it is too early to tell whether or not this decline represents a transitory movement, or a movement toward the equilibrium unemployment rate. Unemployment in Russia and Ukraine coincides with significant underemployment among workers in unrestructured firms or workers employed in the large informal sector. This is a still bigger problem in other CIS countries.

In the low-income CIS countries unemployment has also increased, but the problem is not so much open unemployment as underemployment. It takes the form of employment in subsistence agriculture, irregular or casual employment in the informal sector (e.g. petty trade), or employment in unprofitable and unviable enterprises, which often are not even able to pay wages. Underemployment is closely associated with low productivity and thus translates into lower output and social welfare, similarly as does unemployment. In a way, underemployment is a mirror image of unemployment in societies where people cannot afford to be unemployed, because there is no effective social safety net in place.

20
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 24. Affordability of power and water in Benin and in other low-income countries**

_Source_ : Banerjee and others 2009.

Operational inefficiencies of the power and water utilities cost Benin a further $39 million a year, equivalent to 0.7 percent of GDP. The annual value of inefficiencies in the power sector (at $16 million) is more than twice as much as in the water sector (at $6 million).

Both the power utility, SBEE, and the water utility, SONEB, could benefit from reducing distributional losses and improving bill collection. SBEE’s distributional losses of 17 percent in 2009, almost twice as high as the best-practice benchmark of 10 percent, represent a potential savings of $11 million. Nonrevenue water in the water sector stood at 28 percent of total water production in 2009, in comparison to the best-practice benchmark of 20 percent. Nonrevenue water inefficiencies cost Benin about $3 million a year, equivalent to 0.05 percent of GDP. The power utility presently collects 96 percent of its billings. If 100 percent of bills were collected, SBEE could receive additional $5 million a year. The water utility collects 85 percent of its billings, forsaking $3 million a year. Nevertheless, the overall burden of operational inefficiencies in Benin’s power and water utilities is lower than for comparable countries (figure 25).

40
to about 9 percent more exports and a one percent reduction in transit delays leads to about 1.5 percent more trade.<sup>12</sup> In Table 5 the effects of documentation, inland transit and customs and ports times on the exports of time-sensitive products are presented. Time delays should have a greater effect on exports of time-sensitive goods.13 To examine the extent to which they are hampered, we follow the methodology in Djankov, Freund and Pham (2010) and estimate a difference-in-difference gravity equation using trade data of agricultural (processed and unprocessed) products for which time matters the most and the least. This methodology reduces the endogeneity problem coming from reverse causality because we control for country and industry fixed effects. In addition, the products we consider account for only a small fraction of trade in agricultural goods on average (less than 10 percent) so it is unlikely that they have a large impact on establishing trade facilitation processes.

We base our definition of time-sensitive agricultural products on the information of their storage life (Gast 1991), which includes a range of products going from a minimum storage life of 2 weeks or less, such as apricots, beans, currants, and mushrooms to 4 weeks or longer, for example apples, cranberries and potatoes and canned products. We also include goods with a very long storage life such as dry fruits with a maximum storage life of between 6 months and one year and canned products with a storage life ranging from 1 to 5 years, depending on the good’s acidity. To measure time sensitivity we use the inverse of the median storage life of each product.

To study the joint effect of industry time-sensitivity and country time delays on exports we estimate the following difference-in-difference gravity regression _LnExportsik_ = α _i_ +α _k_ + β 1 ( _Time Sensitk_ ) × ( _Inland transit timei_ ) + β 2 ( _Time Sensitk_ ) × ( _Customs_ & _Ports timei_ ) + β 3 ( _Time Sensitk_ ) × ( _Docs timei_ ) β 4 ( _K abundancei_ ) × ( _canned product k_ ) + ε _ik_ (2)

> where α _i_ and α _k_ represent country- and industry-fixed effects.  The coefficients β 1 , β 2

> and β 3 capture the joint effect of time sensitive products and respectively time delays in inland transit, customs and ports and documentation on export values. We introduce the

> 12 When we include the log of total time as the only trade cost variable in the regression, we get a coefficient of about -1, the same as the  results for developing countries in Djankov, Freund and Pham (2010), although they use a slightly different approach.

> 13 Evans and Harrigan (2005) show that time-sensitive apparel products are more sensitive to distance than time-insensitive products.

10
firms. However, column 6 in Table 6 indicates that the average TFP growth rate for the former group always exceeds the ones for average mixed-owned and state-owned firms.

Following the discussion from above, we are also interested in analysing the impact of (potential) misclassification of firm ownership on our TFP results. For this reason, columns 7 and 8 of Table 6 report our TFP results for the groups of ministry-reporting and non-ministry-reporting firms, respectively. Column 9 provides the results of various t-tests for both groups of firms. Here, an additional advantage of the ministry-reporting information for state ownership is that both types of firm groups are of similar size (439 and 442, respectively) and thus our simple mean-comparison tests are well equipped with statistical power.

Excluding the unreliable fixed effects estimation results from our discussion, the upper parts of columns 7 and 8 clearly indicate that, on average, non-ministry reporting firms exhibit higher levels of TFP. Indeed, according to the results obtained from the other three estimation procedures (OLS, Olley and Pakes (1996) and Levinsohn and Petrin 2003), the TFP levels of non-ministry reporting firms substantially exceed the corresponding figures of their ministry-reporting counterparts. Some TFP estimators even indicate that the productivity levels for non-state-influenced firms are approximately doubled.

The results presented in the lower parts of columns 7 and 8 again do not provide significant differences with regard to TFP growth across ministry-influenced firms and the non-influenced counterparts. All results taken together, the simple mean comparison tests unambiguously document that state-owned and state-influenced firms exhibit lower levels of TFP. Moreover, when focusing on a comparision of state-owned and non-state-owned firms this TFP gap has widened during the period from 2005 to 2010.

# **5. Unveiling the source of productivity differences**

The results in the previous section indicate that large productivity differences exist between state-owned enterprises and private firms in the Belarusian machine building industry. In this 20