rafmacalaba/datause-encoder-data huggingface dataset:
||LOG|INEQUA|LITY|LO|G HOURS|L0|
|---|---|---|---|---|---|---|
|Variable|model1|model2|model3*|model1|model2|model3*|
|Peiex|0.017|0.017|0.013|0.149|0.144|0.142|
||(0.007)|(0.007)|(0.007)|(0.012)|(0.012)|(0.012)|
|apex(EP)|0.034<br>|0.033<br>|0.026<br>|0.090<br>|0.077<br>|0.074<br>|
||(0.012)<br>|(0.012)<br>|(0.012)<br>|(0.022)<br>|(0.022)<br>|(0.022)<br>|
|log(number employees)t-2|0.012|0.012|0.009|0.013|0.009|0.008|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.002)|
|Firms’ age|0.004|0.004|0.002|0.033|0.033|0.032|
||(0.003)|(0.003)|(0.002)|(0.005)|(0.004)|(0.004)|
|Firms’ age<sup>ˆ</sup>2|0.000|0.000|0.000|-0.001|-0.001|-0.001|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|log(wage)t-2||-0.001|0.000||0.009|0.009|
|||(0.001)|(0.001)||(0.003)|(0.003)|
|log(schooling)t-2||0.003|0.003||-0.007|-0.006|
|||(0.003)|(0.003)||(0.008)|(0.008)|
|log(nbr exporters - region)||-0.002|-0.002||-0.008|-0.008|
|||0.002|0.002||0.004|0.004|
|share(engenieer and R&D)t-2||(0.021)|(0.017)||(0.012)|(0.008)|
|||0.008|0.008||0.014|0.014|
|average experience t-2||-0.001|-0.001||-0.012|-0.012|
|||(0.000)|(0.000)||(0.000)|(0.000)|
|number of subsidiaries t-2||0.015|0.013||0.042|0.042|
|||(0.003)|(0.003)||(0.015)|(0.015)|
|Manager exp _M_1|||0.073|||0.049|
||||(0.016)|||(0.026)|
|Manager exp _M_2|||-0.004<br>|||0.128<br>|
||||(0.017)|||(0.028)|
|Manager exp _M_3|||0.010|||0.071|
||||(0.011)|||(0.017)|
|Manager _M_1 N-_Q_4|||0.153|||-0.139|
||||(0.023)|||(0.035)|
|Manager _M_1 F-_Q_4|||0.097|||0.218|
||||(0.054)|||(0.106)|
|Manager _M_2 N-_Q_4|||0.025|||-0.356|
||||(0.048)|||(0.080)|
|Manager _M_2 F-_Q_4|||-0.018|||0.378|
||||(0.085)|||(0.266)|
|Manager _M_3 N-_Q_4|||0.058|||-0.111|
||||(0.023)|||(0.035)|
|Manager _M_3 F-_Q_4|||0.028|||0.430|
||||(0.069)|||(0.162)|
|Constant|0.106|0.126|0.107|8.821|9.024|9.012|
||(0.027)|(0.029)|(0.028)|(0.052)|(0.053)|(0.052)|
|FIXED EFFECT|||||||
|FIRMS|YES|YES|YES|YES|YES|YES|
|Sector-Year|YES|YES|YES|YES|YES|YES|
|Region(State)-Year|YES|YES|YES|YES|YES|YES|
|N|767,572|767,495|767,495|779,356|779,278|779,278|
|R2 (within)|0.003|0.003|0.041|0.014|0.019|0.022|
|Firms (cluster)|255,428|255,428|255,428|255,428|255,428|255,428|
|F stat|5.810|6.210|32.190|32.230|38.850|38.480|
|F-prob|0.000|0.000|0.000|0.000|0.000|0.000|

Table 12: Dependent variables: Log(Number of within inequality) and Log(Hours hired in L0) - Panel Note: Note: Standard errors clustered at the firm level are reported in parentheses. We exclude outliers (equivalent to 1% of extreme values). Managers are classified by occupations equivalent to CEOs and Directors (M1), Senior staff (M2) and Supervisors (M3). N and F refer to their nationalities, where N(Nationals) and F(Foreigners). Additional Manager’s control (Managers - _Q_ 3– _Q_ 1) refers to dummy variables for managers in the second and third quartile of wage distribution. Sector (CNAE 2 digits). Log Inequality refers to the ratio of the average real wage at the top hierarchy (Ln) and production workers (L0). Log hours L0 refers to the log of total hours hired of production workers.
- 13 -

OED review Is clear and concise, It Is repeated here: 'Overall, the cost recovery record hI irrigation projects has not been good. Frequently, the Bank's requirements as expressed In lending covenants, particularly with respect to recovery of Investment costs, have been so vague that compliance or noncompliance Is difficult to determine. In at least two-thirds of the projects reviewed the covenant requirlng that cost recovery satisfy OWM funding has not been complied with. The proportion of OLM costs recovered was frequently between 15 and 45 percent. In addition, there were very few cases where capital costs were recovered." The OED report goes on to note that OLM of Irrigation systems was considered satisfactory at audit In only about half of the projects. Compliance with cost recovery covenants was assessed as satisfactory In only 15 percent of the cases. When the pricing covenants required a soclo-economic survey and It was uIplemented, the recommendations were "generally not applied". The response of Bank operations staff to noncompliance wlth cost recovery covenants has been quite varlable, coverlng the gamut from refusal to consider further financing of Irrigatlon projects to no reaction at all. VL SOME REASONS FOR NONCOWLIANCE **WIlH** RRIGATION **LENDING** CONDTONALITY The 1986 OED review on Irrigation cost recovery singles out three major reasons for the record of noncompliance wlth cost recovery covenants: I) the often heavy burden of direct and Indirect taxes already kIposed on agriculture; 11) unrellable water supply due to poor OWM of irrigatlon systems; and 1ii) the lack of government commitment. The evidence with respect to each reason will be brlefly assessed In turn.
may be related to some of the trends documented in Y. Feng et al. (2024). We also note the decreases in non-labor force participation, but the coefficients are too imprecise for firm conclusions.

Finally, we look at changes in the jobs ladder based on changes in sector of employment (agriculture, manufacturing and services). Table 8 shows that movements out of agricultural employment and into manufacturing employment are associated with increases in formal wage employment but that growth in services does not show the same relationship. We also explore the extent to which changes in trade orientation and capital deepening are correlated with better jobs outcomes. Table A16 looks at correlations between exports and capital stocks and movements on the jobs ladder. However, many of these variables are available for only a subset of country-year surveys and, as such, we do not see any significant within-country correlations.

Table 8: Sectors of employment and changes in the jobs ladder

||(1)<br>Upper-tier<br>self emp.|(2)<br>Formal<br>wage|(3)<br>Upper-<br>tier<br>informal<br>wage|(4)<br>Lower-tier<br>informal<br>wage|(5)<br>Lower-tier<br>self emp.|(6)<br>Unemployed.|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Emp. share in|-0.337|0.811|-0.077|-0.550**|-0.281|0.121|0.313|
|manufacturing|(0.234)|(0.531)|(0.064)|(0.225)|(0.560)|(0.126)|(0.294)|
|Emp. share in|-0.285|0.546|-0.085|-0.380*|0.054|0.041|0.109|
|services|(0.195)|(0.348)|(0.052)|(0.201)|(0.324)|(0.115)|(0.281)|
|Total pop. (log)|-0.081**|-<br>0.340***|0.061***|0.227|0.138|0.033|-0.036|
||(0.040)|(0.105)|(0.022)|(0.151)|(0.142)|(0.066)|(0.151)|
|GDP p.c. (log)|0.045|0.094|0.024*|0.044|-0.090|-0.013|-0.105*|
||(0.040)|(0.071)|(0.014)|(0.054)|(0.101)|(0.022)|(0.056)|
|Observations|466|466|466|466|466|466|466|

Notes. All regressions include country fixed effects. Standard errors are clustered at the country level and are in parentheses. Only older adults are included. * p<0.10 ** p<0.05 *** p<0.01.

31
exchange rate brought the rates in the two-tier system much closer together, and in 1994, the exchange rate was unified, removing this disincentive for trade.

# _2.2 Liberalizing processing trade_

Importantly, in 1979, China introduced zero tariffs and exempted from non-tariff barriers all imported intermediate inputs and capital goods used in the production of exports. These exemptions, described in detail in Ianchovichina (2007) and Hong Kong Trade Development Committee (2018), were part of China’s export processing system. They provided incentives for both the processing of imported raw materials and the assembly of imported parts and components into _finished_ export products. Provisions for processing with supplied materials also allowed firms to deal with the capital market failures that were a serious constraint on export growth in the early reform era.

The effect of duty exemptions was sizable. Right after their introduction in 1979 in four coastal cities designated as special economic zones (SEZs) (Yu and Tian 2012), processing trade comprised only 5% of Chinese trade flows (General Administration of Customs of the People’s Republic of China, 2000), but after 1984, the authorities expanded the scope of the duty exemption system beyond the four SEZs to 14 ‘open’ cities and a range of other zones (Yu and Tian 2012). This gave a significant boost to trade growth, well ahead of accession, with duty-free imports used for export processing quadrupling as a share of total imports between 1981 and 1988 (Ianchovichina 2007 based on data from General Administration of Customs of the People’s Republic of China, 2000). By 1992 this open-door policy was extended from eastern to central and western China and foreign direct investment inflows surged from 1% of GDP in 1991 to more than 6% of GDP in 1992 (Figure 1), speeding up the integration of China into global production networks.

6
3 factors, as it is now. Like other countries, it seems to be balancing two opposing positions: First, _the country’s aims dictate that it rely less on capital from abroad and it is ready to stand this ground for a long time_ . Second, _the country would like foreign capital, so it will be making efforts to show its creditworthiness_ . In other words, an autarkic stance competing with an integrationist stance. Russia’s experience with debt issues, including lessons regarding the defensive value of time, and its gradual shift toward a more autarkic stance, seem to be reflected in the structure of its debts.

A thorough review of the prospectuses (offering circulars) of all 15 outstanding Russian Federation Eurobonds reveals that, in the aggregate, their legal profile is more protective of the debtor than other sets of emerging market Eurobonds (see table 1 on page 15 for a full overview of the bonds). The time prescribed before creditors lose their claims is short in most cases, so creditors would be likely to begin legal action sooner rather than later, if only to preserve their claim. The bonds also maintain all possible hurdles under existing sovereign immunities laws and potentially create doubt as to whether a default actually happened through broad language contained in the alternative payment currency and fiscal laws clauses. Significant changes taking place since 2014 suggests a degree of intentionality.

These characteristics are likely to increase the time spent in litigation rather than proving fatal for all claims. Remember, however, that in these situations, time can be a powerful weapon. In the past, Russia has been willing to eschew resolution for over seven decades, and upon settlement it offered no more than 10 percent of the value of the claims presented. In more recent

> _<u>Default Risk Plummets After Dollar Coupon Payments</u>_ , Bloomberg, March 22, 2022. _<u>A Russian default looks almost inevitable</u>_ , The Economist, April 6, 2022. This is likely incorrect, while the 1998 default was largely on ruble denominated instruments, many holders were external creditors – alas the LTCM debacle. Also, the same default included a relapse into default of previously restructured London Club debts denominated in USD contracted by Vneshekonombank, an instrumentality of the state charged with trade and development finance since the Soviet period.
<!-- Start of picture text --> 7 O68 F BO<br>6 Los 6 7O _<br>= _ L =<br>Ss Ss) " =<br>~ 4 9 — -50 &<br>a 4 e o4 6<br>7 -o3 @ § -49 2<br>ma= owt= 39 68<br>2 o2~ & E<br>a ? a 7 20 a<br>1 a 17 10<br>i] 1 1 a o r i<br>1985 1990 1995 2000 2005 2010 2015 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> mms Modelled incidence (%), adults 15-49, from Spectrum software ons Prevalence (%), females 15-24 (source: national ANC reports) mms ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013
<!-- Start of picture text --> Growth Accounts for India Decomposition of Output per Worker<br>8 7<br>7 6<br>6<br>5<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1 1<br>0 0<br>1960-73 1973-83 1983-93 1993-99 1999-04 1960-73 1973-83 1983-93 1993-99 1999-04<br>Employment Pysical Capital Education TFP Within Sector Growth Reallocation Effects<br><!-- End of picture text --> Source: Bosworth, Collins and Virmani (2007) The analysis by Bosworth, Collins and Virmani (2007) is insightful. While an extension of the analysis for more recent years would be very useful, we are constrained by the scope of this paper, and the effort involved in constructing comparable data. We however decompose GDP growth using the simple Cobb-Douglas production function with capital and (unskilled) labor as inputs, and a constant capital share of 0.3: Y� �A�K���L�����, where α is assumed to be 0.30, and TFP is estimated as the Solow residual. Consistent with Bosworth, Collins, and Virmani (2007), the results further highlight that the growth momentum in India since the 1990s has been fundamentally supported by increases in TFP, which on average accounted for 60 percent of overall growth between 1990 and 2011 and, has since again emerged as a key driver of growth.

Our growth accounts also reflect that investment rates in India have slowed more recently, which has reduced the role of capital accumulation in driving growth. In addition, increases in labor inputs have only been a modest driver of aggregate growth in recent years, as the contribution of employment growth stabilized at around 1 percent a year after the financial crisis. Both the diminishing role of capital accumulation and the comparatively limited importance of human capital in driving growth contrast the Indian growth experience to East Asia, as especially China relied on strong investment and capital accumulation.

Turning to the sources of labor productivity, India has experienced two significant boosts to labor productivity, the first one commencing in 1993 and the second one in 2003 (Figure 6). The rate of productivity increase during these episodes is larger than that experienced by the East Asian countries during the periods of very high growth, but is smaller than the labor productivity increases realized in China, which increased output per worker by 8.5 percent between 1993 and 2004, compared to 4.6 percent in India.<sup>16</sup>

> 16 See Bosworth and Collins (2008) for a discussion.

13
between 1970 and 1975, and the fourth column shows annual growth rates between 1973 and 1985. The last two coiBinns are foi the number of scientists and engineers not normalized by population.' Following the OECD convention, Table 1 puts OECD countries into three groups (major, medium, and small R&D performner) on the basis of the amount of R&D expenditures. The groups based on **R&D** expenditures are not necessarily the same as the groups based on R&D expenditures as a percentage of CiDP. Table 2 breaks down R&D expenditures by source of funds and b,- type of R&D activities. The first two columns in Table 2 report the average ratios of government funded R&D to the sum of government aind privately funded R&D expenditurcs.'" The last two columns report the awerag. ratios of basic R&D expenditures to the sum of basic and applied (including experimental) R&D expenditures. Data for these refined categories are available for a smaller set of countries.

Some characteristics emerge from Tables I and 2. First, R&D activities in developing countries are negligible. The average R&D expenditure as a percentage of GDP between 1970 aa,d 1985 is only 0.36 percen<sup>t</sup> for developing countries, just 25 percent of the average for OECD countries of 1.42 percent. Between 1970 and 1975, the average number of scientists and engineers employed in R&D activities is 172 per million population for developing 91n calculating sample averages and annual growth rates, the number of observations included differs across countries, and we use our discretion to minimize bias due to the differences in sample periods. We tried to avoid the concentration of samples around certain periods; if the values for 1985 are not available, either 1984 or 1986 figures are used.

10 The source of funds code in the UNESCO data set includes other categorie **s** uch as foreign funds and other funds. Since their proportion is usually minimal and we are not sure whether they should be treated as part of privately-funded expenditures, we exclude them in calculating the ratios in Table 2.

_5_
# **5.2 The Impact of the 2012 Education Reform**

We cannot rule out that during the period 2011 to 2014 significant economic changes, other than the inflow of refugees, occurred in Turkey that happen to be correlated with the instrument. The only major policy change in this period that we were able to identify (the impact of which may be correlated with refugee flows) is the 2012 education reform. This reform, most significantly, adds four years to mandatory schooling, increasing the period from eight years to 12 years. School children in grade 8 (typically age 14) and younger were affected by the reform starting in 2013, making it compulsory for them to continue their schooling. In 2011, our baseline year, Turkey’s border regions with Syria had significantly lower school attendance rates among ages 15 – 18, 54 percent compared to 66 percent in the rest of Turkey, and hence were disproportionately affected by the reform. Since the 2012 education reforms coincided with the influx of refugees from Syria and disproportionately affected the same regions, our estimation strategy is potentially confounding the events.

To test whether it is likely that the education reform biases our estimates we check whether the refugee flows are correlated with 2011 levels in school attendance across Turkish subregions. As expected the positive correlation between refugee flows and 2011 school attendance rates is significant at the one percent significance level (controlling for the gender, age and education composition of a subregion the point estimate is -0.17). Even once we instrument for refugee flows the correlation remains significant at the one percent significance level (a point estimate of -0.30). However, once we control for the log distance for the Syria border there is no longer a statistically significant relationship, in either the OLS or IV, between school attendance rates in 2011 and subsequent refugee flows. This suggests that, on account of the inclusion of our distance from the border control, we can rule out the 2012 education reform confounding our estimates.

# **5.3 Robustness to Varying Sample of Turkish NUTS 2 Subregions**

Throughout this paper we use all 26 NUTS 2 subregions of Turkey for identification. However, the results are robust to varying the particular sample of subregions. We report results for two alternative samples. First, we drop the Gaziantep subregion from the estimation. Gaziantep has the highest refugee to population ratio among all regions and reportedly towns with a refugee share of over 30 percent. The inclusion of Gaziantep may skew results if there are any non-linearities in the impact of refugees. Second, we follow Ceritoglu et al. (2015) in only considering nine subregions of Turkey. These are the five Syrian border regions of southeastern Anatolia (Hatay, Gaziantep, Sanliurfa, Mardin, and 27
# **1. Introduction**

This paper studies the long-term demographic consequences of the Rwandan genocide (1994) and, in particular, it analyzes how excess mortality was distributed in the population. This study complements earlier work by Verwimp (2003a, 2003b, 2004 and 2005). The analysis uses the siblings’ mortality module of the 2000 Demographic and Health Survey (DHS) in Rwanda to analyze mortality during 1994. The maternal mortality module collects from female survey respondents the following information: number of brothers and sisters born from the biological mother of the respondent and, for each of the siblings; gender, survival status, age and age at death if relevant. De Walque (2005 and 2006) has used this method to analyze selective mortality during the Khmer Rouge Period in Cambodia.

We find that the excess mortality of 1994 dramatically affected the population composition of Rwanda. Adult males were most likely to die, indicating that violent deaths represented a very large share of the excess mortality. Using the characteristics of the respondent sister as a proxy for the socio-economic status of the family, we also establish that individuals with an urban or educated background were more likely to die. We consider the possibility that these two results are driven by the fact that Tutsi were especially targeted by the 1994 genocide. Indeed, we find, using the 1992 Demographic and Health Survey, that Tutsi were more likely to be educated and to live in urban areas. Since ethnic variables have not been collected in the 2000 DHS, our analysis suffers from an omitted variable bias. We try to tackle this issue by using age at marriage as a proxy for being Tutsi. Even if we can only partially address the omitted variable bias, we can conclude that the genocide represented, over and above the human tragedies, a huge 2
to groups of households. Second, after data collection, consumption of optional modules is imputed for all households. Then, the resulting consumption aggregate is used to estimate poverty indicators.

# Module construction

Food and non‐food consumption for household _i_ are estimated by the sum of expenditures for the full list of consumption items<sup>7</sup> � � where y� and y� denote the food and non‐food consumption of item _j_ in household _i_ . As the estimation for food and non‐food consumption follows the same principles, the upper indices _f_ and _n_ are neglected in the remainder of this section. The list of items can be partitioned into M+1 modules each with _mk_ items: Item assignment to the core module was designed to maximize the core module’s share of total consumption, so that a large share of consumption would be enumerated from each household. Important items were identified by their average food share across households from wave 1 of the SHFS.<sup>8</sup> This strategy relies on the fact that, in Somalia, a few dozen items capture the majority of consumption. The core modules captured 94 percent of food consumption and 79 percent of nonfood consumption, respectively (Table 2). Optional modules were constructed such that items are orthogonal within modules and correlated between modules, using an iterative algorithm (Pape and Mistiaen, 2018).

**Table 2: Item partitions and consumption shares in SHFS wave 2.**

||Number of<br>items<br>|**Food Items**<br>Share Wave 2|Share Wave 2<br>Imputed|Number of<br>items<br>|**Non‐food Item**<br>Share Wave 2|**s**<br>Share Wave 2<br>Imputed|
|---|---|---|---|---|---|---|
|Core|38|94%|79%|29|79%|47%|
|Module 1|21|2%|8%|14|5%|14%|
|Module 2|18|2%|6%|15|6%|15%|
|Module 3|19|1%|5%|16|7%|18%|
|Module 4|18|1%|4%|15|5%|11%|

Source: Authors’ calculations based on SHFS Wave 2.

> 7 The list of consumption items used in wave 2 of the SHFS is discussed in section 3.1.

> 8 Generally, previous consumption surveys in the same country or consumption shares of neighboring or similar countries can be used to estimate food shares. In the worst case, a random assignment results in a larger standard error but does not introduce a bias. The assignment of items to modules is very robust and, thus, even rough estimates of consumption shares are sufficient to inform the assignment without requiring a baseline survey.

18