rafmacalaba/datause-encoder-data huggingface dataset:
# **4.3 Linking sentiment to economic activity**

While the supply and demand sentiment measures provide the relative size of supply and demand shocks over time and their sectoral differences, their impacts on economic activity are not observed. We use a local projection model to trace out the impact of sentiment as measured in the earning call transcripts. The benefit of such approach is we do not need to place onerous restrictions as in the case of VAR models. The model controls for the alternate sentiment measure, consumer inflation, exchange rates, oil prices, and policy interest rates.

Figure 5 plots the impulse response functions from demand and supply sentiment shocks. Both supply and demand sentiment shocks are statistically significant drivers of output. On average, the impact from a 1 unit positive shock to demand is larger than the equivalent supply shock–by about three-quarters–and leads to a 1.25 percent increase in output. One possible reason for why supply sentiment has a weaker impact in comparison to the demand shock is that the worsening sentiment might be transmitted at different degrees to the real economic activity. For example, supply chain difficulties might be addressed by existing inventories and/or alternate supply channels for some firms. Even if the initial disruption is a shock to supply, intersectoral linkages might exacerbate the impact of demand shocks, resulting in significant reductions in aggregate demand (Guerrieri, Lorenzoni, Straub, and Werning, 2020). While the larger impact of demand on output may be contrary to the perspective of real business cycle models (see, for example, Kydland and Prescott, 1982) of the importance of supply shocks, it fits into models of hysteresis and other possible sources of demand shocks (see, for example, Cerra et al., 2020).

A demand sentiment shock does not lead to a statistically significant increase in inflation beyond the first period, with the increase in inflation small (0.1 percentage point) relative to the shift in output. This implies a contemporaneous Phillips curve slope of 0.075; a 1.0 percent increase in growth increases inflation by 0.075 percent. This result is consistent with less sensitivity of inflation to economic activity over time (see, for ex14
- 34 -

Profitability Trends 67. Despite the low profitability of the CPEs, capital goods industries as a whole have had higher profitability than the manufacturing sector as a whole. The profitability ratios for capital goods industries are presented in Table 17.

TABLE 17: INDEX OF RELATIVE PROFITABILITY OF CAPITAL **GOODS** INDUSTRIES /a

||1980/81|<br>1982/83|<br>1984/85|<br>1985/86|<br>1987/88|
|---|---|---|---|---|---|
|RBI Survey:<br>Gross<br>Profit<br>to Net<br>Assets|n.a.|1.31|1.08|0.88|1.30|
|<br>Net<br>Profit<br>to Net<br>Worth|n.a.|1.36|1.04|0.97|1.61|
|CMIE<br>Survey:<br>(Privat**e**<br>S ctor):<br>- All Capital<br>Goods:||||||
|<br>Profit<br>to Sales|1.59|1.56|0.94|0.92|1.27|
|Net Profit<br>to Net<br>Worth|1.76|1.65|0.63|0.71|1.19|
|- Electrical<br>Machinery:||||||
|Profit<br>to Sales|1.66|1.26|0.80|0.61|1.20|

La Rati **o** f capital goods industries profit rate to average profit rate in other manufacturing industries.

68. The decline in relative profitability in 1984/85 and 1985/86 was due to a drastic reduction in machinery tariffs to 45% and 55% respectively. These tariffs were increased to 90% in 1987/88. However, even with these drastic tariff reductions, the profitability of capital goods only declined to about the manufacturing industry average. Since then, further real devaluation and tariff increases have increased the profitability of domestic capital goods producers above the overall manufacturing industry average.

69. Protection and Trade. QRs on capital goods appear to have been significantly liberalize **d** uring the 1980s. More than 1,000 items have been put on the OGL list. However, the industry is still highly protected. First, most of these OGL items represent products not manufactured locally, and OGL imports represented only about 30% of total capital goods imports in 1987/88. Second, most imports of capital goods continue to require the clearance of the Capital Goods Committee. Third, the increasingly high tariffs levied on imported capital goods (official tariffs in 1987/88 averaged 90%, and actual collection rates averaged 62.5%) have successfully substituted for the
# Figure 10 Greece **MCY,** M1Y, QMY and **DCPY** overtime

<!-- Start of picture text --> 0.5.<br>0.45.<br>0.4.<br>> 0.3-<br>0 ......- g H ,- ... . _ , E~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~....<br>0.25 1 1<br>-----------<br>MCY<br>~0.2-<br>0o.15 0- ,. -<br>0.1-<br>*0.05<br>1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990<br>year<br>MCY M1Y ..... QMY ----DCPYI<br><!-- End of picture text -->
# **6.3 Using 2018 Data**

In this paper, we rely on data from the ELMPS 2012. Indeed, the 2012 survey is advantageous as it oversampled areas with high migration rates. As we discussed in section 3.1, the most recent ELMPS data was conducted in 2018 and instead oversampled rural communities in Egypt. Nonetheless, in this section, we replicate our analysis using the most recent publicly available survey as a robustness check.

We examine the effect of migrants’ legal status on hourly wages in 2018, unconditional on return migration, by estimating equation (1b). In Table A.14 in the Online Appendix, we report our results unconditional on return migration. We account for the various selection processes in columns (1) to (3) following the same identification strategy presented in section 4.1. In line with our benchmark results presented in section 5.1.1, we find that only documented migrants witness a wage premium relative to non-migrants, while undocumented migrants witness a wage penalty.

We also check the robustness of our results to using pooled cross-sectional data from the 2012 and 2018 ELMPS. We report the results in Table A.15 in the Online Appendix, unconditional on return migration. Our model additionally includes a year fixed effect for the 2018 survey. Our results consistently show that undocumented migration is associated with a wage penalty, while migrants’ legal status leads to a positive wage premium upon return.

Relying on the 2018 data, we also analyze the dynamics of the effects. We ask whether the effect of legal status remains constant over time after return or varies. Accounting for the triple selection in columns (1) to (3), column (4) reports the results from the structural equation. To capture the dynamics of the effects, instead of focusing on the full 2018 sample as in Table A.14, we restrict our analysis, in Table A.16, to individuals who were interviewed in both ELMPS 2012 and 2018. We therefore track the same individuals included in our benchmark analysis in the 2018 survey. Our results consistently point to a wage penalty for undocumented migrants of 54% and to a wage premium for documented migrants of 16%. Comparing the magnitude of these effects with those from our benchmark model in section 5.1.1, we find that undocumented migrants’ wage penalty grew larger over time. Moreover, while documented migrants still witness a significant wage premium upon return, our results show that legal migrants’ wage premium was actually larger in 2012 (28%). The evidence therefore suggests that the wage gap between non-migrants and undocumented migrants grew larger over time, while the wage gap between documented migrants and non-migrants narrowed down.

# **6.4 Other Robustness Checks**

As we discussed earlier in the paper, the incidence of multiple migration episodes is very low in Egypt. In our data set, approximately 95% of return migrants had one 27
Table [1.2

# Share of SMEs in the Sectors under Study (1990)

|Sectors||SME Share|||
|---|---|---|---|---|
||Number<br>of Firms|Employment|Value<br>added|Exports|
|Manufacturing<br>average|98.3|61.7|44.3|45.5'|
|Factory<br>automation-|99.5|93.0|85.6|20.2|
|Auto parts|97.5|66.5|48.3|44.1|
|Electronic parts|94.1|31.8|17.5|24.4|
|Woven textile|98.1|53.2|67.8|60.6|

- Note: 1988 figure - 1991 figure 'These figures are for the machine tool sector in 1988.

Source: The Korean Sm?all and Medium Industry Promotion Corporation (SMIPC).

Table 11I.3 Distribution of SMEs in Sample <!-- Start of picture text --> Number of Finns<br>Factory Auto Electronic Woven<br>automation parts pans textiles<br>Number of Employees (unit/small batch) (mass/large batch) (continuous process)<br>< 50 18 4 4 8<br>50-99 13 7 5 10<br>100-199 6 4 8 16<br>> ;z00 3 5 3 8<br>Tota 40 20 20 42<br><!-- End of picture text --> 47
Table 2: Summary statistics

|**Variable**|**Mean**|**Std. Dev.**|
|---|---|---|
|**Household characteristics**|||
|Monthly consumption per adult (1996 Ethiopian birr)|1757.374|2083.679|
|Age of household head|44.249|14.374|
|Female headed household|0.181|0.385|
|Head primary education|0.298|0.457|
|Head secondary education|0.051|0.22|
|Head nonformal education|0.044|0.206|
|Household sector of occupation: agriculture|0.352|0.261|
|Household head unemployed|0.003|0.057|
|Household sector of occupation: manufacturing|0.016|0.077|
|Household sector of occupation: services or trade|0.043|0.121|
|Household size|5.858|2.293|
|Proportion of females 16-64|0.249|0.15|
|Proportion of females under 15|0.242|0.179|
|Proportion of females over 65|0.014|0.076|
|Proportion of males under 15|0.249|0.184|
|Proportion of males over 65|0.017|0.067|
|Household dependency ratio|0.513|0.204|
|Household owns more than 3 plots|0.531|0.499|
|Household can raise 200 birr in a week|0.755|0.43|
|Household received income from PSNP in 2010|0.066|0.248|
|Household net consumer of food|0.481|0.5|
|Household owns land|0.913|0.283|
|Household has own fush toilet/pit latrine<br>|0.388|0.487|
|Household uses fush toilet/pit latrine|0.5|0.5|
|Household has corrugated iron roof|0.382|0.486|
|Household has electricity|0.1|0.3|
|Dummy: urban household that rents|0.085|0.278|
|**Geographic characteristics**|||
|Rural Household|0.849|0.358|
|Ln Distance to market (km)|3.726|1.229|
|Close to market (less than median minutes)|0.49|0.5|
|Distance to town (minutes)|288.226|201.758|
|**Shocks**|||
|Household sufered death of member|0.043|0.204|
|Household sufered job loss of member|0.007|0.086|
|Household sufered self-reported food price shock|0.121|0.326|
|Infation rate (3-month moving average)|1.545|1.412|
|Drought: proportion lost crops|14.848|18.928|
|N|3|7,964|

Notes: Descriptive statistics for 2005 and 2011 pooled.

9
_Result 1- Lower transportation costs will induce a switch from farming to rebels (and vice versa) when the costs of stealing marketed goods is sufficiently low (high)._ (i.e. lim<sup>ttheenn</sup> →−∞ ) →0<sup>,</sup> ddΨ KKgg ddtt

# Proof: See Appendix.

Intuitively, lower transportation costs make production for the market more attractive. Recall that Kg defines the cost of looting. As the costs of looting from the market decline, the strategic advantage (i.e. payoffs) accruing to the rebels increases as they are able to loot a greater share of the aggregate output produced by farmers. As a result, payoffs available to the rebels from theft of the higher aggregate output, can rise faster than the increased output of the marginal farmer. A corollary of result 1 is that when more households join the rebels agricultural output would decline in such situations, ceteris paribus, so there can be no presumption that transportation cost reductions induce the desired outcomes.

The next result explores whether an increase in conflict at the market is more damaging than at the household.<sup>7</sup> _Result 2. In general the welfare cost of a marginal increase in looting of subsistence goods relative to marketed goods is ambiguous, and is increasing in the relative welfare weights of these goods in the utility function_ . (i.e. <u>i i</u> _if_ > 1−f) � FF ~~�~~ > � FF� ddUU ddUU αα ddff ddgg ββ 1−gg

# Proof: See Appendix.

Result 2 suggests that the consequences of increased looting at each location depend critically upon the welfare weights in the utility function. When the marketed good is given sufficient weight relative to the subsistence product the marginal welfare costs of theft at the market are higher and vice-versa. An implication of this result is that the combined effects of simultaneous changes in transport costs and conflict area also ambiguous even in this highly stylized framework.

In sum the model shows that transportation costs affect the consumption/utility levels of households through numerous channels, one by directly affecting the revenue of the households and others indirectly, by affecting the amount available to loot and the number of looters. <mark>The model also suggests that lower transportation costs could induce more conflict, and ultimately lead to a reduction in welfare in conflictprone areas</mark> (Result 1). We confront these empirically testable hypotheses with data from DRC. In addition, we empirically analyze the effects of conflict at markets and households (Result 2) as well as the combined effect of conflict and transport cost, as the theory is ambiguous in predicting the consequences.

> 7 As is conventional rebel utility is ignored.

11
# **Table S20. Relationship between the WB Human Capital Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.006***<br>(0.00)|0.000<br>(0.00)|0.000<br>(0.00)|||||
|Open Data Barometer<br>||||0.005***|0.000|0.000||
|Score||||||||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.006|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||0.000||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||-0.001|||-0.001||
||||(0.00)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||0.000|||0.001||
||||(0.00)|||(0.00)||
|School Enrollment,|||0000|||0001||
|Primary (% gross)|||.|||.||
||||(0.00)|||(0.00)||
|Year 2014|||||0.001|0.001**||
||||||(0.00)|(0.00)||
|Year 2015|||||0.000|0.002**||
||||||(000)|(000)||
|Year 2016|||||.<br>0.000|.<br>0.002*||
||||||(0.00)|(0.00)||
|Year 2017||0.021***|0.021***||0.023***|0.026***||
|||(0.00)|(0.00)||(0.01)|(0.01)||
|Year 2018||0.021***|0.022***|||||
|||(0.00)|(0.00)|||||
|Year 2019||0.022***|0.022***|||||
|||(0.00)|(0.00)|||||
|Year 2020||0.017***|0.017***|||||
|||(0.00)|(0.00)|||||
|Year 2021||0.017***|0.018***|||||
|||(0.00)|(0.00)|||||
|Year 2022||0.017***|0.018***|||||
|||(0.00)|(0.00)|||||
|Constant|0.271***|||0.408***|||0.389***|
||(0.02)|||(0.02)|||(0.02)|
|Sigmau||0.09|0.06||0.09|0.06||
|_<br>Sigmae||0.02|0.02||0.01|0.01||
|_<br>R2|0.532|0.991|0.991|0.595|0.998|0.998|0.531|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|149|149|149|100|100|100|92|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1020<br>05, *** p<0.<br>(WDI), Open|1020<br>01. Standard er<br>Data Watch (|1020<br>rors are cluster<br>ODIN), Global|361<br>ed at the count<br>Data Baromete|361<br>ry level. Data<br>r (GDB), and|361<br>from the Worl<br>Open Data Ba|92<br>d Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

69
national market portfolios. Comparisons are made across DM and EM assets, and where appropriate, the results are reported for subperiods to establish time stability.

# - **1.1** Pairwise Correlati!nQ

We begin with a correlation analysis for the period 1989-1990. The price returns arv based on market clearing prices of the NIFs in the abcve host (U.S.) market. The NAV returns are based on home market clearing p;;_es of the underlying securities that constitute the NIF. Note that we do not include dividends in computing price and NAV returns so as to make them comparable with returns on various market indices (which are not adjusted for dividends) used in the analysis of this paper. The results are presented in Table 8 and summarized below. The coefficients of correlation are: between returns on prices (ROP) and NAVs is **CPN;** between ROP and returns of local market index in U.S.$ (RLM) is CP,L;<sup>8</sup> between ROP and returns on S&P500 is Cpsp; between NAV returns and RLM is CNL; and between NAV and S&P500 is **CNSP.**

> **8 Table** 8 also contains correlation coefficients computed between price and NAV returns with respect to IFC local stock market indexes. Waeneveg there is no IFC index, a N.C. (non computable) messages appears.

7
-7-

the neglect of intra-household inequality can throw off estimates of poverty and inequality by a considerable margin (see Haddad and Kanbur, 1990).

However, given the state of information in most developing countries, from an operational point of view we are restricted basically to the distribution of individuals by per capita household consumption or income, and this is the distribution to which the poverty line i, applied.

Given this distribution and a poverty line, the most obvious and commonly used index of poverty is the headcount index, which measures the fraction of individuals below the poverty line. While this index will continue to be the workhorse of poverty analysis, a number of authors (e.g. Sen, 1976) have pointed out that this measure is insensitive to changes **ir, tse** depth of poverty. A good example of the difficulties is given by the World Development Report, based on the work of Ravallion and van de Walle (1988).

Suppose we want to know how an increase in the price of basic food staples affects poverty. A recent study examined this question for Java, Indonesia, using data for 1981. Households close to the poverty line were found to be, on average, net producers of rice, the main food staple. Thus, the headcount index of poverty will fall when the price of rice increases -- assuming that this price change is passed on to the producers. But the study found that the poorest of the poor -- many of them landless agricultural laborers or farmers with little land but with some other source of income -- are net consumers of rice. They are worse off, at least in the short run, when the price of rice increases. The study showed that measures that take the severity of poverty into account tend to show an increase in poverty when the price of rice goes up -- exactly the opposite message conveyed by the headcount index.

We would do well to bear this caution in mir.d, and look perhaps to classes of measures that allow a weight to the depth of poverty (for an
2008 were conducted in the last three quarters of the year after international food prices peaked. Therefore, they cover the period of peak international prices. Some lag in domestic food inflation may still be a problem, although we have already assessed results for surveys conducted in 2009, which may capture the twin effects of slower growth (due to the financial crisis) and higher food prices.

Bearing these caveats in mind, table 10 reports the results of calculating the populationweighted averages of food insecurity prevalence and population numbers for 2007 and 2008. The results thus suggest that there was basically no change in the “global” prevalence of food insecurity between 2007 and 2008. However, table 8 also shows that this result is heavily driven by trends in India, where food insecurity fell 4 percentage points from 2007 to 2008. The bottom half of table 8 calculates trends excluding India (which, admittedly, represents approximately one-quarter of the developing world’s population) and finds that population-weighted food insecurity in the rest of the sample went up by 2.53 percentage points, representing approximately 43 million people. Therefore, using the 2007 round as a base suggests that many developing countries were somewhat worse off in the peak food crisis year relative to the previous year. The largest increases in self-assessed food insecurity occur in Tanzania (23 points), Turkey (21 points), Burkina Faso (14 points), Uganda (14 points), Mozambique (12 points), Kenya (11 points), Ecuador (10 points), Cameroon (9 points), Sri Lanka (9 points), Armenia (7 points), and Honduras (7 points). Although we cannot ignore measurement error and the role of other factors in explaining these trends (the result in Turkey stands out as somewhat implausible), it is notable that many of the countries listed above did experience quite rapid food inflation. Indeed, the average rate of food inflation in these countries was approximately 4 points higher than the rest of the sample.

31
The remainder of the paper is organized as follows. Section 2 describes the context of the health sector in Rwanda and the P4P intervention evaluated. In section 3, we present our data and we describe our identification strategy. Section 4 presents our results while section 5 concludes.

# **2. The health sector in Rwanda and the P4P intervention**

In 2005, HIV prevalence for adults in Rwanda was estimated at 3% (Institut National de la Statistique du Rwanda (INSR) and ORC Macro, 2006). The Government of Rwanda (GoR) decided to address the HIV epidemic by not only aggressively scaling up HIV services nationwide, but also utilizing the planned national P4P model to target HTC, ART for AIDS patients, prevention of mother-to-child transmission (PMTCT), and other HIV-related prevention and care services. The GoR initiated the P4P scheme in 2006 to supplement the input-based budgets of health centers and hospitals with bonus payments based on the quantity and quality of key services (Ministère de la Santé République du Rwanda, 2006).

The scheme pays for different dimensions of services, including maternal and child health, tuberculosis, and HIV/AIDS. For HIV/AIDS, the P4P scheme pays for 10 output indicators, such as the number of clients and the number of couples tested for HIV (US$ 4.59 per couple), the number of newly diagnosed HIV-positive patients on ART (US$ 0.92 per individual), and the number of HIV-positive women on contraception (Table 1). The Ministry of Health (MoH) defined the output indicators and each corresponding unit payment based on health priorities, available budget and the previous NGO pilot experiences (Ministère de la Santé République du Rwanda, 2008). This analysis focuses on the first two indicators dealing with HTC: (i) the number of clients tested for HIV at the HTC center, and (ii) the number of couples/partners tested at the health facility.

Facilities submit monthly reports and quarterly requests for payment to the district P4P steering committee, which is responsible for verifying the data and authorizing payment. Each committee verifies reports by sending auditors to facilities on unannounced random days each quarter. The auditors verify the data reported are the same as the data recorded in facility records. In addition, during the 2006-2008 period the Ministry of Health financed one patient tracking survey to conduct face-to-face interviews with approximately 1,000 patients to verify the 5