rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Impacts on Total Consumption and Poverty**

||_Total Cons_<br>|_umption_<br>|_Moderat_<br>|_e Poverty_<br>|_Extrem_<br>|_e Poverty_<br>|
|---|---|---|---|---|---|---|
|**Measure of Shock**|(1)|(2)|(3)|(4)|(5)|(6)|
|||**Panel A: T**|**otal**||||
|t * (rainfall z-score> 2)|-69.036**||0.030||-0.002||
||[27.814]||[0.019]||[0.018]||
|t * ( 2 < rainfall z-score <= 3)||-50.806||0.023||-0.004|
|||[37.537]||[0.024]||[0.022]|
|t * (3 < rainfall z-score<= 5)||-71.968**||0.035||-0.011|
|||[29.671]||[0.024]||[0.020]|
|t * (rainfall z-score >= 5)||-84.366||0.029||0.014|
|||[53.776]||[0.025]||[0.021]|
|Baseline Mean/Median|598.8|598.8|0.453|0.453|0.112|0.112|
|||**Panel B: U**|**rban**||||
|t * (rainfall z-score> 2)|-181.140***||0.055**||0.000||
||[44.103]||[0.025]||[0.010]||
|t * ( 2 < rainfall z-score <= 3)||-179.968**||0.085***||0.020*|
|||[89.900]||[0.030]||[0.012]|
|t * (3 < rainfall z-score<= 5)||-167.649***||0.025||-0.023|
|||[48.588]||[0.033]||[0.014]|
|t * (rainfall z-score >= 5)||-195.220***||0.070**||0.014|
|||[62.435]||[0.029]||[0.012]|
|Baseline Mean/Median|796.7|796.7|0.306|0.306|0.0469|0.0469|
|||**Panel C: R**|**ural**||||
|t * (rainfall z-score> 2)|8.584||0.015||-0.009||
||[34.086]||[0.027]||[0.028]||
|t * ( 2 < rainfall z-score <= 3)||29.077||-0.015||-0.019|
|||[36.608]||[0.032]||[0.032]|
|t * (3 < rainfall z-score<= 5)||-17.423||0.048||-0.004|
|||[37.397]||[0.034]||[0.032]|
|t * (rainfall z-score >= 5)||25.569||0.003||0.003|
|||[72.903]||[0.044]||[0.046]|
|Baseline Mean/Median|496.9|496.9|0.561|0.561|0.159|0.159|

Observations: 26,587 Total; 11,225 Urban; 15,362 Rural. _Notes_ : Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Total Consumption is the monthly expenditure p.c. of a household. Quetzales of 2006. For Total consumption the baseline median is presented. Moderate poverty means that the p.c. expenditure is under the moderate poverty line. Extreme poverty means that the p.c. expenditure is under the extreme poverty line. For poverty the baseline mean is presented. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1. _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

26
43

- Frydman, R. and A. Rapaczynski (1991): Markets and institutions in large-scale privatization: An approach to economic and social transformation in Eastern Europe, in: V. Corbo, F. Coricelli, and J. Bossack (eds.): _Reforming Central and East European Economies,_ The World Bank, Washington DC, 253-274.

- (1993): Insiders and the state: overview of responses to agency problems in East European privatization, _Economics of Transition, vol._ 1, no. 1, 39-59.

- Frydman, R., E. Phelps, A. Rapaczynski, and A. Shleifer (Frydman et al.) (1993): Needed mechanisms of corporate governance and finance in Eastern Europe, _Economics in Transition, vol._ 1, no. 2, 171-207.

- Hoshi, T., A. Kashyap, and D. Scharfstein (1990): Bank monitoring and investment: evidence from the changing structure of Japanese corporate banking relationships, in R. Hubbard, _Asymmetric information, corporate finance, and investment,_ University of Chicago Press, 105-126.

- Jaros, V. and M. Sanders (1994): Investment privatization funds, _Wood & Company,_ Prague.

- Kotbra, J. (1993): Privatization in the Czech Republic: an overview, _Charles University,_ Center for Economic Research and Graduate Education (CERGE), and Economics Institute of the Academy of Sciences of the Czech Republic (El), Discussion paper no. 18, Prague.

- Mejstrik, M. (1994): Czech investment funds as a part of financial sector and their role in privatization of the economy, _Charles University,_ Institute of Economic Studies, Reform round table working paper no. 14, Prague.

- Mejstrik et al. (1994): Privatization and opening the capital market in the Czech and Slovak Republics, _Charles University,_ Center for Economic Research and Graduate Education (CERGE), and Economics Institute of the Academy of Sciences of the Czech Republic (El), Working paper series, no. 54, Prague.

- Mladek, J. (1994): Mass privatization: a first assessment of the results, _OECD,_ OECD advisory group on privatization, Fifth plenary session, Mass privatization: a first assessment of the results, 2-4 March 1994, Paris.

- Ramirez, C. (1994): Did J.P.Morgan's men add liquidity? Corporate investment, cash flow and financial structure at the turn of the twentieth century, _George Mason University,_ mimeo.

- Roland, G. (1995): Political economy issues of ownership transformation in Eastern Europe, in: Aoki, M. and H.-K. Kim (eds.), _Corporate governance in transitional economies: insider control and the role of banks,_ The World Bank, EDI Development Studies, Washington DC.

- Shafik, N. (1993): Making a market - mass privatization in the Czech and Slovak Republics, _The World Bank,_ Policy research working paper no. 1231, Washington DC.

- (1994): Information and price determination under mass privatization, _The World Bank,_ Policy research working paper no. 1305, Washington DC.
The previous gravity results summarize the estimated relationship between each individual trade agreement reported to the WTO and subsequent trade flows between each country pair. One concern is that all of Cambodia’s trade agreements prior to 2022 were through ASEAN. As a result, the estimated effects of the trade agreement apply to all of the ASEAN countries that signed the agreement.

To get a sense of how Cambodia’s experience differed from those of other ASEAN members, Table 3.5 shows the results of HDFE PPML gravity estimation that interacts Cambodia as an exporter with the estimated effect of the trade agreement. The table has two columns. The second column presents the estimates with the Cambodian interaction term. The results are estimated using the full sample (including zero values for pair-wise trade for those countries that do not trade). The main results suggest that Cambodia gets more of a “boost” from trade agreements than other countries, but this is only marginally statistically significant (at the 10% level). That is, Cambodia’s exports increase relatively more than exports from other countries with similar agreements. Overall, therefore, Cambodia’s trade agreements have played an important role in increasing exports.

**Table 3.5: Cambodia RTA Estimates**

|**VARIABLES**|**Baseline**<br>**RTA**|**Cambodia's**<br>**RTA**|
|---|---|---|
|RTA|0.581***<br>(0.098)|0.581***<br>(0.099)|
|Cambodia||1.127*<br>(0.647)|
|Constant|16.027***<br>(0.045)|16.027***<br>(0.045)|
|n|1,231,334|1,231,334|
|**Notes:**PPML HD<br>pair-wise COMT<br>1996-2021.<br>Robust standard e<br>***p<0.01,**p<|FE estimatio<br>RADE/Baci d<br>rrors in paren<br>0.05,*p<0.1|n using annual<br>ata spanning<br>theses.<br>.|

In addition to these agreements, several other trade programs are relevant for Cambodia. The three most notable are the Everything But Arms (EBA) program of the United States, the 2006 Trade and Investment Framework (TIF) between the United States and Cambodia, and trade with China. We explore each of these in turn here. The European Union (EU) introduced the “Everything but Arms” preference program for the Least Developed Countries (LDCs) on March 5, 2001. Cambodia’s preferences were reduced due to human rights concerns in 2020, which followed the re-imposition of EU tariffs on Cambodian rice (along with rice imported from Myanmar) in 2019.

In 2023, there were 46 LDCs. Table 3.6 shows the results of the Everything But Arms (EBA) program across all participants controlling for the effect of RTAs generally. The results 19 | P a g e
# Conclusion

This survey of Syrian businesses leaves little doubt that, like the rest of the country, businesses have been devastated. Supply disruptions are extensive, loss of employees and customers is dramatic and widespread, and interruptions in basic services like electricity are crippling problems. Firms in Aleppo have been especially hard hit on all fronts, and report strikingly more problems of physical damage and insecurity than the rest of the country. Many firms have been forced to close and of the 508 initially only 160 were confirmed to still be operational –many could not be reached, resulting in significantly fewer firms reporting back from Aleppo. Firms have responded to the circumstances in a variety of ways. Almost a fifth have also moved some of their operations within Syria and 7 percent to outside Syria. Hours have changed for most firms. Some have reduced hours, perhaps unable to operate longer, but almost a fifth have increased their hours to try to make things work. This is a testament to the resilience of business in the face of a devastating conflict.

The survey highlights that beyond the physical problems repairing physical damage and restoring service provision like electricity, there are a set of even more intractable challenges that are tied to the social and human impact of the conflict. Chief among them is the loss of human capital, a result of the tragic loss of life and large‐scale migration of people, with businesses across Syria reporting loss of skilled workers, managers, and unskilled workers alike. Similarly, rebuilding the relationships that form the basis of supply chains, the business networks that allow credit to flow, and the trust that underlies these relationships will be a challenge. The key conclusion of this analysis is that the damage to social and human capital is significant, and rebuilding that social and human capital is likely a greater challenge than the bricks and mortar. The ways in which the international community could support this objective is beyond the scope of this analysis, but the need to end the crisis and begin recovery remains as urgent as ever.

16
index estimated using the surveys’ unit values. Real consumption, temporally adjusted (CPI): uses the Consumer Price Index (CPI) instead of the Fisher index to adjust for inter‐year price variations. Both the HBS and NPS used the Fisher price index to adjust for spatial and intra‐year differences in the cost of living. In the HBS, separate food and non‐food Fisher price indices are estimated based on unit values (value/quantity) from the survey data. The overall (food and non‐food) price deflator was computed using the weighted average of food and non‐food indices, where the weights were the average budget shares on total nominal food and non‐food consumption. Price indicators were calculated by geographic stratum and quarter. The NPS data used a similar method to adjust for spatial and intra‐year price differences, but the Fisher price index was based on food unit values only. Table 6 compares the values of the spatial price deflators by survey quarter and strata and shows no significant differences between HBS and NPS indicators. Both surveys show low variations in the spatial deflators across the quarters, but reveal important differences between the geographic regions, with Dar es Salaam being the most expensive area and the rural areas being the least expensive.

A similar approach was used to adjust for inter‐year (between survey rounds) price variations, where HBS used a Fisher index based on food and non‐food unit values (weighted average of food and non‐ food Fisher indices) while NPS used a Fisher index based on food unit values only.<sup>7</sup> The difference between both surveys in the inter‐year deflators seems to be important. The Fisher food price index between HBS 2007 and 2011/12 is estimated at 2.15 indicating an increase in food prices by over 100 percent and the Fisher weighted index is estimated at 1.9, revealing a price increase of around 90 percent. The NPS shows a much lower inflation rate with the Fisher food price index estimated at 1.21 between the first two rounds and at 1.34 between the last two rounds (taking NPS 2010/11 as the base year).

To better understand the potential effect of the differences in (survey based) inflation rates on the consumption trends in HBS and NPS, we also use the CPI to adjust for inter‐year temporal price variations.

Table 5 presents the average monthly per adult consumption levels as well as their distribution by quintiles. The reference period for calculating average household consumption levels was harmonized to 30.4 days for all surveys rounds.<sup>8</sup> The base years in the HBS and NPS, when adjusting for inter‐year price variations using the survey

> 7 In the HBS, food aggregates were deflated by the Fisher food indicator only. 8 The NPS values were recalculated using this new reference period to replace the original 28 days.

22
- [110] Lichtenberg, F.R., 1992, Corporate Takeovers and Productivity, Cambridge: MIT Press.

- [111] Lichtenberg, F.R., and Siegel, D., 1991, The Impact of R&D Investment on Productivity - New Evidence Using Linked R&D - LRD Data, Economic Enquiry, 29(2), 203-29.

- [112] Lobo, J., and Rantisi, N.M., 1999, Investment in Infrastructure as Determinants of Metropolitan Productivity, Growth and Change, 30(1), 106-27.

- [113] Lucas, R.E., 1988, On the Mechanics of Development Planning, Journal of Monetary Economics, 22(1), 3-42.

- [114] Makin, T., and Paul, S., 2003, The Productive Effects of Public Infrastructure: What Do the Data Say? International Journal of Appplied Business and Economic Research, 1(2), 137-47.

- [115] Mayer, J., 2001, Technology Diffusion, Human Capital and Economic Growth in Developing Countries, United Nations Conference on Trade and Development Discussion Paper, No. 154.

- [116] McGuckin, R.H., and Sang, V.N., 1995, On Productivity and Plant Ownership Change: New Evidence from the LRD”, RAND Journal of Economics, 26(2), 257-76.

- [117] Miller, N.J., and Tsoukis, C., 2001, On the Optimality of Public Capital for Long-Run Economic Growth: Evidence from Panel Data, Applied Economics, 33(9), 1117-29.

- [118] Mitra, A, Varoudakis, A and Véganzonès-Varoudkis, M. 2002., Productivity and technical efficiency in Indian states’ manufacturing: the role of infrastructure, Economic Development and Cultural Change, 2002: 395—426.

- [119] Mourmouras, A., and Tijerina, J.A., 1997, Government Investments in Schooling and Infrastructure: Ramsey vs. Public Choice Paths in a Lifecycle Model, Estudios Economicos, 12(1), 3-21.

- [120] Munnell, AH., 1990a, Why has productivity growth declined? Productivity and public investment, New England Economic Review, January/February 1990: 3—22.

- [121] Munnell, AH., 1990b, How does public infrastructure affect regional economic performance? New England Economic Review, September/October 1990: 11—32.

- [122] Munnell, AH., 1992, Infrastructure investment and economic growth, Journal of Economic Perspectives, 6(4): 189—198.

- [123] National Integrated Resource Plan, 2004, National Integrated Resource Plan 2 2003/2004, National Electricity Regulator.

- [124] Nelson, R.R., and Wright, G., 1992, The Rise and Fall of American Technological Leadership: The postwar era in historical perspective, Journal of Economic Literature, 30, 1931-64.

- [125] Nickell, S., 1996, Competition and Corporate Performance, Journal of Political Economy, 104(4), 724-46.

- [126] Nourzad, F., 2000, The Productivity Effect of Government Capital in Developing and Industrialized Countries, Applied Economics, 32(9), 1181-87.

- [127] Nourzad, F., 2001, Public Sector Capital and Technical Efficiency in Private Sector Production, Konjunkturpolitik, 47(1), 39-50.

- [128] Olley, G.S., and Pakes, A., 1996, The Dynamics of Productivity in the Telecommunications Industry, Econometrica, 64(6), 1263-97.

29
trends assumption is violated 19 times in the 105 regressions, and 10 of the 19 violations occur in DiD regressions whose coefficient estimates would have otherwise led us to conclude there was a statistically significant effect of the IHC program. This evidence, together with the pooled estimates from Table 7 suggest that the DiD estimator is not suitable for inference for any of our outcome variables of interest in these data because the common trends assumption is too frequently violated.<sup>43</sup> This motivates our use of the SCM below. An additional lesson from Table 8 is the evidence of potential heterogeneity in the estimated effects of the IHC program across treated firms. The DiD regressions for import value in particular generate a relatively large number of significantly positive and significantly negative effects.

**Table 8.  Results Summary from Firm-by-Firm DiD Regressions and Common Trend Testing** **<u>Regressions</u>**

|Dependent<br>variable|Median<br>estimated<br>coefficient<br>on treatment<br>variable|Mean<br>estimated<br>coefficient<br>on<br>treatment<br>variable|Number of<br>significant<br>coefficients<br>> 0|Number of<br>significant<br>coefficients<br>< 0|Number of<br>violations<br>of common<br>trends<br>assumption|Number of<br>significant<br>treatment<br>coefficients<br>with common<br>trends<br>violation|
|---|---|---|---|---|---|---|
|Median|||||||
|clearance time|-0.259|-0.321|1|7|5|2|
|Clearance<br>time 75<sup>th</sup>pct|-0.441|-0.571|0|10|4|3|
|Clearance<br>time 90<sup>th</sup>pct|-0.365|-0.556|1|8|5|3|
|Inspection rate|-0.004|-0.007|0|1|2|1|
|Import value|0.083|0.048|6|2|3|1|

Notes: The table summarizes regression results from 105 DiD regressions and 105 regressions that test for common trends. Each of the 21 firms adopting the IHC program appears in one treatment regression and in one common trends testing regression, and the control firms are those within the same size decile as the treated firm. For each dependent variable, the median and mean of the estimated treatment effects across the 21 firms are reported, as well as the number of DiD regressions (out of 21) with significant positive and negative coefficients at the 5% confidence level, respectively, for the treatment dummy. The final two columns show, respectively, the number of regressions that indicate a statistically significant violation of the common trends assumption at the 5% confidence level and the number of cases in which a statistically significant coefficient coincided with a violation of the common pre-treatment trends.

# **5.2 Benchmark Synthetic Control Method Results**

This section presents the SCM estimates for the impact of the IHC program on importing firms, considering five outcomes of interest and addressing the following questions. First, did the IHC program reduce clearance times for the firms that adopted it? We evaluate this question at

> 43 Given a confidence level of 5%, one might expect only one failure of the common trends assumption to arise in 21 regressions, or roughly five in 105 regressions.

32
# **iii. The wage bill: Price versus quantity**

Changes in the public wage bill can be due to variations in wage and/or changes in the public employment level. We observe a positive correlation between the three measures of public employment (as share of the labor force) and size of the wage bill (as percentage of GDP), with the General Government and Public Sector aggregations showing the clearest association (Figure 3).

_Figure 3: Public employment (% labor force) and compensation to employees (% GDP)_ <!-- Start of picture text --> 0 .05 .1 .15 .2 .25 0 .2 .4 .6<br>General Government Central Government<br>30 30<br>20 20<br>CE (% GDP) CE (% GDP)<br>10 10<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> 0 .2 .4 .6 .8<br>Public Sector<br>30<br>20<br>CE (% GDP)<br>10<br>0<br><!-- End of picture text --> Source: ILO Stats and IMF A variance decomposition of the wage bill into the variance of its components (employment and wages) may be a useful starting point.<sup>4</sup> The decomposition is done using country-year level data (80 countries) 4 The variance of the wage bill is equal to the variance of employment, plus the variance of wages (proxied by total wage bill divided by public employment) and twice the covariance between both components: V i )) = V i )) + V i )) + 2 ∗C i i )) Where i corresponds to the wage bill in constant 2011 international dollars, i is general government employment (in thousands), and VV(ln(GG i is a proxy of wage level constructed by dividing VV(ln (EE VV(ln (WW CC(ln (EE ), ln (WWi by i . GG EE WW GG EE 6 6
exchange rates are inappropriate for currency conversions since they do not measure the ‘true’ purchasing power of a currency. For example, on market exchange rates, the price of a haircut in India seems incredibly cheap to an American visiting India or, conversely, a taxi ride in Australia will be almost as expensive as plane travel between two cities in India. PPP provides the adjustments required to market exchange rates such that the price of an item in two countries is identical if expressed in a common currency. A working definition of the PPP is provided in World Bank (2013, p. 19), namely, that ‘ _it represents the number of currency units required to purchase the amount of goods and services equivalent to what can be bought with one unit of currency unit of the base or reference or numeraire country’_ .

There are several reasons for the divergence between the market exchange rates and the PPP rates (however measured). The short term factors include the capital movements between countries, interest rate movements, speculation in foreign exchange markets, etc. The longer term factors include the fact that the exchange rates are almost exclusively dependent on the relative prices of tradeable items, while standard of living comparisons between countries should include both tradeable and non-tradeable items, especially in the context of developing countries. Consequently, the PPP rates are based on a much wider selection of items than market exchange rates including both tradeable and non-tradeable items. It is not surprising, therefore, that the PPP rates deviate from exchange rates much more for developing countries than the developed ones. The use of PPPs has provided some comfort to authorities in developing countries who have used them to argue that the ’real size’ of their economies is much higher than shown by the exchange rate converted figures.

Though the concept of PPP can be traced back to the early work of Cassel (1916), its modern origin is due to Balassa (1964) and Samuelson (1964). The distinction between PPP and exchange rates is underpinned by the distinction between tradeable and non-tradeable items. While both PPP and exchange rates measure the ratio of prices of a basket of items in two countries, the former considers both tradeable and non-tradeable items, but the latter considers only tradeable items. Both Balassa (1964) and Samuelson (1964) base their distinction between PPP and exchange rates on productivity differences between the tradeable and the nontradeable sectors. As Balassa (1964) notes (p.585), “under the assumption of constant marginal rates of transformation, the relative price of the non- traded commodity will thus be higher in the country with the higher productivity levels than the other”. This leads him to conclude that (p. 586), “assuming that international productivity differences are greater in the production of 3