rafmacalaba/datause-encoder-data huggingface dataset:
TABLE 5 Regression results for total factor productivity

|**Dependent variable:**<br>**Baseline**<br>|**3-year mov**|**ing**<br>**5-year mov**|**ing**<br>**7-year mo**|**ving**<br>**Linear-quadratic**<br>|
|---|---|---|---|---|
|<br>**TFP growth**<br>**HP-trend**|**average**|**average**|**average**|**trend**|
|GDP per capita rel. to<br> <br>-0.06***|-0.07***|-0.07***|-0.06***|-0.06***|
|advanced economies<br>(0.000)|(0.001)|(0.002)|(0.002)|(0.001)|
|Working-age population<br>4.16*|3.05|4.70|6.86**|3.13|
|(0.100)|(0.326)|(0.143)|(0.044)|(0.321)|
|Secondary completion rate 0.003|0.003|0.010|0.009|-0.029***|
|(0.701)|(0.807)|(0.375)|(0.397)|(0.002)|
|Secondary completion rate<br> <br>0.009*|0.012*|0.009|0.004|0.004|
|(bottom two-thirds)<br>(0.061)|(0.068)|(0.142)|(0.466)|(0.464)|
|Investment growth<br> <br>0.088***|0.178***|0.185***|0.169***|0.118***|
|(five-year moving average)<br>(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Commodity<br>exporters<br> <br>0.592***|1.094***|0.778**|0.664**|1.001***|
|credit boom dummy<br>(0.000)|(0.002)|(0.035)|(0.040)|(0.000)|
|Number of observations<br>706|694|692|687|706|
|Number of countries<br>125|125|125|125|125|
|Within R-square<br>0.26<br>Source: World Bank.<br>Note: *** indicates significan<br>and * at the 10 percent level.<br>around countries. The meth<br>unbalanced panel of 33 advan<br>|0.27<br>ce at the<br>Estimatio<br>odology<br>ced econo|0.29<br>1 percent le<br>ns are based<br>is defined i<br>mies 92 EM|0.29<br>vel, ** at th<br>on standar<br>n annex.3.<br>DEs for 1983|0.25<br>e 5 percent level,<br>d errors clustered<br>Sample includes<br>-2020. p-statistics|

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Estimations are based on standard errors clustered around countries. The methodology is defined in annex.3. Sample includes unbalanced panel of 33 advanced economies 92 EMDEs for 1983-2020. p-statistics are shown in parentheses.

49
# **1 Introduction**

This paper asks whether we should expect to find the sharp polarization of labor markets found in high-income countries to emerge in the developing world, and then employs global census data to see whether we do.

For the United States, Katz et al. (2006); Autor (2010); Autor and Dorn (2013) document expanding job opportunities in both high-skill, high-wage occupations and low-skill, low-wage occupations, coupled with contracting opportunities in middle-wage, middle-skill white collar and blue-collar jobs. In particular, job opportunities are declining in middle-skill white collar clerical, administrative and sales occupations and in middle-skill, blue-collar production, craft and operative occupations. This especially hits the earnings and labor force participation rates of workers without college education and particularly men. Goos et al. (2014) document that this phenomenon has appeared in each of 16 European countries from 1993 to 2006: middle wage occupations decline as a share of employment in all 16 countries with an unweighted average of 8 percentage points while high wage and low wage occupations increased in the vast majority. Beaudry et al. (2013) show that the collapse of middle level paying jobs has now spread to the high-skill labor market. Even growth optimists, such as Brynjolfsson and McAfee (2014) predict major shifts in the composition of labor and the need for compensatory social policies to offset the resulting inequality.

From the prevalence of polarization across the advanced countries, Autor concludes that a common set of forces are at work. Of these, a leading explanation is the ongoing automation and off-shoring of middle-skilled “routine” tasks that were formerly performed by workers with moderate education. Routine tasks as described by Autor et al. (2003) are sufficiently defined that they can be carried out by a computer executing a program or alternatively, by a comparatively less-educated worker in a developing country who carries out the task with minimal discretion. These would include book keeping, clerical work and repetitive production tasks. Acemoglu and Autor (2011) offer a model where technological progress 1
of the year (June and December).

# **5.1 Risk Score**

We use the firm’s risk score at baseline as a proxy for its likelihood of evading taxes. Like many tax administrations, Tajikistan’s Tax Committee uses a risk-scoring system to assess firm risk as part of its audit strategy.<sup>22</sup> Risk-based audits help administrators allocate limited resources to cases likely to yield significant revenue while reducing the monitoring burden on compliant firms (less likely targets for an audit). A firm’s risk score is the product of a proprietary algorithm incorporating observed firm characteristics and results of prior audits on other firms. Similar to those of most countries (both developed and developing), Tajikistan’s tax authority keeps confidential the details of the risk-scoring process to prevent taxpayers engaging in strategic behavior in order to avoid an audit. That said, risk criteria incorporated into basic models for small firms in different countries include, _inter alia_ , the sector(s) of activity and business structure, quality of record keeping, past compliance behavior, cost ratios and deductions from gross revenue, and comparisons with industry and sector averages (Loeprick and Engelschalk, 2011).

To investigate empirically this variable’s correlates in our setting, we regress firms’ risk scores at baseline against different firm characteristics from administrative and survey data. Table 2 indicates that risk score is negatively correlated with operating in the trade sector and with having had a recent audit, whereas it is positively correlated with claiming to have no other employees besides the entrepreneur and with the amount of tax adjustments and penalties from the prior audit. In addition, we find two pieces of evidence suggesting that the risk score is predictive of non-compliance. First, we find that, controlling for observable firm characteristics such as industry, gender of owner, number of employees 22 Khwaja et al, (2011) provide examples from dozens of countries’ experiences with risk-based audits.

20
# **Agricultural Productivity, Hired Labor, Wages and Poverty: Evidence from Bangladesh**<sup>**1**</sup>

## **Shahe Emran IPD, Columbia University**

## **Forhad Shilpi World Bank**

Key Words: Agricultural Productivity, Home Production, Market Work, Wage, Hired Labor, Labor Supply Response, Poverty JEL Classification: O13, J22, J43, Q10

> 1 We would like to thank Will Martin, Luc Christiaensen, Claudia Berg, Fenohasina Maret for comments on earlier versions of the paper. We acknowledge Yan Sun for excellent research assistance.
|ation country<br>ear, ...rm and<br>columns) and<br>: Destination<br>columns 5-6),<br> parentheses.<br>(8)<br>AF<br>countries<br>0.003<br>(0.117)<br>-0.087***<br>(0.010)<br>0.004*<br>(0.002)<br>-0.031***<br>(0.009)<br>-0.097<br>(0.119)<br>-0.236***<br>(0.053)<br>0.122*<br>(0.064)<br>3690|
|---|
|y c to destin<br> country, y<br> numbered <br> description<br> countries (<br>r level are in<br>(7)<br>AF<br>countries<br>0.017<br>(0.092)<br>0.001<br>(0.008)<br>-0.084***<br>(0.009)<br>0.002<br>(0.002)<br>-0.039***<br>(0.007)<br>-0.095<br>(0.108)<br>-0.200***<br>(0.048)<br>0.097*<br>(0.055)<br>3690|
|ing in countr<br>r destination<br>bsectors (odd<br>le 1. Sample <br>), low income<br>�HS4 secto<br>(6)<br>Low Inc<br>countries<br>0.026<br>(0.130)<br>-0.099***<br>(0.011)<br>0.007***<br>(0.002)<br>-0.033***<br>(0.010)<br>-0.163<br>(0.133)<br>-0.284***<br>(0.058)<br>0.070<br>(0.067)<br>3010|
|m i operat<br> control fo<br>ss HS4 su<br>ned in Tab<br>olumns 3-4<br>g country) <br>tively.<br>(5)<br>Low Inc<br>countries<br>0.021<br>(0.098)<br>-0.0001<br>(0.009)<br>-0.090***<br>(0.011)<br>0.006***<br>(0.002)<br>-0.038***<br>(0.008)<br>-0.192<br>(0.127)<br>-0.218***<br>(0.052)<br>0.019<br>(0.066)<br>3010|
|duct k of ...r<br> Model.<br>We<br> to vary acro<br>est are de...<br> countries (c<br> at (exportin<br>evels, respec<br>(4)<br>High Inc<br>countries<br>-0.381**<br>(0.167)<br>-0.092***<br>(0.011)<br>-0.011***<br>(0.003)<br>-0.055***<br>(0.010)<br>-0.065<br>(0.063)<br>-0.158*<br>(0.094)<br>0.233**<br>(0.095)<br>10220|
|nships for pro<br>onal Hazard <br>eline hazard <br>ables of inter<br> high income <br>ors clustered <br>5%, and 1% l<br>(3)<br>High Inc<br>countries<br>-0.294*<br>(0.174)<br>0.039**<br>(0.019)<br>-0.098***<br>(0.009)<br>-0.010***<br>(0.003)<br>-0.051***<br>(0.007)<br>-0.044<br>(0.062)<br>-0.184**<br>(0.090)<br>0.212***<br>(0.075)<br>10220|
|trade relatio<br>ox Proporti<br>llow the bas<br>s). The vari<br>olumns 1-2), <br>standard err<br>t the 10%, <br>(2)<br>non EU<br>countries<br>-0.267**<br>(0.116)<br>-0.084***<br>(0.007)<br>-0.002<br>(0.002)<br>-0.047***<br>(0.009)<br>-0.113<br>(0.108)<br>-0.105*<br>(0.054)<br>0.116**<br>(0.056)<br>7831|
|azard rate of <br> using the C<br>e¤ects, and a<br>ered column<br> countries (c<br>7-8). Robust <br> signi...cance a<br>(1)<br>non EU<br>countries<br>-0.104<br>(0.076)<br>0.012*<br>(0.007)<br>-0.086***<br>(0.007)<br>-0.002<br>(0.002)<br>-0.049***<br>(0.008)<br>-0.120<br>(0.100)<br>-0.096**<br>(0.048)<br>0.088*<br>(0.048)<br>7831|
|The dependent variable is the h<br>j: All regressions are estimated<br>exporting country x HS2 ...xed <br>across HS8 product (even numb<br>markets considered are: non EU<br>and African countries (columns <br>*, **, and *** denote statistical <br>FDc x sanitary_riskk<br>sanitary_riskk<br>initial_exportcikj<br>NProductsci<br>NDestinationscik<br>Contiguitycj<br>Com_languagecj<br>Colonycj<br>Distancecj<br>Observations|

25
national government to the provincial governments over time (blue line) and the performance capitation associated with the traces (red line).

**Table 3: Public Spending on Health Services in 9 Provinces (millions, USD) 2005–08**

|Years|2005|2006|2007|2008|
|---|---|---|---|---|
|Health services (publicly financed)|486|568|852|1134|
|Health services (Obras Sociales)|278|307|392|498|
|Total health services spending|764|875|1244|1632|
|**Plan Nacer expenditures**|**11**|**31**|**36**|**33**|
|**Plan Nacer** **as %**of total expenditures|**1.4**|**3.5**|**2.9**|**2.0**|

_Source:_ Unidad de Financiamiento Internacional de Salud (UFIS), Ministerio de Salud Nacional. 2005-2008.

The provinces by and large met most but not all of the tracer targets. Figure 4 reports the share of tracer targets meet by the provinces every four months. Except for a few periods, the provinces meet between 70 and 80 percent of the targets and as a result were rewarded with most of the maximum payment possible.

Plan Nacer finances a number of activities (table 4). The largest category encompasses payments to the facilities for care services for Plan Nacer beneficiaries. Payments from provincial governments to the facilities for services <mark>accounted for</mark> $67.11 billion or 63 percent of total expenditure over the whole period. The payment-for-services share rose over time, starting at 23 percent in 2005 and rising to 71 percent in 2008, reflecting the growth in enrollment so that fixed costs are spread over a larger base. Payments to facilities represent 94 percent of capitation payments from the national government to the provinces.

The second largest category of Plan Nacer expenditure was investment in durables such as medical equipment in the medical care facilities and in software, communications, office equipment, and vehicles in the provincial Plan Nacer offices. Investment in medical facilities was designed to ensure that the facilities were able to deliver quality care, and in the provincial program office to facilitate program implementation. Between the years 2005 to 2008, program investment accounted for about 21 percent of total expenditure. As expected, most of this investment occurred in the earlier years, representing about 30 percent of total expenditure in 2005 and falling to 8 percent in 2008.

11
negative effects of volatility are primarily due to the uncertainty it causes, which leads to planning errors by firms. Badinger (2010) contributes to the discourse by introducing a novel instrument to identify the causal effect of output volatility on growth, using exogenous volatility spillovers from abroad. The cross-sectional analysis of 128 countries confirms the negative relationship between volatility and growth, even when institutional quality is controlled for. The link between policy volatility and growth is shown in Fatas and Mihov (2013) who show that an increase in government spending volatility reduces long-term economic growth. More recently, Mumtaz and Ruch (2023), show that fiscal policy uncertainty decreases output, private consumption, fixed investment, and raises prices.

To test the hypothesis that tax revenue levels may affect government consumption volatility and hence output growth volatility, we identify time-varying volatility using quarterly data on real GDP growth and real government consumption (both seasonally adjusted) from Haver Analytics and calculate 10-year ahead standard deviations of the quarter-on-quarter (annualized) growth rate. We then convert these quarterly observations into annual observations (using the first quarter observation of each year). Figure 8A provides a binned scatterplot (where observations along the vertical and horizontal axis are partitioned and then binned into one single point) of the relationship between 10-year ahead average government consumption volatility to the level of tax to GDP. Higher levels of tax revenue are associated with lower levels of government spending volatility. Similarly, figure 8B shows that higher government spending growth volatility is associated with higher real GDP growth volatility.

We use the same methodology as above to identify the potential of a tax threshold on government consumption volatility. Figure 8C shows the coefficient on the tax threshold dummy for various values of tax to GDP. The coefficient on the tax threshold reaches a nadir at 13.5 percent of GDP and is negative (decreasing volatility above the threshold) between 13 and 23 percent.

ten-year-ahead government consumption volatility as the dependent variable. The predicted volatility is shown in figure 8D. The predicted future standard deviation of government consumption is about 19 percent at 7 percent tax to GDP and declines to about 5 percent at 15 percent tax to GDP.

15
and livelihoods. Given these are not exact measures of exposure at the household level, our estimates are still technically ITT. However, we argue that for the short distance buffers based on which exposure is defined, it is unlikely that these ITT estimates differ much from ATE.

Despite these alternative probing specifications our analysis is constrained by some remaining shortcomings, which merit discussion. First, while the pre-conflict (pre-pandemic) sample of the LSMS-ISA in Ethiopia is randomly selected from urban and rural households in each district, the follow-up phone surveys are prone to systematic non-response and attrition. To account for these systematic non-responses in the phone survey, we construct and employ sampling weights as discussed in Section 2. These sampling weights can help to recover appropriate and representative statistics if the observable characteristics we use to construct our weights sufficiently capture these systematic non-responses and attrition (Wooldridge, 2007; Korinek et al., 2007). To the extent that they fail to control for this bias, our results may be taken as a lower bound of the actual impacts of the conflict. A final issue is the potential uncertainty related to the impact of COVID-19 related economic restrictions, which may interact with the effects of the war. Most mobility restrictions and pandemic-related policies in Ethiopia had been relaxed long before the outbreak of the war. In the worst case, our maintained assumption is that these are additive effects which may be captured by the time dummies or household fixed effects. However, it is possible that violent conflict largely substitutes for economic restrictions. If this is the case, then we might worry that economic or mobility restrictions are only imposed (or enforced) in areas not affected by the war. In such a case, our estimates are also lower bound estimates of the actual effects of the conflict.

# **5. Results and Discussion**

In this section, we present three sets of results based on the approaches outlined in Section 4: DID, disaggregated DID and ACLED-based results. In all our estimations, we apply the sampling weights constructed using the procedure described in Section 2.<sup>27</sup>

> 27 We estimated both weighted and unweighted regressions for each outcome variable. However, there is little difference between the weighted and unweighted results, suggesting that our results are robust to applying sampling weights. Thus, we present results based on the weighted regressions and the unweighted results are available upon request.

24
Table 1. Global (Concept 3) inequality in early 19<sup>th</sup> century

|||This paper|Bourguignon and<br>Morrisson(2002)|BFLZ (2009)|
|---|---|---|---|---|
||1750-1880<br>Countries<br>with social<br>tables only<br>(1)|1750-1880 Countries<br>with social tables plus<br>distribution data from<br>BM (year 1820)<br>(2)|1820<br>(3)|1820<br>(4)|
|Gini|38.5|43.3|50.0|47.0|
|Theil (0)|60.9|57.9|52.2|n.a.|
|Average income of<br>population included (in<br>1990 $PPP)|600|646|652*|687|
|Total population<br>included (in m)|650|861|1057*|921|
|Population coverage (%)|~66|~90|~100|>90****|
|Number of countries***|13|29|33|40|
|Number of independent<br>datapoints|591|<767|<<363**|n.a.|

Note: The difference in total population in Columns (2) and (3) stems from the fact that countries with social tables enter population in column (2) with their actual populations in the year when the social table was created while in Column (3) they enter with their populations in year 1820. Thus in Column (2) India’s population is 182 million and in Column (3) it is 209 million.

* Calculated directly from the Bourguignon-Morrisson tables, Data_WD19.xls  (supplied by the authors separately from the paper). The 100 percent coverage by Bourguignon and Morrisson has to be taken with a grain of salt since the data were “forced” (through generous use of estimates) to cover 100 percent of the population.

** See footnote 2 above.

*** The number of countries is not fully comparable. For example, Bourguignon and Morrisson, treat Bangladesh/Pakistan and India as two countries; we treat them as one as the 1750 data refer to the entire subcontinent.

*** BFLF (Table 4) give 88 percent for their coverage but that number seems to be based on a somewhat higher estimate of the world population in 1820 than we use here.

9
-2-

on the difference between the value of output and that of inputs (including non-tradeables), i.e. the gross Value Added before depreciation. The Effective Protection Coefficient (EPC) is defined as: EPC - VAd/VAw , shown to be - NPCi + (NPCo - NPCi)/a (1), where:

- VAd and VAw are the Value Added measured respectively with domestic and international border prices (non-tradeable inputs are in both cases valued with domestic prices); - "a" is the Value Added to output ratio in international prices (a - VAw/POw - 1 - (PIw * q)/POw, where POw and PIw are the respective international prices of output and input, and q is the gj_xe4 quantity of input per unit of output; a is by definition smaller than 1); and - NPCo and NPCi are the respective Nominal Protection Coefficients of output and inputs.' NPCs in turn are defined to be the ratio between the domestic (ex-factory, before indirect taxes) price of a tradeable good and its international border price.<sup>2</sup>

The formula indicates clearly that any difference in nominal protection coefficients between inputs and output is amplified by the factor 1/a into an effective protection coefficient different from NPCI or NPCo.

6. This mechanism is illustrated by Graph 1. The horizontal axis is measured in terms of NPCs, and the vertical axis measures the international prices Pw and domestic prices Pd of input and output. _-PP **-** For NPC - 1 (i.e. no protection), the vertical bar displays the composition of POw betweer. its Value Added VAw and its input cost CIw - q * PIw at international POw _ prices. Point A indicates the input cost at domestic prices corresponding to NPCi, and point D the output domestic price POd corresponding to NPCo. The vertical. bar for NPCo displays rhe components of POd, i.e. the Value Adde 'lkd and the input _ cost CId - q * PId a :tomestic prices. 0 <!-- Start of picture text --> Pnces Graph 1<br>,<br>_-PP -<br>_ _<br>V' 'VAd<br>_<br>, . A<br>VAw<br>, Cld<br>,-w c.-<br>_ ___ -<br>0 1 NPCi NPCO NPC<br><!-- End of picture text --> 1 For the sake of simplification, only one input is considered in the definitior. of "a". The argument is easily generalized to the case of several inputs, including non-tradeables. In such case, NPCi would simply be an average of the NPCis of each input, weighted by the input values consumed per unit.

**2** In a protection regime without Quantitative Restrictions (QRs) which permits unconstrained competition from imports, domestic prices of tradeables align themselves with the import prices after tariff duty. Thus nominal protection coefficients are equal to 1 plus the tariff rate. Otherwise, domestic ex-factory prices (before indirect taxes) are the result of various effects of QRs and tariffs relative to the degree of domestic competition in the industry and of the supply/demand balance generated by the regulatory policies. In such cases, the realized nominal protection is best captured by the observed ratio of the domestic ex-factory price to the international border price (CIF). It is this latter definition of the NPC which is used throughout this note.