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19
inflation on investment: a 7.4% inflation rate (which is the average in our sample of developing countries) generates a 7-11% drop in capital stocks.

It is interesting to note that, while quantitatively relevant, McKinnon’s original argument does not readily generalize to environments with endogenous profits and arbitrary initial capital stocks (rather, the argument requires initial capital stocks that are “sufficiently low”). The intuition follows directly from McKinnon’s reasoning: at high inflation rates, it is more costly to hold money for long periods of time. By increasing its capital stock, the firm can increase its cash flows, making it possible to finance indivisible investments by accumulating retained earnings for shorter time periods. This increases the incentives to accumulate capital, particularly for small firms for which the indivisibility plays a bigger role. Consistent with this reasoning, our calibration suggests that the relationship between investment and inflation may be non-monotone.

We proceed by empirically investigating whether, in developing economies, inflation drives a wedge between investment in large and small firms. Intuitively, firms with lower cash flows must hold money for longer periods of time in order to finance a given size of indivisible investment; thus, McKinnon’s mechanism is likely to be more relevant for small firms. Using the World Bank’s Enterprise Survey, we show that when inflation is high, small firms are more likely to stop investing than large firms. In addition, conditional on investment, inflation reduces investment in small firms relative to large<sup>1</sup> This paper is related to a long line of research about inflation and investment. The theoretical literature on the topic is inconclusive: while McKinnon (1973), Stockman (1981) and, more recently, Smith and van Egteren (2005) suggest a negative relationship between inflation and investment, Tobin (1965) and Fischer (1979) argue that the relationship should be positive,

> 1Following The Enterprise Survey, we define small firms as those with less than 20 fulltime permanent workers, and large firms are those with 20 or more full-time permanent workers.

3
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As Table 1 suggests, Jordanians and Syrians have significantly different characteristics. Jordanians are significantly more educated by an average of three more years of schooling. Jordanians who have ever worked are therefore, on average, older than their Syrian counterparts, with fewer years since their first job. They also have a significantly lower total number of jobs. Moreover, considering the aforementioned segmentation of the market, while 40% of Jordanian youth have informal wage work, Syrians are almost entirely in informal wage employment.

_Table 1: Sample characteristics of Jordanians and Syrians_

||Jordanian|Syrian|Difference||
|---|---|---|---|---|
|Age|23.99|23.65|-0.34||
||{3.2}|{3.0}|(0.25)||
|Years of schooling|11.89|8.65|-3.24|***|
||{2.7}|{3.2}|(0.18)||
|Years since first job|4.50|5.08|0.58|**|
||{3.8}|{4.1}|(0.24)||
|Type of employment for last job:<br>informal wage|<br>0.41|0.94|0.53|***|
||{0.4}|{0.2}|(0.03)||
|Total number of jobs taken|1.25|1.41|0.16|***|
||{0.7}|{0.9}|(0.03)||
|N|510|314|824||

Each row is a regression of the dependent variable on binary variables for each nationality. Standard deviations are reported in brackets and standard errors are reported in parentheses. Significance * .10; ** .05; *** .01.

_Source: Authors’ calculations using SYPJ data_ The SYPJ has two main limitations. First, since the sample is restricted to youth it gives no information about older working populations. Second, retrospective data on job histories 12
percent of revenue from the federal rural property tax (ITR).

Constitutional Amendment No. 42 of 2003 increased the municipal revenue sharing rate by 1 percentage point, to 23.5 percent of IPI and IR, leaving the rate unchanged for the states; introduced the sharing of revenue from the federal excise on petroleum derivatives ( _CIDE-Combustíveis_ ) with the municipalities (25 percent of the state share in revenue); and increased the revenue sharing rate for ITR to 100 percent if the municipality opts for collecting this tax.

Box Table 1. **Intergovernmental transfers to municipal governments, 2002** In per cent of GDP

||All municipalities|
|---|---|
|Total|4.9|
|Contitutional transfers|3.9|
|Revenue sharing|3.0|
|_of which_:||
|FPM (Municipal Participation Fund)|1.2|
|ICMS (Value Added Tax)|1.5|
|IPVA (Tax on Motor Vehicle)|0.3|
|Compensatory transfers|0.8|
|_of which_:||
|ICMS revenue insurance|0.1|
|FUNDEF (Fund for Education)|0.7|
|Non-constitutional transfers|0.9|
|_of which_:||
|Health care|0.5|
|Capital|0.3|
|Other|0.2|

_Source_ : Afonso and Araujo (2004).

# **Compensatory transfers**

Compensatory transfers include: _i)_ the Fund for the Compensation of Exports (FPEx), consisting of 10 percent of total collection of IPI, proportional to the value of exports of industrialized goods,<sup>1</sup> _ii)_ royalties for the exploitation of petroleum and natural gas, hydroelectricity and other mineral resources in their territory or in the adjacent maritime platform, _iii)_ ICMS revenue insurance, consisting of compensation for the exemption of ICMS from the export of primary and semi-manufactured goods;<sup>2</sup> and _iv)_ FUNDEF allocations.

# **Other transfers**

The main additional transfers refer to health care and voluntary transfers associated primarily with investment programs.

____________________

1. It is distributed to the States and, in turn, to the municipalities. The states have to share 25 percent of this transfer with the municipalities.

2. The municipalities are entitled to 25 percent of the ICMS revenue insurance.

24. Municipal borrowing is low in comparison with higher levels of government, reflecting their lower debt repayment capacity, on-going fiscal adjustment and limited access to markets. Municipal fiscal stance, as gauged by the primary budget balance (excluding municipal enterprises), has fluctuated over time (Figure 3). The municipalities of São Paulo, Rio de Janeiro and Salvador account for about 75 percent of outstanding municipal debt. The debt restructuring arrangements discussed above, the introduction of 18
In what follows, we provide more details on the methods used for data collection in domains that are central to our research.

# _Soil fertility_ :

Soil fertility testing was conducted by the World Agroforestry Center (ICRAF). During MAPS fieldwork, enumerators collected plot-level soil samples from each of the selected plots following a protocol carefully designed to maximize the representativeness of the samples while maintaining feasibility of implementation. From each plot, four samples were collected from the top-soil (0-20cm depth) and combined in the field to create one composite top-soil sample. Additionally, a single sub-soil sample (20-50cm depth) was collected from the center of the plot. After being processed locally, the samples were shipped to ICRAF Nairobi where all samples were subject to spectral soil analysis and approximately 10 percent were subject to conventional wet chemistry testing. A portion of this 10 percent sample was used to calibrate prediction models, while the remainder was used to verify the predictions made onto the spectral data. For details, see Shepherd & Walsh (2002). The final results from the soil analysis include key indicators of soil fertility such as pH, texture analysis (percent sand, percent clay), cation exchange capacity, and the concentration of multiple elements and micronutrients, such as carbon, nitrogen, and potassium.

# _Maize yields_ :

A 4x4 meter subplot (divided into four 2x2 meter quadrants) and a separate 2x2 meter subplot were laid on the randomly selected maize plot during the post-planting visit following a strict protocol to ensure the location of the subplots was random. The subplots were roped off until harvest, when the enumerators were alerted and completed the harvest with the assistance of the farmer and a local assistant. The shelled maize from each 2x2 meter subplot was weighed and barcoded separately. The maize was then dried by a dedicated team at a central, monitored location until moisture content was in the range of 12 to 14 percent. Once desired dryness was met, the maize was re-weighed, and the dry weight and final moisture content recorded. For analysis, all maize weights have been normalized to 12 percent moisture content.

# _Plot area_ :

Following conclusive evidence of systematic bias in farmer estimates of plot area among smallholder farmers in the region (see Carletto et al., 2013, Carletto et al., 2015, Carletto et al., 2017), MAPS implemented area measurement using a Garmin eTrex 30 handheld GPS device. Both the area and the raw GPS track outline were stored.

Table 1 presents descriptive statistics of key variables. Every household in the sample cultivated at least one maize plot (1.89 maize plots on average). Plot sizes are small (0.15 ha on average) and 7
until 2014. Per capita GDP refers to the year the country received its first DPL. The same summary statistics are also presented by region.

The statistics reported in Table 1 show mixed results. Indeed, intensive recipients of DPL tend to perform better than the sample average, but with several exceptions. Only 12 among the top 20 DPL recipient countries recorded a larger than average increase in the _Doing Business_ score, and 13 performed better in terms of per capita GDP growth. In Africa, the region with the largest number of DPLs, smallest average loan size and weakest _Doing Business_ performance, 6 of the 9 top recipients managed to achieve larger improvements in their business environment than the region’s average. In contrast, only half of the intensive recipients in Latin America, Europe and Central Asia, and East Asia and Pacific exhibited better results than their respective regional averages.

Of course, this evidence does not say much about whether development policy lending has been effective in promoting better environments for business activity or good growth outcomes. We cannot observe counterfactual performances. Moreover, we do not control for additional factors that may well have affected the outcomes, nor for self-selection biases. This is what we try to do in the next section.

# **3 Estimation**

We estimate the following equation at the country level using data from 2004: where _DBi,t_ is the average distance to frontier of _Doing Business_ indicators in country _i_ and year _t_ .<sup>6</sup> _DPLsi,t_ represents the cumulative number (or cumulative value) of development policy loans for country _i_ in year _t_ . _Zi,t_ is a vector of control variables, which includes the logarithm of GDP per capita to correct for the possibility that policy quality is inferred in part from performance.

As a robustness exercise, we also control for total official aid received by the country in a given

> 6In our baseline specification we use the longest time series of the _Doing Business_ indicator, which includes averages of three indicators available since 2004. As a robustness check, we also consider averages of wider subsets of indicators, which however became available only after 2004. See Section 2 for further details.

7
Appendix 1A: Total direct & indirect value added export (VAX) of financial service, 1995-2007

||X||Ratio1 =|||Ratio2 =|
|---|---|---|---|---|---|---|
|Country|(gross export)|VAX|VAX/X|dVAX|indVAX|indVAX/dVAX|
|AUS|210|579|2.76|133|447|3.74|
|AUT|564|686|1.22|334|352|1.00|
|BEL|620|896|1.45|327|569|1.65|
|BGR|20|47|2.37|13|34|2.69|
|BRA|72|457|6.30|47|410|8.68|
|CAN|487|984|2.02|275|709|2.59|
|CHN|53|1774|33.62|34|1740|42.94|
|CYP|4|12|2.72|3|8|2.79|
|CZE|45|104|2.32|20|84|4.77|
|DEU|1151|2644|2.30|529|2115|3.56|
|DNK|122|282|2.30|76|206|2.83|
|ESP|438|847|1.93|284|563|1.82|
|EST|8|15|1.91|5|10|1.87|
|FIN|33|122|3.76|21|101|3.56|
|FRA|790|1925|2.44|405|1520|3.96|
|GBR|5050|4339|0.86|2591|1748|0.54|
|GRC|39|87|2.27|28|59|2.21|
|HUN|52|118|2.28|29|89|3.74|
|IDN|139|271|1.95|103|168|1.95|
|IND|101|683|6.77|78|605|7.46|
|IRL|1597|1171|0.73|831|341|0.38|
|ITA|552|1604|2.90|331|1273|3.57|
|JPN|789|3603|4.57|522|3081|6.25|
|KOR|199|976|4.91|119|857|6.58|
|LTU|2|12|6.45|1|11|9.35|
|LUX|2910|849|0.29|640|209|0.32|
|LVA|8|16|1.96|5|10|2.09|
|MEX|181|512|2.83|119|393|3.70|
|MLT|7|11|1.49|4|7|1.97|
|NLD|820|1244|1.52|442|803|1.58|
|POL|114|219|1.92|70|148|2.55|
|PRT|93|235|2.52|65|170|2.76|
|ROM|24|67|2.82|17|50|3.07|
|RUS|5|152|29.25|4|149|49.65|
|SVK|20|37|1.89|12|25|2.01|
|SVN|9|38|4.35|6|33|4.66|
|SWE|372|532|1.43|247|285|1.08|
|TUR|7|251|34.48|5|247|45.46|
|TWN|95|1197|12.55|75|1122|13.78|
|USA|10116|11897|1.18|5624|6273|1.07|
|ROW|2382|4798|2.01|1598|3199|1.93|
|**TOT**|**30300**|**46293**|**1.53**|**16070**|**30223**|**1.88**|

Notes: The export values in this table are for financial services sector (WIOD sector 28) in 100 million U.S. dollars at 2005 constant price, using the U.S. GDP deflator from the Federal Reserve Economic Data (website address: http://research.stlouisfed.org/fred2). _X_ is total gross exports. _VAX_ is total value added exports. _dVAX_ is direct value added exports. _indVAX_ is indirect value added exports through other sectors.

41
pp 7-9.

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40 Table 7. Average effect of the Venezuelan migration shock on educa– tional outcomes Secondary school grades (marginal effects)

||(1)<br>(2)|(3)|
|---|---|---|
|Dependent Var.|Ln(Enrollment)<br>Promotion Rate|Dropout Rate|
||**Panel A: Without Controls**||
|Foreign Males|0.0023***<br>-0.0152|0.0032|
||(0.0007)<br>(0.0111)|(0.0075)|
|Native Males|0.0005<br>-0.0147|0.0066|
||(0.0007)<br>(0.0116)|(0.0074)|
|Foreign Females|0.0025***<br>-0.0212*|0.0190**|
||(0.0008)<br>(0.0118)|(0.0089)|
|Native Females|0.0005<br>-0.0104|0.0067|
||(0.0007)<br>(0.0110)|(0.0077)|
|R-Squared|0.8784<br>0.2680|0.2098|
||**Panel B: With Controls**||
|Foreign Males|0.0022***<br>-0.0143|0.0027|
||(0.0007)<br>(0.0110)|(0.0075)|
|Native Males|0.0005<br>-0.0139|0.0061|
||(0.0007)<br>(0.0117)|(0.0075)|
|Foreign Females|0.0024***<br>-0.0203*|0.0185**|
||(0.0008)<br>(0.0117)|(0.0088)|
|Native Females|0.0004<br>-0.0095|0.0062|
||(0.0007)<br>(0.0110)|(0.0077)|
|R-Squared|0.8784<br>0.2684|0.2099|
|Native Males Mean|4.611<br>91.17|4.077|
|Native Males SD|1.292<br>11.50|8.145|
|Native Females Mean|4.614<br>93.83|3.322|
|Native Females SD|1.291<br>9.446|7.659|
|Foreign Males Mean|0.880<br>88.99|6.790|
|Foreign Males SD|0.990<br>25.97|21.11|
|Foreign Females Mean|0.861<br>90.95|6.290|
|Foreign Females SD|0.980<br>23.91|20.44|
|Observations|203,560<br>203,560|203,560|

Clustered standard errors by municipality in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All columns include School FE, Year FE and Department _×_ Year FE. Selected controls include the following variables interacted with year dummies: N. of Terrorist Attacks _×_ 2012, 2014 and 2016, Informal Labor _×_ 2018, UBN _×_ 2013.
32

**Table 6.  Expenditure Categories in Ghana GLSS Surveys, 1998/99 and 2005/06**

|**Category**|**Description **|**Examples**|
|---|---|---|
|Food|Purchased food<br>Non-purchased food|Maize, bread, cassava, milk,<br>meat, fruit, vegetables<br>Food from: own-production,<br>gifts, donations, socialprograms|
|Consumer goods, durables|Consumer goods<br>Household durables|Clothing, shoes, fabric<br>Annual use value of stove,<br>refrigerator, furniture, television,<br>car|
|Housing|Housing value|Annual use value of housing<br>(calculated from rental payments<br>or imputed values)|
|Education|Educational expenses|Books, school supplies, uniforms,<br>registration fees, travelto school|
|Health|Health expenses|Doctor and dentist fees,<br>medicine, hospitalization,<br>antibiotics|
|Other|Utilities<br>Transport, communications<br>Remittance expenses|Water, gas, electricity, telephone<br>Bus and taxi fees, gasoline, faxes,<br>postage<br>Expenses on remittances|

Source: 1998/99 Ghana GLSS 4 and 2005/06 Ghana GLSS 5 Survey (sub-sample).
**Figure 3: Potential Explanations for Market Power by Continent.** Green bars highlight continental Africa. Effectiveness of anti-monopoly policy is measured in a survey of discipline experts by the World Economic Forum (Schwab, 2019). Known cement cartels are those reported in the Private International Cartel database (Connor, 2020). The number of procedures to start a business are from the Doing Business Indicators (World Bank, 2020a). Cement consumption is from the Global Cement Report (Armstrong et al., 2013, 2019). Data are the full analysis sample, pooling 2011 and 2017. Averages are unweighted by consumption.

<!-- Start of picture text --> Effectiveness of anti-monopoly policy (Experts) Cement consumption with known cartel (%)<br>South America Africa Asia North America Europe Africa Asia South America North America Europe<br>Procedures to start new business (#) Average cement consumption (Mt)<br>Europe North America Africa Asia South America Africa Europe South America North America Asia<br>5<br>100<br>4 80<br>3 60<br>2 40<br>1 20<br>0 0<br>10 100<br>5 50<br>0 0<br><!-- End of picture text -->
Table A1.10: Poland: Numbers of tariff headings subject to SSG Actions, 1995- <u>2004</u>

||Tariff Lines subject to a<br>|ctions|
|---|---|---|
|Year|Volume-based<sup>a</sup>|Price-based|
|1995|0|0|
|1996|0|2|
|1997|1|3|
|1998|1|5|
|1999|1|113|
|2000|2|9|
|2001|1|8|
|2002|0|138|
|2003|0|14|
|2004<sup>a</sup>|0|100|
|Memo ite<br>lines for<br>were not|m: Number of tariff<br>which trigger prices<br>ified‘up-front’|140|

Source: Tabulated from national notifications of SSG actions, available on the WTO documents website.

Note: a 01 January – 30 April. Poland acceded to the EC on 01 May 2004 59